In a significant development for the future of Russia’s Central Bank Digital Currency (CBDC), Sberbank, the country’s largest bank and a major tech player, has confirmed its participation in the digital ruble pilot program.

Sberbank Joins Russia’s Digital Ruble Pilot Program: A Step towards Financial Digitization

Sber, Russia’s banking titan and a burgeoning technology heavyweight, recently committed to partaking in the country’s Central Bank digital currency (CBDC) trial slated for 2024. This development follows the financial institution’s surprising departure from the initial pilot stage earlier this year.

Sber rejoins Russia’s digital ruble trials for 2024

Sberbank, Russia's largest bank and a major tech player, has confirmed its participation in the digital ruble pilot program.

Sberbank to join Russian Central Bank’s Digital Ruble pilot

Russia’s Sber, the operator of Sberbank, says it will join the Central Bank’s digital ruble pilot in a coup for the nation’s CBDC. Find out more!

Russian Business Titan Sber to Join Digital Ruble Pilot

{"likeData":{"id":866399,"type":1,"typeStr":"content","state":0,"withUsers":0,"contentId":866399,"subsite":{"id":2201504,"name":"\u041a\u0430\u0442\u0435\u0440\u0438 ...

Сбермаркет не возвращает деньги

EXPERT RA assigns X5 an ESG rating of II(a) Moscow, 14 September 2023 - X5 Retail Group N.V. ("X5" or the "Company", LSE and MOEX ticker: FIVE), a leading Russian food retailer that operates the ...

EXPERT RA assigns X5 an ESG rating of II(a)

A wide variety of grains are used in Russian cuisine. Many people, however, only know traditional Russian porridge (kasha); but that’s just

Top 5 most popular grains in Russia

Russia is seeing a surge in interest in spirituality and mysticism as President Vladimir Putin's war in Ukraine continues. In the 17 months since Putin launched a full-scale invasion of Ukraine, there have been multiple deaths among Russia's wealthiest. In ...

Russian elites keep getting themselves killed with alternative medicine

Another Russian businessman, Dmitry Zhulin, founder of the online shop SberMarket, was hospitalized after self-medicating with an immune stimulating agent based on human placenta, Russian Telegram ...

Russian Elites Keep Getting Themselves Killed With Alternative Medicine

{"likeData":{"id":1752209,"type":1,"typeStr":"content","state":0,"withUsers":0,"contentId":1752209,"subsite":{"id":303542,"name":"\u0421\u0432\u043b\u0430\u0434 ...

Этот сбермаркет троллит щас?

