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274 Stories To Learn About Software

260 Stories To Learn About Team Productivity

Let's learn about via these 69 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Productivity Tools /Learn Repo It's called "Slack" for a reason. 1. Top 10 Productivity Apps to Make 2020 Your Most Productive Year Yet Welcome to Part 2 of the Tech Productivity Series 2. How Task Management Software Helps Achieve Productivity in a Post-Pandemic World Before the pandemic, roughly 30% of the part-time workforce was working remotely. It is estimated this will expand to at least 48% post-pandemic. Until the pandemic is over, it’s unclear exactly how much of the full-time workforce that can work remotely will continue to do so. 3. 5 Best Online Tools to Compress and Optimize Your Images Here are the 5 Best Online Tools to Compress and Optimize Your Images. 4. On Productivity Tools And Time Management With Julien Quintard, Founder Of Routine An interview with Julien Quintard on why he is building Routine and why productivity tools as we know it is going to change drastically in the a few quarters. 5. Maybe, Just Maybe, Writing Your Passwords On Paper Isn't So Bad? This is not a joke. Stationery stores and selling pocket notebooks with the word "PASSWORDS" written across the front. Inside are pages, with alphabetized tabs and spaces for you to record the website name, the username (login), and your password. Plus a space for notes. 6. The Placeholder Tools that Boost Productivity for Developers and Designers 7. Boost Your Productivity as a Developer With These 10 Tools Productivity is one of the important metrics for developers. Stay organized with this list of the essential tools for a developer to increase your productivity. 8. 10 Best Web Dev Tools of 2021 Top 10 Web Dev Tools in 2021 9. Startup Interview with Henry Shapiro, Co-founder at Reclaim.ai Reclaim is an intelligent calendar assistant that enables you to block adaptive time for anything you care about. Vote for us for HackerNoon's SOTY! 10. How to Merge PDFs Without Adobe Acrobat For a task or assignment, different members are responsible for different parts. So, it's very common that the team must conclude the respective results at last. If their results are in the PDF formats, they must merge the PDFs to reach the final result. There are still many other occasions that we need to merge PDF files. 11. 22 AI Tools You Should Know About List of top trending AI tools 12. How to Stay Focused When Coding: 8 Productivity Tips Your well-being, routines, motivations and how effectively you plan, all play a role in your ability to remain focused while coding. 13. apibeats: Get Notified on Slack When The API Changes apibeats is an API maintenance platform that lets backend and frontend developers collaborate, and notifies teams on Slack & Email when the API changes in any way. apibeats provides a complete environment for you to interact with your APIs. It lets you create collections of endpoints and watch them for uptime/downtime and changes in structure, test your collections by making requests without writing any code and document your API collections. 14. How Time Tracking is Reshaping the Future of Work If you are running a business or just started your own startup, it’s important you keep an eye out on every minute detail. In case you are hiring designers, software developers, and/or a team for project management, tracking total working hours is probably what you should be doing. 15. 15 Apps that Can Make You Productive Here, 15 amazing apps have been listed for increasing our productivity. 16. 15 Best Project Management Tools Project management systems are supposed to make the life of teams easier and the work process faster and more efficient. 17. MakeMySummary Software Update:  June 2020 We recently released an update for MakeMySummary. The following are the highlights of the update. 18. 🏅year in Review: Tools That Helped my Productivity as a Software Engineer As a software engineer, you have to find the perfect mix of tools and templates that can help make you more efficient in your day-to-day tasks. 19. 4 Productivity Hacks That Yield Quick Results Having trouble hitting deadlines and staying focused? Well, have no fear! This article will introduce four productivity hacks that almost anyone can implement immediately and see fast results. 20. Writing a Slack Bot that Responds to Action Commands I recently wrote a slack bot that responds to action commands (like /ech yolo ) but I ran into some trouble finding exactly how to do this, so I’m documenting it here for other peeps to find. The exact error I hit was /echo failed with the error "dispatch_failed" . 21. NPX Introduction: Fast Work With NPM Package Binaries With Node.js, building CLI utilities and development tools have gotten so much easier. 22. Essential Productivity Tips Every Software Developer Should Know Here are the game changing productivity hacks, tools, and tips that have helped me most as a software developer. 23. 17 Killer Web Apps to Increase Productivity 🚀💯 24. 5 Team Communication Tools That Will Skyrocket Your Productivity Teams come in all sizes and these days from all parts of the world. Geography is no longer a limitation with messaging tools that can connect you with anyone in the world. The right communication tools can skyrocket your team’s productivity, as ability to easily share information and ideas with all your stakeholders is key to driving productivity. 25. Windows 11's New Focus Mode Is Pretty Useful The new Focus Mode feature is a useful tool that helps you get more productivity out of Windows 11 in a simple way. Though that simplicity does cut both ways 26. How to Improve Developer Productivity Become more productive as a developer in the next 10 minutes using researched backed tips and hacks. 27. Summarizing Youtube Videos and  News/Blog articles I made a new app/service that lets users summarize the deluge of info that comes our way every day. Its unambiguously named MakeMySummary. 28. Introducing CatalyzeX: A Browser Extension for Machine Learning Andrew Ng likes it, you probably will too! 29. How Nonprofits Can Get Adobe Discounts via TechSoup: FAQs Learn more about Adobe for Nonprofits, including information on eligibility, product licensing, downloading and installing software, and more. 30. How to Hack Your Team: Top 5 Tools For Team Management Automation in 2021 Wondering how to increase your remote team's productivity? These tools will help. 31. Top 10 Productivity Apps For iOS To Take Your Productivity To The Next Level Find the best productivity apps for iPhone or iPad. These best iPhone productivity apps help you get more things done faster and more efficiently than before. 32. Mind Hacking: Using Technology to Rewire Your Brain Like a Monk Not so long ago my life was an exercise in excel spreadsheets, over-scheduled phone calls, and constant emails. I was working in a high-pressure venture capital job, and putting myself in a state of mental fragmentation. 33. 4 Reasons You Shouldn't Use MoSCoW for Product Prioritization I often see MoSCoW being used to prioritise product development work, but for anything mildly complex (almost anything, really), and especially any product  with more than one customer, it isn’t appropriate. Here’s why. 34. How to Install RStudio on the WSL System If you are using Windows and don’t want to use the RStudio client on the Windows side, this method will work perfectly. 35. 6 Chrome Extensions That You’ve Never Heard Of But Should Try Out There are two types of people when it comes to using browser extensions. You have them all or you have them none. The adventure starts with just one install button. Then, you're lost in the extension world.  You’re adding a new one to your browser week by week. But what are the best Google Chrome extensions that you need to have? 36. Top 10 Javascript File Managers File management is a helpful tool in any business application. Here is an overview of the most functional, elegant, and popular JavaScript File Managers ready to be built into the client-server solution. Even if you do not have a direct need to work with files, you can borrow the classic file explorer interface for other tasks. For instance, there are some cases of the classic interface becoming the basis for a new web application in this article. 37. And Then He Said: Let's Fly Like an Eagle! Hi there, I'm Vadim, founder of Wedding Planning Assitant, and this is my three short stories about that. 38. How To Choose A Time Tracking Solution For Your Business: A Step By Step Guide As the impact of the COVID 19 epidemic on the global economy and societies starts to feel like less of a momentary blip on the radar and more of the new normal that we are all going to have to adjust to and get used to, people are getting used to the idea of working from home or remotely in some capacity. One of the technologies that have enabled this to happen and thus proven indispensable to the pandemic economy is time tracking solution. 39. The Fintech CTO's Guide To Remote Team Tools and Reviews Remote culture has never been so in demand before. The pandemic has changed the way we thought the world was likely to evolve in the nearest future. However, changes always go hand in hand with opportunities. 40. How Did Pocket Create a Habit Forming Product? A simple analyses of Pocket app using the infamous Hook Framework 41. 10 Features Where Coda Is Better Than Notion Notion vs. Coda: a look at 10 features in Coda that are better than Notion from the perspective of a Coda employee. This article has been updated for 2022. 42. 5 Productivity Tools Every Solopreneur Needs This pandemic is the perfect time to focus on your side hustle 43. Productivity 101: How to Implement the Pomodoro Productivity Technique If you’re looking to be more productive in your life, you may have heard of the Pomodoro productivity hack or technique. 44. 6 Incredible Productivity Tools for Programmers Productivity is key when it comes to getting work done with a smile. Burndowns are natural, here is how to minimize them and keep hustling. 45. Invoicing Tech Can Boost Brand Appeal Your brand is the essence of your company. It’s what your customers perceive you to be, and it’s how you portray your business to the world. 46. The Art of Mastering Atomic Habits If you want to change your life, you need to start with your habits. Atomic habits are the building blocks that make up our lives. 47. 5 Small Business Technologies You Can’t Afford to Ignore In an ever-changing world, staying up to date with modern business technologies can provide business owners with the leverage they need to be successful. This means being prosperous in their ventures and relevant in the competitive market. In most cases, using the correct types of technology will facilitate greater efficiency and even productivity. 48. Celebrating 10,000+ stars on GitHub repository⭐ ❤️️ Postwoman is celebrating the milestone of reaching 10,000 GitHub stars 🎉 49. 5 Tech Tools to Sharpen Your Creativity Creativity is important today because it helps us innovate and think outside the box. Knowing how to use your creativity can open up a world of opportunities, so it's worthwhile taking the time to sharpen these skills with the right tools. 50. Get Rid Of Your Self-Imposed Restrictions: An App Review of Freedom Want to improve your focus so you can get more done? Stop relying on self-discipline and use the Freedom app instead. 51. Without Digital Adoption, Your Enterprise will Struggle It’s clear to everyone that we’re living in a digital age. In a time when people expect to run their lives through their smartphones, competitive enterprises have no choice but to adopt digital tools. 52. 13 Must-Have Tools for Content Marketing in 2020 Hey there! If you’re looking for the best Content Marketing tool for your Business, stick here! Because I’ve made a list of tools for you that you need for your Content Marketing in 2020. I’ve used a lot of tools for my Content Marketing and shortlisted a few that are a must-have if you are to market your business through social media and Content Marketing. 53. 17 Apps to Boost Developer Productivity To help you build the ultimate workflow, we went looking for the developer productivity tools that are currently trending in coding communities. 54. 5 New AI Tools That Can Help You To Be More Productive How much of your productive time is taken away by all those meaningless news, stories and activities? Especially if you are a high performing executive or professional, how productive would you be if you weren’t distracted? We lose a lot of time on the activities that seem to be crucially important, while they are not. Although social media and numerous web-based communication platforms have definitely made communication a much less bothersome task, it is simply mind boggling to realize just how much of our time is spent on social media. 55. A Critical UX Review of Trello's New Features Trello recently launched new features and a business version. This could only mean one thing. It's time for a design review. 56. The Era of Opinionated Productivity Software: Superhuman, Roam, What’s Next? Productivity software is indispensable for all of us. Dedicated tools for creating spreadsheets, writing down notes, managing our to-dos etc. ensure that we are productive in our professions. Incumbent vendors (e.g. Microsoft, Google) have dominated this space for many years — yet, there is a chance for new players to enter the stage. New challengers such as the E-Mail client Superhuman or the note-taking tool Roam Research can compete by developing highly opinionated products which deliberately omit flexibility. Instead, the software is stringently crafted around a very particular way of approaching a process, which is inherently seen as superior by the firm. 57. How to Use Dora Metrics to Become an Elite Team Tradeoff between speed and quality is a myth. 58. How to Create a Habit Tracker in Notion Why Even Track Your Habits? 59. Automating Pull Request Merges with Mergify Mergify is a tool that helps prioritize, queuing, and automatically merge your pull requests. 60. 20+ Extraordinary Apps For Remote Teams I am sure that you already have Zoom, Slack and Asana on your computer. But daily office life is more than written tasks and planned meetings. There are lunches with your team, spontaneous talks near the water cooler, common tasks and goals you want to acheive. It’s your comfortable workplace and it by itself motivates you to be productive. Just look at the bunch of stickers on the wall — big things are waiting for you! 61. How To Improve Concentration: 2021 Edition How to improve your focus - especially as a software developer. Book review of Deep Work by Cal Newport. 62. Upgrade Your Note-taking With Roam Research “Personal Knowledge Management (PKM) is the practice of capturing the ideas and insights we encounter in our daily life, whether from personal experience, from books and articles, or from our work, and cultivating them over time to produce more creative, higher quality work.” 63. Notion: Superior Productivity For All Notion is an All-In-One workspace. Notion’s platform allows you to create task-oriented lists and projects, wikis, databases, lightweight CRM’s, tables & more! 64. InVision Gains 2 Million Users, Adds Freehand Templates (Interview) After a year that included 40% growth in user base and a 130% surge in demand for online whiteboard products, InVision has released an update with Freehand template gallery. 65. Top 10 Business Development Tools in 2020 and 2021 Slack, Rocketbolt, and Trello are among the best business development tools in 2021. In this article, we'll go over 10 tools your business should consider. 66. 6 Tools to Help SMBs Hack Productivity in 2020 The approach of the new year is always a great time to revisit all the awesome things your SMB has achieved and pat yourself in the back about how great things are running. 67. The Zen of Python: As Related by Masters The Zen of Python saw light for the first time in 1999. It's one of the many aspects that adds to the awesomeness of Python. It's a set of expressions which corners the spirit of the language. It was enounced by Tim Peters, a reputable software engineer, master Pythonista and Python's 'most prolific and tenacious core developer' in the words of none other than Guido [18]. This article bases itself mostly on the saying of core devs and highly reputable members. It makes a great gift to all those interested in the history of the sysadmin script which took the world by (pleasent) surprise. 68. All You Need To Know About SharePoint Migration Learn how you can simplify SharePoint Migration process easily with the best SharePoint Migration Tools. 69. The Best 75 Evernote Alternatives:  Complete Review Has Evernote become a unicorn that has stopped caring about its core users while trying to attract broader markets? Thank you for checking out the 69 most read stories about Productivity Tools on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

