NAX Group

naxgroup

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Limitations of the study and possible future studies Despite its inherent limitations, including its single case design and the potential for bias, the exploratory depth of the study uncovered hidden potential within these systems, even amid serious concerns about generalizability. Future research could include: • Refining the crafting of prompts. • Exploring new features of these and other GenAIbots being introduced with increasing frequency. • Establishing standardised benchmarks to evaluate and compare chatbots and AI systems' performance, accuracy, and reliability. • Conducting long-term studies to observe the evolution of chatbots' capabilities and their impact on user interactions over time. • Conducting research with real students in classroom settings and beyond to assess these AI systems' practical educational applications and challenges. • Investigating chatbots' learning and adaptation capabilities to individual user needs and preferences over time. • Researching the integration of multimodal inputs (e.g., text, voice, image) to enhance chatbot capabilities and user interaction experiences. When implementing GenAIbots in Chemistry learning, it is crucial to evaluate benefits and drawbacks judiciously, ensuring accurate information delivery and considering the implications of reduced human interaction. These concerns may be alleviated by integrating GenAIbots with other educational tools or activities promoting collaborative dialogue among learners. Author Contributions The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation. Conflicts of interest There are no conflicts to declare. Acknowledgements The authors warmly acknowledge Melanie Swan for her invaluable suggestion, which led to the transition from using the term 'objects-to-think-with' to 'agents-to-thinkwith'. References Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionising education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152 Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4337484 Bardin, L. (1977). L’analyse de contenu. PUF - Presses Universitaires de France. Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3), ep430. https://doi.org/10.30935/cedtech/13176 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. ArXiv, 2005.14165. http://arxiv.org/abs/2005.14165 Castro Nascimento, C. M., & Pimentel, A. S. (2023). Do Large Language Models Understand Chemistry? A Conversation with ChatGPT. Journal of Chemical Information and Modeling, 63(6), 1649–1655. https://doi.org/10.1021/acs.jcim.3c00285 David, E. (2023, September 21). Microsoft to add DALL-E 3 to Bing Chat. The Verge; Vox Media. https://www.theverge.com/2023/9/21/23873690/microsoft-new-ai-features-bingsearch-shopping-dall-e-3 Dewi, C. A., Pahriah, P., & Purmadi, A. (2021). The Urgency of Digital Literacy for Generation Z Students in Chemistry Learning. International Journal of Emerging Technologies in Learning, 16(11), 88–103. https://doi.org/10.3991/ijet.v16i11.19871 Dunlop, L., Hodgson, A., & Stubbs, J. E. (2020). Building capabilities in chemistry education: happiness and discomfort through philosophical dialogue in chemistry. Chemistry Education Research and Practice, 21(1), 438–451. https://doi.org/10.1039/C9RP00141G Flavell, J. H. (1976). Metacognitive aspects of problem-solving. In L. B. Resnick (Ed.), The Nature of Intelligence (pp. 231–236). Lawrence Erlbaum. Floridi, L., & Chiriatti, M. (2020). GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1 Franciscu, S. (2023). ChatGPT: A Natural Language Generation Model for Chatbots. https://doi.org/10.13140/RG.2.2.24777.83044 Gregorcic, B., & Pendrill, A.-M. (2023). ChatGPT and the frustrated Socrates. Physics Education, 58(3), 035021. https://doi.org/10.1088/1361-6552/acc299 Latour, B. (1991). Nous n’avons jamais été modernes: Essai d’anthropologie symétrique. La Découverte. Leon, A. J., & Vidhani, D. (2023). ChatGPT Needs a Chemistry Tutor, Too. Journal of Chemical Education. https://doi.org/10.1021/acs.jchemed.3c00288 Liu, F., Budiu, R., Zhang, A., & Cionca, E. (2023, October 1). ChatGPT, Bard, or Bing Chat? Differences Among 3 Generative-AI Bots. https://www.nngroup.com/articles/ai-botcomparison/ Metz, C., & Hsu, T. (2023, September 20). ChatGPT Can Now Generate Images, Too. New York Times - Technology. https://www.nytimes.com/2023/09/20/technology/chatgpt-dalle3-images-openai.html Mishra, A., Soni, U., Arunkumar, A., Huang, J., Kwon, B. C., & Bryan, C. (2023). PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models. ArXiv, 2304.01964. https://doi.org/10.48550/arXiv.2304.01964 Mollick, E. R. (2023, April 26). A guide to prompting AI (for what it is worth): A little bit of magic,but mostly just practice. One Useful Thing Blog. https://www.oneusefulthing.org/p/a-guidetoprompting-ai-for-what Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033 OpenAI. (2023). GPT-4 Technical Report. OpenAI. https://cdn.openai.com/papers/gpt-4.pdf Papert, S. A. (1980). Mindstorms - Children, Computers and Powerful Ideas. Basic Books. http://www.arvindguptatoys.com/arvindgupta/mindstorms.pdf Permatasari, M. B., Rahayu, S., & Dasna, W. (2022). Chemistry Learning Using Multiple Representations: A Systematic Literature Review. J.Sci.Learn.2022, 5(2), 334–341. https://doi.org/10.17509/jsl.v5i2.42656 Pimentel, A., Wagener, A., Silveira, E. F. da, Picciani, P., Salles, B., Follmer, C., & Jr., O. N. O. (2023). Challenging ChatGPT with Chemistry-Related Subjects. ChemRxiv. https://doi.org/10.26434/chemrxiv-2023-xl6w3 Schlosser, M. (2019). Agency. In The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/win2019/entries/agency/ Swan, M. (2015, January 21). We Should Consider the Future World as one of Multi-Species Intelligence. Response to The Edge Question 2015: What Do You Think About Machines That Think?; Edge.org. http://edge.org/response-detail/26070 Taylor, C. A., Hogarth, H., Hacking, E. B., & Bastos, E. (2022). Posthuman Object Pedagogies: Thinking with Things to Think with Theory for Innovative Educational Research. Cultural and Pedagogical Inquiry, 14(1), 206–221. https://doi.org/10.18733/cpi29662 Timilsena, N. P., Maharjan, K. B., & Devkota, K. M. (2022). Teachers’ And Students’ Experiences In Chemistry Learning Difficulties. Journal of Positive School Psychology, 6(10), 2856–2867. https://www.journalppw.com/index.php/jpsp/article/view/13764 Tümay, H. (2016). Reconsidering learning difficulties and misconceptions in chemistry: emergence in chemistry and its implications for chemical education. Chemistry Education Research and Practice, 17(2), 229–245. https://doi.org/10.1039/C6RP00008H Turkle, S. (1984). The Second Self: Computers and the Human Spirit. Simon and Schuster. Yin, R. K. (2011). Applications of Case Study Research. SAGE Publications, Inc. This paper is under CC BY-SA 4.0 DEED license. available on arxiv

Overcoming Limitations in AI Chatbot Research for Future Educational Impact

ভবিষ্যতের শিক্ষাগত প্রভাবের জন্য এআই চ্যাটবট গবেষণায় সীমাবদ্ধতা অতিক্রম করা

Überwindung von Einschränkungen in der KI-Chatbot-Forschung für zukünftige Bildungseffekte

Superar las limitaciones en la investigación de chatbots de IA para lograr un impacto educativo futuro

Surmonter les limites de la recherche sur les chatbots IA pour un impact éducatif futur

भविष्य के शैक्षिक प्रभाव के लिए AI चैटबॉट अनुसंधान में सीमाओं पर काबू पाना

将来の教育への影響のために AI チャットボット研究の限界を克服する

미래 교육적 영향을 위한 AI 챗봇 연구의 한계 극복

Superando as limitações na pesquisa do AI Chatbot para um impacto educacional futuro

Преодоление ограничений в исследованиях чат-ботов с использованием искусственного интеллекта для будущего воздействия на образование

Gelecekteki Eğitim Etkisi için Yapay Zeka Chatbot Araştırmalarında Sınırlamaların Aşılması

Khắc phục những hạn chế trong nghiên cứu Chatbot AI để tạo ra tác động giáo dục trong tương lai

克服人工智能聊天机器人研究的局限性，为未来教育带来影响

I once downloaded this fancy productivity app. At first, I was all in. Tapping away on my phone, making lists, and setting reminders felt awesome. Life happens. Work gets hectic. Netflix released the latest season of my favorite show. And my productivity app? Buried under forgotten apps. Why did this happen? The app was great. But I didn't fully adopt it into my routine. This scenario happens when businesses think more about product design and launch than product adoption. Launching is half the battle. The other half is to nail product adoption. Read on to learn: What exactly is product adoption Common barriers to product adoption How to increase product adoption (with real examples and expert tips) What is product adoption? Product adoption is when users find, try, and incorporate your software into their lives. Some products are daily, e.g., your go-to podcast app for your favorite daily podcasts. At the same time, others are weekly (think of the collaboration tool you use for your weekly standups). The win is when users link your product to getting their jobs done. How do users adopt products? Users move through six stages of product adoption, not necessarily in this order: : Potential users recognize their problem and discover your solution. Awareness : They actively seek more information about your product. Interest : They dive deeper to assess if your product meets their needs and compares to competitors. Evaluation : They opt for a trial or freemium tier. Trial : The 'aha moment' occurs when users realize the product's value for the first time, often by performing a crucial task. Activation : Convinced of your product's value, customers make it an integral part of their everyday lives. Adoption How to increase product adoption Customers stick around when they see value and churn when friction occurs. Here are five straightforward techniques to help you provide value, reduce friction, and increase product adoption. 1. Personalize the user experience A common mistake hindering adoption is focusing on the wrong crowd. That is introducing your product to the wrong user category. Users fall into these five categories: Five Product Adoption Categories: Early Adopters Trendsetters who can't wait to dive into the newest products. Innovators These folks are all about new tech, even if it's a bit wonky. They're the pioneers. Early Majority They need solid recommendations and testimonials before trying something new. Late Majority These users want rock-solid support. They prioritize convenience. Laggards Change? No thanks. They only switch when there's no other choice. Think of someone still rocking a 2020 smartphone. Make your product's value crystal clear in a way that resonates with each group. If you're trying to win new customers, your message should be different from what you tell your current customers. How to personalize the user experience for product adoption Segment users according to their adoption stage Use product experience tools such as Logrocket or Fullstory to identify issues facing different user profiles. Create targeted content and campaigns based on each profile. You can approach this by targeting early adopters with exclusive offers and upcoming releases. Their feedback lets you improve your product before rolling it out to more resistant late adopters. Personalize the email experience. Segment users based on their behavior or customer journey. Using tools like Userlist or Encharge, you can send behavior-based emails. It is a way to appeal directly to customers, depending on where they are in the journey. For example, you can email product guides to help users remove friction if they're stuck. When users finish a task, send them a congratulatory email. If their trial oe subscription is about to expire or renew, schedule a reminder email. 2. Provide a smooth user onboarding experience Users are reluctant to try a product after a disappointing first experience. An excellent user onboarding process ensures new users get value from your product right off the bat and integrate it into their lives. Here are ways to ace user onboarding Use a checklist to guide users through essential product features, leading them to the activation point. Gather customer information, such as their team or role, with a welcome survey. This helps you understand their unique requirements or job-to-be-done. Provide optional product guides so users can control their onboarding. Tech-savvy users do not need a walkthrough, but others will benefit. Offer contextual support with a chatbot or pop-up forms within the app, minimizing users' need to leave for assistance. 3. Remove adoption barriers A major friction of product adoption is getting people to change habits or overcome the fear of the unknown. Aside from early adopters, later adopters distrust new solutions. Here are ways to cut friction and reduce adoption barriers: Find out the blockers from customers Create a customer feedback channel where you can collect and act on user feedback. Monitor customer feedback regularly and prioritize the most critical issues. Send an email or in-app notification to acknowledge customer feedback and show you value their opinions. Integrate your product into users' existing workflows. Changing habits is challenging, and inertia often prevails. For instance, convincing a team accustomed to standups on Slack or Discord to switch platforms is a tough sell. founder and C.E.O., Michael Villar, formerly an engineer at Stripe with a decade of product development experience, shared how integration helped Height increase adoption. Height’s "We built our team AI standups to be shared within Height, Slack, and Discord. Height's standups include task links. If one team uses Height in a larger organization, Slack and Discord standups can increase adoption velocity. As the links open to Height on the web, other teams will find it easier to adopt." Address users' objections before they arise Address any potential obstacles as soon as possible. For example, some users might hesitate to add their card to a free trial. It is now common for many companies to offer free trials without credit cards and to indicate that on their pricing or signup pages to ease any objections. Source: Uscreen Display testimonials and show the results of switching to your product. This convinces both early and late majority users. They need to see that your product's value exceeds the required learning effort. This makes it easier for them to switch from a competitor or choose your solution. I love Basecamp's approach. There is a testimonial section on their website that serves as social proof. Source: Basecamp 4. Remove friction Strive for a user experience that requires minimal effort to reap rewards. Avoid anything that makes them overthink, which may lead to frustration and abandonment. How to cut friction to drive product adoption strategy Watch session recordings to identify points where users face challenges or disengage Using product experience tools, like Logrocket you can watch app and website interactions. Then, personalize your experience based on this data. Let's say users struggle to locate a specific feature. Consider redesigning its placement for better accessibility. Address user barriers at each adoption stage A helpful way to convince late adopters is to show testimonials from satisfied users before they commit. Ease their concerns with risk-free experiences like free trials and money-back guarantees. Make it easy for users to self-serve Provide resource centers or knowledge bases with adequate visual product guides for users to find answers. Also, ensure your customer support team is always on hand and easily reachable if they can't find what they're looking for or need clarification. Ensure your product works across all browsers and devices. Except in rare or specific cases, your product should work across all browsers, devices, and operating systems. This ensures flexibility and opens your product to a larger audience. Another critical point is to consider the needs of users with disabilities and ensure your product is accessible to everyone. Be proactive and innovate Be proactive and keep innovating and introducing new features your users need. Slack introduced huddles in 2021, their fastest-adopted feature during the remote work peak. Slack's senior product director, Olivia Grace, shared how constant innovation drives their success. "After launching Huddles, Slack kept innovating. We added video, screen sharing, and jukebox features to Huddle. Including seasonal backgrounds for Halloween and Christmas. These details matter. Users love them". The results? Slack has over 10 million daily active users (DAUs). That's ten million+ people who log in daily. 5. Drive feature adoption Getting users to embrace features extends beyond onboarding. You want to make sure they keep coming back and finding value. Getting new customers is expensive, so you want to maximize their engagement and repeat subscriptions. Ways to boost feature adoption: Use in-app messaging Use in-app messages strategically to highlight key features. Instead of bombarding users with notifications, send targeted nudges at the right moment when the feature is most relevant and valuable to them. Send post-onboarding email campaigns Send post-onboarding email campaigns packed with valuable tips tailored to specific roles. These tips will entice users to revisit your platform and explore its features further, fostering ongoing adoption among new and existing users and teams. Make the latest features prominent Update your user interface to make new features more noticeable, like adding a clickable button. You can also move them to a more prominent position. A study showed people spend 80% of their time on the left side of a page. NNGroup Another tactic is including notifications to inform users about the latest features. Invite them to provide feedback or test updated features. And offer rewards. Optimize for product adoption Product adoption isn't instant; it requires continuous effort. The more your product integrates into their routine, the more they will stick around. Test, test, test. That's the most common advice for product owners and managers. Run experiments to see how easy it is to use your product. For example, sign up to see if your checklist's progress bar works properly. You can also check if the tooltips got triggered at the right time and what the results were. Keep reminding users about the value of your product, because you can bet your competitors are doing the same. Send out emails or use in-app pop-ups to share content that gets customers thinking about your product in fresh ways. Remember, it's not about telling your customers to adopt; it's about creating an environment that makes it a no-brainer.

