Sunnyvale, CA and Ra’anana, Israel, December 19, 2017 – Anodot, the AI-powered analytics company, today announced a Series B round, bringing its B funding total to $23 million. The additional $15 million investment was led by Redline Capital Management together with existing investors Aleph Venture Capital and Disruptive Technologies Venture Capital. Over the past year, the company more than tripled its revenues, with customers such as Foursquare, Lyft, Microsoft, Upwork and Waze (Google), and is gearing up for further expansion in 2018. Headline-making glitches that interrupted sales for major retailers on Black Friday and frustrated thousands of shoppers illustrate the severe damage to revenue and reputation that these often hidden issues cause. Anodot’s patented solution enables businesses to track and correlate massive volumes of business and technical data in real time to identify these business incidents. Anodot is saving its customers millions of dollars and increasing revenue opportunities for top-tier brands in ecommerce, retail, Internet, mobile and other industry segments. The company is one of 17 globally to have achieved Amazon Web Services’ Machine Learning competency announced last month at its re:Invent conference. It won Ventana Research’s “Digital Innovation Award in Analytics” and was recognized as a Cool Vendor in Analytics by Gartner. With the new investment, Anodot will open offices in London and APAC, grow its US team, and invest significantly in sales, marketing and customer success, while continuing to innovate in its machine learning platform. “Business Intelligence presents a multi-billion-dollar market and Anodot’s differentiated approach adds AI to BI,” said Benno Jering, Principal at Redline Capital. “Traditional BI focuses on dashboards and other tools that analyze historical data, focusing on specific portions of data and addressing pre-defined queries. Anodot addresses a completely different need by surfacing the issues you wouldn’t know to ask about, across constantly changing massive amounts of data. Anodot’s analytics provide the full picture across all data at a granular level so companies can understand what’s going in every market, with every product, across every device.” The most diligent BI and monitoring teams using traditional visualization tools will still miss important issues that affect their quality of service and operational efficiency. By automatically delivering actionable alerts in real time with proprietary AI technology, Anodot prevents revenue loss, reputation damage and end-user frustration. Anodot’s insights enabled a retailer to update its pricing to address competitor’s bidding activity that was affecting product revenue; a mobile games company to resolve customer engagement drops resulting from its AB tests; and a fintech company to protect its revenue by reducing incident resolution time by 99%. “We are very excited by this new round of investment and see it as proof that there’s life beyond dashboards, with AI,” said Anodot CEO and co-founder David Drai. “Our rapid growth and the adoption of our technology by so many recognizable brands is a testament to the value of  leveraging automated machine learning to create a new type of analytics solution.” David Drai, who previously cofounded Cotendo (acquired by Akamai), teamed up with Ira Cohen, formerly Chief Data Scientist at HP, and software R&D executive Shay Lang to found Anodot in 2014, combining their collective experience managing huge software systems and the big data analytics required to understand them to solve one of the most costly problems plaguing the online realm. About Anodot Anodot illuminates business blind spots with AI analytics, so companies will never miss another revenue leak or brand-damaging incident. Its automated machine learning algorithms continuously analyze all business data, detect the business incidents that matter, and identify why they are happening by correlating them across multiple data sources. Anodot customers in fintech, ad-tech, web and mobile apps, and other data-heavy industries use Anodot to drive real business benefits like significant cost savings, increased revenue and upturn in customer satisfaction. Founded in 2014, Anodot is headquartered in Ra’anana, Israel, and has offices in Silicon Valley.  Learn more at www.anodot.com. About Redline Capital Management Redline Capital is a global Venture Capital and Growth Equity fund investing in fast-growing companies with differentiated technologies across North America, Europe and Israel. Redline is backing strong management teams, and lends its support to enable their vision and strategy through all stages of their development. For more information about Redline Capital, please visit Redline Capital, please visit www.redline-capital.com.   For more information, please contact: Molly Meller [email protected] +1-732-865-3998

