Ad Campaigns: How to Reduce False Positive Alerts for Ad Budget Caps   The massive scale and speed of online advertising means that adtech companies need to collect, analyze, and act upon immense datasets instantaneously, 24 hours a day. The insights that come from this massive onslaught of data can create a competitive advantage for those who are prepared to act upon those observations quickly. Traditional monitoring tools such as business intelligence systems and executive dashboards don’t scale to the large number of metrics adtech generates, creating blind spots where problems can lurk. Moreover, these tools lead to latency in detecting issues because they act on historical data rather than real-time information. Anodot’s AI-powered business monitoring platform addresses the challenges and scale of the adtech industry. By monitoring the most granular digital ad metrics in real time and identifying their correlation to each other, Anodot enables marketing and PPC teams to optimize their campaigns for conversion, targeting, creative, bidding, placement, and scale. False positive alerts steal time and money from your organization   In any business monitoring solution, alerts for false positive anomalies or incidents are troubling in several ways. First of all, they divert attention from investigating or following up on positive anomalies detected. The fact is, no one knows the difference between a false positive and a true positive until at least some investigative work is done to determine the real situation. In the case of false positives, this is time (and money) wasted while true positives are on the back burner waiting for the resources to investigate them. Time lost = money lost in the adtech industry. Too many false positive notifications create alert fatigue and eat away at confidence in the monitoring solution. Analysts may begin to doubt what is found and ignore the alerts—thus real problems are not being found and mitigated. When excessive false positive alerts are issued, the monitoring solution needs to be tuned in terms of the detection logic in order to reduce the noise and improve accuracy. This is precisely what happened in a recent case with an Anodot adtech client, and the resulting fix will help anyone in adtech and marketing roles.  Capped campaigns create false positives in business monitoring   In this scenario, an adtech company’s account managers are responsible for helping their customers manage campaign budgets and allocate resources in order to attain optimal results. Working closely with Anodot, this company has set alerts for approximately 7,000 metrics to monitor for changes in patterns and to detect any technical issues that might result in unexpected drops in their impressions, conversions, and other critical KPIs. It’s all very standard for any adtech company. So what’s the issue? Capped campaigns create false positives.  Many of this company’s customers have a predetermined budget for each campaign that is used to pay for the various paid ads, clicks, impressions, conversions, and so on. When the budget is exhausted, or nearly so, the account manager is notified by an internal system. At the same time, there’s a rather large drop for the relevant KPIs that are being measured and monitored, which makes sense given that no additional money is being put toward the purchase of ads. This usually happens without a relevant detectable pattern. While the account manager expects the drop in KPIs, the business monitoring system does not—and thus the detected drop in KPIs appears to be an anomaly. The system often sends a corresponding alert, which in this case is a false positive because the drop was expected by the account manager. Capped campaigns are not unusual in this industry, so the monitoring system needs to be tuned to accommodate these occurrences to reduce the number of false positive alerts. Anodot’s unique approach eliminates false positives in capped campaigns   Anodot’s first attempt to resolve the issue was to add the capped events as an influencing event. This failed to fix the issue because the influencing event did not correlate to a specific metric, only to an alert. The result was still false positive alerts which often went to many people, resulting in redundant “noise.” A successful resolution came when Anodot suggested sending notice of the capped event as an influencing metric in its own right so that it can be correlated on the account dimension level or on a campaign ID. So, the adtech company sends a metric – “1” for a capped event, “0” for uncapped – via an API to Anodot. The API call is triggered on each significant KPI change. In response, a watermark is sent respectively to close the bucket, ensuring the metric’s new value to be registered in the quickest way possible. When a KPI drop occurs, Anodot looks for the corresponding business metric of the capped event on an account level. If the latter metric contains a “1” then no alert is triggered because the system now knows this is a capped campaign. The influencing metric will go back up to the last 10 data points, looking for the last one reported, meaning that if Anodot gets the capped event before the drop is reported, Anodot is still able to detect it. The illustrations below show how this technique prevents false positive alerts. The anomaly of the dropping KPI is detected in the orange line. The corresponding capped campaign metric is reported in the image below. When the metrics are correlated and placed side by side, the resulting image looks like this: Note the lack of the orange line indicative of an anomaly that will trigger an alert. Anodot’s approach works for any company with capped campaign budgets   While Anodot designed this approach for a specific client’s needs, it has application for other companies in adtech that have capped campaigns. The goal is to eliminate the false positives that arise from campaigns reaching their end budget, causing a drop in KPIs like CTR, impressions, revenue, and so on.  The adtech company must have granular campaigns data, registering both capped and uncapped events to be sent to Anodot via API. Seasonality is recommended on the campaigns being monitored, meaning that capped and uncapped events are to be sent to Anodot at the same intervals as the campaigns; for example, every 5 minutes, hourly, etc. The process is easy to set up, with campaign monitoring as an existing condition. The first step is to send the capping events as metrics (0 or 1) with the relevant dimension property, such as the account ID, campaign name, or campaign ID. Next, Anodot will use the capped metrics as influencing metrics within the alert. If this sounds like a scenario that will help your company reduce false positive alerts while monitoring campaign performance, talk to us about how to get it set up. By eliminating false positives, your people can concentrate on what’s really important in monitoring performance.   