5 Best Product Adoption Techniques + Examples and Expert Tips

5টি সেরা পণ্য গ্রহণের কৌশল + উদাহরণ এবং বিশেষজ্ঞ টিপস

Die 5 besten Techniken zur Produkteinführung + Beispiele und Expertentipps

Las 5 mejores técnicas de adopción de productos + ejemplos y consejos de expertos

5 meilleures techniques d'adoption de produits + exemples et conseils d'experts

5 सर्वोत्तम उत्पाद अपनाने की तकनीकें + उदाहरण और विशेषज्ञ युक्तियाँ

5 つの最良の製品導入テクニック + 例と専門家のヒント

5가지 최고의 제품 채택 기법 + 예시 및 전문가 팁

5 melhores técnicas de adoção de produtos + exemplos e dicas de especialistas

5 лучших методов внедрения продукта + примеры и советы экспертов

5 En İyi Ürün Benimseme Tekniği + Örnekler ve Uzman İpuçları

5 kỹ thuật áp dụng sản phẩm tốt nhất + Ví dụ và lời khuyên của chuyên gia

5 种最佳产品采用技术 + 示例和专家提示

Every player wants to beat the odds. But few actually do. Have you ever wondered how slot machines in casinos can be beaten? Or how to win in Web3 games that rely on chance? In this article, we explore the role randomness plays in Web3, whether it’s possible to beat random number generators (RNGs), and how to go about cracking RNGs. In the vast expanse of Web3, randomness isn’t just a happenstance. It’s a pivotal element in various scenarios, influencing outcomes that range from gaming experiences to critical blockchain functionalities. Where Randomness Plays a Role in Web3 In Web3, randomness operates as a fundamental force influencing various aspects. One prominent domain is within the gaming sphere, where the chance of unveiling rare in-game items at specific locations is contingent upon the mechanisms of random number generators. These random encounters often dictate the rewarding experience of players upon defeating opponents. Lottery draws a quintessential example of randomness, requiring an unequivocally transparent and verifiably random process, especially within the Web3 ecosystem, where fairness and openness are paramount. If not, the lottery draw may potentially favor only specific groups or players. Additionally, the random selection of block producers through RNGs is crucial in certain layer-one blockchains. If this selection process lacks verifiable randomness and exhibits discernible patterns, block producers might exploit this to their advantage, strategically producing blocks that serve their interests. Such actions compromise the network's security, underlining the critical role of truly random and unbiased selection mechanisms in Web3 blockchain operations. While randomness plays such an important role in Web3 gaming and the Autonomous World, it's crucial to differentiate between pseudo-random number generators (PRNGs), which use algorithms to produce sequences of random numbers, and true random number generators (TRNGs) that use physical processes for higher security. Why Predicting Random Numbers Is Possible Predicting numbers generated by pseudo-random number generators (PRNGs) might be feasible due to specific vulnerabilities and patterns inherent in their algorithms. The generation process of PRNGs follows patterns that can be exploited if the initial seed (starting point) and state (current condition) of the generator are known. Let’s dive deeper into what a “seed” and “state” mean in this context. The seed, a critical input, initiates the random number generation process. It can be something as simple as the current time. The state refers to the current condition of the generator, which evolves with each number generated. Knowing the initial state allows for the prediction of subsequent numbers, presenting a significant security risk in scenarios where unpredictability is crucial. Moreover, the use of centralized sources for RNGs introduces additional vulnerabilities. Centralized systems become single points of failure, making them prime targets for malicious attacks. Historical incidents have demonstrated that brute force attacks can compromise these systems. Attackers can inundate the central server with numerous random guesses and make incremental adjustments to fine-tune their predictions, ultimately increasing their accuracy in guessing the next random number generated. These vulnerabilities emphasize the need for more secure and decentralized RNG solutions in various applications, especially within the Web3 landscape, where data integrity and randomness are of utmost importance. Exploiting RNG Vulnerabilities: Theoretical Methods Finding the Seed Through Hypothesis Testing The initial step in exploiting an RNG is to uncover the seed, which is the starting point for number generation. This process involves formulating and testing various hypotheses about potential seed sources. For example, if the hypothesis for the seed is the time of RNG operation, then generating numbers at consistent times and analyzing the outputs for patterns could confirm or refute this hypothesis. Advanced statistical techniques can be applied to detect subtle patterns that might not be immediately apparent, thereby increasing the accuracy of seed prediction. State Analysis and Reverse Engineering Beyond identifying the seed, understanding the state transitions within the RNG algorithm is crucial. By analyzing how the state changes with each number generated, it's possible to reverse-engineer the RNG algorithm. This method involves sophisticated mathematical and computational techniques, including algorithmic analysis and potentially cryptographic reverse engineering, depending on the complexity of the RNG. Leveraging Machine Learning for Pattern Recognition Machine learning, a subset of artificial intelligence (AI), excels at deciphering complex patterns in large datasets. This capability extends beyond simple pattern recognition, enabling the algorithm to learn and adapt without explicit programming. A prominent example of this is Google's AlphaGo. This AI system, trained on the board game Go, mastered the game by learning from vast quantities of game data, eventually outperforming world champions by anticipating their moves. This principle is directly applicable to analyzing RNG outputs. Machine learning algorithms can process extensive datasets of RNG-generated numbers to detect underlying patterns, including subtle irregularities and algorithmic resets that might not be apparent through traditional analysis methods. By training on a sufficiently large dataset, machine learning models can identify the cyclical nature of these RNGs, including their reset points and output patterns. Understanding these patterns allows for the prediction, and potentially the manipulation, of future RNG outputs. This capability is particularly crucial when RNGs are used in environments where predictability can lead to security vulnerabilities or unfair advantages, such as in gaming or cryptographic applications. Cryptographic Analysis Cryptographic analysis of RNGs is a multi-faceted approach that involves scrutinizing every aspect of their cryptographic design and implementation. This process starts with a thorough examination of the entropy sources, which are the raw materials for randomness. Entropy quality, its collection method, and the rate at which it's gathered are crucial factors; low-quality entropy or predictable collection methods can weaken the RNG's output. Next, the cryptographic algorithm itself is examined for resistance to known attacks. This includes statistical analysis to detect any biases or patterns that emerge over time, and testing the algorithm against known cryptanalytic techniques like differential cryptanalysis or linear cryptanalysis. The goal here is to ensure that the algorithm doesn’t produce outputs that are disproportionately influenced by certain bits of the seed or state. Moreover, the implementation of the RNG in software or hardware can introduce additional vulnerabilities. This part of the analysis involves code audits to find bugs or oversights that might compromise randomness. Flaws like insufficient mixing of entropy, predictable incrementation of state variables, or improper seeding can be exploited by attackers. Cryptographic analysis also extends to the operational environment of the RNG, checking for side-channel vulnerabilities like timing attacks or power analysis, where an attacker could infer internal states by observing indirect characteristics of the system. Verifiably Random and Tamper-Proof RNGs The process begins when the Randcast Adapter Contract receives a randomness request from a DApp or Web3 game. It then initiates a BLS-TSS (Boneh-Lynn-Shacham Threshold Signature Scheme) task, sending an on-chain event to the ARPA Network, which consists of multiple nodes capable of performing these tasks. The network acknowledges this event and completes the task, subsequently returning the signature as the random seed to the Randcast Adapter Contract. This seed is then transformed into the desired randomness type—be it a dice roll, shuffled array, or any other form—and is utilized in the subsequent DApp or Web3 game logic. How Randcast is different Randcast's approach is particularly vital in scenarios where reliable randomness is essential. In the deterministic environment of blockchain, where smart contract outcomes hinge on input, generating a random number can be easily manipulated. For example, using a block hash or timestamp as a randomness source could be exploited by miners for their benefit. Randcast circumvents this issue by generating random numbers through a decentralized network, ensuring transparency and verifiability in the process. A crucial aspect of Randcast's design is its resilience against tampering. Traditional single-entity randomness generation poses the risk of manipulation. Randcast circumvents this problem by utilizing the collective capabilities of the ARPA Network. Through BLS Threshold signature tasks, it ensures that no individual node can influence the final randomness outcome. This decentralized approach not only enhances security but also upholds the integrity and impartiality of the randomness generated, making Randcast an indispensable tool in the Web3 developer’s arsenal. Reference links https://www.roulettephysics.com/how-to-beat-rng-roulette-slot-machines-other-casino-games/ https://research.nccgroup.com/2021/10/15/cracking-random-number-generators-using-machine-learning-part-1-xorshift128/ https://lemire.me/blog/2017/08/22/cracking-random-number-generators-xoroshiro128/ https://crypto.stackexchange.com/questions/24646/can-machine-learning-analyze-random-number-generator https://www.reddit.com/r/askscience/comments/56r419/is_it_possible_to_find_the_algorithm_for_a_random/?rdt=57618 https://lcamtuf.coredump.cx/oldtcp/tcpseq/print.html#tcpseq

Securing the Unpredictable: Random Number Technology In Web3 Games

অপ্রত্যাশিত সুরক্ষিত করা: ওয়েব 3 গেমগুলিতে র্যান্ডম নম্বর প্রযুক্তি

Das Unvorhersehbare sichern: Zufallszahlentechnologie in Web3-Spielen

Asegurar lo impredecible: tecnología de números aleatorios en juegos Web3

Sécuriser l'imprévisible : la technologie des nombres aléatoires dans les jeux Web3

अप्रत्याशित को सुरक्षित करना: वेब3 गेम्स में यादृच्छिक संख्या प्रौद्योगिकी

予測不可能な状況を保護する: Web3 ゲームの乱数テクノロジー

예측 불가능한 보안: Web3 게임의 난수 기술

Protegendo o imprevisível: tecnologia de números aleatórios em jogos Web3

Защита непредсказуемого: технология случайных чисел в играх Web3

Tahmin Edilemeyenin Güvenliğini Sağlama: Web3 Oyunlarında Rastgele Sayı Teknolojisi