SberMarket

sbermarket

is an ML expert with over six years of hands-on experience in ML engineering and product management. As an ML Tech Lead at Deliveroo, Gusev developed a proprietary internal experimentation product from scratch as the sole owner. As part of the innovative stream of Yandex Music transforming the product to add podcast listening experience to the service, he built a podcast recommendation system from scratch as an at Yandex and achieved a remarkable 15% target metrics improvement. Additionally, as Head of Recommendations at SberMarket, his tech-driven roadmap elevated AOV by 2% and GMV by 1%. Petr Gusev ML Engineer Can you provide examples of specific ML projects that you have managed successfully? How did you ensure the project's success? I led a project to personalize the ranking of products featured on the main page of a grocery delivery service. Our main task was to increase business metrics through personalized product offerings. To make sure the product would be successful, I did: Interviews with users, with the intention of understanding what is valuable to them and the type of products they would like to see on the main page. In this project, I also invited ML engineers to participate in the interviews so that they could get context by listening to the users. Checked whether the hypotheses that arose after interviews with users were confirmed by the data we gathered Recorded the metrics that we expected to reach as a result of developing this functionality Before starting actual development, we developed the model and made sure that it gives us the necessary quality and that we can achieve the necessary results in the A/B test. This is a very important point because developing an is a research project that may not give us the results we expect. And if development has already started, we will waste the entire team’s time without achieving the necessary results. ML model Assembled a cross-functional team of analysts, developers, and ML engineers and made sure that all people working on the project were on the same page throughout every stage of product development. What are the specifics of managing an ML team? A key aspect of managing an ML team is defining the project scope with precision, given the exploratory nature of ML projects. This requires setting clear, structured tasks. For instance, to delineate tasks accurately, I often use a template like "Implementing X will impact metric Y by Z.” This approach helps us set tangible goals and measure progress accurately. Talent acquisition and skills development are also vital. Building a team with the right mix of skills is essential, and continuous learning should be encouraged to keep up with the rapidly evolving field of machine learning. This includes nurturing both technical skills and soft skills, such as problem-solving and teamwork. Cross-functional collaboration is another important element. Especially when integrating and building a new ML model, the ML team needs to work closely with other departments, such as data engineering, product management, and marketing, to ensure that the ML model goes live. Lastly, in this field, risk management and experimentation are crucial. Managing an ML team involves balancing and mitigating the risks associated with new and untested models while encouraging innovation and experimentation. It's about creating a safe space for trial and error, learning from failures, and continuously improving the models and techniques used. How do you delegate tasks and responsibilities within your ML team to maximize efficiency and productivity? Structuring Work by Product Functions: I organize responsibilities based on different product functions such as consumer, operations, and marketing. This approach ensures that team members can specialize and develop deep expertise in specific areas, leading to more efficient and effective problem-solving. Rotating Team Members Between Streams: Periodically, I rotate Machine Learning Engineers (MLEs) between different streams. This not only prevents monotony but also broadens their experience and perspective. For instance, an MLE who has completed an initial project in the consumer stream may be moved to work on improving the model in a different stream. This rotation fosters a well-rounded skill set and promotes adaptability within the team, and reduces the so-called “bus-factor.” Assigning Tasks Based on Seniority and Growth Potential: Senior MLEs are often tasked with more complex, end-to-end projects or major research initiatives, such as building initial model prototypes. Meanwhile, junior MLEs are assigned to support these projects, providing them with opportunities to learn and hone their skills. This mentor-mentee dynamic accelerates the growth of junior team members and ensures effective knowledge transfer. Setting Clear Objectives and Expectations: For each task or project, I clearly define the objectives, expected outcomes, and timelines. This clarity helps team members understand their roles and responsibilities, enabling them to work more autonomously and efficiently. What strategies do you employ to ensure effective communication and coordination between data scientists, engineers, and other team members? Scheduled regular meetings where team members from different functions can update each other on their progress, discuss challenges, and brainstorm solutions. Ensured all work, whether code, data analysis, or research findings, is well-documented and accessible on shared platforms. Established and agreed with team members on clear channels for communication specific to different needs – such as urgent queries, brainstorming, or status updates. Involved all relevant team members in goal-setting and project-planning sessions. How do you handle challenges that may arise during the development and deployment of ML models? What potential bottlenecks can occur when managing an ML team? Often, the key factor is the model's execution speed. Initially, it's crucial to understand the specific constraints needed, as these can vary across different segments of a product. For instance, a model might be required to deliver results in less than X milliseconds or operate on less powerful machines. To effectively manage this, a rapid prototype should be developed to evaluate the model's performance in a production setting. If meeting these constraints is impossible, then it’s important to negotiate with stakeholders to determine which aspect we should prioritize: the model's accuracy or its execution speed and resource consumption. Another common challenge in , particularly those with a stronger focus on science and mathematics rather than engineering, is optimizing the model for high performance in a production environment. Teams with a predominantly scientific and mathematical orientation might excel in creating robust models but may lack the skills to enhance their operational efficiency. Therefore, incorporating diverse profiles into the team is vital, as this ensures that we have both engineering and scientific backgrounds. ML teams Additionally, managing the required accuracy constraints of the models is a critical aspect of the process. Tasks often necessitate careful balancing of precision and recall. For instance, in scenarios where false positives have significant implications, prioritizing precision might be more crucial. Conversely, in cases where missing true positives is costly, focusing on recall becomes essential. Tailoring the model to effectively manage these trade-offs according to the specific requirements of the task is fundamental to the successful deployment of machine learning models. Can you share an example of a time when you had to make tough decisions regarding resource allocation within your ML team? with a fast-growing user base, our team ran out of resources to store personalized recommendations, which were generated offline. Problem: As an immediate measure, I ceased generating personalized recommendations for users with insufficient history feedback. Recognizing that the quality of these recommendations would not be optimal due to the lack of data, I’ve replaced them with non-personalized recommendations. This approach leaned more towards popular recommendations, utilizing a different ML model to manage the immediate storage issue. Short-Term Adaptation: To sustainably address the problem, my team developed an online recommendation system. Unlike the previous method, this new system did not require storing recommendations for every user. Instead, it generated recommendations dynamically, in real-time, when a client requested them. This innovative solution effectively resolved the storage constraints and adapted the recommendation process, and made it more scalable and responsive to user demands. Long-Term Solution: How do you ensure that the ML team's work aligns with the business goals and objectives of the organization? Ensuring that the ML team's work aligns with the organization's business goals and objectives involves a strategic and proactive approach: I have established a process for consistently sharing the relevant business context of our project with the team. This involves frequent updates and discussions about the company's broader goals, market trends, and customer needs, ensuring that the team's work remains aligned with the overarching business objectives. Regular Business Context Sharing: In collaboration with the team, we set quarterly Objectives and Key Results (OKRs) that are directly derived from and support the company’s own OKRs. This ensures that our projects and initiatives are contributing to the company's strategic objectives. Setting Quarterly OKRs in Alignment with Company Goals: To monitor our progress towards these OKRs, we conduct bi-weekly check-ins. These sessions allow us to assess our current standing, identify any challenges early, and make necessary adjustments to stay on course for achieving our objectives. Bi-Weekly Progress Tracking: I proactively engage with stakeholders, providing them with monthly progress updates. This not only keeps them informed about the ML team's contributions and advancements but also fosters transparency and trust. It allows stakeholders to provide feedback and ensures that our efforts are in sync with the company's evolving priorities and needs. Monthly Stakeholder Updates: How do you measure and track the impact of ML projects? Prior to the actual development of the ML model, I conducted a thorough data analysis to estimate the potential impact of the project. This step involves evaluating historical data and predictive analytics to forecast the expected outcomes and benefits of the ML model. Pre-Development Impact Estimation: I implement A/B testing to empirically assess the effectiveness of the ML model and its impact on business metrics. Before initiating the experiment, I identify the key business metrics that are most likely to be influenced by the model. These metrics are carefully selected based on their relevance to the project goals and are analyzed to gauge the model's performance. A/B Testing: Post-deployment, I actively monitor a set of dashboards that track various company and proxy ML-model metrics. This includes, but is not limited to, metrics like the number of purchases facilitated by the recommendation system or distribution of travel time model predictions. Regular monitoring of these dashboards, typically on a weekly basis, allows for real-time assessment of the model's impact on business operations. Dashboard Monitoring: To further understand the value added by the ML models, I conduct quarterly degradation tests. These tests involve temporarily disabling the ML models in specific parts of the business flow or across the entire application for a randomly selected, small user group. This helps in quantifying the by observing changes in business metrics in its absence. However, it’s very important to align with all the departments involved, so everyone is aware and actively agrees on performing such a test. Quarterly Degradation Testing: impact of the ML systems What steps do you take to foster a collaborative and inclusive team environment where all members feel motivated and valued? I emphasize the importance of every team member's perspective and actively encourage open communication. It's crucial not only to state that all opinions are valued but to demonstrate this in practice by giving equal attention and consideration to everyone's ideas and suggestions. Valuing Every Team Member's Opinion: I prioritize building and maintaining a diverse team. This includes diversity in skills, backgrounds, experiences, and perspectives. A diverse team is more likely to generate innovative ideas and solutions, and it also fosters a richer, more inclusive work culture. Maintaining Team Diversity: During team meetings, I ensure that everyone has the opportunity to speak and contribute. I actively facilitate discussions in a way that every member feels comfortable sharing their thoughts. This might involve directly inviting quieter team members to share their views or setting up meeting structures that ensure balanced participation. Inclusive Meeting Management: Organizing team-building activities and informal networking opportunities helps in building rapport and understanding among team members. Encouraging Team Building and Networking: I support the professional development of each team member through opportunities like personal development plans, workshops, and conferences. Providing Opportunities for Professional Growth: Regularly acknowledging and celebrating the achievements and contributions of team members. Recognizing and Celebrating Contributions: How do you encourage continuous learning and professional development within your ML team? Are there any specific initiatives or programs you have implemented? We have established a reading club where team members take turns to present and discuss a machine learning science paper relevant to our work. This not only keeps the team updated on the latest research but also encourages critical thinking and knowledge sharing. Reading Club: Every two weeks, we dedicate a day to R&D, where each team member explores a research project or tests new hypotheses. This initiative is designed to foster innovation and practical application of new ideas that could benefit our daily work. Biweekly Research and Development Days: To stimulate creativity and team bonding, we organize quarterly ML hackathons. These events span a few days and are focused on working on exciting, out-of-the-box projects. They provide an opportunity for the team to experiment with new technologies, collaborate in different team configurations than usual, and offer a fun and stress-free environment to innovate. Quarterly ML Hackathons: How do you handle conflicts or disagreements within the ML team? Can you provide an example of a conflict that you resolved successfully? Initially, I conducted one-on-one meetings with the involved parties. This allows me to understand each person's perspective and the core issues at hand. It's important to listen actively and empathetically during these discussions. Following the individual meetings, I arranged a joint meeting with both parties. Here, I share my observations, facilitate a constructive discussion, and encourage both individuals to express their views. This step often helps in clearing misunderstandings or mismatched expectations. Most conflicts are resolved at this stage. However, if the disagreement persists, I involve HR to assist in finding a resolution, ensuring that all parties feel heard and that a fair solution is reached. For technical disagreements, I advocate for a to conflict resolution. I encourage team members to support their arguments with measurable metrics or empirical evidence. For example, a conflict arose over which algorithm would perform faster for a particular application. To resolve this, I asked both parties to provide benchmarks and empirical data supporting their claims. This approach not only resolved the conflict but also fostered a culture of evidence-based decision-making. data-driven approach How do you ensure that your team is also up-to-date with the latest technologies and techniques? I arrange for experts from other companies within the industry to visit and share their experiences with our team. This exposure to external expertise provides valuable insights into new practices, tools, and methodologies being used in the industry. It's an opportunity for the team to learn from the successes and challenges faced by others in similar fields. I actively encourage team members to both attend and present at relevant conferences. This serves a dual purpose. Attending conferences keeps them informed about the latest trends and breakthroughs in machine learning while presenting their own work fosters a deeper understanding of their areas of expertise and enhances their professional profiles. This involvement in the professional community not only benefits individual team members but also brings fresh perspectives and ideas back to our team.