Big Data: How AI Analytics Drives Better Business   “Data-driven” is the latest buzzword in organizations in which data-based decision making is directly connected to business success. According to Gartner's Hype Cycle, more than 77% of the C-suite now say data science is critical to their organization meeting strategic objectives.  For top organizations looking to adopt a data-driven culture to stay competitive, what does that mean? The term evokes images of data analysts huddled in a dimly lit office, watching numbers and visualizations pass on a dashboard as their observant eyes search for anomalies.  But as data scientists and analysts become increasingly expensive and in high demand, many organizations are questioning why these highly skilled knowledge workers should be relegated to a role where they observe dashboards and react to changes?  Companies that lead in data-driven organizational analytics know that for knowledge workers to deliver value, they need tools that free them from laborious tasks to spend more time on meaningful strategic initiatives and less time wrangling data for insights. Growth of Big Data    Industry analysts predict that digital data creation will increase by 23% per year through 2025. The global market for Big Data is expected to exceed $448 billion by 2027. So what's driving this growth? Businesses across the globe now recognize the force multiplier that data-driven business intelligence represents to improve business outcomes.  The only legitimate restraining forces for the development of Big Data are the costs associated with staffing data science and business intelligence competencies and the time-intensive nature of analytics work.  With over 81% of companies planning to expand their Big Data capabilities and data science departments in the next few years, the competition for resources will only increase.  Traditional BI Dashboards Can't Keep Up With Big Data   In today’s data-driven economy, managers struggle to keep up with the myriad of business intelligence reports from traditional BI tools – which fail to effectively and efficiently analyze and interpret the data in real-time. The fact is, conventional BI approaches and tools were not designed for and are not suited for the growth of Big Data. While most of the existing BI solutions can process and store a vast amount of data with many dimensions, they don't offer analysts a manageable way to get real-time business insights, and they certainly don't help data science teams predict the future.  Traditional BI tools lack detailed analysis, offer little correlation, and don’t provide real-time actionable insights. That leaves data science teams and business analysts spending hours with data stores instead of working on delivering value with predictive analytics.  Gain Business Value With Big Data Empowered by AI Analytics   Many companies overextend their BI tools and teams on use cases they were never built to handle. That leaves knowledge workers trying to extract insights from traditional solutions. To put that in perspective, it’s like tying an anchor around their waist and asking them to swim.  The answer is extending business intelligence capabilities with analytics capabilities empowered by AI and machine learning. Rather than developing new views, models, and dashboards, teams leveraging AI analytics gain real-time actionable insights to react to change and predict the future.    Big Data AI Analytics With Anodot   Regardless of the industry or how far along your business might be in its data analytics journey, Anodot's AI-powered analytics can empower your knowledge workers to focus on leveraging business insights to deliver value. Instead of digging into dashboards for answers, Anodot delivers the answers to them, automatically.  Anodot monitors 100% of business data in real time, autonomously learning the normal behavior of business metrics. Our patented anomaly detection technology distills billions of data events into a single, unified source of truth without the extra noise that can leave teams flatfooted.  Anodot delivers the full context needed for BI teams to make impactful decisions by featuring a robust correlation engine that groups anomalies and identifies contributing factors. This helps teams know first, before incidents impact customers or revenue.  Data-driven companies use Anodot's machine learning platform to detect business incidents in real-time, helping slash time to detect by 80 percent and reduce false-positive alert noise by as much as 95 percent.