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User experience has become a paramount concern for designers and developers in the ever-evolving digital landscape. Microinteractions, subtle animations and interactions that engage users on a small scale play a crucial role in this endeavour. According to a by NNGroup, a leading UX research and consulting firm, microinteractions can improve user engagement by 50% and satisfaction by 22%. In this article, we will explore what microinteractions are, why they are essential, their benefits, and how to design effective microinteractions that enhance user experience. study What are microinteractions? are small, subtle design elements that serve specific and often repetitive user tasks. They focus on providing a delightful and seamless user experience by incorporating subtle animations, sounds, and visual cues. These interactions can be as simple as a button changing colour when pressed or as complex as a loading animation. Microinteractions Why are Microinteractions Important? Microinteractions significantly contribute to the overall user experience. They make interactions more engaging, intuitive, and satisfying. Imagine using an app or website without any feedback or visual cues when you act—how would you know if the action was successful? Microinteractions bridge this communication gap, ensuring users know their actions and the system's responses. Benefits of Using Microinteractions Microinteractions are attention-grabbing and with the interface, effectively capturing and sustaining their attention. This increased engagement can lead to longer sessions within the app or website, which can be pivotal for the product's success. Enhanced User Engagement: encourage users to interact Microinteractions offer immediate and intuitive feedback to users, informing them about the status of their actions. This guidance helps users confidently navigate the interface, reducing errors and improving overall user satisfaction. Feedback and Guidance: A positive user experience, driven partly by well-designed microinteractions, often results in higher user retention rates. Users are more likely to revisit and continue using an application or website that offers a smooth, enjoyable interaction, ultimately fostering a loyal user base. Improved User Retention: Microinteractions provide an opportunity to infuse a brand's unique identity into the user interface. Tailored animations, colours, and sound effects can help create a distinctive brand personality, setting the product apart from competitors and contributing to brand recall. Brand Differentiation: By breaking down complex tasks into smaller, manageable steps, microinteractions guide users through processes seamlessly. This efficient task completion enhances user satisfaction and encourages users to utilize the application for its intended purpose more frequently. Efficient Task Completion: Types of Microinteractions , despite their small scale, can significantly impact user engagement and overall user experience. They can be broadly categorized into two main types, each serving specific purposes: Microinteractions User-initiated microinteractions are triggered by explicit actions or inputs from the user. These interactions are often designed to provide immediate feedback, guidance, or assistance during user interactions. User-Initiated Microinteractions: When a user clicks or taps a button, it responds with a change in colour, animation, or a brief pop effect, confirming the interaction. Button Press Animation: As a user fills out a form, the microinteraction provides real-time feedback on the correctness of the input, such as turning the input field border green for valid entries and red for invalid ones. Form Field Validation: When users scroll through content, microinteractions can include animations or visual indicators like a fading scroll bar or content animations to enhance the scrolling experience. Scrolling Feedback: System-initiated microinteractions are automatic responses generated by the system based on specific conditions without direct user interaction. They often provide feedback or notifications to inform users about system status or events. System-Initiated Microinteractions: An animated icon (often a spinning wheel) is displayed during loading processes, indicating that the system is processing or fetching data. Loading Spinner: A pop-up, sound, or vibration alert triggered by an event, such as receiving a message, email, or other notifications. Notifications: After a user acts, such as saving a document, a subtle animation or notification confirms the action, providing reassurance. Auto-Save Confirmation: Examples of Microinteractions in Popular Apps and Websites Instagram's Heart Animation: Instagram utilizes a microinteraction tied to user engagement through the double-tap action to "like" a post. When a user double-taps an image, a heart icon momentarily enlarges and gently animates, providing visual confirmation of the action. This dynamic animation acknowledges the user's engagement and adds a playful and enjoyable element to the platform, reinforcing positive interactions and making the experience more captivating. Tinder's Swipe Gesture: Tinder leverages a fundamental swipe gesture for users to express interest or disinterest in potential matches. As a user swipes left or right, the profile card smoothly moves toward the motion. This microinteraction mirrors real-world actions, making the user experience intuitive and engaging. The fluidity of the animation, aligned with the user's natural interaction, enhances the app's usability and encourages users to continue engaging with the platform. Uber's Ride Request Animation: Uber employs a microinteraction to inform users during the ride request process. When a user requests a ride, an animated car icon appears on the screen, symbolizing the process of matching with a driver. This visual representation offers real-time feedback, reassuring the user that their request is being processed. The microinteraction effectively manages user expectations and reduces perceived waiting time, contributing to a smoother user experience. How to Design Effective Microinteractions Creating impactful microinteractions involves adhering to certain design principles and implementing best practices. Let's delve into the key aspects of designing effective microinteractions. Principles of Good Microinteraction Design Clarity and Simplicity: Ensure that the purpose and functionality of the microinteraction are transparent to users. It should be immediately evident what action is expected from the user and the system's response. Keep microinteractions simple. Avoid unnecessary complexity to prevent confusion and maintain a seamless user experience. Feedback and Communication: Provide immediate and relevant feedback to users for every action they perform. Feedback can be visual, auditory, or haptic, indicating the system's response and confirming the user's movement. Use appropriate feedback mechanisms to guide users and reassure them about successfully completing their actions, minimizing uncertainty. Consistency and Standardization: Maintain consistency in the design elements used across microinteractions. Use consistent colors, icons, animations, and language to create a unified and cohesive user experience. Adhere to industry standards and established design patterns to ensure that users can easily predict how a microinteraction will behave based on their prior experiences with similar interfaces. Tips for Creating Engaging Microinteractions Understand Your Users: Conduct comprehensive user research to understand the target audience, preferences, behaviours, and pain points. Identify what microinteractions would resonate with them and add value to their experience. Prototype and Test: Create prototypes to visualize how microinteractions will look and function within the interface. Conduct usability testing with these prototypes to gather feedback and insights, allowing for iterative improvements. **A/B Testing: Test different versions of microinteractions to identify the most effective and engaging design. A/B testing helps understand user preferences and optimize the microinteraction design for better outcomes. Conclusion Microinteractions, though seemingly minor, play a significant role in enhancing user experiences across digital platforms. Incorporating well-designed microinteractions can significantly impact user engagement, satisfaction, and brand perception. By adhering to sound design principles and considering user preferences, designers and developers can create meaningful microinteractions that elevate the overall user experience and drive the success of their products and platforms.

How Microinteractions Can Improve User Engagement

When we hear the word "group," we often think of various associations, from social circles to musical bands or even a gathering of animals. A Fabric.js group is a collection of objects that are clustered together. These groups serve multiple purposes, such as moving and modifying objects collectively or applying property changes uniformly across multiple objects. In this blog post, we will explore how to create object groups using Fabric.js, empowering you to streamline your object manipulation and modification tasks. Why Use Object Groups in Fabric.js: Object groups offer several advantages and functionalities, including: With object groups, you can conveniently move multiple objects as a single unit, simplifying the positioning and arrangement of complex scenes. Moving Multiple Objects Together: By grouping objects, you can apply modifications uniformly across the entire group, saving time and effort when making changes to related objects. Modifying Objects as a Group: Object groups enable simultaneous property changes, allowing you to apply transformations like skewing, scaling, fill color, stroke, and more to multiple objects at once. Applying Property Changes: Creating Object Groups in Fabric.js: To create an object group in Fabric.js, we utilize the fabric.Group constructor. Let's take a closer look at its syntax and usage: Syntax: new fabric.Group(objects: Object, 
                  options(optional): Object, 
                  isAlreadyGrouped(optional): Boolean); The parameter accepts an array of objects that we want to group together. objects Optional can be passed as an object, allowing customization of group properties like position and angle. options The parameter is used to indicate if the provided objects are already grouped. isAlreadyGrouped Creating a Basic Object Group: Follow these steps to create a basic object group: You can group shapes, images, or text elements by creating individual objects. Pass these objects as an array to the constructor. fabric.Group Example: var circle = new fabric.Circle({ radius: 30, fill: "#FF0000" });
var image = new fabric.Image(...);
var text = new fabric.Text("Hello", ...);

var group = new fabric.Group([circle, image, text]);
canvas.add(group); A circle, an image, and a text element are combined in the above example. The variable represents the newly created object group, which is then added to the canvas. group Customizing Object Groups: You can further customize object groups by providing optional properties to the object parameter. These properties allow you to define the group's position, rotation angle, and other visual attributes. options Example: var group = new fabric.Group([circle, image, text], {
  left: 70,
  top: 94,
  angle: -10,
}); In the above example, we set the and properties to position the group on the canvas. Additionally, we specify an to rotate the entire group. left top angle Creating a Group of Two Objects: Creating a group of two objects follows a similar approach. However, we need to ensure proper alignment within the group by setting the and properties to "center" for each object. originX originY Example: var rect = new fabric.Rect({
    width: 100,
    height: 85,
    fill: "#FFC0CB",
    originX: "center",
    originY: "center"
});
var text = new fabric.Text("Hello world@", {
    fontSize: 30,
    originX: "center",
    originY: "center"
});

var group = new fabric.Group([rect, text], {
    left: 150,
    top: 100,
    angle: -10
});
canvas.add(group); In the above example, we create a group of a rectangle and text element. By specifying the and properties as "center" for each object, we ensure they are properly centered within the group. originX originY Ungrouping Fabric.js Objects In addition to creating groups of objects in Fabric.js, you also have the ability to ungroup them when needed. Ungrouping objects allows you to restore their individual properties and manipulate them independently. Let's explore how to ungroup objects using Fabric.js with an example. Example of Ungrouping Objects: You want to ungroup a group of objects and treat them separately. Consider a group consisting of two rectangles and a text element. We will demonstrate how to ungroup them using Fabric.js. var canvas = new fabric.Canvas("canvas");

// Create the rectangles
var rect1 = new fabric.Rect({
  width: 100,
  height: 50,
  fill: "blue",
  left: 50,
  top: 50,
});

var rect2 = new fabric.Rect({
  width: 100,
  height: 50,
  fill: "red",
  left: 200,
  top: 50,
});

// Create the text element
var text = new fabric.Text("Hello", {
  fontSize: 20,
  fill: "white",
  left: 120,
  top: 60,
});

// Group the objects
var group = new fabric.Group([rect1, rect2, text], {
  left: 100,
  top: 100,
});

canvas.add(group);

// Ungroup the objects
var activeObj = canvas.getActiveObject();
var activegroup = activeObj.toGroup();
var objectsInGroup = activegroup.getObjects();

activegroup.clone(function(newgroup) {
    canvas.remove(activegroup);
    objectsInGroup.forEach(function(object) {
        canvas.remove(object);  
    });
    canvas.add(newgroup);
    canvas.setActiveObject(newgroup);
    canvas.requestRenderAll();
});

canvas.renderAll(); In the code above, we assume that canvas is the Fabric.js canvas object. First, we retrieve the active object from the canvas using . We then convert the active object into a group using the method, and store it in the activegroup variable. getActiveObject() toGroup() A list of objects within the group is obtained using the method and stored in the variable. getObjects() objectsInGroup A clone of the activegroup is then created using the clone() method. Inside the callback function of clone(), we remove the activegroup from the canvas using . remove(activegroup) We iterate over the objectsInGroup array and remove each individual object from the canvas using . remove(object) After removing the group and its objects, we add the cloned group back to the canvas using . add(newgroup) Finally, we set the newgroup as the active object on the canvas using , and request the canvas to render the changes using By executing this code, you will ungroup the objects and have them restored as separate entities on the canvas. setActiveObject(newgroup) requestRenderAll(). Conclusion Mastering object grouping in Fabric.js empowers you to simplify object manipulation and modification tasks. By creating object groups, you can move, modify, and apply property changes to multiple objects simultaneously, enhancing your productivity and efficiency. Whether you're working with complex scenes or need to apply uniform transformations, object groups in Fabric.js are a valuable tool in your development arsenal. Start leveraging the power of object grouping in Fabric.js and take your canvas-based applications to the next level.