Interview with Petr Gusev: How To Manage an ML Team the Right Way

Hi everyone. My name is Ivan Prokofyev, I am a payment fraud lead and this is my first (hope not last) post. HackerNoon I have over 10 years of experience in the fraud prevention domain. In my career, I’ve spent time working at Tinkoff Bank, ForexClub(aka LibertEx), Gett, and Booking.com. I’ve also worked on a couple of projects with Sbermarket. I’m currently working in Bumble(Badoo). A bit more about myself: Experience in these companies allowed me to work in different domains and different business models where fraud prevention was part of IT, Security, Marketing, Finance, and Operations. Most of my fraud positions were related to payment fraud (everything which relates to credit cards, PayPal, and the rest of payment methods such as Klarna, boleto, etc). I also have experience working with marketing abuse (coupon, loyalty fraud), collusion, resellers, etc. In today’s post, I’ll aim to answer the following questions: Do you need fraud prevention as well as cybersecurity? Why do you need a Fraud Specialist? Why is it hard for a Business Analyst to become a Fraud Analyst? Why does a fraud specialist have to know how to work with SQL? Let’s get to it! 1. Do we need fraud prevention if we already have cybersecurity? Cybersecurity and fraud prevention are 2 different professions that respond to different aspects of a business. Work in cybersecurity focuses more on access, DDOS attacks, protecting devices, networks, data breaches, etc. Fraud prevention covers payment disputes, coupon abuse, testing fraudulent credit cards, reselling services or/and goods, etc. There is of course overlap and both areas aim to protect companies from financial loss. However, expecting a cybersecurity expert to handle fraud prevention cases and vice versa is not the right approach and may cause issues for both sides because the focuses are so different. This would be the equivalent of expecting a Backend Developer to work on the Front end — it might be achieved, but the time and resources wouldn’t be used effectively. TL;DR point 1: Cybersecurity and fraud prevention are 2 different professions. Cybersecurity is about protecting your infrastructure, while fraud prevention is about protecting your business processes. 2. Why do you need a Fraud Specialist? . This is only the number of confirmed losses that have to be opened to the public, the real numbers are expected to be much higher. It was ± 17B$ two years ago in 2020 and the prediction for 2023 is ± 50B$. Payment fraud losses were ± 41B$ in 2022 This should be reason enough! But let’s not stop there. Whenever a company decides to start accepting payments there are several payment fraud prevention options: In-house solution (An in-house fraud team). Payment provider solution with a guaranteed high acceptance rate and low fraud decline rate. An external solution that prevents all possible fraud. However, it is important to remember when choosing one of the last two solutions that you need to have someone on your side. Otherwise, solutions will focus on their own success, not the protection of your interests. What could go wrong? Without regular communication, the company can face an increase in reject rate, decline payments from genuine customers, and chargeback(dispute numbers) will grow, which can lead to consequences such as fraud/dispute programs from Payment systems ( , , Amex, etc) and as a result initiate even more declines from banks because the company becomes risky and on top of this there will be a monthly fee, starting from 10k $. Visa MasterCard Fraud specialists will lead all conversations with providers, communicate about changes on the business side, feedback about false positive declines with requests to review solutions, and most importantly keep your company away from fraud/dispute programs. You might think that if you’re not working with payments, you don’t need Fraud Specialists, and you might be right. It might be true. For example, in some regions even when you accept payments, domestic fraud is so low and you will never have a rate higher than 0.001%. But these markets have other fraud risks, which you might have never known about. Let’s take as an example “Invite friends”. I believe each of you knows about these marketing campaigns. After launching the campaign most of the time all dashboards/charts show only the growing number of new customers. But if you start to analyze “new” customers you might find that 20 new users are related to one email or phone. The main account already has a super high balance and 20 accounts will never have any activities after being created. In the end, you have misleading information and expectations from numbers. Businesses that work with 3rd party companies or people (for example delivery or taxi services) are faced with collusion and fake orders which cost a lot. For these cases, you have to have a fraud specialist, that will focus on analyzing all these areas, work closely with the product team and find and resolve cases that affect company revenue. Also, preventing you from making a decision when you don’t have any fraud, but also don’t have any orders and customers. It is very difficult to prevent fraud and keep it under a specific level to make sure that the business is operating and all fraud prevention solutions aren’t affecting real customers and prevent only fraudsters. TL;DR point 2: Fraud losses are growing year over year. The worse the economic situation, the more fraud there is. External providers are good but better to have someone on your side to focus on your company’s interests. Fraud isn’t just about fake credit cards. It also relates to coupons, collusion, etc. 3. Why is it hard for a business analyst to become a fraud analyst? I know some people who started out as Business Analysts and became amazing Fraud Analysts, but it’s not always the case. Usually, when Business Analysts are asked to work on fraud prevention, it starts with simple requests from the business side — define fraud or fish out fraudulent activity. This task doesn’t contain enough information or clarity about fraud and what fraud actually means. Analysts will usually start analyzing traffic and identifying anomalous activity. For example, 20 orders in a specific area at a time you would usually find three. After a couple of these attempt approaches, the percentage of fraud (usually it is 5–7%, in some cases 17–20%) is erroneously defined, and the “party” begins. Everyone stops their work and tries to find out why we have so much fraud. Unfortunately, finding anomalies is not enough even if it is a good process. But finding anomalies, reviewing the number of orders, finding a pattern, linking issues, and explaining why they are fraudulent is different. On top of this, fraud prevention requires manual work, most of the time, and it becomes boring for business analysts. They lose motivation and are ready to switch to another project. Finding a pattern is not the end of the story, the hardest part is to make a decision and say — yes it is fraud and we have to block these accounts and refund money back to prevent more losses. Additionally, it's important to note that while business analysts typically rotate between departments, focusing solely on fraud allows for a deeper understanding of emerging trends and potential risks. With fraud, there are no established manuals or descriptions outlining how to identify it, so it requires sustained focus and expertise. Waiting three months to uncover fraudulent activity while calculating EBITDA can prove challenging without this specialized knowledge TL;DR point 3: Business Analysts can make excellent Fraud Analysts but the skills needed for the roles are very different. Not every analyst wants to find fraud, review orders, and review conspiracy theories. Fraud Analysts can’t be rotated as you’ll have a risk of missing trends. 4. Why does a fraud specialist have to know SQL? It's much easier to detect issues and solve problems when equipped with the proper tools. For instance, identifying suspicious orders, analyzing historical data, and recognizing current trends can be done swiftly through SQL queries (or Python, R, etc.). Conversely, manually comparing multiple Excel files or examining orders individually and recording them in a text document can be a time-consuming process that may result in outdated information by the time the task is complete. If you intend to employ a fraud specialist without SQL expertise, it may make sense under specific circumstances, such as: outsourcing work to an external provider whose primary focus is reviewing individual orders, having a dedicated analytical resource to provide full support, or employing junior specialists to assist a skilled individual who understands how to work with databases. While Python or R may offer additional options for data analysis and pattern recognition, my view is that SQL suffices. Nonetheless, some companies, including large ones, still require expertise in Excel, and SQL is viewed as an added advantage. TL;DR point 4: Without SQL, Python, or other tools that allow you to work with big data, you can work to a maximum of 50 orders per day. SQL is a must-have. The information above is based on my experience and situations that I have encountered in different companies. Also published here. Lead image from giphy.

Do You Need to Hire a Fraud Specialist?