December 14, 2017 - InsideBIGDATA How to Keep the Bah-humbug Micro-glitch from Ruining Your Holiday Sales This time of year, a better name for the micro-glitch, might be the “Micro-Grinch.” They do share similar traits, after all, such as the capacity to begin small and snowball into something that has the potential to steal a retailer’s holiday season. Here’s a few big data analysis tips to help retailers keep the glitch-grinch from ruining both the holidays sales season and their bottom line. The Micro-Glitch is More Common than You Think All online retailers and customers experience them at some point. The mis-coded product that disappears from the cart during check-out. Or, high-volume traffic overloads payment processing servers leading to credit card failures. During an average sales day, these glitches and errors might only cost the typical online retailer about $40,000. This is certainly no drop in the bucket. But consider what happens when you change the phrase “average day” to “pre-holiday day.” Suddenly a run-of-the-mill glitch, becomes both a $250,000 revenue loss and a personal call from the C-suite wondering why your company is trending negatively on Twitter. It is possible to avoid the glitch. Consider taking these steps: Track web traffic—You can identify glitches faster if you know which pages your customers visit, where leads are generated from, and what offers they are clicking. Use analytic tools to watch for traffic drops or spikes on pages, page elements, or products on your app or website. A run-up in sales against a drop-off in revenues for a specific product could indicate product mispricing. Or a drop in visits to a specific page could suggest an app error occurring only on a certain type of device or browser. The greater the level of granularity you examine, the faster you can pinpoint problems and respond. Watch social media—We’ve all seen it happen—a celebrity shares their look on Instagram or Snapchat and suddenly their jewelry or clothing is selling like crazy. If you only identify it after the fact, you might run out of inventory or miss the opportunity to bundle that product with others to increase basket size. Tracking social media metrics brings true operational value if it’s correlated in real time with product demand changes so you can adjust your business strategy in real time. Monitor third-party data—External influences, such as competitor advertising and weather, can often significantly influence sales. For example, an impending snow storm may result in an increase in demand for cold weather gear. Companies need to seamlessly connect all data sources and key performance indicators (KPIs) in order to correlate and analyze trends in real time to ensure the best response to findings, such as a shift in inventory or pricing. In the old days, this used to be done by a team of people watching KPIs. Today, no team can integrate enough data quickly enough to effectively respond—especially during the holidays. Solutions need to be automated. Combine DevOps data with other KPIs—Monitoring DevOps and IT data and integrating it with other KPIs can help determine root cause of an error more quickly. For example, is a sudden drop in product sales the result of a coding error or is it because a celebrity dissed the product on social media? Without the context of business KPIs, Dev/Ops and IT offer only half the story. Today, data must encompass the bigger business picture, avoiding the siloed approach. Make Your Holiday Retail Season Merry & Bright During the holiday season, it pays to be aware that revenue leaks from micro-glitches can add up fast. In a data-rich, e-commerce ecosystem, the key to effectively avoiding the glitch is automation and technology. Make your holiday retail season merry for both your customers and your bottom line by looking for solutions and workflows that enable you to identify and respond to micro-glitches in near real time. And, say goodbye to the glitch-grinch! About the Author David Drai co-founded Anodot out of his frustration with not being able to get real time insights in spite of having access to real time big data. Together with 2 partners and a growing team, they have developed state of the art AI-powered analytics, that illuminates business blind spots, so companies will never miss another business incident. They are growing super quickly, and currently customers in industries such as ecommerce, social media adtech, fintech, mobile, telcos, etc. where our SaaS solution automatically identifies drops or increases in: conversions, clicks, impressions, revenue, load time, events, and more.