Simplifying Object Manipulation: Grouping and Ungrouping Fabric.js Objects

Using Redis Streams with NestJS: Part 3 - Consumer groups

A tested framework that helps stakeholders digest research insights and recommendations efficiently. Have you ever delivered research insights to your team but never heard back? Or lost touch with how and when those insights were used, if at all? This is an issue that I dealt with far too often, especially as a team of one, and felt invalidated from time to time. I was unsure if I was doing meaningful work and contributing to the product development cycle. With that, I knew I had to create processes to cultivate my work’s impact. Thus, I created a framework that works well for me, which proposes the steps the UX Research team should take to endorse their work and the research insights at their workplace. This framework suggests that there are four vital compartments that researchers should follow: Deliver the deliverables Tell a story with actions Increase the research’s visibility Meet regularly with PMs 1) Deliver the deliverables Whether it is the research report, persona, competitive analysis, or user journey map, the deliverables keep the research together and help stakeholders digest the smaller pieces better. For example, suppose the stakeholders need to understand users’ goals, motivations, and needs. In that case, it is essential to include a detailed user persona on the research report highlighting user information, use cases, and (possibly) stats regarding the problem space. Or, if we need to discuss our product’s competitive advantage and the opportunity gap it fulfills, a comprehensive competitive analysis highlighting what works well or what does not on other products could help stakeholders understand the components that users need from a product. As the users and the product evolve, researchers need to iterate on deliverables constantly to keep them relevant and practical. It is important to guide the stakeholders through digestible and easy-to-comprehend reporting materials so that both the users’ needs and the product offerings mature. 2) Tell a story with actions Being the user’s voice is all about how you tell the research story and turn the insights into actionable items. It is about building empathy between the users and the stakeholders with a catchy and memorable story to keep them engaged and inspired. But how do you tell a compelling story? While there are great articles that tackle this topic already*, I believe some of the essential components of telling a good story are Explain the findings from the users’ perspectives. Show what users did and what users said. Do not try to include you heard. everything Make sure that your audience knows what to expect during the introduction Last but not least, have actionable insights. Yes, with a good research story, researchers must also ensure they share actionable recommendations that help process and progress the project. For instance, do not just report that “Users do not like to log in with usernames.” Richen your insights through a better narrative: “Users do not like to log in with their usernames because they often forget the usernames on their accounts. They believe it is easier to log in via email, as they can easily request a new password through their email address if they forget their passwords. This is what users said about logging in with a username vs. an email address . Therefore, we should allow users to select whether they want to log in via username or email to provide flexibility.” [include user quotes and visuals here] In other words, as researchers, we should focus on telling a captivating story about why our research is necessary and include finishing touches about what should come next from the research’s perspective. 3) Increase the research’s visibility To make the research findings more accessible to the stakeholders, UX research should allow visibility through constant communication channels — whether it is a research repository or a slack channel. In those channels, it is crucial to be transparent about the research roadmap, updates, and backlog to ensure everyone has regular access to the most recent research information. You can see the research repository template that I created . here Another strategy I follow for visibility is to invite stakeholders to user conversations and sessions and ask them to ask questions about what they have heard or seen. That way, we can uncover what follow-up questions or requests stakeholders might have since they consumed the latest research insights. 4) Meet regularly with PMs In my opinion, one of the most essential components of this framework is the habit I built that strengthened the bridge between research and cross-functional teams. Once I started regularly following up with product managers regarding how my research findings fit into the current product roadmap and which research recommendations could inspire product decisions, my confidence in my work increased immensely. Think about it as a reminder to the product teams to apply research insights and incorporate research into the roadmap. Through this habit, I also understood which research recommendations were being deprioritized and why. With that, I get to design the following research study to revisit those left-over research questions. These are the steps I follow to understand how (and make sure) stakeholders are the research insights into their product decisions. What has been working for you? What is your framework’s secret sauce? adopting Articles that I referred to https://www.notably.ai/blog/how-to-use-storytelling-in-ux-research-reports https://www.nngroup.com/articles/two-tips-better-ux-storytelling/ Also published . here

How to Help Team Members Adopt Research Insights Better

I was recently going through my notes of and was shocked to realize how actual this book still is. I'm going to share my summary of the book. I guarantee you'll find at least a few antipatterns in your current database design. SQL Antipatterns Logical Database Design Antipatterns Jaywalking Storing a delimiter-separated value string in a varchar field instead of creating an intersection table. It might be easier, but it makes queries harder. And updating the field is also a pain. And your field might have all kinds of consistency errors (for instance, ids are normally numbers, but now you have varchar. The DB cannot ensure consistency for you). Antipattern: when the data you're storing in the varchar is not needed to be used in the queries (gets displayed as-is). Legitimate Uses of the Antipattern: create an intersection table. Solution: Naive trees When you have multiple level parent -> child relationship and you represent it using an Adjacency list (you have a parent id in every row). It becomes impossible to get the whole tree in one query (because by default you can only get the next level). Antipattern: when you're certain that you will only have 1 level of queries. The alternatives are harder to implement. Legitimate Uses of the Antipattern: Solutions: Path Enumeration (storing the string of ancestors as an attribute of each node, just like the filesystem directories). Drawback: limitations of the Jaywalking antipattern. Nested Sets (add to each node nsleft and nsright: the nsleftnumber is less than the numbers of all the node’s children, whereas the nsright number is greater than the numbers of all the node’s children. These numbers have no relation to the id values). General tree reading becomes easy, but editing the tree becomes hard because all the nsleft and nsright need to be reprocessed. Closure Table: an extra table that holds all the relations between ancestors and descendants (including node indirect relationships and a reference to itself). The only drawback is the extra table size for big trees. But other operations are very easy to implement. "Joe Celko’s Trees and Hierarchies in SQL for Smarties" Reference: ID required Using a primary key that is not the appropriate primary key for this table (e.g., there is a natural primary key like social security number or there could be a compound key in there). If a table has a unique column then, most probably that could be the primary key. Antipattern: declare a primary key on a field that is easy to index and makes the most sense for that particular table Solution: Keyless entry when you don't use the constraints offered by the DB (especially foreign keys). Antipattern: never, unless your DB doesn't support it Legitimate Uses of the Antipattern: always declare constraints Solution: Mixing data with metadata For example, by appending the year onto the base table name, we’ve combined a data value with a metadata identifier. Antipattern: This is the reverse of mixing data with metadata from the Entity-Attribute-Value and Polymorphic Associations antipatterns. In those cases, we stored metadata identifiers (a column name and table name) as string data. In Multicolumn Attributes and Metadata Tribbles, we’re making a data value into a column name or a table name. If you use any of these antipatterns, you create more problems than you solve. Entity-value-attribute when you have a table with the columns: entity_id, attribute_name, attribute_value Antipattern: It appears when you have OOP inheritance and the children have various fields. If you use it, you lose referential integrity and data type validation. you shouldn't use this in a relational DB. Just use a non-relational DB or one of the solutions below. Legitimate Uses of the Antipattern: Solutions: Single table inheritance: one table with all the attributes of the children (it's going to be a sparse table Concrete table inheritance: completely independent tables for each subtype Class table inheritance (one table for the parent properties and tables with the child-specific properties) Semistructured data (one table with the parent properties and an extra blob field with the child-specific attributes). Disadvantages similar to the Entity-value-attribute. Polymorphic associations When you need to reference from a single table, or multiple parent tables (e.g., comments to features/bugs). You're using the pattern if you store in a column the name of your type of parent table. Antipattern: Solutions: Create intersection tables Create a common super table (with only ids) and reference that with your new table. The children will reference the super table (ex: comments references issues; bugs references issues, features references issues) Multicolumn attributes When an entity's attribute can have multiple values, you create multiple columns called attribute1, attribute2, etc. Antipattern: Solutions: Create a dependent table (a table that references the initial table by id and has the attribute as the other column) Store each value with the same meaning in a single column. Metadata tribbles When to support scalability and performance, you clone tables or new columns to support new partitions: ex: sales_2013, sales_2014, etc. (tables of the same schema with different entities). Basically, in your database, you will have a table per "some data value". Antipattern: when splitting the database sensibly makes database administration tasks easier after the database size passes a certain threshold. Legitimate Uses of the Antipattern: Solutions: Using Horizontal Partitioning: rows are separated into partitions. Mostly managed by the DB. Using Vertical Partitioning: splitting a table by columns. Databases usually do this for TEXT and BLOB. Physical Database Design Antipatterns Rounding errors (when you want to store floating point numbers in the DB) When you use float, double, or any other related DB type. The problem is how the number is represented internally by the DB. It will do all kinds of unpredictable roundings. Antipattern: scientific applications. Legitimate Uses of the Antipattern: use NUMERIC or decimal Solution: 31 flavors When you want to restrict a column to specific values (e.g., the status of a ticket), you define the allowed values in the table's schema or as a trigger. Antipattern: when you have very few values that will never change. But you might still get it wrong (e.g., gender). Legitimate Uses of the Antipattern: create a lookup table. Solution: Phantom files When you want to store Store Images or Other Bulky Media (when you reference them via the DB), and you use the FS directly, instead of the DB. These need to be specifically backed up, and the DB does not manage them. Antipattern: when the data files might affect how your DB behaves. Legitimate Uses of the Antipattern: Use BLOB Data Types As Needed Solution: Index shotgun When you want to Optimize the performance of the DB, and you throw indexes here and there without thinking too much about it (no indexes, too many indexes, queries that cannot benefit from indexes). Antipattern: none Legitimate Uses of the Antipattern: Solution: Measure the query times Get the query execution plan (QEP) Analyze the QEP and find where the index needs to be added
after creating the index, test Optimize: covering (compound) indexes could speed up things even more; use in mem vs. on disk indexes Rebuild: because indexes become fragmented as rows are deleted/created/updated Query Antipatterns Fear of the unknown thinking that null is just another value. Actually, SQL treats null as a special value, different from zero, false, or an empty string. As a general rule, all operations with null will return null. Or using a value to represent null. Antipattern: Treat null as a unique value. Declare Columns NOT NULL when it makes sense. Also, consider if using default makes sense. Solution: Ambiguous groups What you're already doing: Get Row with Greatest Value per Group. When you're using group by and are trying to get extra information from the row that you selected (like other columns that are not included in the group by expression), in other words, referencing Nongrouped Columns in the select list of the query. Antipattern: Every column in the select list of a query must have a single value row per row group. This is called the Single-Value Rule. Columns named in the GROUP BY clause are guaranteed to be exactly one value per group, no matter how many rows the group matches. none because most DBs will throw an error Legitimate Uses of the Antipattern: Solutions: Don't add an extra column to the select list Using a Correlated Subquery (own note: seems pretty hard to implement) Using a Derived Table: use the group query as a subquery of a query that selects the columns that you need. But merging their results with left join https://stackoverflow.com/questions/7745609/sql-select-only-rows-with-max-value-on-a-column Using an Aggregate Function for Extra Columns -> apply a function to the extra column in the select list so that you ensure that there's only one value coming back (e.g., max or GROUP_CONCAT) Random selection When you must fetch a random sample from one of your tables, you sort data randomly -> ORDER BY RAND(). This operation does a full table scan. Antipattern: only when you know for sure that the size of the data that you're randomizing will not be bigger than 50-100 rows. Legitimate Uses of the Antipattern: Solutions: Pick a Random primary Key Value Between 1 and MAX; only when primary keys are contiguous Choose Next Higher Key Value: just like before, except you'll be picking the next available key (assumes that the keys are non-contiguous). The results will not be evenly distributed Get a List of All Key Values. Choose One at Random (do it in the application; does not scale well) Choose a Random Row Using an Offset: select random between 0 and total_rows and use it in an OFFSET command Search the docs of your DB of choice Spaghetti query When you want to achieve everything in only one SQL query (solving a complex problem in one step, Antipattern: You might create a cartesian product without wanting to do that It's going to be hard to maintain when you're going to use that query as a data source in a 3rd party app Legitimate Uses of the Antipattern: Solutions: Split your initial query into multiple smaller ones Use the union label (to combine smaller queries) Writing SQL Automatically—with SQL -> use concat to create a list of queries to run; you can also do this with a script in bash Readable passwords Store Password in Plain Text Antipattern: hash your passwords with salt directly in your application. Solution: SQL injection when writing dynamic SQL queries, you end up executing unverified input as code. Antipattern: Solutions: Parameterize Dynamic Values (use query parameters) Filter Input Do your best not to write standard SQL. Rely on your data access framework as much as possible Diplomatic immunity when you want to code fast, completely skip engineering best practices. Make SQL a Second-Class Citizen, not giving the DB the same importance that the code gets in terms of quality. Antipattern: Solution: Forget about self-documenting code. It's a myth. Use Entity-relationship diagrams, break them down into functionalities, and mention triggers and stored procedures Use source control Have tests that test the interaction with the DB Magic beans when designing an MVC application and you don't consider enough how to separate the app logic between the M, V, and C. (Own note: I'll probably need to re-read this chapter from the book) Antipattern: Active record is a design pattern that maps objects to DB tables. The Model Has an Active Record -> basically, build services that compose the models. Aim to make your model a domain model, not a database model. Solution: Rules of normalization Should be considered only in the context of your app. How much do you want to normalize/denormalize? It's a tradeoff. And it would be best if you benchmarked it. The objectives of normalization: To represent facts about the real world in a way that we can understand To reduce storing facts redundantly and to prevent anomalous or inconsistent data To support integrity constraints The forms First Normal Form: no repeating groups (no multicolumn attributes, no multiple values in one column) Second Normal Form: when you're repeating values on a column, instead of creating a dependent table Third Normal Form: single responsibility principle for tables (put the column in the table that it belongs to) Advanced 3rd form (Boyce-Codd Normal Form): table doesn’t contain any field (other than the primary key) that can determine the value of another field. Example: teacher, subject, student. The subject is always dependent on the teacher. Fourth Normal Form: BCNF without compound keys Sixth normal form: It’s typically used to support a history of changes to attributes (e.g., an audit log). See no evil discarding DB error messages or not looking at the raw SQLs that get run when debugging the application. Antipattern: Recover from Errors Gracefully -> log potential exceptions every time. Solution: Antipatterns that are not that current anymore? Implicit Columns when you want to reduce typing (like explicitly mentioning all columns in the select list), you use: Antipattern: For insert without specifying the columns, when you refactor you might break the insert or, even worse, the database A select with * will fetch and display all the data when you're writing queries to try things out Legitimate Uses of the Antipattern: Name Columns Explicitly Solution: Poor man’s search engine when you want to do a full-text search, you use pattern-matching predicates (e.g., LIKE, REGEXP). Antipattern: don't use SQL. Elasticsearch has made this one easy. If you have to use SQL (for simple cases, each vendor has a solution for this already) Solution: Pseudokey neat-freak when you want to have contiguous keys in a table (e.g., when you have deletions in the DB), you want to fill in the gaps. Antipattern: Instead of allocating a new primary key value using the automatic pseudo key mechanism, you might want to make any new row use the first unused primary key value Renumbering Existing Rows -> changing the existing keys so that they are contiguous. none. Legitimate Uses of the Antipattern: just let it be. You can see the primary key id as UUIDs. The fact that they are consecutive is just a coincidence of the implementation. Solution: Also published . here