69 Stories To Learn About Productivity Tools

Let's learn about via these 36 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Business Development /Learn Repo 1. The Trifecta of AI: Market Adoption, Business Innovation, and Technical Development Explore the three aspects of AI driving the industry: market adoption, business innovation, and technical development. Discover how they shape the future of AI. [2. 9 Essential Things to Consider Before Buying a New Office (for Your Small IT Company)](https://hackernoon.com/9-essential-things-to-consider-before-buying-a-new-office-for-your-small-it-company-161d31wj) Would you like to have more control over your office space or build up equity instead of paying rent? Buying a commercial property comes with many advantages. 3. AR Smart Glasses Used for Business Success in 2020 AR Smart Glasses while being used by employees for working more effectively show the features of a perfect device that helps to create corporate values for..... 4. Hidden Costs of Starting a Small Business If you own a small business, limiting your costs is just as important as maximizing your revenue. 5. Growth Trends in Tech and Retail Industries This article is exploring AI, CDP, and personalization as emerging trends in Growth — helping companies  to  identify and implement new opportunities for growth 6. Startup Planning 101: How to Write Your Business Plan Want to know how to write a Startup Business Plan? We will take you through all the steps of creating a business plan and show you what to be aware of. 7. Why Interactive Sales Proposal Is the Next Big Thing? Paper sales proposal is rapidly becoming an old fashioned thing in a connected and interactive world. It's clear that sales proposal have evolved in many ways. Sales proposal files have been replaced by fancy proposal tools. You can also call it a technological leap. 8. How Giveaways Can Support Explosive Startup Growth Capitalize on our collective love for free stuff and get your startup more exposure online. 9. Shoshin: How To Foster This Zen Concept In Companies and Personal Development Shoshin is a Zen concept that companies, science researchers or regular people can practice for a better mindset. 10. 4 Tips for Building a Successful Business from Scratch in 2022 Four useful tips on starting a business from scratch, finding your purpose, and avoiding mistakes. 11. 6 Benefits Brand Tracking Brings To Your Business A great tool to better segment your audience is brand tracking. To learn more about your audience, you need to track their likes, dislikes, and demographics. 12. 8 Key Factors That Justify The Unparalleled Growth Of The Managed Services Market More and more businesses are willing to cut down their costs by hiring managed service providers. Here's why. 13. How to Run a Tech Business in 2023: 4 Tips From a Ukrainian Startup CEO Running a tech business in 2023 can be difficult. Here are four tips you can follow to develop your business this year. 14. Top 10 Tips for Choosing a CMS Platform for Your Website The most effective and dynamic Top 10 Tips that you might want to work on and choose in order to choose the right CMS platform for your business website. 15. 6 Effective Tactics To Nurture Leads Find out the importance of lead nurturing, why you should implement a strategy, and top tactics to create an effective one that drives results. 16. If Composable Commerce is the Future, Where is it? Composable Commerce was predicted to be the future, but remains out of reach for most companies. Integrations will be key to ensuring this approach succeeds. 17. What Future Business Investors can Learn from Past Trends If you want to protect your assets and seize opportunities during major crises, you need to consider the consequences. 18. Should Your Business Start Accepting Cryptocurrency? Wanting to get your business to be able to receive cryptocurrencies might be important for your business but how do you start and are there any good reasons? 19. 6 Ways Business Analysts Contribute in Software Development Business analysts ensure that the company's products/solutions meet its objectives. This post goes over the roles and responsibilities in detail 20. About Our Strategic Partnership with Hive Uquid, a blockchain e-commerce ecosystem, has continued to make significant gains towards fostering adoption in the global market. It has developed an innovative platform that incorporates DeFi, NFTs and metaverse. 21. What is a 409a Valuation and How Do You Get One? This article was prepared to help you in choosing the right and top 409A valuation providers. 22. Importance and Tips of Financial Management in Small Business Managing your business finances is important for creating stable financial future. Keep reading to know useful tips for financial management in small business. 23. 5 Most Common Data Quality Issues For Business With the advent of data socialization and data democratization, many organizations organize, share and make information available to all employees in an efficient manner. While most organizations benefit from liberal use of such a source of information available to their employees, others struggle with the quality of the data they use. 24. Top 10 Business Development Tools in 2020 and 2021 Slack, Rocketbolt, and Trello are among the best business development tools in 2021. In this article, we'll go over 10 tools your business should consider. 25. Why You Should Focus on Value and Not on Busy Work Stop with the busy work, and focus on the value drivers. When business or life are not going well, people often look for anything to do. Doing something may make you feel better, but doing the wrong thing won’t help you, and it can even cause harm. 26. How Automation in Contract Management Can Optimize Business Negotiations Contract management software for small businesses optimizes negotiations so you can spend more time expanding your brand. 27. How to Precisely Validate and Test Startup Ideas through MVP Techtic lists down 6 ways to validate & test business startup ideas through MVP.  Also, know the advantages of MVP and how MVP assists businesses. 28. How Does Procurement Transformation Help Achieve Sustainable Business Results? Procurement involves more than sourcing and buying. It encompasses diverse corporate activities focused on promptly and efficiently acquiring goods and services 29. How Startups Can Stay on Top of Their Cash Flow Millions of Americans launch their own startups each year, with over 423,153 new business applicationsin April 2022 alone. 30. Difference Between Sales and Business Development: 8 Business Metrics “You only have to do a few things right in your life so long as you don’t do too many things wrong.” — Warren Buffett 31. How to Write a Thorough Grant Funding Proposal for a Small Business Regardless of the small business, numerous grants of all sizes are up for grabs if you can persuade gifters through a well-written proposal. Find out how here! 32. 4 Tips for Selling Your Business Online Selling a business is a very important and emotional process. There’s a certain amount of joy and relief that comes from the process, but there is also plenty of stress and challenges. 33. Ways To Increase The Conversion Rate On Landing Page To get the highest possible conversion rate surely have tweaked your landing page, your ads are laser targeted and your bids are competitive. All the time in which you have stayed profitable your competitors would surely be thinking and figuring out the ways to improve, enhance and build trustworthy relations with their clients that could be yours, and it all happened in front of your eyes because their impression was better than yours and we all know that the first impression is the last impression. But still, there's some time in which you could increase the conversion rate on your landing page. To make it work we first had to discuss the ways to separate the fact from fiction. 34. Growth Marketing vs Growth Hacking: Difference and Benefits This article discusses the difference between growth marketing and growth hacking. It covers what are the benefits of growth hacking and rules of growth hacker 35. How to Generate Leads in B2B Hello and welcome to the overview of the basic techniques and methods that are a must for you if your goal is to make your B2B sales lead generation more efficient and less time-consuming. 36. Dealing With Negative Email Replies: To Give It Up or To Work It Out When you consider all outcomes of your email campaigns, don’t be afraid to ask the right questions. Even if they scare you. For example, don’t say “What should I do if I get negative replies?”. Say “What should I do when I get negative replies?”. Thank you for checking out the 36 most read stories about Business Development on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