Last month Gartner published its first ever “Market Guide for AI Offerings in CSP Network Operations,” and we’re excited to share that Anodot has been identified as a Representative Vendor in the report. According to the Gartner report, “CSPs are focusing on automation of their network operations to improve efficiency and customer experience, and mitigate security concerns.” The market guide presents many new and actionable insights. We’re taking a closer look at a couple of them and sharing our perspective on their significance for operators. The strategic role of data correlation The first insight we’d like to discuss is the reason behind why it’s so challenging for operators to meet their top objectives for AI implementation, i.e.: Network monitoring Network optimization Root cause analysis According to Gartner this is due to the fact that: “Despite CSPs having multiple data sources, most of it is uncorrelated and is thus processed separately. Vendors are working on ways to unify these data silos in order to create a large dataset for their ML algorithms.” The great news for operators is that they don’t have to wait to overcome the correlation hurdle. This is because collecting and correlating all data types from 100% of the network’s data sources (however siloed) in real time, is exactly what Anodot’s autonomous network monitoring platform does. Say goodbye to silos  Anodot’s unique, off-the-shelf data collectors and agents, collects data from every network domain and layer, and service and app, aggregating inputs from sources that include network functions and logic such as fault management KPIs, xDRs, OSS/BSS tools, performance management KPIs, probe feeds, counters, alerts, and more. So, the days of monitoring the network in silos, with separate tools for each domain and layer, are over. Kudos to correlations As for correlations, Anodot correlates anomalies across the entire telco stack (including the physical, network, and data layers), and between KPIs, alerts, and network types (i.e., mobile, fixed/broadband, and wholesale carriers/transmission). Root cause at the speed of AI Through this combination of eliminating siloes and correlating data, Anodot also provides early detection of service degradation, outages, and system failures across the entire telco ecosystem, sending alerts in real time with the relevant anomaly and event correlation for the fastest root cause detection and resolution. Benefits for CSPs include: Time to detect incidents accelerated by up to 80% Time-to-resolve incidents improved by 30%  Root cause analysis improved by 90% And this is how operators can address the top three objectives for AI in their network operations. A quick & important look at KPIs Another important insight presented by Gartner in the report includes the following: “CSP CIOs who are looking to leverage automation and AI offerings to support their organizations with evolving business requirements, network operations, and continuing digital transformation and innovation should: Prioritize the critical success factors of your network operations through a structured analysis of your network operations center (NOC) and other related operations that can benefit from AI. Identify your service-level objectives and select AI vendors that contribute to your business-driven key performance indicators (KPIs).” 3 Let’s focus on KPIs. Operators have millions to billions of network-centric KPIs that help uncover performance issues. But without autonomous anomaly detection on 100% of the data, it can be impossible to deal with the volume and velocity of data, identify the anomalies within network big data, and resolve issues before they impact business-driven KPIs. The impact of impact scoring Here too, Anodot comes with an innovative approach. Not only does the platform continuously and autonomously monitor and analyze millions (and billions) of performance and customer experience KPIs by leveraging patented algorithms. It takes this even further. Once it detects an anomaly, it assigns a score based on its impact on what’s important to the operator. In fact, every single KPI that comes in goes through a classification phase and is matched with the optimal model from a library of model types. Then, Anodot’s statistical models group and correlate different KPIs in order to analyze them based on the use case.  Moreover, it automatically groups related anomalies and events across the network into one alert, reducing alert noise by 90%. Indeed, at Anodot, we believe in the importance of aggregating and correlating data from multiple data sources to address the key AI objectives of CSPs. We are also committed to assuring that AI brings a strategic contribution to business-driven KPIs. And, being identified as a Representative Vendor in Gartner’s report serves as a great validation of this approach.  The  Market Guide for AI Offerings in CSP Network Operations is available to Gartner subscribers.