Dec 13, 2017 - MarTech Series MTS: Tell us about your role at Anodot and the team you handle. Ira Cohen: I am the Chief Data Scientist and co-founder of Anodot. The company was founded in 2014 as a new way to provide insights from big data that is changing and growing in real time. Our goal is to illuminate business blind spots so companies will never miss another business incident. So we have gathered together a “dream team” of a half-dozen data scientists that include several PhDs, an engineer from France, a former research engineer at HP Labs, and our “whiz kid,” who began his academic studies at 14 and completed his M.Sc at age 19 & then went on to obtain his PhD. Besides the data science team, however, we also, of course, have product people, devops, UI/UX, and marketing and sales. MTS: How is Anodot different from contemporary business intelligence tools that service digital media and ad tech companies? Ira: Traditional tools rely on dashboards and alerts. Dashboards are only good for what you KNOW you want to look at, but when you’re dealing with big data, you need to track everything. AI asks all the possible questions of the data and provides you with insights so you know what to focus on. Alerts are problematic as well in that most traditional alerting tools will either overwhelm you with alerts or miss crucial issues. Media and advertising data is often seasonal, meaning that it will go up and down predictably during the day and the week. If you set an alert at the top or bottom of the data “wave”, you will miss any issues that happen in the middle of the wave, which is most of them. Setting alerts on seasonal data without AI is an impossible task so this is something our platform overcomes. Anodot uses unsupervised machine learning – a type of AI — to automatically learn the normal behavior of every metric a company wants to track, so our solution can then alert them to every abnormality or anomaly. An anomaly may be a bad thing that needs to be resolved (such as a drop in clicks or revenue) or a good thing that should be leveraged for increased business or better customer satisfaction. By providing crucial insights into what is happening across the company and its customers in real time, companies can make informed decisions and keep their businesses running smoothly and profitably. MTS: What are the core tenets of AI-engine driving Anodot’s automated anomaly detection? Ira: The most significant tenet to know is that our anomaly detection works at scale. Many data science algorithms work fine in academia, but if it doesn’t work at scale, it can’t work in a production environment in advertising. We have customers that process more transactions daily than Nasdaq and Visa combined. This is true scale. Another is that we work for any type of data stream. Many analytics solutions work fine for smooth, easily predictable metrics, but there are dozens of types of metrics, and you need to have algorithms that will work for all of them. Our first layer of machine learning determines the type of metric it is, so we know which machine learning algorithm to apply. We continuously re-apply this test since the shape of metrics can change over time. Lastly, we know how to correlate across multiple data metrics of all types. We didn’t want to be in a situation where we overwhelm people with alerts on single metrics, since when an incident occurs it often affects multiple metrics, sometimes thousands at once. If you’ve ever received 15,000 alert emails or text messages one after the other, you know that this is NOT a good thing for a technology solution to do. We provide another layer of machine learning that correlates related alerts, which not only reduces the number of alerts to a manageable number, but it also provides a clear story to the recipient of what is going on. If you know that the issue is affecting customers in all regions, who use Chrome desktop browser version 30, but is not affecting mobile at all, then that helps you very quickly get at the root cause of the issue so you can fix it quickly. MTS: What are the major pain points for CIOs and CTOs in adopting full-scale AI Analytics platforms for business anomalies and predictive modeling? Ira: Many companies recognize that they need an AI Analytics platform, and because they already are dealing with big data and have data scientists in house, they figure it’s a piece of cake to simply build what they themselves. Then they get 6 months or even a year or more into the project, sucking up the their data scientists’ time, and dragging along with it some product managers, a UI/UX person, possibly even devops, before they start to realize they are being sucked into a black hole of developing a highly specilaized product, that despite their best efforts, may still not meet their needs. Algorithms that work in a testing environment may not work at scale in production, leading to missed incidents, and revenue losses. We strongly recommend that companies analyze the costs from the standpoint of human resource time as well as time to value before making this decision. We have outlined the key cost elements in a white paper about the build or buy dilemma. MTS: Would you tell us more about Anodot’s anomaly identification and monitoring for Uprise? How could other ad tech companies benefit from Anodot? Ira: Uprise is an ad-tech company that specializes in performance-advertising, using its machine learning algorithms to target that best performing ad placements for its customers. As a successful advertising technology company, Uprise needs to keep track of hundreds of thousands of metrics, which is the epitome of its business. The company develops its software by each team pushing around 20 new software releases into production each day. Each new release can affect the ad-tech platform’s performance. Because of this it is pertinent to monitor results in a timely way, this can determine if the new release should stay or be rolled back. Uprise uses Anodot to track KPIs like revenue, spend, fill-rate and performance.  