36 Stories To Learn About Business Development

261 Stories To Learn About Software Development

Duel on Syrtis

RocketBot aims to give users an easier way to send and receive crypto through social media. It’s bridging the divide between crypto and social media platforms. Thanks to RocketBot, advertisers can now easily pay users for engagement, making it a simple engage-to-earn solution. You can reward followers by running airdrops and giveaways. It also provides users with a fun way to earn crypto for likes, retweets, and other social media activity. So what is RocketBot, and who should use it? Here is what you should know about RocketBot for engage-to-earn activities on Twitter. What is RocketBot? RocketBot is one of the latest engage-to-earn platforms and is likely to become a major part of how people use crypto on social media. It offers a streamlined method for sending and receiving crypto via Twitter, Telegram, and Discord. RocketBot is intended as a bridge between social media platforms and cryptocurrencies. Users can use the platform to exchange, send, receive, and airdrop cryptocurrencies. RocketBot also allows members to subscribe to news and tips. RocketBot is supported by Merge, which is a platform designed to support blockchain and non-blockchain services. It acts as the intermediary between you and Software as a Service (SaaS) products. The Merge ecosystem includes a variety of services, including RocketBot. The simplicity of RocketBot makes it the ideal solution for an engage-to-earn campaign. What is Engage-to-Earn? Engage-to-earn refers to a model in which users are rewarded for participating in specific activities on social media. For example, advertisers may run a giveaway for followers who retweet or like a specific tweet on Twitter. The users are rewarded for engaging with the Twitter account. In the past, setting up an airdrop or giveaway using crypto required multiple steps on separate platforms. RocketBot streamlines the process. It allows advertisers to pay users directly for their engagement. Here are the steps for earning crypto with RocketBot: Install the mobile RocketBot crypto wallet app Link the crypto wallet app to your Twitter account Start earning crypto for using Twitter After installing the crypto wallet, users can look for airdrop opportunities that utilize RocketBot. Engaging with certain accounts may result in free crypto with minimal effort. How Does RocketBot Work? RocketBot integrates with Discord servers, Telegram groups, and Twitter accounts. Users can also access RocketBot services through private messaging. The web-based RocketBot platform allows you to register a main account for use with all supported social media networks. To get started, you just need to install the RocketBot crypto wallet on your smartphone. After installing the app, link your Twitter account to start earning or giving away crypto. RocketBot is a type of bot designed specifically for robotic process automation (RPA). RPA allows bots to automatically carry out specific requests, such as initiating a crypto giveaway or sending crypto to a specific user. The transactions are completed using commands. The RocketBot website and YouTube channel provide tutorials for issuing commands on supported social media networks. With a single line of code, you can instantly check past transactions or launch a new crypto giveaway. Some of the main features of RocketBot include: Universal native wallet Instant deposits & withdrawals No extra fees Rewards Tokenomics Security RocketBot users receive access to a personal wallet that they can use to store cryptocurrencies, including Merge (MERGE). After signing up, you can complete instant deposits and withdrawals using your linked social media accounts. RocketBot does not charge extra fees for deposits. However, withdrawals are subject to the standard fees incurred by each crypto coin. Users can also reward other users. Reward options include tips, airdrops, and giveaways. The platform also supports Tokenomics by locking tokens that users receive after a service period ends. RocketBot provides a secure solution for sending and receiving crypto through social media. It uses the captcha system to verify that a human is completing the action instead of another bot. Advantages of Choosing RocketBot RocketBot stands out compared to the other engage-to-earn options that have appeared online. Users can sign up quickly and start using the platform to launch giveaways and complete crypto transactions. Other advantages of using RocketBot include: Supports a variety of crypto Easy to use commands Convenient crypto wallet Airdrops with increased transparency Get more out of your advertising RocketBot is also free to use. Most of the features are provided for free. You can create a wallet, receive crypto, send tips, and complete giveaways without extra fees. The only fees come from withdrawals and extended usage. Supports a Variety of Crypto RocketBot supports a growing list of cryptocurrencies, including: Merge (MERGE) Binance Coin (BNB) Bitcoin (BTC) Litecoin (LTC) PIVX Whale Exploder (WHEX) Absolute (ABS) French Digital Reserve (FDR) Martkist (MARTK) Block-Logic (BLTG) Doge (DOGE) Ultragate (ULG) Firo (FIRO) And more The current selection of cryptos includes Bitcoin (BTC) and Doge (DOGE), which are two of the most well-known currencies. The selection of cryptocurrencies should continue to grow as the popularity of the platform increases. Easy-to-Use Commands Sending crypto on Twitter using RocketBot is easy. You insert a single line at the end of a tweet to schedule giveaways, tip someone, or send crypto to a friend. Immediately after sending the tweet, the RocketBot account sends a confirmation message to your Twitter account. RocketBot transfers the crypto out of your account and sends it to the intended recipient. You get another confirmation message after the transfer is complete. RocketBot commands allow you to: Complete a deposit List deposits Complete a withdrawal List withdrawals List transactions View balances Complete an airdrop Complete a giveaway Send a tip List all coins Most commands are available on Discord, Telegram, and Twitter. However, some commands are only available on Discord or Discord and Telegram. For example, you cannot list all transactions on Twitter but can view all withdrawals or all deposits. Convenient Crypto Wallet The mobile crypto wallet is another reason to use RocketBot. Users can download the mobile wallet on their iPhone or Android smartphone or tablet. The wallet is powered by Merge. After signing in, users can exchange crypto and view the value of each currency in real time. The wallet is useful for tracking the value of your assets and viewing transactions without relying solely on commands on social media. Airdrops with Increased Transparency Using RocketBot allows for greater transparency when completing airdrops and crypto giveaways. Crypto users have grown skeptical of airdrops due to the rise of scams and worthless airdrops. RocketBot provides complete transparency, as the transactions are automated. The advertiser or brand has no control over who receives the reward during a random giveaway. The transparent nature of RocketBot offers a trustworthy solution for completing airdrops and giveaways. Get More Out of Your Advertising Campaign RocketBot offers many advantages for both consumers and advertisers. As an advertiser, you can increase engagement levels with your target audience. Offering real rewards for liking, commenting, and retweeting can boost the visibility of your advertising campaigns. You can use airdrops and giveaways to attract and retain more followers. Basic Usage Limits with RocketBot RocketBot is free to use for the basic features. You can create a wallet and complete most of the available commands for free. However, the service includes limits. Here are the basic limits for using RocketBot for free: Send up to five tips per day for free Complete one airdrop per day Complete one giveaway per day Simply holding more MERGE tokens in your RocketBot wallet can increase your limits. Extended usage increases the basic limits by one for every 300 MERGE tokens that you hold. For example, if you hold 900 MERGE in your wallet, you can complete up to eight tips (five free tips plus three extra tips) per day. You can also complete up to four airdrops and four giveaways. Users may pay fees for withdrawals. Each blockchain charges a small fee for completing a transaction. RocketBot does not earn a commission for the transactions. They are simply facilitating the transaction. Users may also pay fees for monthly listings. There is no setup fee for a basic free listing for a new coin on RocketBot. However, setting up a monthly listing includes a one-time setup fee and a small recurring monthly fee. Who Should Use RocketBot? RocketBot is designed for everyone, whether you are a developer, advertiser, business owner, or consumer. Developers may choose to use RocketBot to promote their newly developed coins. Advertisers and business owners can use RocketBot to boost engagement with followers. Users can benefit from RocketBot by engaging with their favorite brands and receiving rewards. Conclusion RocketBot is a crypto wallet and platform for sending and receiving crypto. It currently offers one of the most streamlined options for crypto airdrops and giveaways. However, it may also eventually simplify the way that people complete peer-to-peer transactions, such as sending money to a friend or family member. If you are looking for a simple method for sending crypto or completing airdrops or giveaways, download the RocketBot mobile wallet today. Install the app, link your social media accounts, and familiarize yourself with the commands. Download RocketBot today. Also Published here