AI-driven business monitoring could have prevented expensive glitch for Santander Bank As most people were preparing to celebrate the new year, the UK's Santander Bank was dealing with a crisis. On Christmas day, roughly 75,000 people who received payments from companies with accounts at Santander Bank received a duplicate payment transaction. The total damage amounted to £130m, and recovery in these situations is a painful process for both the bank and its customers. Making things even more complicated is that many of those who received the erroneous funds are customers of different banks. It's a big mess, but could it have been prevented? Preventing revenue critical incidents with AI analytics The right monitoring approach could have prevented or at the very least mitigated the incident at Santander Bank. It appears that most of the transactions happened in a short time, on the same day and perhaps in the same hour. Even if the bank was monitoring this type of use case, manual processes and traditional monitoring don't cope well when disasters quickly progress. AI models are far more adept at catching anomalies in real-time and alerting intervention teams before the damage gets out of hand. To respond to costly anomalies in real-time, organizations need systems that thoroughly understand the expected behavior of all components and transactions. Integrating machine learning and AI-empowered monitoring tools can aid this process by establishing a clear baseline of anticipated behavior across any business metric. Things like seasonality, customer behavior, and routine transactions help these monitoring solutions identify anomalies as soon as they occur. None of this is to say that it's a simple problem to solve. On the contrary, modern banking systems are incredibly complex, with integrated systems and transactions fragmented into multiple streams and sophisticated interactions with external partners (and competitors). Human observation of traditional dashboards can't keep up with all this complexity. How Anodot helps banking and payment companies detect revenue-critical incidents As the Santander incident demonstrates, glitches within the complex banking and payments ecosystem can lead to a significant incident costing companies millions and eroding customer confidence. In cases like these, timing is everything. AI and Machine Learning empowered monitoring is simply essential to oversee it all and ensure that incident responders receive real-time alerts so they can intervene before it's too late. Anodot is an AI-driven business monitoring solution that helps companies protect their revenue with a platform that constantly analyzes and correlates every business parameter. It catches revenue and customer impacting anomalies across all segments in real-time, cutting time to detect revenue-critical issues by as much as 80 percent. Anodot helps global banks immediately identify issues such as failed and declined transaction rates, login attempts, device usage and the transaction amount per type – all of which are valuable in detecting potential revenue and customer experience issues. For example, Anodot can immediately identify an unusual drop in transactions, which could mean a potential issue with a bank card or payment gateway. Real-time alerts ensure the issues can be resolved before impacting customers or revenue. Real-time financial protection for the world's most complex businesses Anodot's AI models provide proactive protection by catching potential issues immediately. It doesn't matter how complex the system is or how diverse the customer base might be. Problems are remediated quickly before minor nuisances turn into massive incidents. With Anodot, integrations are simple Managing financial systems is complicated enough. Anodot adds robust, automated monitoring capability with no painful integrations and no learning curve for your operators. Within minutes, Anodot's built-in connectors learn the expected behavior of every single business metric in a system and start monitoring all streams for abnormal behavior. Actionable alerts that suit your processes Not only does Anodot autonomously monitor billions of events for every metric in a company's revenue streams, but it distills them into singular alerts scored by impact and immediacy. Anodot notifies your teams via existing comms systems like email, Slack, PagerDuty, or even Webhook with minimal false positives. The rise of digital solutions, payment channels and transaction volumes has caused an exponential surge in the amount of data that must be monitored and managed. Leaders in the financial services and payments industries are using AI-driven technologies to monitor high volumes of data, from multiple sources, in an efficient manner.

The fintech market grows larger and more diverse each day. The financial news website Market Screener says the global fintech market will be worth $26.5 trillion by 2022, with an average annual growth rate of 6%. In Europe alone, the use of financial technology increased by 72% during 2020. Competition in this market segment is also on the rise. In the first eleven months of 2021, over 26,300 startup companies joined the fray—more than double the number of new entrants just three years earlier. As competition for customers’ engagement and loyalty heats up, players need to address much larger audiences, spread across ever-growing geographic regions. Monitoring and managing business operations becomes more challenging as the number of customer accounts and financial transactions continue to grow. Consequently, more solutions that solve fintech-related problems are needed. There is a focus on solutions that help fintech companies optimize all phases of their operations, from customer acquisition to payments processing and forecasting of payouts. In all aspects of the business, there is little margin for error or unexpected disruptions or downtime. Optimizing performance is key to succeeding in this industry. The explosion of activity spawned by all these companies is generating massive amounts of customer and payment data as well as information about the underlying business processes. Deep insights hidden within this data can help companies optimize their payment approval rates, transaction costs, and fraud mitigation, as well as retain customers and give revenue growth a boost. Time is Critical When Issues Arise in Payment