44,000 startups. 4,553 cities. “4 billion votes” It was such a blessing to get to know so many of your ideas and innovations from every corner of the world in 2021. The startup community amazed all of us with your hard work and persistence despite all of the challenges that we faced globally throughout last year. Not gonna lie, the competition got our hearts racing like Eurovision. We won’t let you wait for much longer, below are long-awaited results! ✨✨✨ To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series 🔥 Central Europe Central Europe has always caught our attention with the talented pool of people in tech community. But were they able to get through 2021 despite the pandemic? Let’s cheers to the most outstanding amongst 4,500+ startups who have been working hard this year: Basel, Switzerland An overwhelming vote for ~ in their mission of helping machinery companies drive more service revenues from their installed base MachiQ Software Berlin, Germany We have with a whooping 538 votes! Let’s support Plan A with its mission to help companies to measure, monitor and reduce their environmental footprint. Plan A Düsseldorf, Germany It doesn’t get any easier with Kicking off in helping people organize online schedules more easily. EasyWeek! Geneva, Switzerland Manage code quality? We have to give props to for their startup! SonarSource Hamburg, Germany Taking over Hamburg is , an acoustic device to reduce noises in vehicle cabins. No more annoying sounds on the road 🚗 ReCalm Köln, Germany Small business? Hoping to manage your marketing campaign? is winning the vote! rankingCoach Lausanne, Switzerland is a Swiss-based company focused on cryptocurrency management solutions built for financial institutions. Metaco Leipzig, Germany empowers companies to operate and utilize drones for the most effective industrial data applications. FlyNex Mainz, Germany is growing sustainable digital mobility startups! Next Mobility Labs Munich, Germany wins for top-line, high-security international Identity Management! ORY Stuttgart, Germany Remote desktop application? Ask anywhere. AnyDesk Vienna, Austria , an IT company enabling privacy-preserving big data applications in the digital age. Mostly AI Zug, Switzerland Way to go 1762 votes for the first provably live pure Proof-of-Stake (PoS) CBC-Casper blockchain designed for real-world applications. CasperLabs! Nyon Calling for robust data solutions for impact-driven organizations! Havuta LLC Zürich is revolutionizing small business lending to give easy access to capital to those who need it most: SMEs ✨ Teylor Heidelberg, Germany is an innovative HR startup providing a solution for fast and efficient recruiting of employees with simple job profiles. Talk'n'Job Stuttgart, Germany Congratulations for winning this title! AnyDesk Moving on to our buddies in... 🔥 Eastern Europe Eastern Europe is home to many familiar big names in our start-up world. Trace history for Miro, Telegram, and Grammarly and you know where they are from. Who is the next generation for Eastern European startups? Budapest, Hungary is a platform for mobile app development, enabling tens of thousands of developers to automate integration, testing, and deployment. Bitrise I__ __ stanbul, Turkey Leading here is - a brand new software intelligence platform- helps organizations deliver faster, high-quality products by connecting their DevOps tools. Oobeya Kraków, Poland is a human cloud platform that connects global enterprises with European software houses, offering tech talent as a service. Talent Alpha Kyiv, Ukraine 281 votes for , an AI powered app where people can swap faces in the videos, GIFs and images in just a few seconds. Reface Sofia, Bulgaria 387 votes for __ __378, leading Blockchain and DLT solutions company that specializes in helping enterprises and blockchain startups design, build and scale innovative solutions using Blockchain & DLT! LimeChain Warsaw, Poland is a next-generation blockchain document management software (DMS). DoxyChain Yerevan, Armenia Whoever clicked to vote for was sure a vote, because it ended up winning as a full-service software provider delivering Artificial Intelligence & Machine Learning solutions for businesses. SmartClick smart Almaty Just like the name suggested, provides one pass to dozens of gyms and studios. 1FIT Debrecen got all the coders ranked figured out! CodersRank Kharkiv is complete solution digital assets management! Pics.io Minsk, Belarus with 243 votes is the first blockchain-powered sports media platform. Scorum Moscow, Russian Federation 1 e-grocery company in Russia with 26 votes and #1 on our HackerNoon board for Minsk. SberMarket Saint Petersburg, Russian Federation leads this area as a personal finance application that rewards responsible spending and savings. CoinKeeper Kazan, Russian Federation Smelling a sweet victory? 3d App designers for flower bouquets is in the house! Flowito Western Europe 🔥 Brexit? Pandemic? It seems like startups in Western Europe still keep their spirits regardless. Startups in Western Europe that stood out in votes this year go to: Antwerp is on a streak! As a bug bounty platform, it ensures online security through a crowd-sourced platform for computer experts. intigriti Belfast A record-breaking 10,003 votes? is an independent coffee shop. Come grab a coffee! Bobs & Berts Birmingham What’s happening in Birmingham? We have , an innovative UK-based advanced lithium battery developer aiming to accelerate the global shift to cleaner. Aceleron Bordeaux 30 is an open-source web framework for building modern apps and websites with Vue.js. NuxtJS Boulogne-billancourt is a software company that provides an in-store analytics platform to optimize operations. DiGEiZ Brighton In for some jam? offers musical education services. BIMM Bristol Another tasty victory for Western Europe is enabling the strongest digital encryption on the internet. KETS Quantum Security Brussels A fashionable win for , a social fashion network. You scan barcodes in fashion shops and see every influencer wearing those outfits! Share your wishlist with your friends and earn cashback. SCANDALOOK Cardiff is a technology company developing end-to-end digital connectivity for the point-of-care diagnostics market. Bond Digital Health London A whooping number of 1085 vote__ __ offers hardware based cyber-security solution for authentication and encryption in a wired local network. Crypto Quantique Edinburgh Hopping up on the crypto movement, just launched last year but they are already rocking it at making crypto accessible to everyone. Zumo Ghent 5379 votes for ! A Belgian InsurTech startup that helps insurance providers all over the world to better connect with their digital customers. WeGroup Glasgow specializes in single-cell analysis transforming the life sciences. Biomage Paris Show some love in the city of love for , a sports platform that is connected to a mobile sports coaching application. AirFit Aberystwyth enables artificial intelligence, data science & digital innovation solutions for the agriculture, biotech & life sciences industries. Agxio Norwich Congratulations for the amazing win of 1124 votes! WATR Charleroi Congratulations for your amazing win! Hirering Gibraltar Give a round of applause to Fortknoxter! Waltham Abbey is ruling the area with its amazing 72 votes! RPAIR Antwerpen Rocking up Antwerpen area is the rising startup ! Willow Egham What’s happening in Egham? Proudly announcing winning the Noonies this year! ATA.ONE Leicester Leicester is calling the name of as the winner for the region! DeadHappy Macclesfield Who else but ? Congratulations on dominating the award! AeroCloud Northern Europe 🔥 Want to listen to some music? Spotify. Need some games to kill time? Candy Crush. What about a quick call to someone you haven’t seen in a long time? Skype. These are all startups that originated from Northern Europe! Noonies, let’s take a look at the upcoming generation of startups from Northern Europe that hopefully will see an even bigger and brighter future. Gothenburg, Sweden Shipping was never made for small shippers, so took on the mission to built an end-to-end logistics platform to empower small businesses to spend less time and money on shipping and more time on growing their businesses. Sendify The Netherlands provides a virtual platform for many kinds of conferences and events. Let's Get Digital Helsinki, Finland A curated collection of public basketball courts in the Helsinki Metropolitan Area called . Everyone is welcome to submit to their local court. Koripallopaikat Lund, Sweden develops an antibiotic-free, safe and effective treatment of vaginal fungal infections. Gedea Biotech Luxembourg, Luxembourg 🚀 is a job-matching platform that connects developers and tech experts with CTOs and team leads from the best tech companies across Europe. 👾 nexten.io Malmö, Sweden Feeling studious, Noonies? __ __e is a tool for Google Classroom, that enables formative matrix assessment towards course learning objectives directly. StudyBe Oslo, Norway is the platform supports a unified API across all connected device categories, removing significant friction for app developers. Domos Labs Reykjavík, Iceland __ __s is a mobile-first technologically advanced neo-bank. Indo service #S__ __ tockholm, Sweden is the biggest blockchain start-up in Sweden. Double winner of the most prestigious nomination in Nordics 'NyTeknik 33-listan', winner of the Deloitte Fast 50, and now Noonies Award! ChromaWay AB T__ __ allinn, Estonia Passing 1000 votes is a fully digital and decentralized ecosystem for all actors in supply chain. OBORTECH Vilnius, Lithuania is an easy-to-use money app for cards and accounts. Via Genome, users can open a business or a merchant account or a personal one with dedicated IBANs, access transfers, exchange currency, process payments, and manage the funds. Genome Aalborg Digura A legaltech offering tenants fast, digital and reliable legal assistance Aarhus Partisia Market Design provides secure, simple, efficient solutions for auctions and exchanges realizing the potential of the market. Helsingborg is a hi-tech company developing cardiac occlusion devices and other vascular implants. Occlutech Tallinna is an AI-powered system for marketing automation in social networks. POSTOPLAN 🔥 Southern Europe Southern Europe’s economy went through a hard time this year due to a huge reduction in tourism. But our startups are of course always headstrong, from Spain to Italy and all the way through Greece. Who are the heroes rising amidst changes of Southern Europe? Skopje is a versatile software development company that solves the business challenges of SMBs, big brands, and brave hearts with awesome ideas. CodeChem Sliema Sounds like just a relaxing exhale. is a holistic health and wellbeing app built to allow users work on the development of their physical, mental and spiritual health Few Tajonar is a company of experienced business & industry entrepreneurs offering solutions to industries based on cutting-edge technologies. das-Nano Tarragona is a technology-based company focusing on the production of integrated microsystems. IMICROQ Terni is an ICT-based integrated solution that is able to modulate, respond, assist and learn to control most home activities. Mind Tirana is a video content and e-commerce platform for the Balkans built on top of an Albanian language specialized search engine. Gjirafa Torino develops and commercializes algorithms based on Artificial Intelligence to increase the autonomy of small satellite missions. AIKO T__ __ orres Buy online groceries like you would buy offline with personalized picking as fast as in 4 hours with Vedras 360hyper! Trento is a music-based platform built around streaming platforms that allow users to discover new emerging artists and make new friends. Brots 2021, what a YEAR. Congratulations to all of the startups that made it to the top winning list for Europe! Stay tuned for Startup awards in 2022 and let’s cheers to a new year of more innovative ideas, bold innovations, and robust creations from you, Europe 🌷 More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written by An Le and edited by Natasha Nel. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Europe

Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. . Browse the archive here Hi, everyone, I’m Ryan Tate, an editor here at The Markup. It’s Saturday morning, and I’d like to talk about your most mundane daily experiences, and how to keep them boring and harmless. . . . Jon Keegan, one of our reporters at The Markup, has carefully documented how these common human experiences now put people’s most sensitive data at risk for harvesting by strangers. That’s because companies in seemingly every industry—from automakers to app creators to political campaigns—now believe they can make money off tracking their users. Driving a car Looking at your phone Voting “Everyone’s a data broker now,” Jon told me recently, quoting a friend. That’s so true, it hurts. In 2021, Jon about the multibillion-dollar market for your phone’s location data. He then stepped back and started looking at various ways our personal information leaks out in day-to-day life—data sinks we are vaguely aware of but don’t scrutinize. After cars and political ads, supermarkets seemed like a natural next step. wrote Jon told me he always pauses at checkout at the supermarket and doesn’t share his phone number to use his family’s loyalty program membership, leaving real money on the table and causing an ongoing argument with his wife. Skipping out on discounts? In this economy? While the entire tech journalism industry is focused on probing the potential sentience of AI-powered chatbots, Jon suspected that everyday supermarket data was an underreported topic that was due for fresh inspection. Based on his prior reporting on how consumer data is bought, sold, and traded with few limitations, Jon theorized that supermarkets today are likely gathering more data about us than ever before. Loyalty card programs, after all, go back to before smartphones and before ubiquitous online tracking, back to before billions of dollars could be made from data collection. He wondered: How have supermarket discount programs evolved? Jon accelerated his research in the following weeks, paying particular attention to the two largest supermarket chains, Kroger and Albertsons, which had proposed merging into the nation’s largest supermarket chain. Politicians and advocacy groups were worried the merger would drive up food prices, drive down wages, and result in a massive, highly sellable, pool of sensitive data about shoppers. (Albertsons told us that “we appreciate the importance of privacy and take appropriate handling of our customers’ data seriously” and that it had recently updated its privacy policy. Kroger did not respond to Jon’s requests for comments.) , including those for other brands the company owns, like Ralphs and Food 4 Less, help collect information that goes well beyond the list of items in your  shopping cart. The company “enriches” information it has on what you buy and who you are with additional data. Jon found that Kroger loyalty programs Some of this data is acquired from brokers, some comes from Kroger apps, some of it comes from Bluetooth beacons and facial recognition systems in stores. Some is even systematically inferred from all the other data. Drawing from these sources, Kroger can collect your financial information, including bank and credit card account numbers; track demographic data, including race, ethnicity, age, and employment information; make inferences about your health and behavior; track your precise physical location in the store if you consent; and recognize your face in select locations, with signs providing notice. Jon also found that Kroger had two business units focused on making money from all the information Kroger collects. One, known as Kroger Precision Marketing, sells advertising to product makers, not only in stores, per long-standing industry practice, but also by following shoppers onto the internet, their smartphones, and their home entertainment devices. Kroger’s other data-related business carries the startup-like name 84.51, corresponding to the longitude of Kroger headquarters in Cincinnati, Ohio. This unit sells information and “insights” gleaned from what the company describes as “35+ petabytes” of customer data. (That’s the equivalent of several million downloads of the film “Black Panther: Wakanda Forever,” in case you were wondering.) Kroger sees 84.51 and Kroger Precision Marketing as key sources of growth. These units, along with a personal finance division, are part of its “alternative profit business,” offering much higher margins than selling produce and canned goods. A promotional presentation about the Kroger-Albertsons merger (see page 18 ) shows that the company believes these types of business units are worth “+$1B Annual Alternative Profits” to “Kroger Today” and represent a “~$1.5B Annual Alternative Profits Opportunity” to Kroger and Albertsons combined. here That would be big growth from the $150 million in incremental operating profits Kroger derived from these businesses in 2021. Clearly, Kroger is as excited about monetizing shopper data as it is about selling groceries. The ad industry is excited, too. As Jon was putting the finishing touches on his story, I spent a day earlier this month at the AdExchanger Industry Preview event in New York. Industry players at the conference are grappling with major challenges, including economic headwinds that have seen many ad budgets slashed and privacy concerns that could bring new regulations. (Our editor-in-chief, Sisi Wei, who you heard from a few weeks ago, wrote about being how advertisers could limit their data collection.) invited to the event and explaining But advertising sold by supermarkets and other stores, categorized as “retail media” to the people at the conference, emerged as a clear industry bright spot, alongside ads for video streaming services like Netflix. Over and over again people on stage talked about how effective it was to buy ads from retailers like Amazon, Walmart, and Kroger, and how it paid to leverage their customer data. One executive, Jed Dederick of ad-buying platform The Trade Desk, explained that retailers sometimes know more about how to reach a company’s likely customers with online ads than the company itself. Two speakers from Arete Research highlighted retail media as the industry’s fastest-growing segment. Retail media “are outgrowing all other forms of advertising in ’22” by 10 percentage points or more, said analyst Rocco Strauss, “and will likely do so again in 2023.” If the power and extent of shopper tracking is taken as a given in the ad industry, it still seems to be news to the shoppers themselves. When Jon’s story , it caused a minor sensation. Jon was by NPR’s “All Things Considered,” and his revelations were by Business Insider, by The Washington Post, and on the techno-political podcast “This Machine Kills”. published last week interviewed about his findings written up highlighted featured I know that while Jon appreciates that sort of impact, what he really wants is to find out precisely what information the supermarkets are collecting, and if any of it is potentially harmful. You can help him. If you’re a frequent shopper at a , and a Kroger Plus or Boost member in California or Virginia and want to help find out what Kroger has, email Jon at . Kroger-owned supermarket keegan@themarkup.org And if you’re wondering, Jon still refuses to hand over his number at the checkout, much to his wife’s annoyance. Cheers, Ryan Tate Editor The Markup P.S. In case you missed it, I also wanted to share two important pieces that we published this week. First, into how L.A.’s scoring system for subsidized housing gives Black and Latino people experiencing homeless lower priority scores, along with scoring in other cities. our investigation our tips for how to investigate We’ll dive more deeply into this investigation with you next week. Second, we released a tool this week called the (USPS), which lets you easily sample random U.S. street addresses without knowing how to code. Soon, we will be releasing a guide on how to replicate in other American towns and cities using USPS. United States Place Sampler our investigation into internet disparities By Ryan Tate Also published here Photo by on Hanson Lu Unsplash

How Much Data Are Supermarkets Like Kroger Collecting?

The Noonification: Even Supermarkets Are Tracking Your Data Nowadays (8/14/2023)

Even Supermarkets Are Tracking Your Data Nowadays

এমনকি সুপারমার্কেটগুলি আজকাল আপনার ডেটা ট্র্যাক করছে

Sogar Supermärkte verfolgen heutzutage Ihre Daten

Incluso los supermercados están rastreando sus datos hoy en día

Même les supermarchés suivent vos données de nos jours

आजकल सुपरमार्केट भी आपके डेटा को ट्रैक कर रहे हैं

最近ではスーパーマーケットでさえあなたのデータを追跡しています

요즘에는 슈퍼마켓에서도 데이터를 추적하고 있습니다.