Send Crypto Via Twitter, Discord and More Using This Platform

Slack, Rocketbolt, and Trello are among the best business development  tools in 2020 and 2021. In this article, we'll go over 10 tools your business should consider, especially if a part of your team is working remotely. Whether you have a new startup or an established business, top are the need of the hour to manage the flurry of activities and keep your team productive. business development tools From relationship-building to managing pitches to reporting and prospecting! Technology is empowering us with the latest innovations and business development tools to smoothen out our workflow, ease out the excess workload, and enhance employee productivity. As per the latest surveys on social media monitoring, CRM, research services, SEO, the following are the top benefits these tools provide. list building software, social media, prospecting, market automation, and project management, 4 Primary Benefits of Business Development Tools - For better integration of all activities under one roof, smart business software is much needed. Better Integration of Data An easy-to-learn CRM, user-friendly interface, well-integrated with marketing automation platforms is definitely priority for business managers. Simple, Practical CRM - Tools that integrate well with other software to have up-to-date accurate data, better tracking business development tools are must. Improved Tracking Facility - Business managers are always on the lookout for efficient reporting on the metrics for quick and easy management of workflow. Smart Reporting Tools - Each business development tool or app comes along with certain specialty features to serve that particular organization. Hence, it is crucial to analyze your business needs and then pick up the best tool to boost up your business. Below our my picks for the to survive the cut-throat business challenges of your business. top 10 business development tools 1.Twitter is strongly emerging as one of the most important , with special features of providing real-time information. Twitter business development tools It is an American microblogging and social networking service founded in 2006. Twitter facilitates public conversation, allows you to send & receive posts called tweets. Twitter is often looked upon as a common ground for the whole world to connect, share, learn and solve their problems 2. LinkedIn is an American business development, employment-oriented portal founded in 2003. They operate via websites and mobile apps and have carved a niche for themselves in connecting business professionals all over the world.  LinkedIn is mainly used for professional networking, where employers post their jobs and job seekers post their CVs. LinkedIn LinkedIn is yet another important business development platform for all professional networking requirements. Mainly, LinkedIn serves online professional network, jobs, people search, address book, advertising, company searches, professional identity, and group collaborations. 3. Join.me is a cloud-based business Development software that specializes in collaboration, mobility, screen-sharing, collaborative whiteboard, connectivity, meetings, and technology. Join.me SAAS It is considered as one of the essential business development tools for remote teams with budget constraints, allowing instant online meetings, sharing of tasks effortlessly. Coming from the suite of services,join.me is a big success amongst small businesses, it has an easy-to-use modern interface with plenty of features to enhance your online meetings. LogmeIn 4. Slack offers connectivity, group work, mobility to remote teams staying continents apart. It is one of the most useful business development tools for sharing files, group collaborations, and connecting on calls. Slack This tool offers IRC-style features including persistent chat rooms organized by private groups, topics, and direct messaging. It is one of the simplest applications to connect with your team and work together. 5. Rocket Bolt is one of the key business development tools for lead activation, conversion optimization, engagement, SaaS, B2B, email productivity and lead tracking. It seamlessly integrates with current marketing and sales workflows to record all the lead activity. Rocketbolt Rocketbolt is a simple yet powerful lead tracking tool that saves time on researching and monitoring leads. This trendy app easily manages your emails and make them easy to read. Also, it can be easily added to any website in less than a few minutes and quickly drive more sales & social media engagement without any additional maintenance. Overall, it provides a great UX/UI with clean features, super easy to use application! 6. Trello is a smart task management application with special features for organization, collaboration, software, and projects for teams to work more collaboratively. Trello Trello’s boards, cards, and lists enable teams to organize & prioritize projects in a fun, flexible way. This business development tool gives a visual overview of what is being done and who is working on what, making a systematical approach to track the project and contribute where it is most needed. 7. Google Drive Launched in 2012 by Google, this is a file storage, synchronization service that allows users to store files on their servers, share files and synchronize across various devices. Google Drive is a specialty business development tool for online storage and file sharing. It is one of the best free cloud-based storage services that enables users to store and access files online by providing the15 GB free space to back up your important files. It allows simultaneous data sharing with the whole team preventing the hassle of sending emails separately. 8. Prezi is a cloud-based presentation platform launched in 2009 by a Hungarian presentation software company. This business development tool helps you connect more powerfully with the audience. Prezi Prezi is strongly growing its base with each passing year, has more than 150 million users who have created more than 400 million presentations worldwide. Its open canvas allows you to navigate freely through topics, encouraging easy interaction and collaboration between you and your viewers, resulting into conversational presentations that are more natural, engaging, and memorable. 9. Canva has successfully established itself into an efficient business development tool for graphic design platforms. It allows its users to create social media graphics, posters, presentations, documents and several other useful visual contents. Canva With Canva, you can design anything from business cards to consultant-worthy presentations and proposals in a matter of minutes. It is built more intuitively to provide design efficiency where users can choose from many professionally designed templates, edit the designs and upload their own photos through drop and drag interface. You can start by selecting the type of project you want to build like- a presentation, letter or a Facebook cover, etc. Then you can browse through the provided templates, pick one, customize it, and you are ready with a stunning presentation to send your prospects. And you do not have to be an expert designer to make these presentations, anybody with an interest in designing and a keen eye for details can use this business development application with ease and  confidence. 10. Fileboard specializes in online meetings, screen sharing, track analytics and sales presentations. Fileboard This business development tool is an AI-enabled software that shows how sales professionals are engaging efficiently through insights with their prospecting, automate tasks and then comes with a suite of tools to enhance productivity. Wrap Up The are endless out there, varying as per the business needs of that organization. business development tools We tried presenting the top 10 tools which are mostly useful for every organization’s work structure. You surely want the best business development tools and software to have an edge over your competition. Also, the ease of work experience these tools provide is unmatchable. We recommend to select the best business tools after thoroughly scrutinizing their payment plans, applications benefits and after reviewing their full reports to extract maximum benefit out of these.