Processes The fintech industry comprises many different sectors and industries, including retail banking, acquiring banks, payments facilitators, trading platforms, crypto-currencies, P2P payments, and more. While the industry is diverse, all players have at least one thing in common: a sophisticated technology platform that processes upwards of millions of transactions daily, often with calls to third party partners in the value chain. Throughout these platforms are points where data can be collected, measured, and monitored for changes, anomalies, and trends that can be indicative of an issue in operations or the business outlook. For example, the payment services company Payoneer closely monitors 190,000+ performance metrics in every area across the company. They are watching for any indication that something is even slightly off kilter with the business – such as, an unexpected decline in people registering for a new account, or a glitch in an API with third party software – in order to address issues quickly. Payoneer also monitors customer withdrawals in order to accurately forecast the funds that must be available for withdrawal, in the currencies that customers prefer, without over-allocating funds and losing the opportunity to use that money elsewhere in the business. Time is critical when it comes to identifying and resolving issues in payment processes. Consider what happens if there is a glitch in an API to a backend banking system that is crucial for payment approvals. If transactions can’t access this API, the payment acceptance rate will plummet and the fintech company is going to lose revenue during this unexpected downtime. Not only could the monetary losses be quite significant but the company could suffer reputational damage as well if customers can’t complete their payment activities. To make sure every payment transaction is completed as expected, operations teams must be able to find and fix payment issues as they’re happening anywhere along the end-to-end transaction path. [CTA id="3509d260-9c27-437a-a130-ca1595e7941f"][/CTA] Traditional Dashboards Can’t Keep Up Traditional dashboards and analytics solutions cannot keep up with the complexity and volume of payment data and channels. Operations staff and analysts have to manually dig into multiple dashboards to uncover (if possible) the root cause of a payment incident and then remediate the problem. Given that dashboards typically present historical rather than real-time data, analysts lose the ability to make decisions and act on something that is happening right now. This further delays mitigation and drives up revenue losses resulting from low success rates. To optimize the payment process from end-to-end, fintech companies need the benefits of AI-powered insights. Payment transaction monitoring is the practice of observing customer transactions and payment data (payment approval/failure, payment fees and rates, payment behavior, etc.) in production to ensure performance and availability. AI-based real time monitoring ensures that networks, applications, and third party service providers perform as expected. When transactions fail, it often means that a business’s most critical, time-sensitive applications fail as well. And even if a payment transaction doesn’t fail outright, performance degradation and data anomalies can wreak havoc on the user experience and signal problems in essential system functions. That’s why monitoring transaction behavior is just as crucial as monitoring critical servers and infrastructure. Get Real Time Insight into the Behavior of Payment Data Anodot’s highly scalable automated payment monitoring helps companies gain real-time insight into the behavior of their payment data. Using sophisticated autonomous machine learning, Anodot learns the patterns and behavior of each metric across the payment chain and discovers hidden relationships among the metrics. By understanding the expected behavior of metrics, Anodot detects when something anomalous happens, filters through the noise and false positives, and alerts on the issue before it can seriously impact customers or revenue. As an example, consider a global payments company that uses Anodot to continuously monitor payment approval rates across multiple dimensions such as country and currency. Anodot spots a drop in approvals for the Indian rupee. At the same time, the approval rates for the Indonesian rupee and the Pakistani rupee drop. The company recognizes that all three currencies are going through the same processing provider, indicating a problem with that provider that must be investigated. So, Anodot doesn’t just recognize a drop in payments; the system recognizes the correlations of the incidents that enables a conclusion about what changed in the business. This example shows how Anodot transaction alerts help companies react to changes that can affect payment optimization, but Anodot also delivers business insights that can be used proactively to really optimize payments. For example, merchants use smart routing of payments using simple rule engines. When Anodot notifies a merchant of a problem area resulting from routing payments in a specific way, the merchant can change the routing rules to go through a different processor. In fact, the alert can trigger the routing change automatically in order to respond even more quickly. This actionable alert allows the merchant to be proactive and avoid payment issues over the troublesome route. Issues Show Themselves as Anomalies If a picture is worth a thousand words, the graphic examples below show Anodot alerts to fintech clients that help them keep payments – and business – on track. [caption id="attachment_10744" align="alignnone" width="842"] A drop in approval rates in a particular payment gateway[/caption]   [caption id="attachment_10745" align="alignnone" width="1211"] A spike in partner API activity[/caption]   [caption id="attachment_10746" align="alignnone" width="1208"] Transaction count dropped to zero[/caption]   [caption id="attachment_10747" align="alignnone" width="878"] Drop in success rate for payment gateway[/caption]   Fintech companies can optimize their revenue with Anodot’s automated payment monitoring. Anodot’s AI-powered platform helps companies detect payment issues faster, intelligently route payments, optimize approval rates, and gain a competitive advantage in a crowded market.