An Overview of SQL Antipatterns

Tl;dr The ‘Hierarchy of Design’ concept put forward by Lidwell, Holden and Butler in their 2003 book is popular, but poorly developed. It relies on a highly criticized and often misunderstood theory by Maslow which represents fundamental human needs as hierarchical. Furthermore, the representation of the relationships between relevant design characteristics as a pyramid is inaccurate and misleading. This article aims to elaborate on these claims. ‘Universal Principles of Design’ Maslow’s Hierarchy of Needs In the 1940s Abraham Maslow published a psychology paper called . In it, Maslow argued that people are motivated by a set of needs that can be represented in a hierarchy. This hierarchy starts with basic physiological needs, such as food, rest and sleep and progresses to higher-level needs such as love, self-esteem, and creativity. "A Theory of Human Motivation" According to this theory, people generally seek to address the lowest of their needs before trying to attend to those on higher levels. When a lower level of need fulfillment is not in place, any fulfillment gained on a higher level is deemed unstable. When a person has no food, hunger is eventually going to overwhelm them. Then they will stop whatever they are doing and devote all their time and energy to satisfy their hunger instead. The Design Hierarchy of Needs In their book Lidwell et al. applied the hierarchy of needs concept to design. The theory proposes that the foremost purpose of designs is to meet human needs and that design needs can be thought of and depicted in the same way as human needs, namely in a hierarchy. ‘Universal Principles of Design’ Good designs, it is claimed, attend to the lower levels of the hierarchy before attempting to meet the needs of the higher levels. “... a design must serve the low-level needs (e.g., it must function), before the higher-level needs, such as creativity, can begin to be addressed.” Lidwell et al. have proposed the following five-levels for this hierarchy: Functionality: Functionality needs concern the most basic design requirements. This level pertains to the need for products to perform the tasks they were created to perform.  Designs at this level are perceived to be of little or no value. Reliability: Reliability needs have to do with establishing stable and consistent performance. This aspect relates to the technical implementation or the mechanics of a product. Designs at this level are perceived to be of low value. Usability: Usability needs have to do with how easy and forgiving a design is to use. Designs at this level are perceived to be of moderate value. Proficiency: Proficiency needs have to do with empowering people to do things better than they could previously.  Designs at this level are perceived to be of high value. Creativity: Creativity is the level in the hierarchy where all needs have been satisfied, and people begin interacting with the design in innovative ways.  Designs at this level are perceived to be of the highest value. Products that exhibit only lower-level characteristics are deemed to be of no or low value, while those with higher-level characteristics are assumed to be of high value. As products exhibit higher-level characteristics so their value is deemed to increase. A large number of articles, talks and blog posts have picked up on the idea of a design hierarchy. The simple order it leads to appeals to practitioners and inspired many to reproduce, refine and extend the ‘design hierarchy of needs’. A web search reveals a plethora of articles depicting altered versions of this pyramid with a variety of extensions and amendments. The concept has, arguably, found its way into what one might view to be a generally accepted body of design knowledge and hence seems worth a critical review. Since the idea of the ‘design hierarchy of needs’ stems from Maslow’s theory, it seems worthwhile to first investigate how scholars assess Maslow’s original hierarchy of needs. Some critiques of Maslow’s hierarchy One of the main general criticisms of Maslow’s theory is that human needs are too complex to be reduced to a simple hierarchy. The representation of human needs in this manner has been called . Indicators for human motivation are highly variable and multifaceted and cannot be reduced to five static factors. simplistic and reductionist the implication of the strict ranking the proposed hierarchy implies. There is no evidence that physiological needs have to be met before people are motivated to seek out higher-level needs, like love and belonging. A variety of examples are put forward to underpin this assertion. For example, when people get hungry, they don’t immediately disregard their safety needs or ignore the needs of their family and friends. Another example is one of the starving artists who do their best work while their basic physiological needs are hardly being met. Authors challenge It has more specifically been highlighted that the order of items in the hierarchy and that the ranking furthermore . Maslow conceived his theory in America and at a time when . This has led him to select needs that represent specific mid-twentieth century U.S. middle-class values and not universal ones. The order in which he placed these needs was also influenced by his environment and is hence rarely applicable outside of this context. varies across cultures varies with age American culture emphasized individualism and individual achievement A critique of the ‘design hierarchy’ The design hierarchy concept put forward by Lidwell et al. consists of a brief description on a single page in their encyclopedia ‘Universal Principles of Design’ and is hardly detailed or descriptive enough to amount to a theory comparable to Maslow’s. However, the authors explicitly state that the concept relies on Maslow's work and it thus seems reasonable to make general inferences and draw parallels regarding the details of the design hierarchy concept as well as the criticisms thereof. The first question to ask is . On the face of it, there seems to be some validity to the bottom layers of the hierarchy. After all, every product needs some functionality that can be used and that performs its functions as expected. Functionality, Reliability and Usability hence seem necessary characteristics for any design. whether the categories proposed by Lidwell et al. are appropriate measures to determine a design’s success Higher-level needs, such as Proficiency and Creativity are more questionable measures. A cheaper, but less reliable version of a product, for example, or a more environmentally-friendly, but less robust version might allow people to become more creative with a product precisely because of these characteristics. So, a as some characteristics are more relevant in specific contexts than others. customized set of characteristics might be more appropriate than the universal set proposed One of the main claims put forward by Lidwell et al. is that their design hierarchy is to be interpreted as rigid and strict and that lower-level design needs must be completely fulfilled before higher ones can ‘begin to’ be addressed. In the context of the design hierarchy, , the product cannot be assumed to ever be reliable, without complete reliability, there can be no usability and without 100% usability, there can be no proficiency and so on. this claim asserts that without functionality that satisfies the product purpose Firstly, it is and a design can move on to the next level. What does it mean for a complex design, such as a car, for example, to satisfy the product purpose? Is it enough for it to transport people from one place to another? What about safety, entertainment, comfort and all the other design aspects that make a car well-designed? When is a car sufficiently reliable? What does complete usability look like and to whom should it apply? There are no obvious or easy answers to any of these questions and many of them also vary depending on the purpose and target users of any particular design. difficult to assess when a lower-level need has been sufficiently addressed Secondly, it is also . It is also not uncommon in complex designs to find some well-designed elements and some badly designed ones. Nobody would expect a space rocket to be easy to use, but it allows us to put satellites into space that undoubtedly improve our lives in many ways. Similarly, cars have a high proficiency, but are not that easy to operate. Some people take many years to become competent drivers, others never quite seem to make it. Some consumers are enthusiastically using Tesla's “Full Self-Driving” mode, but not always without crashing. While the utility of such a feature is undoubtedly significant, the technology often performs inconsistently, thereby making it sometimes unreliable. Similarly, a design might be very difficult to use or always function reliably, but provide a high level of proficiency at the same time. possible for designs to exhibit these characteristics to varying levels Lidwell et al. also claim that designs gain in value as they satisfy various levels of the hierarchy. So, designs that satisfy the Functionality and Reliability levels, but none of the higher ones have some value. While people who satisfy only the lowest level of Maslow’s hierarchy, like air, nourishment, sex and sleep are undoubtedly still humans, it is difficult to defend this view for products. In practice, designs only gain value when they fulfill, to some degree, all three base levels of functionality, reliability and usability. Something can only be called a design when it has useful functions, performs those reasonably reliably and is also usable. If any of those characteristics are missing, it is an idea, but not a design. The depiction of these characteristics in terms of a hierarchy is hence flawed and misleading due to the implication of a sequential relationship between the levels. Furthermore, the value of any design is defined by the observer and the market and is not inherent in the design itself. How we determine the value of a product is not universal and can vary depending on circumstance, interest, culture or age. A surfboard is only useful to someone who likes surfing, a cross might only have value to a christian and acne cream is rarely useful to old people. Value is not universal and is defined differently in varying contexts, regardless of functionality, reliability, usability and so on. Conclusion: Stop Using the Design Hierarchy Concept While the design hierarchy concept recognises that certain characteristics of designs are dependent on others, it is inherently flawed in so far as it represents a strict succession of specific characteristics. The relationship between design characteristics is interdependent, rather than hierarchical and one characteristic cannot be designed separately from other, related ones. Any characteristic may exist in any design to a varying degree, and for some designs specific characteristics are more relevant than others.  A universal set of characteristics is unlikely to be appropriate for all designs. The context of use and the users may determine the characteristics necessary to make a design good. It is also not a function of the creator to assess when a design has the necessary characteristics to make it good or valuable as the definition of these characteristics is a function of the user. A more accurate definition of characteristics that make designs good cannot focus on the design alone and must take the user into account. The simplicity of the ‘Hierarchy of Design’ concept is appealing, but it is not very helpful in assessing the value of a design. Designers should stop using the design hierarchy concept and find a more flexible, less hierarchical way to evaluate designs. References Universal Principles of Design https://arc345ergofactors.files.wordpress.com/2016/03/william-lidwell-kritina-holden-jill-butler-universal-principles-of-design-rockport-publishers-2003.pdf Motivation and Personality https://www.eyco.org/nuovo/wp-content/uploads/2016/09/Motivation-and-Personality-A.H.Maslow.pdf A Theory of User Delight: Why Usability Is the Foundation for Delightful Experiences https://www.nngroup.com/articles/theory-user-delight/ Maslow's hierarchy of needs https://en.wikipedia.org/wiki/Maslow's_hierarchy_of_needs A Theory of Human Motivation https://www.academia.edu/9415670/A_Theory_of_Human_Motivation_Abraham_H_Maslow_Psychological_Review_Vol_50_No_4_July_1943 There is no such thing as UX Design https://medium.com/@james.mingardi.elliott/there-is-no-such-thing-as-ux-design-d2c912158498 Needs Before Wants in User Experiences – Maslow and the Hierarchy of Needs https://www.interaction-design.org/literature/article/needs-before-wants-in-user-experiences-maslow-and-the-hierarchy-of-needs Designing For A Hierarchy Of Needs https://www.smashingmagazine.com/2010/04/designing-for-a-hierarchy-of-needs/ Gratification, Metamotivation, and Maslow, K. G. Agrawal and B. R. Sharma, Vikalpa, Vol. 2, No. 4, October 1977 https://journals.sagepub.com/doi/pdf/10.1177/0256090919770402 Praise & Criticism: Hierarchy of Needs https://blog.dtssydney.com/praise-criticism-hierarchy-of-needs-maslow The Cultural Relativity of the Quality of Life Concept http://teaching.up.edu/bus360/readings/Hofstede, 1984, Cultural relativity of quality of life.pdf Cooke, Bill, Mills, A. J., and Kelley, Elizabeth S. (2005) “Situating Maslow in Cold War America. A Recontextualization of Management Theory”, Group & Organization Management, 30 (2), pp.129-152. https://www.researchgate.net/publication/237332912_30_Cooke_Bill_Mills_A_J_and_Kelley_Elizabeth_S_2005_Situating_Maslow_in_Cold_War_America_A_Recontextualization_of_Management_Theory_Group_Organization_Management_30_2_pp129-152 Needs and Subjective Well-Being Around the World https://academic.udayton.edu/jackbauer/Readings 595/Tay Diener 11 needs WB world copy.pdf Evaluating the applicability of Maslow's theory of motivation to ancillary staff https://shura.shu.ac.uk/17880/1/40201_LSmith.pdf Psychology of Web Design https://www.scribd.com/document/395722847/Psychology-of-Web-Design-Smashing-Magazine Challenging Maslow’s Hierarchy of Needs https://billbennett.co.nz/challenging-maslows-hierarchy-of-needs/ Maslow’s “Hierarchy of Needs”: A Critique https://medium.com/@lodder77/maslows-hierarchy-of-needs-a-critique-f2669ef9fcc1 Maslow’s Hierarchy: Separating Fact From Fiction https://www.td.org/insights/maslows-hierarchy-separating-fact-from-fiction The Failure of Self-Actualization Theory: A Critique of Carl Rogers and Abraham Maslow https://journals.sagepub.com/doi/10.1177/0022167882222004 Usability 101: Introduction to Usability https://www.nngroup.com/articles/usability-101-introduction-to-usability/

Is There Really a Hierarchy in Design?