Top 10 Business Development Tools in 2020 and 2021

Context The world is advancing at a faster pace and new technologies are coming up to reshape the business world and society. One of these emerging technologies is Artificial Intelligence (AI). Artificial Intelligence has gained momentum in the world like other technologies such as the Internet of Things (IoT), Blockchain, 3D printing, Robotics etc. AI is leaping all around us everywhere we look, both in areas that border on science fiction, like self-driving cars to the more ordinary tasks like what show should I watch on Netflix/Amazon. AI is continuing to have an enormous influence on almost every walk of life, from businesses to civilisation. With these technologies, the business will gain the vigour needed to solve problems that humans can’t possibly solve. Three Components of AI Apart from the context of the domain where AI is being applied, there are three main components of AI: The first one is the AI algorithm itself. Open-source machine learning libraries like Keras, Theano and TensorFlow have shed a lot of the low-level complexity involved in designing and building AI applications. These tools are free, well documented and supported by vibrant communities. The availability of these tools has made building AI applications far more accessible to developers. The second one is computing power, both in the form of raw CPU power and large scale data storage solutions. Cloud services like Amazon Web Services, Google Cloud, Microsoft Azure and others make renting servers, virtual machines and big data tools are as simple as pushing a few buttons. The third but the most important one is data. Before you can contemplate hiring data scientists, renting or paying for servers and installing open-source machine learning libraries, you must have data. The quality and depth of data will determine the level of AI applications you can achieve. Data & AI Source: https://www.ibm.com/cloud/garage/architectures/dataAnalyticsArchitecture While AI is a reasonably wide area of study in computer science, most of the excitement these days is centred on an area of AI called machine learning and in particular, deep learning. Machine learning trains the algorithms to learn and predict answers to problems by analysing data to make predictions on their own. As we discussed above, the promise of AI depends on certain ingredients for successful adoption; one of the critical ingredients for a successful AI implementation is a clean, representative and large amount of historical data. However, unless you are Google or Facebook with vast amounts of representative data, you will struggle to harvest historical data that are enough to give the required inference for machine learning techniques to be an effective enabler for AI initiatives. Therefore, to make AI work, there is a need for an improved level of data preparation, engineering, enrichment, and contextualization. As with most reports about groundbreaking technology, this discussion is way ahead of current industry practices. The vision serves a useful purpose in suggesting what’s possible. But with many businesses lacking the data infrastructure necessary to obtain real AI and ML capabilities, the journey towards perfect production can also be so abstract that it perplexes the very people looking to achieve it. AI algorithms learn from data. It is critical that you feed them the right data for the problem you want to solve. Even if you have good data, you need to make sure that it is on a useful scale, format and even that meaningful features are included. Understand the key capabilities you need for collaborative, operational data preparation pipelines to serve the needs of both your data and business analysts. During the global launch of the Outside Insight book, author and Meltwater CEO Jorn Lyseggen, alongside AI experts, discussed the importance of the data fueling AI, and the need for executives using AI outputs for decision-making to both understand the data informing those outputs and ensure it’s as comprehensive and unbiased as possible. “Artificial Intelligence is your rocket, but data is the fuel. You can have the best algorithms in the world, an amazing rocket, but you’re only going to get as far as your data gets you. Data is fundamental — data is AI,” said Gerardo Salandra, Chairman of the AI Society of Hong Kong and CEO at Rocketbots, at the Hong Kong launch event. Similarly, Monica Rogati’s Data Science Hierarchy of Needs is a pyramid showing what’s necessary to add intelligence to the production system. At the bottom is the need to gather the right data, in the right formats and systems, and in the right quantity. Any application of AI and ML will only be as good as the quality of data collected. Source: https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007 In the growing AI market, International Data Corporation (IDC) predicts global spending is expected to increase 50% per year, to a total of $57.6 billion by 2021. Business leaders are catching on to the importance of implementing an AI strategy globally. However, it’s not enough just to introduce AI-driven tools; you need the right data inputs to find valuable insights. Importance of Data A lot has been written about AI recently, but one element that is often not stressed is the value data plays in allowing AI to function. Take self-driving cars — probably the most recognized application of AI. Building a self-driving car requires a humongous amount of data ranging from signals from infrared sensors, images from digital cameras, and high-resolution maps. NVIDIA estimates that one self-driving car generates 1 TB per hour of raw data. All that data is then used in the development of the AI models that actually drive the car. While we have seen recent advances in other AI techniques like reinforcement learning that use less data (like the success of Deep Mind’s recent Alpha Go — Zero in the game of GO), data is still critical for developing AI applications. Enterprises are overwhelmed with silo IT systems built over the years that contain data designed to do very specific individual ‘System of Record’ tasks, but unfortunately, these records are duplicated across multiple ‘Systems of Record’ resulting in massive data proliferation but lacking complete representation of an entity in any single system. This reality has given rise to fragmented and often duplicated data landscape that requires expensive and often non-efficient means of establishing ‘Source of Truth’ data sets. Data provides ‘Intelligence’ to AI AI applications improve as they gain more experience (means more data) but present AI applications have an unhealthy infatuation with gaining this experience exclusively from Machine Learning techniques. While there is nothing inherently wrong with Machine Learning, the main caveat for a successful Machine Learning outcome is sufficient and representative historical data for the machine to learn from. For example, if an AI model is learning to recognize chairs and has only seen standard dining chairs that have four legs, the model may believe that chairs are only defined by four legs. This means if the model is shown to, say, a desk chair that has one pillar, it will not recognize it as a chair. Preparing data for AI While your organisation may not be at the stage where you are able to start building AI applications, at the least you should be preparing for a future where your data will be utilised to power smart solutions. Treat every new initiative or project as an opportunity to build a foundation for future data models. Data Collection This aspect has become crucial in light of the GDPR legislation. Are there clear and followed guidelines about what and why data is collected when a new feature or product is being developed? Does that data have a purpose or is it being collected just like that? Data Format When you are collecting data, is it being saved in a usable format across all our data collection touchpoints? Are the field names the same? Is the same level of validation and error checking applied across products? Data Storage Data needs to be flowing into data stores and be available in real-time to all areas of the business. Given that AI applications usually become more reliable the more they can correlate different sources of information, siloed data sets that are hard to access become an obstacle to discovering value in an organisation’s data. Data Literacy AI is basically biased in how it was created, trained, programmed. One of the most significant things for AI to be successful is that executives and decision-makers have the data literacy to beat up the model, to challenge the model, to massage the model and to fully understand what the underlying assumptions are to make sure the answer it produces actually matches the terrain that you want to operate in. What’s vital to get the best predictive models and forward-looking insights is that the data informing them comes from a variety of external sources. More Data Just like humans, AI applications improve with more experience. Data provides examples essential to train models that can perform predictions and classifications. A good example of this is in image recognition. The availability of data through ImageNet transformed the pace of change in image understanding and led to computers reaching human-level performance. A general rule of thumb is that you need 10 times as much data as the number of parameters (i.e., degrees of freedom) in the model being built. The more complicated the task, the more data needed. Data Understanding For cooking the perfect meal, it’s great to know the tastes of your diners. Similarly, data is essential to tailoring an AI model to the wants of specific users. We need to learn how users use their applications and search content in order to generate meaningful personalized recommendations. By knowing what content users read, download and collect, we can give them advice on potential content of interest. Furthermore, techniques such as collaborative filtering, which make suggestions based on the similarity between users, improve with access to more data; the more user data one has, the more likely it is that the algorithm can find a similar a user. Diverse Data A key problem in building AI models is overfitting — this is, where the model focuses too specifically on the examples given. For instance, if a model is trying to learn to recognize chairs and has only been shown standard dining chairs with four legs, it may learn that chairs are defined by having four legs. If the model is then shown a desk chair with just one pillar, it wouldn’t recognize it. Having diverse data helps combat this problem. During training, the AI model can view more examples of different types of things. This is particularly valuable in working with data about people, where there can be the potential for algorithmic bias against people from diverse backgrounds. This point was made by Prof. Dame Wendy Hall in her interview at the World Summit AI. Prof. Hall focused on the need to make sure that AI was trained on diverse datasets. A good example of combating this through data is the lengths that Apple went to in training their new Face ID recognition algorithm. External Data As the race to implement AI tools at an enterprise-level reaches new heights, it’s important to note that the data informing those tools is of paramount importance. Relying only on internal information to inform algorithms will produce insights gleaned only from the information you already have. Rather, it’s vital that decision-makers also look to insights from external data for a much more comprehensive and unbiased view of their customers and industry landscape. Hypothesis Testing Even in cases where techniques can be used that require less training data, more data makes it easier to test AI systems. An example of this is A/B testing. This is where a developer takes a small amount of traffic to a site and tests to see whether a new recommendation engine or search algorithm performs better on that small set of traffic. The more traffic (means data), the easier it is to test multiple algorithms or variants. At the World AI Summit, Netflix explained how they use A/B testing to select artwork that maximizes the engagement with films and TV series on Netflix. Data Reusability Finally, it is usually the case that data can be reused for different applications. For example, a technique called transfer learning allows data developed for one domain to be applied to another domain. Moreover, recent work has revealed that background knowledge can further improve on tasks like object detection in images. Recent work from Google has shown how training using data designated for a different task like image recognition can help performance on another completely different task like language translation. Conclusion In summary, data is the pivotal element in developing any AI system today. In the near future, what we are now calling AI will be embedded in our culture and we won’t call it AI any longer. It will just be how things work. What you have in your control today is your data. It’s crucial that you start preparing for a future where AI applications can start using your data and that starts with the quantity and quality of the data itself. Embracing AI with business & society is a journey, not a silver bullet that will solve challenges instantly. It begins with gathering data into simple visualizations and statistical processes that allow you to better understand your data and get your processes under control. From there, you’ll progress through increasingly advanced analytical capabilities, until you achieve that utopian aim of perfect production, where you have AI helping you make products as efficiently and reliably as possible. Ankit Rathi is an AI architect, published author & well-known speaker. His interest lies primarily in building end-to-end AI applications/products following best practices of Data Engineering and Architecture. Why don’t you connect with Ankit on Twitter , LinkedIn or Instagram ?