You place your mug on your desk and boot your computer. Like every morning, you skim over various dashboards on one screen and sift through your email alerts on the other before you start pulling the regular reports. But this morning turns out to be nothing like other mornings. It is about to take a mean twist that will keep you from ever finishing your morning coffee. As your eyes run over the payment dashboard you realize there was a huge drop in transaction approvals overnight. You sit down slowly, rub your eyes, and secretly hope it’s a mistake. What in the world has happened? It takes a minute to get your thoughts together. When Dashboards Fail: A FinOps Nightmare Where is the failure? How much money is lost? Which merchants are affected? What will account managers say? A scenario like this can’t be ruled out in any company relying on dashboards to monitor payments and transactions. Dashboards help FinOps, BI, and commercial teams visualize business activities to ensure the payment ecosystem is running smoothly. However, as the amount of data to collect and monitor increases, the efficiency of traditional dashboards for digital payments monitoring becomes questionable. Why is payment transaction monitoring using a dashboard insufficient? 1. Siloed monitoring and disconnection of data sources and teams Business units monitor their data separately, gathering from various sources using different tools to cope with the abundance of data. This stifles collaboration and hinders comprehensive analytics efforts. In our little story, you are completely in the dark regarding the incident's causes or impact. You need to start involving programmers, IT, finance and product to get to the bottom of the issue. 2. Lack of granularity and context A dashboard monitors specific metrics separately but can not provide information on if and how specific data behavior might be related. You could be facing a software failure, configuration error, or malicious fraud. It’s up to you to connect the dots and gather clues. But time is critical. With every passing minute, the company loses money directly on unaccomplished transactions and indirectly on the time analysts and developers spend on the investigation. 3. Alert storms and false positives Alerts assume a respectable position next to dashboards in payment systems monitoring. However, the more data comes flooding in and the more agile it becomes, the higher the chances of false negatives and false positives. Alert storms are becoming normal. It’s easy to understand how a failure like the one in our example virtually vanishes among the influx of alerts. 4. Retroactive monitoring and static thresholds A dashboard monitors historical data. Thresholds and alerts are defined by past data behavior. Therefore, every time market or user behavior patterns change, the settings and definitions are no longer valid. Also, each metric has regular data fluctuations, which are overlooked by static thresholds. Just imagine the same story, with the drop in payment approvals remaining within the normal range because transaction numbers are usually the highest at the time in question. You wouldn’t even get an alert, and a lot more time would have passed till you realized something was wrong. It might even take a customer complaint to identify the glitch. 5. Extensive manual work Thresholds require manual adjustment. Additionally, each business unit focuses on its unique operational needs and goals. Extensive manual work is necessary to get the whole picture and understand the connections in data behavior. Teams end up filtering, organizing, prioritizing, and comparing data on excel files. The coffee in our example has long gone cold, and you are still busy downloading the relevant reports that help you figure out who needs to be informed about what and which steps to take. 6. Human errors So much manual work is not only time-consuming but also leaves much room for human errors. [CTA id="3509d260-9c27-437a-a130-ca1595e7941f"][/CTA] How to Save Time and Cost with Smarter Digital Payment Monitoring   1. Gain control over data volume and complexity The amount of data to monitor is challenging for dashboards but not for AI-driven platforms. Machine learning capabilities also add a level of autonomous data processing, identifying patterns, similarities, and connections in data from various sources in various formats. The insights answer actual questions that come up in the initial examination. It takes out a lot of guesswork and points you in the right direction. You’d be surprised at the amount of time you save and the stress you avoid. 2. Connecting and correlating data for higher resolution A tool that monitors 100% of your business data and isn’t limited to the preferences of a specific business unit can bridge the gaps between teams and data silos, provided it correlates all data points to identify connections in data behavior. Now imagine, the system synchronizes the plunge in payment approvals with data from other teams’ sources and detects a simultaneous drop in server activity. You see the position this puts you in? Instead of pulling the emergency brake and getting the entire company on board to solve the crises, so you don’t lose any more money, you’d tell IT to redirect traffic towards the affected server. Transactions would run smoothly within minutes again, and the relevant team could investigate and fix without the extra pressure of financial loss and loss of customers’ trust. 3. Accurate alerts - no more thresholds There’s a better way to identify anomalies in data than static thresholds. Your payment monitoring dashboard tool is happy as long as metrics are within the defined normal range. However, to get a precise assessment of what normal data behavior looks like, you need to consider time-specific and seasonal fluctuations. By recognizing regular patterns in every metric, the machine learns what is normal for which metric at which time. Real-time data monitoring becomes significantly more precise and eliminates irrelevant alert noise. 