Até os supermercados estão rastreando seus dados hoje em dia

Günümüzde Süpermarketler Bile Verilerinizi Takip Ediyor

Ngay cả các siêu thị cũng đang theo dõi dữ liệu của bạn ngày nay

如今，甚至超市也在跟踪您的数据

COVID-19 is ruthlessly shaping ordinary stores into supermarkets of the future. They must accommodate all the new grocery shopping trends from panic-buying to delivery and pickup options, all with disrupted supply chains. Grocery retailers are responding differently. Some are cutting back on the number of products they sell, some are skipping distributors and contacting product manufacturers directly, and some are giving their brick-and-mortar stores a complete digital overhaul. No matter which line of action your company has selected, will enhance your operations significantly. Stephen Hawking predicted that artificial intelligence will transform every aspect of our lives. Grocery stores are no exception. The global AI in retail market is expected to reach rising from $3 billion in 2020 and displaying a year-over-year growth rate of 29.7%. According to Statista, the consumer goods and retail sectors have been using AI throughout 2020 for various purposes: AI retail solutions $24 billion by 2027, Statista If you want to follow the supermarket industry trends and take your existing to the next level with AI, this article will show you some compelling use cases, warn you of possible implementation challenges, and share five tips to help you through deployment. retail software solutions Physical stores adopting AI to enhance the shopping experience and become more efficient McKinsey & Company found that even during the pandemic still occurred in physical stores. It appears that brick-and-mortar stores are part of the future of grocery shopping. People still like to see, touch, and smell goods. Remember how you feel when you look at colorful packages of sweets or smell freshly baked pastries. Fabio Parasecoli, a professor at NYU’s Department of Nutrition and Food Studies, says that this kind of experience “gives you an idea of choice, abundance, quality, and pleasure.” 85% of the US grocery sales Contrary to popular assumptions, physical stores will not be replaced by their online alternatives. But they will rely on AI to tackle the pandemic-induced challenges.However, this doesn’t mean that grocers are expected to go all in with technology and leave their less technical clients behind. Walmart is an excellent example of a grocery store that does not aim for tech-savvy buyers only but is willing to improve the shopping experience of the average customer. The chain experimented with different technologies to find what matches customer comfort levels. With this learning approach, Walmart discovered that using cameras and real-time data helped them and sales in the meat aisle by 90% and 30%, respectively. increase overall shelf-stocking efficiency 5 ways AI is shaping the future of supermarkets and grocery stores 1. Sparking consumer interest through personalization A few years ago, promotions were advertised in catalogs or through broadcasts. Both options were rather expensive and displayed the same information for all consumers coming to the store. In supermarkets of the future, AI and advanced analytics offer plenty of information on every individual buyer, such as their meal preferences, food allergies, and motives behind their purchases. By employing AI in grocery personalization, retailers gain extensive knowledge of who is walking down their aisles. This approach enables retailers to craft customized promotions to attract buyers and increase sales. Attesting to the capabilities of AI is Gary Hawkins, the CEO of the Center for Advanced Retail and Technology based in Los Angeles: “AI technology can go incredibly deep and continues to learn over time, so it gets better at knowing which items to promote at what price. This will likely lead to different prices for different people, thanks to offers they are sent.” For example, Woolworth in Australia employs supermarket technology of the future to customize its marketing emails considering not only consumers’ taste but also their past shopping behavior. The chain can predict which items every shopper is likely to run out of based on their previous purchases and suggests these items as well. Another example of customization comes from Kroger, an American retail company. When consumers activate the store’s mobile app while inside, sensors detect them and send a personalized selection of items together with the prices via their preferred communication channel (e.g., video, voice, text). Effortless, time-saving navigation is a part of supermarkets of the future — especially if we’re talking about spacious retail facilities like the Mall of America, which incorporates over 520 stores and 60 restaurants. It can be daunting to move around such a space. To relieve its customers, the Mall of America deploys location-based AI chatbots, which operate through Facebook or a mobile app and assist customers in finding products and services. The dynamic pricing concept revolves around using machine learning and artificial intelligence in shopping to determine the best pricing strategy for different products. For this, algorithms analyze data from different sources, such as historical sales, competitor prices, stock levels, and special occasions. Personalized promotions In-store navigation Dynamic pricing One of the tactics of dynamic pricing is cross-selling a discounted item (e.g., buns) with a complimentary product (hot dogs) at a full price. This strategy helps reduce food waste by lowering the prices of goods nearing their expiration date. 2. Improving inventory management Grocery store robots Supermarkets of the future increasingly adopt robots to tackle inventory-related problems, such as preventing out-of-stock items, incorrect labeling, and pricing. California-based Fellow Robots developed an autonomous retail robot, which can scan your store shelves as high as 2.4 m above the floor daily taking high-definition pictures of products and their prices. Machine learning algorithms examine these pictures, searching for misplaced products and price discrepancies. Store employees can view the results through dedicated dashboards and take immediate action. Additionally, robots can help futuristic grocery store employees cope with the increasing demand for deliveries. Ocado, a UK-based online supermarket, in demand since the lockdown in March 2020. The company is using robots to scan the inventory and pick the right items for each delivery. Another grocery shopping trend is using AI-powered forecasting systems. AI algorithms do not purely depend on the historical data available at stores. They can self-learn and create forecasts even when the data is limited — for example when introducing a new product or testing a new promotion technique. reported a ten times increase AI-based forecasting in grocery stores Some older forecasting techniques work on products as clusters and can’t take the local dynamics into account. A good AI model can make predictions about products at a granular level considering local and regional trends. 3. Reducing theft According to a National Retail Federation survey, retailers due to theft, which is almost $10 billion increase from 2018. AI-powered supermarket technology can detect inappropriate behavior among both buyers and cashiers. Incorporating computer vision in shopping can help store security managers identify theft attempts. Sainsbury’s, a UK-based supermarket chain, employed ThirdEye’s concealment detector, which uses computer vision to detect shoppers who take an item and place it in their pocket. The system records suspicious activities and notifies security personnel. Thanks to this technology, Sainsbury’s stopped almost between September 2019 and March 2020. Sweethearting is another form of theft when cashiers fake the act of scanning items at checkout in favor of consumers. Smart store technology can help spot such behavior. Computer vision systems, such as ScanItAll, use ceiling-mounted video cameras to “watch” cashiers and detect sweethearting events, like covering barcodes and stacking items on top of each other. The US-based supermarket chain Piggly Wiggly reported a loss of almost $10,000 per month due to checkout shrinkage at one of its locations. After installing ScanItAll and retraining cashiers, shrinkage lost $62 billion in 2019 Shoplifting 6,000 theft attempts Sweethearting costs declined to $1,000. 