The problem is there are no simple “right” answers for most Web design questions (at least not for the important ones). What works is good, integrated design that fills a need—carefully thought out, well-executed, and tested. - Steve Krug One of the difficult things in UX Design is that there are very few hard and fast rules designers can follow to make any specific design useful or easy to use. What is the correct task flow? What controls will our users need? Can they efficiently operate them? How should we layout our screens so users can be efficient? How can we make sure we can find all the functions and features they need? While this feels like a standard list of questions designers ask before starting to get creative, there is no definitive answer for any of these questions. For every project and screen, we need to find solutions specific to the context of the project, the users, and their tasks. However, designers can all follow many laws, rules, and principles that can help us avoid some mistakes. One handy list of principles was compiled more than two decades ago by Jacob Nielsen. He described ten heuristics for interaction design that every UX designer should probably know. Wikipedia describes them as “... strategies derived from previous experiences with similar problems”. So, Heuristics are loosely defined guidelines for problem-solving and are not guaranteed to be optimal, perfect, or work in every situation. Nevertheless, Nielsen heuristics are general rules of thumb we use to guide our design decisions. Here is an example of how we applied Nielsen’s “Match Between System and the Real World” principle when creating the Linx Designer. Match Between System and the Real World The Linx Designer is a visual backend, integration, and automation programming environment and low-code IDE. So, when we started designing it, one of the first questions we asked ourselves was what a “match between the system and the real world” actually means in the context of Linx. The competition seems to widely agree that backend applications, data processing, and automation should be represented with views that look like workflows. Here users develop their applications by connecting various blocks on a free-form canvas. The data flows along with the diagram and is processed in each block. However, while a workflow diagram does an excellent job at representing simple process flows for marketing materials, real-world applications can quickly become extensive and very complex. We were concerned that workflow diagrams would become too messy and not scale well enough for serious applications. We wanted to make sure our users could keep the overview even in very involved applications. The target users for Linx range from professional developers to business people with some programming experience and knowledge of coding concepts. We expect that such people have used IDE’s to code applications before and possibly know one or two of them reasonably well. So making them develop applications in a workflow designer did not feel right. Instead, we wanted to design something more familiar and make them feel like they were still programming an application. We also wanted them to organize applications they developed in Linx in the same way they would think about and arrange them in a coding environment. So, we decided that a match between Linx and the real world means that Linx should look and feel like an IDE rather than a workflow designer. A low-code application development environment is also an IDE, and we decided that Linx should have more similarities with IDE’s. This design approach should help to bridge the gap between developing by writing code and developing using low-code. In Linx, they could feel that they were programming applications, just on a higher level and without writing any code. So, the concepts we created in the Linx Designer and the terms we use to describe the concepts developers use when coding applications. The terms we use to describe these concepts are terms used in programming. In Linx, users create custom functions with inputs, called parameters, and outputs called results. Functions are used to encapsulate functionality, as they are in code. Functions can also be nested and call each other to create functional hierarchies and simplify developing very complex applications. Each user-created function opens in a tab in the center of the IDE. Users program a function by dragging and dropping other functions into sequences. The sequences users create look like high-level code. The logic is built from the top down. We use indentations to denote function scope, just like many programming languages and code editors do. To make development quicker, Linx comes with a large number of pre-written functions. Some of these functions are very generic, like , which can be used to call any REST endpoint. Others are specific to an API, like all the Amazon S3 or Google Drive functions that enable users to quickly use the specific endpoints by these services. CallRESTHost These functions are contained in sets we call “Plugins.” Plugins are NuGet packages and include libraries with pre-written functions. When writing this article, we had released almost 40 plugins, each catering to a different technology, resource, or API. Plugins are picked from a list and added to a solution. Adding plugins to a solution in the Linx Designer is like referencing namespaces or importing classes in other languages, like in java and python or in C#. Once added, the premade functionality therein can be used in the application. import using We also created a typing system, strings, integers, dates, and so on that are used to store in-program data. Users can combine these simple types to create complex ones. Individual plugins can also have types. Internally, Linx handles all data in JSON already anyway, making it easy to work with REST services like the types they expect can just be dragged into a function and populated with data. Finally, we created services that listen to events and initiate the execution of the functionality. The most straightforward service is our . It has one event called that the service fires at specific times or intervals, as configured by the user. Users define the logic they want to execute when the event fires in the . Another popular service is called . It contains various endpoints that contain the logic to process incoming calls. Timer TimerEvent TimerEvent RESTHost Modeling Linx after IDE’s rather than workflow designers was an excellent strategy to choose. Staying close to the programming world allows our target users to get familiar with the concepts in the IDE quickly and helps to flatten the learning curve considerably. In addition, the design will enable users to follow sound programming principles, like the single responsibility principle, DRY (Don’t Repeat Yourself), or the separation of concerns principle, and enables Linx applications to scale well even when applications become large and complex. References 10 Usability Heuristics for User Interface Design https://www.nngroup.com/articles/ten-usability-heuristics/ Also published . here

First-Hand Experience of Applying Nielsen's Usability Heuritics in Design

In my I described how to apply the Double Diamond - a tool of design thinking (https://www.designcouncil.org.uk/news-opinion/what-framework-innovation-design-councils-evolved-double-diamond) - to product discovery.  In I will be diving deeper into diamond two (D2) where we address four types of risk as defined by : previous post this post SVPG Is it valuable to the customer? Is it usable by the customer? Is it feasible for us to build it? Is it viable for our business? D2 in a nutshell: conjure as many solutions as possible (divergent thinking) and then validate each idea to eliminate all but the optimal solution (convergent thinking). The final convergent step should not be confused with the “Learn” step which is intended to reflect a “ship to learn” mindset (https://www.intercom.com/blog/intercom-product-principles/). NB: D2 is an iterative process and starts with some hypotheses which can be validated at varying levels of fidelity.  A good hypotheses takes a format something like this “We believe suggesting improvements to authors at point of submission will increase the quality of submitted manuscripts as measured by a decrease in the desk reject rate from 50% to 45%”.  See a great post on hypothesis here < >.  Surveys, interviews, wireframes, live data prototype and AB testing are typical methods to validate a hypothesis. On occasion, for a big bet, an idea may pass through multiple levels of validation, say starting with a survey, then wireframe and then AB test (assuming at each validation your hypothesis is not disproven).  Commonsense is needed to determine how much time and effort to spend on validating an idea (i.e. cost vs benefit).  Ray Dalio principle 5.6 ( ) “Think of every decision as a bet with a probability and a reward for being right and probability and a penalty for being wrong.  A winning decision is one where rewards times it probability of occurring is greater than the penalty of it occurring”. https://www.producttalk.org/2014/11/the-5-components-of-a-good-hypothesis/ https://www.principles.com So let’s dive into D2. The discovery workshop First we need to make sure you have the right people in the room - physical room where possible - this should include product, engineering, design and, depending on your situation, a data scientist.  In my current role we always include a subject matter expert from the area of the business where the problem is causing the most pain. It is important to find a real subject matter expert rather than someone with a lot of opinions. I start this kind of workshop with a slightly modified version of the lean canvas ( ).  I prepare a first draft of the canvas prior to the meeting and then, in the meeting, walk through the canvas box by box and update the canvas.  Its important for everyone to contribute and feel part of this process - I find this helps get everybody in a ‘product thinking ( ) mindset.  This should take . http://leancanvas.com https://www.mindtheproduct.com/why-product-thinking-is-the-future-for-product-management/ about 30min to max an hour At this point everybody should be focused on the problem. Its important to note that the outcome of these workshops are shared with other key stakeholders to mitigate  viability risk early on.  For example, it may not be neccessary to include a participant from your ethics department, but sharing your ideas early with ethics can save you a lot of time and wasted effort.  Unfortunatly I speak of this example from personal experience :/ Ideation There are many methods to conjure up ideas to solve the problem at hand (selected in diamond 1).  Often the ideas come from outside the room but typically ideas come from: Random gut feel Iterating existing features Customer feedback Tech eg scale Quality eg bugs/performance  (cite this) Which method is best for your team is an experimentation process in itself.  Using Jobs to be Done ( ) is one of my favorites.  A method I haven’t yet experimented with but am super keen to try is the opportunitiy assessment tree ( ). https://hbr.org/2016/09/know-your-customers-jobs-to-be-done https://www.producttalk.org/2016/08/opportunity-solution-tree/ Easier said than done One of the things I struggle with is speed - validating ideas quickly to reduce the cost of failure. Iterating at speed (without forgoing too much quality) is difficult.  This is what I like to refer to as . This is, in my imagination, when dual track development ( ) is working in perfect harmony (NB: I have never achieved perfect harmony).  Setting up a Customer Advisory Board (CAB) ( ) and having recuring scheduled calls with users on an ongoing basis is key to having good Discovery Flow.  I, with my awesome UX lead, are recruiting our CAB at the moment - so any academic researchers out there wanting to help please reach out [end plug]. Discovery Flow https://www.jpattonassociates.com/dual-track-development/ https://www.productplan.com/glossary/customer-advisory-board/ The other thing I find difficult is designing experiments to validate ideas which are both meaningful and .  One of my heroes Don Norman ( ) (if you havent read this book it really is one of the classics - I think Design of everyday things, along with and are the 3 essential product management books).  In this example Mr Norman and his team want to understand how pedestrians will respond to driverless cars - so they go to an auto-store, buy some car seat covers and make suits out of them.  They then drive around in these suits and it appears there is no driver.  Genius! as small as possible https://www.nngroup.com/books/design-everyday-things-revised/ Lean Analytics Inspired

Double Diamond Discovery - Part 2

Predicting the likelihood of a customer to make repeat purchases using logistic regression

Whenever I am rushing for an office meeting and have to take an , I press the elevator button multiple times, even after seeing it already ON. It is an act I do to alleviate my stress in the moment. I act irrationally, inspite of knowing that I cant speed up things. This happens to all of us in our daily lives, especially when we are in a hurry and can’t wait for things to happen on their own. elevator , the ride hailing service in India, has introduced a cool feature on their app recently, that asked the user to let them (Ola) keep looking for a ride for five more minutes, in case the user didn’t get one in the first go, in an attempt to make things easier for the users. Ola While I think this is a really good problem to solve that actually helps users from having to try multiple times to book a ride, I felt it could have been implemented in a slightly better way. Let me explain you why I feel so. Be it going to theatres to watch a movie, or heading to a railway station to catch that train to my hometown, whatever the situation is, I always get ready in the nick of time and then try to find a cab. Sometimes, I don’t get a cab when I try for the first time, but I keep trying. I count every second as I stare impatiently at the progress bar hoping to find a ride as soon as possible and whenever I feel it is taking longer, I cancel the ride and try to book again. Not that doing it this way gets me the cab faster but I feel I am in control of things by doing so. I reduce my anxiety by doing so. This is precisely why I felt this feature could have been implemented in a better way. As you can see in the screenshots below, whenever I choose the option of letting Ola look for a ride for five more minutes, I don’t see the progress bar anymore. I don’t see anything inching forward. Sure, I am notified that the app is going to search for a ride in five minutes. Sure, I have an option to cancel this attempt and place a request one more time. But, by not showing the progress, the app is not making me feel that I am in control. Showing the progress of an action that takes longer is a fundamental principle of user-interface design. References: Progress indicators make a slow system insufferable and reduce uncertainty as explained in the article . https://www.nngroup.com/articles/progress-indicators/ Here is another article from NewYork times. https://www.nytimes.com/2016/10/28/us/placebo-buttons-elevators-crosswalks.html

Does Ola need an elevator button?