4. Real-time monitoring There are many factors causing transaction and consumer behavior to shift frequently: your business expands, your competitors change strategies, and new trends emerge. A monitoring tool that autonomously adapts to the new normal without the need to administer manual work can reduce uncertainty, time, and effort. With an AI-driven tool such as Anodot, you stop pivoting endless excel files and leave it to the tool to learn and make the necessary adjustments. Combining the above, you understand how much manual work becomes obsolete when you start using an AI-driven tool. At the same time, the chances of human error are reduced.   Payment Transaction Monitoring With Anodot   Anodot monitors all your business data and correlates data into actionable alerts classified according to severity and financial impact. Prioritization Anodot correlates real-time data and can therefore pinpoint the affected business areas and predict potential operational and financial impact. This allows for the prioritization of each alert. Moreover, you can rank alerts, so the system only shows what truly matters to you. Actionable Alerts Anodot goes yet a step further. In addition to receiving an alert, you can instruct the software to effect a defined action in a specific event. By configuring actionable alerts for critical incidents, the system prevents significant damage to the business. In other words, in case a server failure is detected, the system would redirect all traffic to an alternative server based on your API instructions. A Happy Ending with Anodot Let's rewind to the coffee mug on your desk. You quickly recover from your shock. The combined incident alert tells you that traffic from a specific vendor's server in Iceland failed at 3 a.m. The immediate necessary action - before investigating further - is to redirect traffic to another server. Oh wait, that's already taken care of by an actionable alert. You experience minimum financial loss because traffic was directed within minutes of the incident, customer experience remained unaffected, and operations keep running. What's left is to find what caused the failure in the server and fix it. But that's a job for the technical team.

Marketing teams routinely struggle with monitoring the performance and cost of their ad campaigns. Now, they have a solution that can be as easy as just a few clicks. We recently joined our partners at Rivery for a webinar demonstrating the new Anodot Markering Analytics Monitoring Kit. The kit allows users to track marketing campaigns in real-time and take the action needed to make the most of ad spend. Watch Webinar Anodot's Head of Product, Yariv Zur, joined Rivery's Solutions Architect, Alex Rolnik, to demo the kit in real time. Anodot is an AI-based business monitoring platform helps customers monitor, analyze, and correlate 100 percent of company data in real time. Rivery is a DataOps platform that extracts value from data sources, deploys data operations faster, runs transformation logic to structure data, and ingests data for usage in third party business applications, such as Anodot. In the webinar, Alex explained the challenges of implementing data models and workflows, including: Ingestion Transformation Orchestration Rivery makes these challenges more manageable with pre-engineered data workflows that enable instant insights, called kits. The benefits of kits include: Instant insights Structured to conform to industry best practices Maintenance free Can be used by anyone The new Anodot Marketing Analytics Monitoring Kit instantly deploys all of the data infrastructure you need — from data pipelines, to SQL transformations, to orchestration — to transform raw marketing data (Google Ads, Facebook Ads, etc.) into structured data (in this case, JSON) for Anodot. Joint customers can autonomously monitor their advertising performance while spending more time on mission-critical projects. Anodot monitors all your advertising channels for campaign anomalies, including: Clicks Impressions Conversions Reach Revenue Spend And much more. But of all the conveniences, the most important facet of the Kit is that it allows our mutual customers to take action fast.  

AI Analytics for eCommerce Digital trade and eCommerce companies are generating transactions in more significant quantities than ever before. In 2020, eCommerce sales made up 19% of all worldwide retail transactions, representing $26.7 trillion in revenue. The cornerstone of any eCommerce company is providing a seamless, reliable experience where customers can log into a clean interface, browse products, and make purchases quickly and on-demand. Increased digitization after the pandemic has only heightened the stakes. The scale, distribution, and speed of today's eCommerce generates millions of daily metrics, including orders, shipping, campaigns, application performance, APIs, log-ins and payment gateways, and countless others. It's simply impossible to notice every potential incident that may harm the customer experience or present an opportunity to increase revenue. That's because old-school analytics reporting, static monitoring, and traditional dashboards can only tell us what's already happened. The data streams which impact the customer experience in eCommerce are far too volatile for businesses to rely on static tools and manual intervention. In today's market, only real-time business monitoring based on AI and machine learning is robust enough to eliminate blind spots and deliver intelligent decision support to achieve competitive business outcomes. What types of issues impact eCommerce the most? For eCommerce companies that rely on online transactions as their bread and butter, incidents are costly. For a company with $1 Billion in annual revenues, annual