4. Improving social distancing As social distancing is here to stay, supermarkets of the future are looking for ways to control the number of people indoors. In-store customer tracking is a grocery store technology of the future that can prevent overcrowding inside your store. A German supermarket company Aldi is using AI in shopping through an automated traffic light system, which controls people flow to its stores. When the number of shoppers in a particular location is below a predefined threshold, the light is green, and the door is open for others to come in. After the threshold is reached, the light turns red and the door closes. COVID-19 speeds up grocery store robots’ deployment. Big supermarkets see robots as an opportunity to reduce the number of human workers, which makes consumers perceive the store as a safer place. For example, Walmart uses robots to wipe the floor so that buyers will not be surprised by a human worker disregarding social distancing to clean up a stain under consumers’ feet. A common complaint among warehouse workers is that they cannot maintain distancing given their job conditions. AI-enabled supermarkets can incorporate robots in such an environment to reduce the friction among human employees. Trackers monitoring social distancing Robots replacing human employees in grocery stores 5. Improving the checkout process Statistically, people spend about 60 hours annually waiting in checkout lines. “In response to the COVID-19 pandemic, the demand for autonomous checkout technology is driving grocers and retailers to innovate and adopt new technologies that keep shoppers safe and streamline checkout.” - Lindon Gao, Co-founder and CEO of Caper AI, a software company specializing in AI-powered solutions for retail and grocers. Grocery stores equipped with AI have the tools to correctly identify all the items collected by a particular consumer and charge that person’s bank card upon exit without any intervention from store employees. If you don’t want to let go of your staff or deprive your customers of human interaction, you can mirror what Walmart is doing. Cashiers at this AI store assume a new role of a “Host”. The host’s responsibility is to make sure all consumers are treated in their preferred way. If they favor self-checkout, a host will guide them to an open register. If they like the traditional checkout method, a host will pack their items and process the payment. Caper AI produced a shopping cart that uses artificial intelligence to instantly recognize items and measure their weight (if relevant). The cart contains a built-in navigation and product location system, helping customers to traverse the store. It displays available promotions and can compose a list of recommendations. Caper cart also incorporates a credit card reader, enabling consumers to checkout without cashiers. Caper AI has already partnered with the US-based Kroger and Foodcellar & Co, and Canadian Sobeys Inc. AI for smart shopping carts AI-powered cameras for self-checkout Supermarkets of the future use cameras and sensors to support . For example, Amazon Go has 26 cashier-free locations equipped with hundreds of cameras and computer vision algorithms to monitor which products every shopper picks to charge the total amount off their credit card immediately after they leave the store. Additionally, store owners can use AI and video to facilitate grocery checkout. Ireland-based Everseen developed a visual platform where AI watches videos of customers performing self-checkout in real time. The program can identify errors and correct users immediately. For instance, if a customer encounters an item that doesn’t scan properly at the self-checkout AI kiosk, Everseen’s system will register this incident and notify one of the employees to assist the troubled client. Kroger began deploying this system at its stores in 2020. An intelligent shelf is another application of AI in shopping that facilitates the checkout process. Such a system typically includes computer vision and sensors mounted on shelves. This technology can identify shoppers picking items from a shelf or returning them. It correctly matches buyers and products and charges consumers for their purchase at the end of the shopping journey. checkout-free shopping Smart shelves technology This grocery shopping technology has additional applications. It can display relevant promotions as consumers pass by, monitor stock levels, and notify employees when the store runs out of some items, and detect the presence of ethylene gas, which is released when fresh items start to spoil. The idea of self-checkout was appealing before the pandemic. A retail startup Wheelys designed a prototype of another grocery technology — a self-driving grocery store with no employees. The store would move autonomously from one location to another. Consumers would enter the shop through a sliding glass door using the Wheelys’ app. They would pick their items, scan them through the app, pack, and exit the store. Their bank card would be charged automatically. Autonomous self-driving stores Barriers to adopting AI in grocery shopping Independent of the industry, . According to IDC’s 2019 Global AI Survey of 161 retailers, the major barriers to adopting AI in retail include: implementing AI is a challenge 1. High cost of AI solutions: vary greatly depending on the software type, its intelligence level, the quality and quantity of data that you want your program to process, and the accuracy of algorithmic predictions. To implement an AI application from scratch, you can easily spend $50,000 on a basic version. costs of developing AI systems 2. Lack of skilled personnel: The IDC’s authors interpret it in two ways: there is either a general lack of credible AI talent on the labor market, or the retail sector (including grocers) is struggling to attract AI talent. 3. Data-related trust issues: Artificial intelligence in shopping applications heavily depends on data, while grocers need to show transparency in data usage to win consumer loyalty. If AI algorithms abuse client data, the relationship will be damaged beyond repair. According to a recent survey by Deloitte, agreed to share their data with grocery stores. This survey demonstrates that the grocery sector comes third after hospitals and governmental institutions if we look at people’s willingness to share their private data. This shows that consumers trust grocers, but there will be dire consequences if this trust is breached. 70% of responding consumers 4. Unclear business objectives: AI demands a considerable investment upfront, while it is difficult to find compelling use cases to present to investors. Udai Chilamkurthi, the Chief Architect of Technology Strategy & Architecture at Sainsbury’s confirmed this challenge at the AI Summit London saying: “I haven’t seen a sensible business case that makes sense yet. The technology itself isn’t mature and comes with its own problems.” How supermarkets of the future can succeed in AI adoption Despite numerous challenges, the future of AI in grocery shopping looks bright. Here are the five steps that will help you painlessly employ AI at your store and reap the benefits faster. Define your vision for the role you expect this technology to play in your organization and the returns on investment. Deloitte recommends adopting the following paradigm: Start small, scale fast, and build iteratively. Find the right talent to fill different expert roles. Throwing data scientists in a data lake and expecting them to present new strategies will not yield results. You will need to recruit diverse internal and external talent who can derive, transform, and sustain value in the long term. Adapt the organization’s culture to develop the right attitude towards AI. Many employees still view AI as an intimidating black box, which they fear and cannot understand. Make it clear that AI is there mainly to support humans and help them make informed decisions. Clean up your data. The amount of data that AI can use is constantly increasing as grocers gain access to information coming in video format, from social media, and via geo-location apps and devices. Despite the data growing in complexity, many retailers overlook the need for a clear . data strategy Brian Kilcourse, Managing Partner at Retail Systems Research emphasizes the importance of data in a successful AI adoption by saying: "The top challenge is dirty data (it's the elephant in the room). Models are only as good as the data that creates them. One recent study estimated that over 80% of the effort of implementing AI relates to data cleansing.” Gain an ecosystem advantage. Artificial intelligence capabilities for grocery stores are evolving fast, and working with an ecosystem partner will give you access to the right team to get the job done. For example, a grocery retailer can join forces with predictive analytics and demand-sensing company. Final thoughts Supermarkets of the future are not necessarily online stores. Implementing AI in grocery shopping enables you to overcome all the pandemic-related hurdles while still letting your customers enjoy the sight, smell, and feel of products. However, incorporating AI is not a one-time task. It is a long process that requires changes of a store’s internal processes and culture. As Sanjeev Sularia, the CEO of Intelligence Node, puts it: “Retail organizations often get deterred by the costs of building the infrastructure and data processing capabilities needed for AI adoption. However, flexible businesses have successfully integrated AI across all business functions and upskilled their people to efficiently reorient to a data-driven mindset without trying to build everything from scratch.” Thinking to enhance your grocery store with AI capabilities? Feel free to ITRex retail and AI experts, and they will make sure your supermarket is well-equipped to face challenges of the future. contact