You can read this article on my own blog, if you prefer that to medium. is a fairly new column store database. It’s developed by the guys over at Yandex (the Google of Russia), made to scale horizontally reasonably well and run high speed aggregate queries on hundreds of billions of rows of data. Clickhouse It uses its own SQL dialect and it matches pl/pgSQL in terms of expressivity and simplicity. It even includes higher order functions for working with nested object within rows, such as and . arrayMap arrayFilter What is Clickhouse used for ? In short, Clickhouse is used to run fast analytics on very large amount of data. It’s rather bad as a transactional database, but it can run aggregate queries on billions of rows in sub second times. It fills the ever increasing niche that technologies like Hadoop, Spark, Druid, Big Query, Redshift, Athena and MonetDb aim for. It allows a team to easily store and analyze large sets of data with high dimensionality. More importantly, it blows the aforementioned solutions out of the water in terms of speed and query complexity. Clickhouse is meant solely for analytic workloads, so as long as you have a lot of and aggregated functions such as , and it will be blazing fast, even if your number of rows is in the 11 to 12 digits area. But if you want to do a lot of large scale joins and point queries, a typical RDBMS is still going to be the better choice. GROUP BY COUNT SUM DISTINCT It should be noted here, Clickhouse does not yet support queries like and in its stable syntax. Even when it will, they will be slow due to its compression mechanism. Again, this is not a database meant for transactional workloads. DELETE UPDATE It will also compress your data, the space this saves is based on the column granularity and on how much query speed you are willing to sacrifice. The way Clickhouse compresses and partitions data is a key to its quick queries. If you want to read more about how column stores work, I would suggest you start with the , since most of the information there is quite easy to understand and accurate. Wikipedia article What exactly makes Clickhouse amazing ? 1It’s extremely easy to setup and use,even if you need data replication and data distribution on a few dozen machines. Deploying a Clickhouse cluster on 3 machines is almost instant if you are familiar with what you are doing and could take you a few hours to do properly if you are learning everything from scratch. No need for obscure libraries, no endless configuration files, user permission issues and arcane problems with data distribution and replication. 2 It’s not fragile. One horrible thing I’ve noticed in my rendezvous with various “big data” technologies (spark is a chief offender here) is that they tend to often break with no warning and for no apparent reason. Even worse, they give little information as to why the problem happened within the humongous logs they generate. Clickhouse is designed to recover from failure and it produces quite a reasonably sized system log with clear explanations of the problems it encountered. 3 It doesn’t reinvent the wheel, it adds a steam engine to the carriage. Perhaps the most amazing thing about Clickhouse is that it doesn’t try to push a lot of limitations on you nor does it force you to completely re-think the way you store and query your data. Its data types cover everything from nested structure to matrices, it has a gigantic library of functions and one of the most versatile query language that I’ve ever seen. Unlike Map-reduce or other column store solutions, that will force you to rethink your entire schema, you can pretty much duplicate a transactional database schema with Clickhouse and it will do just fine. 4It’s actually got quite an amazing documentation and a great community. Perhaps the nastiest thing with “big data” technologies nowadays is that most of them are designed in order to sell support. A prime example of this would be Druid, open source and free in theory, but an unbreakable bastion of weird complexity coupled with a complete lack of documentation and community involvement. is relatively easy to search through and it’s being groomed, expanded and improved constantly. Clickhouse’s documentation The community is nothing short of amazing, it’s quite tiny, consisting of the developers and users from Yandex plus a few other early adopters, but that hasn’t stopped every question and issue I raised on Github and StackOverflow from being answered within hours. The developer also seem to very receptive to contributors external to Yandex. Enough talk, let’s install the thing The first step is to install Clickhouse, for the purpose of this tutorial, it’s best to have a machine running Debian or Ubuntu, since that’s the official OS Yandex supports. It’s also quite easy to install it on , I’ve personally installed it with success from the on Arch, it exists as a . If all else fails, you can use the or . Fedora, CentOS, Suse and RHEL AUR repository package for Gentoo docker image build it from source To install it on Ubuntu, the execute the following: sudo apt-add-repository 'deb http://repo.yandex.ru/clickhouse/deb/stable/ main/' sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 sudo apt-get update sudo apt-get install clickhouse-client clickhouse-server Now you can start the server as a daemon by running and connect to it via the client by running . sudo systemctl start clickhouse-server clickhouse-client If you want to play around with the configurations, you can find them inside and respectively. /etc/clickhouse-server /etc/clickhouse-client Playing with some data For this introduction, I chose to use the , it’s a rather small dataset of ~1 million rows, but it means downloading and insertion will be very quick. The query speed will scale linearly with larger datasets as long as they fit on a local RAID and won’t suffer to much if data is distributed. NYC property taxes dataset In case you’ve installed an older version you may want to run: SET experimental_allow_extended_storage_definition_syntax = 1; This will allow you to use the syntax used throughout this tutorial. If your version is new enough, trying to run this will just throw an exception, no need to worry, you are all set. Let’s create our database and table: CREATE DATABASE test_database; USE test_database; CREATE TABLE tax_bills_nyc (bbl Int64,owner_name String,address String,tax_class String,tax_rate String,emv Float64,tbea Float64,bav Float64,tba String,property_tax String,condonumber String,condo String,insertion_date DateTime MATERIALIZED now()) Before we insert that data, let’s look at the things which might be a bit unclear here. is a way to create to column that is automatically set whenever a row is inserted based on the function after the keyword. In this case, the function I chose to use is , in order to get the insertion timestamp for each row. MATERIALIZED now() sets the storage engine for the table. There are many types of that clickhouse supports. Including your bog standard engine, engines that are meant to buffer rows when a very high write rate is needed, engines that are meant to speed up joins and many others. MergeTrees are the best table engines for most analytic queries, and the engine is the most simple of those, and usually the best choice. ENGINE = MergeTree table engines MEMORY BUFFER JOIN MergeTree Whilst I the specific of the engines would take too long to explain here. If you are familiar with , , , or … then thing of MergeTrees as being something conceptually close to that. MergeTree long-structured merge-trees Radix trees immutable “Vectors” Merkel trees Patricia trees If you aren’t, I’d like to invite you to the when your local church of Functional programming next meets. data structures sermon will partition the table using the column. Partitions are the largest storage unit under tables in clickhouse. You can delete partitions, attach partitions and temporarily drop partitions. Efficient clickhouse replication and distribution also depends on good partitioning. PARTITION BY tax_class tax_class From the end user perspective, what you should care about most is that partitions are very fast to operate on using , and . > < == For the column you partition by, you should chose one with somewhat low granularity, as a general rule of thumb. A good rule of thumb is to have less than 1000 distinct values per billion of rows. When your data associated with a point in time, a good partition key is the date associated with every row. is similar to a btree index in an RDBMS, from a user perspective. It speeds up queries using comparison operators. Unlike , you can use high granularity data here without losing performance. Again, in the case of a table where you often query based on a time associate with each data point, a good candidate for this value is the timestamp of each row. You can order your table by a combination of columns if you so desire. ORDER BY PARTITION Now you should be familiar with most of the new concepts you need to get along with Clickhouse, the rest should feel quite natural to you. Let’s insert our data: curl -X GET 'http://taxbills.nyc/tax_bills_june15_bbls.csv'  | /usr/bin/clickhouse-client --input_format_allow_errors_num=10 --query="INSERT INTO test_database.tax_bills_nyc FORMAT CSV" That should take a few seconds or minutes based on your internet speed, once it’s done, we can start testing some queries. If you want to work with a bigger dataset but don’t have time to define your own table, just run the above command a few more times :) Let’s take it for a ride Ok, lets run our first query, don’t blink: . That query should have been relatively instant, probably done in sub ms time if you are using server grade hardware. SELECT COUNT(*)/pow(10,3) FROM tax_bills_nyc For the purpose of this tutorial I’m using an E3–1270v6 with 32GB of EEC DDR4 at 2.133 GHz, but really, any old server or laptop will do. Lets try something a bit harder: SELECT SUM(emv) as total_emv, SUM(tbea) as total_tbea, SUM(bav) as total_bav, tax_class,tax_rateFROM tax_bills_nycGROUP BY tax_class, tax_rate That should have taken somewhere between 50 to 10ms depending on your machine, quite the feat for doing a GROUP BY 2 dimensions and summing up 4 different columns. Ok, what if we add a high dimensionality column in the , let’s say the owner’s name, since there are about 885,000 distinct names in the dataset: GROUP BY SELECT SUM(emv) as total_emv, SUM(tbea) as total_tbea, SUM(bav) as total_bav, tax_class,tax_rateFROM tax_bills_nycGROUP BY tax_class, tax_rate, owner_name That was probably slightly slower, but it still probably took under half a second, despite the high granularity of our new grouping criteria. But, if we built our schema correctly, we should be able to filter most queries using our order or partition column, let’s add a where clause based on the column: tax_class SELECT SUM(emv) as total_emv, SUM(tbea) as total_tbea, SUM(bav) as total_bav, tax_class,tax_rateFROM tax_bills_nycWHERE tax_class='1c - condo in 1-3 unit building'GROUP BY tax_class, tax_rate, owner_name Suddenly, our query time drops in the sub 10ms range again. Clickhouse not only offers speed, but it has various type and value manipulation functions that can allow us to easily write a query which output something that’s easily human readable: SELECT concat(toString(uniq(owner_name) / pow(10, 3)), ' thousand distinct names among the owners of ', toString(uniq(address) / pow(10, 3)), ' thousand properties') AS result FROM tax_bills_nyc This should give you a result similar to: 85.058 thousand distinct names among the owners of 989.466 thousand properties Ok, let’s try and see what happens if we try grouping by every single column in this table. To save you some typing, we’ll use the function, which will essentially do just that. DISTINCT(*) SELECT COUNT(DISTINCT(*)) FROM tax_bills_nyc Surprisingly enough, this once again takes under half a second. It might actually be quicker then some of the previous GROUP BYs since we didn’t have to transform any of the string typed columns into a printable version. In my books, that was quite impressive when I first saw it. Have I peeked() your interest ? Hopefully you can see why I’m very excited about this database due to the expressive syntax and amazing query speed alone. This was mostly a toy dataset, meant to make this tutorial easy to follow. So I encourage you to try and go up to a few billion rows and see for yourself how unbelievable this database is. As for the dataset you want to do that with, I leave that up to you. Maybe try one of your large internal tables on which you often have trouble running analytical queries. Try formatting some of the data in your “data lake”, which you’d usually use a tool like Spark or Athena to query, and insert it into Clickhouse. If enough people are interested, I might produce a followup article or series with some more tips, tricks and real world usecases for Clickhouse. If you enjoyed this article you may also like: Imaginary Problems Are the Root of Bad Software If I were to select the worst Linux syscall