“AI Sells, Data Delivers!”

conducted by Econsultancy in association with IBM Watson Marketing surveyed over 1,000 professionals in marketing, ecommerce, and digital. They found that customer segmentation was the topmost priority among these professionals. 72% of them said that they use customer data for segmentation. In fact, even the most laggard businesses consider . Research segmentation as a standard strategy This really shouldn’t come as a surprise. Every buyer has individual . And who have had a personal experience with brands think that personalization has a significant influence on purchases. However, it’s impossible to cater to each and every person based on their individual traits. And that’s why you need customer segmentation. In this post, we will discuss four benefits of customer segmentation for higher profitability. preferences, needs, and behavioral patterns 59% of people 1. Personalization HubSpot analyzed over 330,000 calls-to-action and their performance over a 12-month period. They found that personalized calls-to-action performed better compared to generic calls-to-action. Providing a great customer experience is the need of the hour for every business today. In fact, perceive customer experience to be as important as a brand’s services or products. And personalization can go a long way to ensure that you provide exceptional customer experience. 202% 80% of people is a jewelry discovery and shopping service provider. Customers can rent unlimited fashion jewelry from top brands and designers by paying a monthly fee. As part of their growth strategy, they require each of their customers to participate in a survey. This survey helps Rocksbox get a better sense of the personal style preferences of their subscribers. Rocksbox Image via Rocksbox Using such data, they create various customer segments so that they can send their customers jewelry choices that are customized. Such segmentation helps to group your customers so that the communication with each group is suited to their taste. Bottom line: Segmentation can help you provide your customers with more personalized communication and experiences, which can lead to higher profitability. 2. Effective Acquisition and Retention Customer segmentation allows you to learn a great deal about your customers so you can cater to their needs more efficiently. You can also tailor your communication depending on the customer lifecycle. in a CMO survey said that they use customer segmentation to decide their acquisition strategy. And 38% of them said that they use it to decide their retention strategy. 44% of marketers For example, when you’re trying to convert a prospect, your communication needs to be more informative. At this stage, they would prefer to learn about the solutions to their problems. In later stages, you may need to talk about other related products in your communication. created hyper-personalized newsletters by using the Smart Content feature from HubSpot. They customized the opening and the CTA of their newsletter emails according to a customer’s life cycle stage. This increased their open rates by 43%, while their CTR increased by 89%. Denamico Image via Denamico Customer segmentation also helps to create an effective retention strategy. You may need to apply a recovery strategy for some of your older and more valuable customers who have not made any purchases recently. You may also need to terminate potentially less valuable customers so that you can focus more on valuable ones. took the time to identify their top 5% of customers who drove the highest revenue for them. They also identified a segment of high potential customers and optimized their marketing strategy to focus on these two segments. In addition to better customer retention and revenue stability, they managed to increase their customer value by 28%. There was a 40% increase in revenue during their key selling period. LiftPoint Consulting Image via LiftPoint Consulting 3. Better ROI from Marketing Braze (previously Appboy) analyzed their they ran over two years. They found that campaigns that had been sent to well-thought-out customer segments saw 200% greater conversions than those sent to broad audiences. marketing data from over 30,000 campaigns Better Targeting Successful marketing not only requires knowledge about who your customers are, but also where exactly they are in the buying process. And customer segmentation based on such information can ensure that your marketing campaigns are truly effective. offers a comprehensive range of residential real estate services that includes renting, selling, buying, remodeling, and even financing. They have a database of over 110 million homes for rent and sale. Zillow Zillow uses customer segmentation to ensure that the right marketing messages are sent to the right people based on their life cycle stage. For example, to consumers who are looking to buy a home, they send emails about homes in their preferred locations. They also send notifications about price changes to homes they’ve saved. Using this strategy, they were able to achieve a and an 18% lift in their click-to-open rate. 161% lift in their open rate Effective Channels and Content Formats Customer segmentation can help you decide on the right kind of promotional content to send to your customers. For example, not all of them may like to receive notifications about discounts or offers but some of them might. B2C buyers are to favor newsletters or emails while B2B buyers are 22.X more likely to find them important. When it comes to retargeting, B2C buyers are 1.1X more likely to favor them while B2B buyers are 1.8X more likely to do so. 1.3X more likely If you are aware of the preferences of your customer segments, you can send them information that they’ll appreciate. In fact, expect that businesses are aware of their needs and preferences. 76% of consumers Segmentation also helps you select the right marketing channels for the right audience set. For example, if you’re planning to start a retargeting , which customer segments should you target? campaign on Facebook Image via Facebook The bottom line is that customer segmentation can help you ensure that you send the right marketing messages to the right people. It can help you leverage the most effective channels and content formats so that your chances of conversions are greater. 4. Unearth New Opportunities With customer segmentation, you will never miss out on what’s trending. And you can even come up with new products or variations according to the changing preferences of your customers. If you are vigilant enough, you may also have the first mover’s advantage in a product segment. helped a bank identify a new and profitable segment, known as the “confident entrepreneur” segment. They helped develop a card offering and a new service package. They considered the segment’s emotional and functional needs to craft a new value proposition to resonate with them. Six months after they launched the new card, their portfolio share in premium cards increased from 8% to 51%. Their actives rate also saw a 7% incremental increase. EMM Group Final Thoughts The standards of good customer experience are ever increasing. In fact, don’t mind paying a premium if businesses can provide them with an amazing experience. Segmentation helps to grow your business by building long-term relationships with your customers and providing exceptional customer experiences. And above all, it helps you stay innovative, or at least helps you to stay at par with your customer needs. 67% of customers Having discussed all of the benefits, it’s important to emphasize that good data and analytics are the basis of segmentation. Data should be clean, updated regularly, and error free. If the data is not correct or misinterpreted, then all segmentation efforts will be meaningless. Have you experienced any other special benefits after exercising segmentation? Feel free to share them with us in the comment section below. Originally published at PostFunnel.com . About The Author is the Founder and CEO of and . You can connect with him on , , , and . Shane Barker Content Solutions Gifographics Twitter Facebook LinkedIn Instagram