incident costs can range anywhere from $8-32 million dollars. The total impact depends largely on what type of monitoring solutions they use and how quickly they notice an incident. Let's take a look at some of the most impactful incidents in the eCommerce space: High-volume traffic overloads In retail, seasonality is always critical. Many eCommerce operations rely heavily on limited time windows for the bulk of their revenue. For many businesses, Q4 accounts for more than 50% of annual sales. During these periods, demand can spike dramatically, and the cost of an outage can cripple revenue for the entire year. Issues with Online Shopping Carts One issue that eCommerce companies continue to struggle with is online shopping cart abandonment. There are several reasons why shoppers might forgo their purchase, but companies must know immediately if it's happening because of a technical issue. Unfortunately, traditional monitoring falls short in this area because there are too many metrics and dimensions (products, devices, sessions, campaigns, etc.) to detect issues before impacting the customer experience. Price Glitches Price glitches can price items far above or below their actual price resulting in a loss of revenue. While these issues are comparatively rare today after many high-profile and costly cases, they can still have a tremendous impact when they occur. Mistargeted online advertising Targeted advertising is crucial to eCommerce in today's hyper-competitive marketplace. Mistargeted advertising causes companies to waste opportunities and promotions to fail. Aside from targeting the wrong people with the wrong products, these sorts of issues can also cause companies to miss opportunities to upsell or cross-sell customers based on faulty analytics. Real-time Analytics and Autonomous Business Monitoring Countless metrics describe the customer experience, with each exhibiting multiple dimensions like seasonality. Incidents that impact that experience occur across a vast number of data streams as well. Manual analytics simply can't keep pace because they can't be performed in real-time or the data presented requires time-consuming analysis from a data scientist. Even a robust data science division will have difficulties monitoring and digesting millions of metrics to produce actionable information for business leaders. AI/ML-based analytics is a giant leap forward in capability and can create actionable, contextualized information to get ahead of incidents before they occur, or at a minimum, mitigate their worst impacts. Here are some of the most critical benefits of real-time analytics powered by AI/ML: [CTA id="3509d260-9c27-437a-a130-ca1595e7941f"][/CTA] Use Cases Conversion rate monitoring As conversion rate directly impacts revenue, monitoring for sudden drops can alert a company to errors in their checkout process and save a significant amount of otherwise lost revenue. Revenue monitoring Companies can leverage autonomous monitoring on all revenue-related metrics, including revenue from each acquisition channel, completed purchases and sales velocity. Customer fraud alerts Operators can apply AI/ML analytics to fraud detection and protect merchants against unexpected patterns in user behavior. Techniques Correlation Analysis eCommerce companies can use correlation analysis to reduce time to detection (TTD) and time to remediation (TTR) by guiding mitigation efforts early. Further, correlation analysis helps to reduce alert fatigue by filtering out irrelevant anomalies and grouping multiple anomalies stemming from a single incident into one alert. Anomaly Detection The data points most commonly tracked by eCommerce companies include purchases, page views or unique website visitors, failed payment transactions, or abandoned sales carts. Then, there are additional dimensions like the product category, geographical region, and the device, operating system, or app used for the transaction. All of these metrics are tied directly to eCommerce revenue. AI/ML-based anomaly detection can establish a baseline of expected behavior across all data points and detect anomalies in real-time. Real-time eCommerce Analytics with Anodot Every day that passes with an incident or opportunity undetected has a negative impact. Lost revenue, degraded customer experiences, and failed promotions can add up a horror stories for the business. These problems are often lurking in overlooked eCommerce analytics metrics, missed by overworked operators and data scientists relying on manual methods. The only performance monitoring solution that can meet today's eCommerce challenge can monitor a given metric's dimensions in real-time. Anodot's approach to business monitoring in eCommerce is autonomous. No manual dashboards. No operators sifting through false positives. Real-time analytics with Anodot can help you detect incidents 80% faster and reduce incident costs by over 70%. In this example, it took this company’s internal solution 3 days longer than Anodot to notice a drop in completed purchases, resulting in a loss of more than $200K. Anodot identified the root cause to be a version upgrade in Android devices, an incident that could not be detected with traditional dashboards and manual thresholds. Real-time analytics empowers e-commerce business leaders and teams at every layer of the organization by distilling millions of data points into actionable insights. That's critical because so much e-commerce revenue comes during the holidays when systems are under their heaviest loads of the year. For the companies that embrace it, real-time e-commerce analytics with AI/ML can provide the competitive advantage they need to avoid costly incidents, retain customers, and keep revenue rising.

Freshly uses Anodot Autonomous Business Monitoring to monitor their prepared meal delivery service in real time for anomalies that can have a material impact on revenue and costs.