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What is one of the greatest myths startuppers tend to believe?“If you have a great product, it will sell itself!” Right?! Well, not quite…If you’ll try to execute this belief as a marketing strategy, you will probably find yourself headed for a failure. And that’s not me saying that, but Peter Thiel, the author of “ ” and one of PayPal co-founders, and part of the so-called “PayPal mafia”. Zero to One “No matter how strong your product- even if it easily fits into already established habits and anybody who tries it likes it immediately- you must still support it with a strong distribution plan” — Peter Thiel So, what is a B2C startup biggest challenge you ask? It’s hacking your channel distributions. And you need to start thinking about it from day one! In this article, I want to share with you what we, as “ Me ” did, to drive hyper-growth to our product. How we managed to bring our chatbot to be featured in Facebook Messenger’s Discover Tab, but more importantly — what we did to keep it there, and reach our first One Million users. So, meet , our Ultimate Reminder Assistant, developed by to balance our daily mental overload and help families become more organized, productive, and connected than ever before. Me Tiv.ai Discovery Discovery is one of the main challenges for the evolution of bot technologies. The rapid proliferation of messaging bots and the increasing number of messaging platforms is translating into a discovery nightmare for the ecosystem. , there are over 300K bots just on Facebook Messenger right to Jan 2018. According to Expanded Rambling Back to “Me” — At the beginning of our journey, we too were captivated by the myth that our great product will sell itself! And it did! All it took was a simple weekly approach and patience ( ). However, our growth was still low, and even though people mentioned “Me” in their blogs, shared it with friends and we even got featured on , we didn’t experience the exponential growth we were hoping for. you can read more about it here BotList Screenshots of User’s reviews from Me’s Facebook page User acquisition Let’s skip to the bottom line for a second — Me is serving over one million users around the world. How did we do that? As a public service, and to save you some time (and money), here are some of the techniques we applied to acquire high and sustainable traction. The first thing we tried was organic marketing. We believed that if we manage to hack this, we will enjoy good marketing. After we released some blog posts, we found that having a peak of dozens of new users once a week isn’t good for us. Since we were releasing versions on a daily basis and checking things all the time we needed a significant amount of new users to come every day and try our product. Later we thought about trying Paid advertising, but when you choose this channel, you need to have a sustainable business model, so the Customer Lifetime Value (CLTV) should be three times than the Customer Acquisition Cost (CAC). Otherwise, you need to stop right now, or you might be doomed to failure. Since we developed a chatbot that sits on top of Facebook Messenger, using a paid acquisition strategies wasn’t the right way to go from one main reason: “The market isn’t ready to pay for bots’ micro-services yet, so paying to acquire new users without monetizing, eventually will get you bankrupt.” Our second try was to build an Inherently Viral Mechanism into our Product, so with every core-action, an existing user takes, new users found out about your product. When your product has a natural social competent, an internal growth could form. Let’s take as an example. NearGroup NearGroup is a dating bot, which matches users (based on their preference) & allows them to start chatting without viewing images or profiles, pretty cool right?! Well, this chatbot is adding 800K new users every month. Our core-action was setting a reminder, 10% of the total reminders that a user creates, were for other people. For example: ‘remind John to pick up the kids tomorrow’s afternoon.’ That sounds great right, but when looking on the average reminders created by users and crossing it with the number of people users interact within the bot, you found out that salvation will not come from here. Last hope was the Discover Tab. Discover is where people can browse and find Messenger bots. Getting featured on any app or bot store is probably THE best thing that could happen to any product. That’s a free channel to acquire users but more importantly — getting their trust in your product. “But to get featured not only should your product deliver a unique value proposition, but it should be better than 99% of the market.” Old school, new neighborhood. A quick look at the Discover Tab will show up to 20 bots in each list, 80% out of them represent enterprises or big tech companies. These companies, are also Facebook’s business partners, which means they are not going anywhere in the near future, and leaves us with only four places for bot developers. That’s a challenge! But we’re not the ones who are afraid of a good challenge, and we decided to embrace it, and to do what it takes to get on the list. For more than 2 months, I ate and drank the “Discover Tab” — studying how to reach it, exploring what KPIs we need to achieve, for how long bots stay in the list, and more importantly — what’s the expected outcome. Lucky for you, here are some of important the things I have learned during the process: Every week your bot will be tested against more than 300K bots on Facebook Messenger, you have to keep up your performances and keep getting better all the time. Getting featured is the ‘easy’ part, staying there is the real battle: Being ranked at the top 3 will attract 5K new daily users; the top 10 will attract daily 1–3K, depends on the place, and at the top 20 will draw a couple of hundreds. (Of course the higher the penetration rate, the more users will encounter your bot). During our exploration time, we tried to get our hands on numbers such as: good retention and stickiness rates, the recommended number and lengths of sessions, does it make any difference who start the conversation the user or the bot? Does the page or the bot reviews affect the rank, how does Blocked, Deleted or Marked as Spam conversations make any difference? And so on. Facebook, as you can imagine, won’t provides the answers. But we had some luck. Magically, we found that “Me” is featuring under the Productivity Category (if you scroll down the Discover Tab, you will see more lists based on the topic). We use this as an advantage and figure out on which KPIs we should focus. Discover Tab, Categories Conquer the Productivity Category Our first objective was looking for the productivity apps’ benchmarks. We released daily versions, tuned the product with incremental advance, until we improved on the competition. Sources: SimiliarWeb, Adjust.com & Appsflyer Since the list got updated on a weekly basis (Friday to be specific), we focused on one KPI every week and tested if our rank is improved. In other words — if that particular KPI influences the Discover Tab. After a while we found out that the relevant KPIs we need to focus on are the following: Retention Day 2, Day 7 and Day 21 Stickiness (DaU/MaU) Session length There aren’t golden numbers for each KPI since every week you are measured against all the bots out there, based on the concept of “may the best Bot win”, and by “best” I mean the best performing. Once “Me” dominated the productivity niche, we decided it’s the right time to scale up and look at the bigger goal, the Featured list on the Discover Tab. Moving on to the Multinational League After we discovered what we need to focus on, and which KPI makes the difference, it was easier to scale up. When you open Facebook Messenger and click the Discover Tab, the first thing you will see is the top three bots worldwide, this is the Holy Grail. To get there, you will not only need to get into the Featured list, but also to be at the top three. That was our next mission — we didn’t know what good metrics are, so, we turned to the amazing bot community, and thanks to them, we managed to do this in just a couple of days. I was asked not to expose our findings, but I can share with you the way there: Read every blog post about successful bots and gather stats I reached bot developers and asked them to share some info. Paid for Analytics platforms that suggested ‘Competitive Intelligent’ features. These features can help you understand how good your product is relative to other products. After two weeks, I got the sense of what we need to achieve and started to work on these metrics until we accomplished the KPIs. One important thing, when we first improved our KPIs, we were already featured in the Productivity Category, but the second time was different. We couldn’t just ask to be featured. First, because we needed to go through the whole process by Facebook, and secondly, we didn’t want to waste our one chance of getting in. From my research, ‘regular’ bots didn’t hold more than two weeks on the Featured Tab, and we didn’t want to miss that. Submitting and Getting Featured To be featured, you need that a Facebook Partner will submit your bot for a review. You probably think what Facebook Partners are, and how do I reach them? Well, as an entrepreneur one of the most important lessons I learned is to get my foot in the door and say what I have to say. After I send messages to every person that could potentially help me, a Facebook employee heard about us and introduced me to a Facebook Partner. We quickly send her an email with a background, explained why Me is essential to people and how it can affect their life positively, and some of our KPIs. Let’s break down the process: Submit your bot for review in specific countries — They review all potential bots for Platform Featured Adoption, Policy compliance, User Experience, and use case. I’d highly recommend going over the before you submit your bot. This step usually takes one month, and you want to get the maximum out of it. Messenger Bots Best Practices 2. Once the review completed, you’ll get you a slide deck with feedback that you can work on, if relevant. — basically they are making sure that your bot complies with the messaging policies for the Messenger Platform. This step usually takes one week. Policy review On February 16th, “Me” got featured in the US and the Philippines and ranked at the 22nd place in his first week. Remember the fact that every week Facebook revise the list? The week after, we reached the 8th place, and in the 3rd week we conquer the top 3!!! Since our launch, “Me” has processed over 200 million messages, assist over 1 million users worldwide, and has become the #1 Most popular productivity bot on Facebook’s Messenger in the U.S, Canada, and the Philippines. User Activity from Jan 01 to May 01 I hope you enjoyed reading this article. . Please feel free to ask any questions in the Responses section below Also, feel free to check out our bot Me . 👏👏Don’t forget to give a round of applause 👏👏 If you’ve enjoyed this piece, go ahead, give it a clap 👏🏻 (you can clap more than once)! You can also share it somewhere online so others can read it too.

How I growth hacked my bot for 10x better user acquisition

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The human attention span is mere 8 seconds, less than that of a goldfish i.e. 9 seconds. So, the fact is you have a very few seconds to convince the visitors to stay on your website. And if you miss that chance, they will leave the page and move to another website. Here a proper web design plays a key role to grab the attention of the visitors and not only that but also converting the visitor into customers. Top web development companies and web designers already know the secret of increasing conversion rate with the design and in few moments even you’ll know it. The following are the 10 web design tips and principles that will help you to boost your conversion rate. 1. Make Your Web Design Responsive Users are 5X times more likely to abandon the task they are trying to complete if the site isn’t optimized for mobile use. Users no longer visit the websites just on PCs/Desktops. In fact, the share of mobile traffic is more. In 2017 mobile traffic dominated the total global web traffic with 52.64%. This shows the . And if your website is not responsive, you’ll be losing a huge amount of audience. importance of responsive websites Accessing a website designed for PC on mobile or other small screens can be an extremely annoying experience. Sluggish pages, zooming in to read the text, awkward buttons, etc. does not provide a pleasing user experience and as a result, the user will leave your website in no time. Designing a responsive website provides a fulfilling user experience as the layout adjusts automatically according to the screen of the device. So next time you hire web developers or a , ask them to make your website responsive. web designing company 2. Use Faces Using faces on your website attracts the user’s attention as they convey emotions. Humans empathize the faces and if it depicts a type of emotion, consumers are more likely to connect with the website. Mirroring the feeling of the user with facial images creates an emotional connection between your brand and user. And emotions drive customer’s decision, This has been widely used by many advertising companies all over the world. However, you must take care that the images represent your brand. And try to avoid the stock photos. The following is a snap from Verizon website. The US telecommunication giant has used various faces portraying happy customers using their services. 3. Testimonials and Reviews If you want to boost your credibility and establish trust with your customers, testimonial and reviews are must have on your webpage. Testimonials and reviews work as a social proof. According to this principle, we humans use other people’s information and experience to make our decisions. For instance, if one of your colleagues has appreciated the food of newly opened restaurant in your neighbourhood, you are more likely to visit there. What if the same guy has scorned about its service? Would you be still excited to visit there? So, add real testimonials of your services on the webpage. Make sure those are good ones and it will provide a shortcut to judgment for the users. 4. Call to Action Button Call to action button is a crucial element of your website. Any interaction between you and your users happens through it. You really need to make sure that it as inviting as a red carpet welcoming the user to click on it. And of course, it should be clearly visible. The position and colour of the call to action button plays an important role. Make sure the colour is highlighting and it is positioned at the right place considering the visual pattern of the user. This brings us to our next principle of content placement. colour ) (Source-Netflix. Check the attractive and placement of the CTA button 5. Content Placement The placement of your content is a great tool to influence your customer’s decision. Research has shown that humans follow a visual pattern on the page. The users read from the top-left side of the page and gradually move to the right. This forms an “F” pattern. Here the top-left portion of the page gets most of the attention. The following heat map shows the reading pattern of the user. The red, orange and yellow area is where the user pays maximum attention. (Source- nngroup.com) (“F” pattern content placement) However, the attention of the user gradually diminishes towards the bottom of the page. But if you add an image between the text, the attention of the visitor is renewed once again. This forms a “Z” pattern. The following is Dropbox’s website forming a “Z” pattern for content placement. The CTA button is placed on the left side. You can also use the to place the main elements of your website. If you have ever used a professional camera or even a camera on your smartphone, you must have seen a grid of nine square boxes. These boxes work as a guide for the photographer to place the elements in the boxes according to their importance and capture the photograph. Use the same to place the elements of your website according to their importance. rule of thirds 6. Unique Selling Point The unique selling point of your website differentiates you from the huge pond of websites out there. To draw the user’s attention your web design must have a one of a kind design that separates you from the crowd. However, apart from drawing the user’s attention, make sure that your USP also sticks to the user’s mind. 7. Keep it Simple 76% of consumers say the most important factor in a website’s design is “the website makes it easy for me to find what I want.” One of the most important principles of web designing is to make it simple for your visitors to get what they want on the website. I have heard many people complain about websites, “Why is it so hard to find a thing on this website.” And you never want to hear it from your visitors. This is one of the things that increases the bounce rate. You need to make sure that they can easily navigate through the pages and get what they need. Try to step into the shoes of the visitor and understand how you can make it easier for them. Organised navigation pattern, uncluttered content, a neat design, etc. Just remember- keep it simple. 8. Utilize the White Space/Negative Space White space, often regarded as negative space in designing has a positive impact on the user. A negative space is an area on the page that is kept vacant. It includes the space between the sidebars and content, header, text and paragraphs. All these negative space helps in making your website look neat, legible and easy on the eyes of the user. Using a proper combination of negative and positive space can help to provide better user experience and convey your idea efficiently. One of the best examples of proper utilization of white space is Apple’s website. Take a look. 9. Consistency in Colour, Branding, Images and Fonts One thing that can destroy your web design is inconsistency and it even looks unprofessional. A website depicts you as a brand in the digital world. So, any inconsistency in your web design will affect your brand image. Ensure that the elements of your page are consistent. The fonts style, type of images, colour scheme, logos, etc must be of the same style. 10.Choose the Right Colours Colours tell a lot about you and your brand. They can attract the consumer’s attention and influence their buying behaviour. Consumers form an initial judgment of a product within in 90 seconds of interaction and 62%-90% of them are based on the colour. Different colours have been proven to evoke different emotions in humans. So, using the right colour scheme while designing can represent your brand and mood of your website. Using contrasting colours can make your essential elements such as the banner headlines, call to action button, testimonials, etc. stand out. The principle that works here is — the thing that stands out is more likely to attract and remembered. So, you can use high contrasting colours in the background and catchy highlighting colours for the elements you want to draw attention. Wrap Up Now you know how your web design can boost your website conversion. Just implement the above principles and tips for your website and boom! You’ll observe more visitors and surprisingly more conversion rate. Hope you are benefited reading it. We are Techuz, a top . We have been providing web development services to our clients for almost half a decade now. If you have any questions or inquiries, feel free to . web development company in India get in touch with us Originally published at www.techuz.com on April 6, 2018.

How to Boost Conversion Rate with Web Design?

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