4 Benefits of Customer Segmentation for Higher Profitability

Today, we’re going to catch up with Optimizely Director of Product Management, Jon Noronha , to discuss data experimentation, the product developments at Optimizely, and his own professional journey in the website experimentation space. Optimizely is the world’s leader in digital experience optimization, allowing businesses to dramatically drive up the value of their digital products, commerce and campaigns through its best in class experimentation software platform. : David What is the scale and scope of Optimizely? : Optimizely is the world’s largest platform for online experimentation, with over 1 million experiments run so far and 600 new ones started every day. We power A/B testing and feature flagging for over a quarter of the Fortune 100, including companies like Microsoft, Visa, and HP. We also power app experimentation for digital disruptors like Blue Apron, Sonos, and Missguided. Chances are that every day you browse the internet, you’re bucketed into hundreds of different experiments powered by Optimizely. Jon “Two-sample hypothesis tests” date back to 1908 ( according to wikipedia ), and Google engineers ran their first A/B test in the year 2000. Optimizely started in 2009 and you joined in 2014. Could you walk us through how you see the history and future of A/B testing on the internet? I’d go back even further — the scientific method has been driving human progress since before Isaac Newton! Online experimentation just applies that same mindset to product development and website design. What’s more striking to me is that it’s taken this long. As you point out, it’s been almost 20 years since Google ran its first A/B test. But in 2018, most companies still aren’t experimenting on most of their key decisions — and they’re generally being outcompeted by a small minority like Amazon and Netflix who are famous for experimenting relentlessly. Optimizely’s mission is to change that balance by bringing Google-level experimentation technology to everyone else. We’ve been pretty successful: in the last decade, most online businesses have started to experiment online, and those who haven’t are realizing they’re falling behind. Interestingly, though, online A/B testing has mainly taken off with marketing teams. It’s now established practice to experiment on email subjects, ad copy, and landing page layouts to drive conversion rates. The bigger trend I see for the next few years is the spread of experimentation to product and engineering teams. The top tech companies already have a model where every new feature is launched as an experiment, but we’re starting to see that mindset grow in smaller start-ups and technical teams within more traditional companies. I think in a few years, every competent product team will be adopting and experimenting on their features after launch. feature flagging In your 4 years at Optimizely, what product work are you the most proud of? Optimizely already had a strong product when I joined, but it was focused on a very particular kind of experimenter: a semi-technical lone wolf running sporadic growth A/B tests. We had ambitions to expand far beyond that, to larger teams running more fundamental experiments across all parts of their product stack. So in 2016, we completely revamped our platform to adapt it for the needs of enterprise teams with more modern websites and apps. The result was , a completely redesigned product built for both marketers and developers. It’s been amazing to see this product work translate into culture change across some of the world’s most important businesses. Optimizely X What do you see as Optimizely’s impact on the internet? Until a few years ago, the only sites experimenting heavily were tech giants like Facebook or Amazon. These companies built a culture of experimentation that allowed them to disrupt one sector after another. I see Optimizely as leveling the playing field. I’m proud of how we’ve helped iconic companies stay competitive through experimentation. For example, we’ve helped the New York Times navigate a major digital transformation and become a leader in digital subscriptions by testing and personalizing their paywall experience. I’m also excited about how we’ve helped smaller companies like Blue Apron and Rocksbox grow their businesses and refine their messaging as they go. Most of all, I’m excited about what that means for all of us as end users of these products. The internet is full of broken apps, janky UX, and dumb features that nobody asked for. By helping more companies experiment, we help make these user experiences so much more effective for all of us. What are your favorite / least favorite misconceptions about data driven experimentation? I hate the term “data driven” in the context of experimentation. Unless you’re the one behind the steering wheel, your data will drive you straight off a cliff. Data is a tool for informing tradeoffs and understanding your customers, but it’s no substitute for a vision or strategy. You should be data-informed, not data-driven. There’s a misconception that experimentation is all about tiny, incremental changes — like Google famously testing 40 shades of blue for their links. But the best experiments are actually bold — like Amazon introducing Prime, or Facebook building the News Feed. Experimentation lets you bring controversial ideas out of a meeting room into the real world, while mitigating the risk if something goes wrong. Done right, experiments should make you fearless in trying out bold ideas. What industries are gaining the most traction from the Optimizely X platform? I imagine behavioural targeting is groundbreaking for ecommerce sites. We certainly see strong traction in retail and travel, where companies can very quickly see a significant impact in online revenue from experimentation. As a typical example, one ecommerce site we worked with redesigned its category page and saw a 20% increase in conversion rates yielding over $250k/quarter in additional revenue. We’ve also seen experimentation thrive at media companies, where it can be applied to everything from application design to editorial content to subscription flows and advertising. Lately I’m noticing more growth in industries like financial services and food and beverage. Both industries have had a strong brick and mortar presence that didn’t always translate to the web — but we see rapidly growing adoption in mobile, where experimentation is a must to both improve the user experience and mitigate development risk. In your series, Product Experimentation Pitfalls , you discuss picking a “north star metric” and how costly it was to pick the wrong one while working at Bing (total number of search queries). How hard was it to admit that your team’s assumption was wrong? And how did the team regain confidence in a new north star? And is a true north star metric even possible? Choosing the right metrics is essential for experimenting effectively. Two companies with similar businesses, like Airbnb and Booking.com, can end up with completely different user experiences by optimizing for different things. As I describe in the post, it took us years at Bing to realize we were optimizing for the wrong outcome — driving more searching when really users want to search less. But the truth is it was easy to tell that something was wrong, because our quantitative numbers weren’t lining up with qualitative feedback on our experiments. That was a powerful hint that something was off, and pursuing that chain made it clear that we needed a metric more in line with our users’ expectations. This is another example of why you can’t be “data driven” — you have to drive the data! I think most teams aren’t well served by a single, fixed metric. Instead, we should all be picking a couple different metrics and re-evaluating them every 6–12 months to make sure they’re still serving their purpose. And when you change metrics, don’t forget to re-test some old ideas! It’s the cheapest experiment you can run. You spent 2 and half years at Microsoft. Why did you decide to leave Microsoft for Optimizely? My time at Microsoft showed the power of experimentation first-hand. In my time there, we went from running just a handful of A/B tests to hundreds of experiments every week, transforming the entire culture of the company in the process. I saw how online experiments could drive a broader trend of agile development and bend the growth curve of a business. I also saw how difficult it was. It took an enormous effort to build experiments on our in-house platform, and we ran into so many issues along the way with statistics, performance, etc. I thought, “everyone should be experimenting, but there’s no way they will if it’s this hard.” As soon as I discovered Optimizely, it was a no brainer — the perfect chance to join a growing company working on a problem I was already passionate about. Across your customers, could you share an anecdote of an unexpected change that yielded major results? There’s a misconception that A/B testing is all about finding “one weird trick” to get big results, like changing a button color and doubling your conversion rate. And there are real experiments like that, but it misses the point. The biggest benefits come from the impact of many experiments, and the culture of fearless development that they enable. compounded That collective impact can be very large. One customer of ours tallied this at $21M in incremental revenues after their first 500 experiments. Instead of asking, “what’s the one experiment that will get me a big impact?” I recommend asking instead, “how do we scale to a hundred experiments to get a guaranteed gain?” Optimizely X Full Stack is A/B testing and feature management for product development teams. What are some of the most advanced algorithms you’ve seen customers build atop Optimizely? Full Stack is great for testing backend changes. We’ve seen it used effectively for product sorting, search algorithms, and dynamic pricing and offers. Maybe the most common one is recommendations algorithms. Almost every ecommerce site has product recommendations, but I’m always surprised to see how rarely they’re refined with testing. We’ve run several experiments where recommendations actually conversion rates due to a distracting user experience or low quality results. Conversely, we’ve seen that testing the placement, algorithm, labeling, and number of recommendations can lead to as much as 5–10% incremental revenue per visitor! reduce How far will personalization on the internet go? Will every site I visit soon say, “Hello David, you’re looking handsome today”? Personalization is a hot topic, but I think the hype mostly misses the point. “Hello David” doesn’t meaningfully impact the user experience, and if anything it can hurt it by seeming creepy or invasive. Coming from a search background, I think about relevance. I expect to see more of the internet resemble Netflix’s show list or Facebook’s news feed. Every user coming to these apps sees something different tailored to them, but it doesn’t scream “this is personalized!”. The key to achieving that kind of relevance is constant experimentation on the algorithms that power these experiences and the user experience around them. Michael Phelps is an interesting choice to Keynote this year’s Opticon 18 . But I imagine his training was data driven… What do you think he’ll speak about? Experimentation and personalization are buzzwords, but the real point is winning! Michael Phelps is a living example of what it means to come in first place. The difference between gold and bronze can come down to mere milliseconds. We see this with our customers every day: the difference between the market leader and everyone else can come down to just a few impactful changes to the user experience. Could we see a screenshot from Optimizely’s dashboard of the product being used on Optimizely.com? Sure, here’s a list of some of the experiments we’re running on our own users: And here’s a live example of how we personalize our own homepage — in this case, to a prospect from the travel industry: What mindsets or traits do you think drives effective product development experimentation? You have to be incredibly curious about your users and open-minded about how they use your product. You have to be skeptical of conventional wisdom and naive readings of the data. And most of all, you have to have conviction. Think of experiments as a tool for building something new and bold — it’s the safety net that lets you try something new without too much risk. So be fearless! What roles are you currently hiring for? What traits do you value when building your product team? We’re always looking for great engineers, and we’re hiring in San Francisco and Austin! We’re also hiring for a range of customer-facing roles like sales engineering, customer success, and technical support in SF, New York, Amsterdam, London, Cologne, and Munich. to learn more. If you’d like to learn more about our product team’s culture, check out our for Q&As with some of our developers. Check out our jobs page engineering blog Connect with Jon on or . Twitter LinkedIn is the world’s leader in digital experience optimization, allowing businesses to dramatically drive up the value of their digital products, commerce and campaigns through its best in class experimentation software platform. Optimizely Disclosure: Optimizely has previously sponsored Hacker Noon.