In one case revenue and click through data for a particular country rose significantly and Anodot traced it to a Facebook outage (people were not able to access Facebook so they browsed elsewhere on the Web). Uprise uses Anodot to cut through the noise of all of the data they collect.  This is important for all ad tech companies, as well as companies in ecommerce, online companies and mobile companies. They all receive so much data and need real time analytics to help them pull out the significant information in a timely manner. We work with many leading Internet, mobile and ecommerce companies, such as Waze (Google), Lyft, Rubicon Project, AppNexus, Outfit7 and others, all companies that need to understand in real time what is happening with their company and their customers through their vast data. MTS: How critical is it for modern IT businesses to leverage AI and machine learning capabilities for better data and analytic management? Ira:It is paramount. They have vast amounts of data and should leverage them for competitive advantage, and most importantly- to keep their customers happy. There are so many moving parts in online businesses that it is impossible to keep track of them manually, or using traditional BI tools like dashboards. They will always miss something crucial, and these types of issues typically lead to revenue losses, customer dissatisfaction or damage to the brand. The only way to manage such vast amounts of data quickly and accurately is with AI. MTS: Thanks for chatting with us, Ira. Stay tuned for more insights on marketing technologies. To participate in our Tech Bytes program, email us at [email protected] Ira Cohen Co Founder, Chief Data Scientist, Anodot Anodot recently won an award for innovation in analytics. We spoke to Ira Cohen, Chief  Data Scientist,

LAS VEGAS, Nevada, November 28, 2017 -- Anodot, an AI-powered analytics company, announced today at AWS re:Invent 2017 that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status. This designation recognizes Anodot as a SaaS/API Provider, offering a solution that enables predictive capabilities within customer applications. Achieving the AWS ML Competency differentiates Anodot as an AWS Partner Network (APN) member that has built solutions that help organizations solve their data challenges, enable machine learning and data science workflows or offer SaaS/API based capabilities that enhance end applications with machine intelligence. Attaining the AWS ML Competency demonstrates to its customers that Anodot has validated ML expertise on AWS. "Achieving AWS ML Competency status recognizes Anodot's proven track record of using machine learning to uncover our customers' business blind spots automatically and in real time," said Anodot Chief Data Scientist and Co-Founder Ira Cohen. "Our team is dedicated to helping customers leverage their massive amounts of data to gain competitive advantage and prevent revenue or brand damage." AWS is enabling scalable, flexible, and cost-effective solutions from startups to global enterprises. To support the seamless integration and deployment of these solutions, AWS established the AWS Partner Competency Program to help customers identify Consulting and Technology APN Partners with deep industry experience and expertise. Anodot's AI Analytics uses patented machine learning algorithms to isolate and correlate issues across multiple data metrics in real time, supporting rapid business decisions. In effect, Anodot uses machine learning to discover and solve incidents that otherwise would become major losses for companies that could hurt revenue or damage a company's brand. Anodot correlates the anomalies it discovers across multiple systems, such as AWS, business systems, application events, and more. Anodot and Amazon Web Services are working together to provide organizations critical observations using Machine Learning to give valuable business insights based on real time data analysis. "Not every machine learning problem requires starting from scratch and building a custom solution, and not all of our customers have access to a dedicated data science team with the time and expertise to build a production workflow for large scale predictions," said Joseph Spisak, Global Lead for Artificial Intelligence and Machine Learning Partnerships, Amazon Web Services, Inc. "We are delighted to welcome Anodot to the Artificial Intelligence and Machine Learning Competency Program to provide off-the-shelf machine learning solutions that can help speed time to market and bring intelligence to any application." One of the largest ad exchanges in the world, Rubicon Project uses proprietary computing systems to automate the buying and selling of advertising. Working with Anodot for more than two years, Rubicon's systems process more than twice as many transactions as the Nasdaq stock exchange. "We want the Supply and Demand Partners to know that we're watching things very closely. Deviations in trading can have a devastating effect on both the advertisers and the exchange," said Rich Galan, Director of Analytics at Rubicon Project, "With Anodot's alerts and correlation capabilities, Rubicon can easily track all of the data in real time. We can determine if we need to reach out to a Partner in real time to fix an issue. We're not just helping our business, we're helping our partner's business. That's the level of service that we want to provide." About Anodot Anodot illuminates business blind spots with AI-powered analytics, so you will never miss another revenue leak or brand-damaging incident. Its automated machine learning algorithms continuously analyze all your business data, detect the business incidents that matter, and identify why they are happening by correlating across multiple data sources. Anodot customers in fintech, ad-tech, web & mobile apps, and other data-heavy industries use Anodot to drive real business benefits like significant cost savings, increased revenue and upturn in customer satisfaction. The company was founded in 2014, is headquartered in Ra'anana, Israel, and has offices in Silicon Valley. Learn more at: http://www.anodot.com .