5 Ways AI is Improving Supermarkets

The context In March 2020, the world froze. A damn virus has spread across the globe and people have locked themselves up in their homes, safe. Getting out was a risk. Go shopping, another one. The problem is that the longer you stay out and in contact with other people, the more likely you are to contract the virus. Especially in closed and unventilated environments. The project that I am presenting to you was created primarily for personal use and then, by word of mouth, it was used worldwide (geographically). The project was not for profit but only to help others. Since June 2020, it has been discontinued due to the slight improvement in cases and the clarity that has been made about the virus, as well as the various security measures that have been applied at the health level. The reason why In the days following the global lockdown, supermarkets, pharmacies, and parapharmacies were attacked by people in search of basic goods and necessities. Immediately after the announcement of the lockdown, many supermarkets struggled to manage the influx of people, forcing them to gather outside the supermarket, standing in line, and waiting to enter the building. The gathering of people in line in front of shopping malls and pharmacies made me think. Why not create some sort of tracking based on Google Maps data? Kind of how it works for traffic. So the goal was to create a map with the various points of interest, such as food, bakers, pharmacies, supermarkets, liquor stores, etc. where the waiting times retrieved by Google were shown (at the time this API was not public, that is, there was no endpoint, while now it *) giving the user the possibility to add temporal feedback for the store in which he was located. The use of the map aimed to schedule the exit of the house towards our point of interest at the moment in which there was the least waiting time to enter. Crowdsourcing was then fundamental for the success of all this, since Google's data is based on the time, the popularity of the place, and how many devices are nearby. User feedback was therefore crucial. * Google now displays the wait data in one place directly in Maps. Costs The project is open source and development costs are not considered as those developed for personal use and then immediately shared publicly. At the start of the whole project, i.e. Redis, API backend in Python and the frontend resided on my Raspberry PI 3B model. After about 7 days from the publication online, the site already counted about 10K visits per day and Raspberry was no longer able to satisfy the requests at the same time. So I decided to move everything to the cloud using OVH and DigitalOcean for the backend and Github Pages for the frontend. The backend, with Redis and the API in Python, I replicated on 4 VPS in the cloud divided as follows: 3 DigitalOcean (New York, London, San Francisco) and OVH (Europe). The specifications were almost the same, that is 2GB of Ram and 3.2GHz dual-core. In total, I spent about $ 25 per month. The total is $ 75 for 3 months. For the domain, not being a commercial project, I reused my former domain thejoin.tech, creating the subdomain https://covid19-waiting-time.thejoin.tech/ The included traffic was 10GB per month, so it wasn't a backend issue. By importing, via git, all the setup on the Cloud allowed me to satisfy (in the whole life cycle) more than 1Mln of users and more than 2.1Mln of sessions in 3 months of the life of the project with more than 1.5K contemporary user. How it worked I'll briefly explain the idea and the mechanism behind it. For the more technical part, I leave you the link to the post. The Github repository is available on my profile: https://github.com/TheJoin95/covid19-market-waiting-times The idea was born with a simple map created in JS (using Leaflet.js) where the various markers of the various points of interest were added, previously retrieved from the Here and Google API. The data was persisted on Redis and, thanks to the Geo operator, could be easily searched through geospatial queries in order to provide geo-localized data and markers. Redis was the most logical choice to provide fast, precise responses and with a TTL on persistence to the various requests so as not to overload the memory. All the functionalities, from the search by location or by address/place to the WebIAM through to the map navigation and crowdsourcing features have been developed incrementally and thanks to the help of some contributors for the graphic part. All this within 25 days, after 8 hours of work. The front-end does not use any framework other than the task runner Grunt to be able to include the various pieces of HTML, minify the CSS / JS / HTML files, and the web workers to be able to use it in the form of PWA (progressive web app). This choice made development very fast with no dependency or compatibility issues. However, compatibility is guaranteed both on mobile (being responsive) thanks to polyfill. The APIs were instead developed in Python and deployed on the 4 instances mentioned above and use Flask as a micro-framework for API and uWSGI as a web server. I must say that uWSGI was the best choice due to the low level of configuration it makes available to us. Turn-ups There have been a lot of likes, tweets, and shares on all kinds of social media and blog/news sites. I was really pleased to help people all over the world, to receive negative and positive feedback in order to improve a tool that was helping thousands of people in the same situation and with the same problem. It was certainly one of the best projects I've worked on and I've had the pleasure of dedicating time to, as well as my passion. Understanding how a prototype released locally on the Raspberry at home to then be made public for friends and then globally taught me a lot… this project was aimed at helping myself, but in the end, it also helped other people. I may not have solved many problems or complexities, but the testimonies make me understand that there was a little help… and this is enough for me.

How I Created The COVID -19 Supermarket Waiting Times App That Was Used Worldwide

that’s no secret, but some of them had to suffer a lot, especially during the lockdown. Supermarkets are on the ‘victims’ list, as it wasn’t enough that the flow of clients was seriously affected since governments recommended staying at home and, if possible, do your shopping online, but they were also forced to stop all sampling campaigns, for example. A lot of businesses were affected by the COVID-19 outbreak, Yes, things were that rough and we weren’t surprised at seeing some locations even closing their doors for undetermined periods. But hey, the show must go on and business must go back to normal while respecting social distancing and the entire set of measures imposed by governments. In this scenario, we wondered what could happen with some of the regular promotional campaigns, like sampling, since we’ve already mentioned it, we were used to seeing in supermarkets before the lockdown? The answer is simple: Improvise, adapt, overcome Specifically, brands are already rethinking the way they are doing in-store promotions by implementing some witty alternative campaigns (see video below), designed in such a way that they respect all social distancing and safety measures, while also making sure that their messages reach the right audience. Sounds pretty cool, right? And trust us, you shouldn’t worry that much about sampling, as these in-store promotions ideas have everything it takes to put a big grin on any customer’s face. Therefore, let’s see what a bit of creativity combined with marketing genius can do, shall we? AR is your friend Augmented reality has been around for some time and some retailers have been putting it to great work, managing to come up with some ridiculously good campaigns. And something tells us that now it’s the best time to use AR in supermarkets and make those classic sampling campaigns - but with good results, don’t get us wrong - forgotten. Let’s take beauty retailer Sephora, for example, who created a 3D, augmented reality-based mirror, thus giving customers the possibility to see how cosmetics would look on them without actually applying something on their faces. Sure, this is just a use case for AR in retail, meaning that it can easily be adapted for supermarkets! Walmart did it back in 2018, for Christmas, when shoppers were encouraged to scan codes through the chain’s app, to unlock exclusive AR experiences featuring some of the brands available in its offer. Yes, shoppers weren’t able to try products, but and it was just enough for this campaign to be a real hit for Walmart! they were able to see them interacting with the environment These are some great starting points, if you ask us, and can easily replace sampling in a post-COVID-19 world. After all, almost everybody who enters a supermarket has a smartphone capable of delivering a top AR experience, so why not give it a shot? Flashy displays aren’t going anywhere We’ve all seen them as they’re in every supermarket. But screens can now be one of the best alternatives to sampling campaigns if used well. Generally speaking, displays show a static ad or, sometimes, an actual ad of the product they’re trying to promote. Nothing special, of course, since most shoppers usually ignore them and mind their groceries session. But if the content shown on these displays is improved, they can successfully replace sampling campaigns! Costco is one of the supermarket chains to look at, as they had an excellent in-store sampling approach before the pandemic, which turned into a planned activity for many shoppers. However, things changed a bit and they can’t rely very much on brand ambassadors who know everything about the products they’re promoting and are enthusiastic about them since this doesn’t fall in line with the social distancing measures we need to take. But hey, the flashy displays are still alive and kickin’. Ok, but what should shoppers see on those screens? Well, product demos, of course, in an attempt to educate and entertain, at the same time. We know, it’s easy to talk and difficult to implement, but you can experiment in the first place. After seeing what kind of content makes shoppers tingle and buy the advertised products you can improve the process and deliver even better stuff in terms of product demos! Opt for a touchless, safe, and engaging shelf display Sounds too good to be true? In this case, we have some good news: this alternative to sampling really exists and we’re so damn proud of it. Tokinomo, the ultimate customer engagement device, as we like to call it, does more than just presenting a product! The multi-sensory experience it creates for the shopper, with the help of motion, sound, and light is almost impossible to ignore, no matter how concentrated on their shopping lists they are. The idea is simple: , not just facts. Through sampling, these two aspects can be influenced. But, uhm, COVID-19 and increased safety measures happened and sampling is something you barely see. So why not let Tokinomo’s light, sound, and motion capabilities create new emotional connections with shoppers? customers heavily rely on their feelings when choosing a product If you ask us, this is the perfect alternative to sampling and the only one able to transform any product into a storyteller for its brand. Why is this so important? Because it manages to create that so-craved-after emotional connection between the buyer and the brand, which, once completed, transforms them into trusty clients or, why not, brand ambassadors. Of course, these are just three examples of , after all, imagination has no limits and we’re convinced that some locations will come up with some great ideas themselves! how sampling campaigns can be replaced in supermarkets nowadays as As a side note, we won’t be surprised to see these alternatives working some well and making retailers implementing them into their long-term strategies. They do have what it takes, let’s be honest. Speaking about ideas, we are very curious to find out your thoughts on our selection and, obviously, do let us know if you are thinking about or using alternatives to sampling yourself, with great results. The comments section is waiting so make sure to drop off a few lines. Previously published at https://www.tokinomo.com/blog/smart-alternatives-to-sampling-campaigns

Stories, News & Mentions about SBERMARKET

Sbermarket’s Stories on HackerNoon

Sbermarket's News & Mentions