Experimenting Your Way to a Better Performing Website

Motivation: Rocksbox was founded on a simple principle: that modern women want to access the latest trends and explore their own fashion sense without spending money on things they don’t want. We offer a monthly subscription, where our members receive a set of three pieces of jewelry curated just for her. We establish a unique relationship with each of our members by learning her style preferences with the goal of getting her items that she’ll love in every box. If she really loves a piece, she can buy it right at home having experienced it in real life. One of the ways that we learn her preferences is through the wish list. Our members can express interest in pieces that they’d like to see in their boxes by placing items on their wish list. Together with feedback on items she’s experienced in her box, we learn her personal style. after Personalization and the automated learning of style space is ripe for . The attributes of the jewelry (e.g. metal, stone type, color, shape…) and the combinations of these attributes together are the key determinants of the style of a piece. Similarly, customers have a range of dimensions and preferences that are uniquely combined in each person. The monthly subscription provides a platform for recurring data collection on both the product and the customer so that we can quickly learn about both parts of the equation (product and customer). Together, these factors make Rocksbox well positioned to augment the domain knowledge of our stylists with high quality recommendations for each box that’s curated. Data Science Personalization of the member experience is a fundamental value proposition of ours and one that we believe helps to create enduring and valuable member relationships. Similar to the original Netflix experience, our members can rate the items they receive and tell us how well they meet their personal style. Our stylists, who curate each box that goes out to our members, are awash in feedback data and face a puzzle in matching up varied feedback from her to our inventory in order to create a set that she’ll love. How can we more efficiently learn our members personal style and help our stylists create awesome experiences in every box? Specifically, how can we learn the similarity between our products in order to both place them within a learned style space and use that style space to drive personalized recommendations? By knowing where in style space the products that she has previously liked and bought reside, we can recommend similar things that are both close enough to her known style preferences to be safe bets but controllably dissimilar to historical likes such as to provide a zone of serendipitous discovery. Inferring Her Style Space: Data Collection through Internal “Crowd-Sourcing”: An important but often overlooked aspect of many classes of is the need for labeled data. Public datasets frequently come already labeled. However, many real datasets captured by businesses either lack labels or lack the right type of labels to facilitate machine learning. In order to learn and model jewelry product attribute space we first needed to have high quality attributes on our products. Initially, the attributes on our products were minimal and not deeply meaningful. They were mainly comprised of metal tone (i.e. gold or silver) and high level subjective style labels such as “boho” and “edgy”. These labels can mean different things to our stylists than our members, and have loose semantic meaning with respect to product attributes. To address this, we launched a multi-functional effort within Rocksbox, leveraging the strengths of our merchandising, engineering and data science teams. Despite the complexity, we undertook this effort because we believe that modeling style space is fundamental to our ability to personalize each set according to our member’s preferences. machine learning Data Science worked with our merchandising team to define over one hundred new attributes covering physical attributes such as clasp type, shapes, secondary colors as well as style elements such as studded, fringe, and perforated. These attributes were chosen that would be most likely to discriminate pieces along style preferences in the desired style space. The growth in attributes was necessary for Data Science but presented a problem of scale. We had thousands of active products that needed to be backfilled with these new attributes. We considered generic crowd-sourcing but quickly discounted it due to the the jewlery specific domain knowledge that we believed was necessary to accurately tag our products. Instead, we “crowd-sourced” internally to Rocksbox with a few employees tagging the products over the course of a couple of weeks. This itself was a learning process. We learned which attributes we had clearly defined and which we had not. We also learned about attributes that we had left out and occurred frequently enough that they warranted inclusion in the set we tagged products with. For instance, we realized that we had left out the attributes crystal clusters and chevron while needing to better define the difference between an ear huggie and an ear jacket. Products were previously tagged using a limited set of tag categories. To facilitate style space learning, the number of tag categories was increased over twofold to include more discriminating physical attributes of the jewelry. Product Tagging: Dimensionality Reduction and Normalization: With new product attributes in hand, we sought to develop a vector space model, in which each product would be represented by an vector learned from its attributes. We sought to use this vector space model to assess the similarity between products in our inventory, thereby forming the backbone of a content based recommendation engine. Backfilling our inventory with the new tags wasn’t the only challenge that the growth in the number of product attributes created. When it comes to machine learning, feature dimensionality poses problems in terms of the burden on training data to learn the importance of any one feature. Furthermore, with a large number of sparsely supported features (203 features with the average feature supported by 136 examples and the median by just 36 examples.), the most common features will tend to dominate and less common features will lose importance. In many applications, it is the rare features that are most discriminating of the individual entities that you are trying to model. To account for both the increased dimensionality and sparseness of the product attribute data, as well as counteract the dominance of common attributes, we applied principal component analysis ( ) and normalization to our data. PCA TFIDF PCA is a tried and true dimensionality reduction technique that applies a linear transformation to the data by projecting the original data onto a reduced set of axes that account for the majority of the variation observed in the original data. However, PCA doesn’t come free. Each new axis in the projected data set is composed of parts of the original features. This linear projection makes interpretability significantly more challenging. The resulting components don’t have a straightforward relationship back to the original features. Furthermore, there is no single best method to choose the number of components (axes) to use. Depending on your application you may want to choose the number of components that captures the most variance observed in the data, or you may simply want to reduce 1000s of dimensions to 100s to facilitate machine learning that might suffer from very wide data. We chose the clusterability of products in the resulting style vector space as a metric to choose the appropriate number of components in PCA. This was based on our goal of efficiently discriminating products in the reduced dimensionality vector space. By driving coherent clusters in the reduced dimensionality space, we could be assured that we had defined regions of product attribute space that our products mapped to. Visualization of product clusters in learned attribute space. The space was reduced to 2 dimensions for display. Sampling of products from these clusters reveals distinct regions of product attribute space. Product Feature Space: Along the road to successfully learning a vector space representation of product attribute we first encountered challenges that made us wonder whether this approach would work out at all. With data transformation, there is as much art as there is science that goes into a successful approach. Initially, we assumed that the high level labels in our products such as “boho”, “glam”, “classic”, and “chic” had low semantic meaning and would bias the learning of style similarity away from what could be learned from the product attributes. Furthermore, given the sparsity of the data, we expected that features with very low support in the dataset would hamper our ability to infer product similarity. In contrast, including the high level style levels helped bolster PCA because many of these style labels covary with sparse, granular attributes. Product Similarity in the Learned Style Space: To further assess the quality of the style vector space that we had learned, we leveraged the between the resulting product style vectors to fetch similar products. For each product in our inventory we fetched the 10 most similar products based on the cosine distance between the seed product and the rest of the inventory. The visual similarity between the similar products was stunning and gave us confidence that we had learned a robust product attribute vector space. cosine distance The 10 most similar products for a given seed product can be queried using the vector space model. While images were not used to assess similarity, the similar products share visually similar styles. This suggests that the product attributes effectively capture the visual styles of the products. From the set of 10 most similar products, an algorithm could choose a subset of these products based on other attributes that are relevant such as popularity or price. Similar Products: Once we had a jewelry attribute style space where we could reasonably assess the similarity between products, we sought to build a content based recommendation engine. By knowing where in style space the products that she has previously liked and bought reside, we can recommend similar things that are both close enough to her known style preferences to be safe bets but controllably dissimilar to historical likes such as to provide a zone of serendipitous discovery. We found that traditional content based recommenders that leverage user item preferences and item attributes tend to overly weight historical style preferences (i.e. serving up pieces based on previous interactions but not serving enough diversity for discovery). We need to balance staying within her taste graph while avoiding fatigue from serving up items that are too similar to those she’s previously purchased. To address these limitations, we developed a taste graph based approach to recommending similar items. The 15 most recent item that has positive feedback (liked or loved), has been wishlisted or has been purchased become the starting nodes in our query. From those starting nodes we fetch the N most similar items. In this manner we can control how similar or dissimilar we want the fetched items to be. There is a lot of flexibility here. For instance, we could fetch pieces less similar to pieces already bought under the assumption that she won’t want to buy or experience more pieces that are very similar to pieces that she’s already bought. Before returning the set of recommendations, we score the resulting set of products. The scoring incorporates both similarity with the starting product node as well as the weighted popularity of that item in terms of both feedback and purchases. Specifically, we compute a popularity score for each product that is a normalized weighted sum of the feedback for the item with the purchase frequency of the item. This popularity score is then weighted by the feedback on the seed piece (i.e. whether it was liked, loved or bought). Each product receives a score that is its distance from the seed piece plus the weighted popularity scores. In this manner, rather than simply relying on raw similarity we rank items based on how likely they are to have a positive member experience and business impact. : With the goal of surfacing pieces that she’ll love, we applied unsupervised machine learning to learn a product attribute based similarity space. Using the products that she has previously rated or expressed interest in (wish list) we can query the learned jewelry feature space to find the pieces that are stylistically similar to her expressed preferences. This set of pieces is ranked based on similarity to the seed products and popularity The top 40 ranked pieces are surfaced as recommendations. Recommendation pipeline To determine the likelihood of this approach to deliver negative experiences for our members, we looked at the incidence of recommending items that she has previously disliked. For customers who are newer to our service and for those from which we have less feedback data, we found that we return items that she has disliked about 1% of the time. This drops to below 0.5% once we have more than 10 data points coming from either feedback or the wish list. In tracking the success of this recommender in production, we can compare the performance of recommended items to non-recommended items in general and also the performance of the same items when they are recommended and when they aren’t. Since deploying the product similarity recommender to our wish list, the wishlist CTR more than doubled (from ~3 wish list per session to ~7 wish list per session). This indicates that the recommender is surfacing relevant items that resonate with our members’ personal styles. Retrospective: We built a graph based product recommender from scratch, leveraging human tagging of our inventory and unsupervised learning to learn style space based on product attributes. We’re excited to have launched this recommender to power our wish list, where members can express interest in pieces and help stylists to create boxes that our members will love. We learned that even with small data, it’s possible to build a high quality recommender that goes beyond what typical content based recommenders can do by exploring known style space as well as allowing for serendipitous discovery. We welcome your thoughts and questions in the comments. is hiring and doing awesome things to transform the jewelry and fashion rental spaces with data. If you’re interested in what we are doing, we’d love to talk! Rocksbox

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