Businesses are flooded with constantly changing thresholds brought on by seasonality, special promotions and changes in consumer habits. Manual monitoring with static thresholds can't account for events that do not occur in a regularly timed pattern. That's why historical context of influencing events is critical in preventing false positives, wasted resources and disappointed customers. Typically, when forecasting a metric's future values, its past values are used to learn patterns of behavior. However, in many cases, it is not possible to produce accurate forecasts without additional knowledge about what influences a metric’s behavior. Specifically, the observed patterns in the metric are often influenced by special events that are known to occur at specific times, both in the past and the future.  What is an Event? An event is a special occurrence in time that is known in advance of it happening. An event recurs in the lifecycle of a metric but not necessarily on a regular basis or cadence. This is better explained with a few examples:  Holidays that aren’t a fixed date but rather are dependent on a time of the month or year. Consider the U.S. observation of Thanksgiving, which is always the fourth Thursday of November; the Jewish observation of Hanukkah, which may occur at any time from late November to late December; or the Muslim observation of Eid al-Fitr, whose date of celebration is dependent on the cycle of the moon.  Sales and marketing events that are often tied to a season or special celebration. Examples would be the Black Friday shopping day(s) that follow the U.S. Thanksgiving holiday; Back to School sales; post-holiday clearance sales; or Amazon’s annual “Prime Day” sale. Sporting events, which may be local or regional. For example, both the Super Bowl and the World Cup have a big effect on sales of beer and snack foods, attendance at sports bars, and sales of team merchandise. In a more local case, regional sporting events can have a similar effect. Other examples of events include weather (blizzard days, heavy rains, hurricanes, etc.); financial events (earnings releases, bank holidays, changes in interest rates, etc.); and technical events (deployment of new software version, hardware upgrades, etc.). These events are generally known (or at least can be anticipated) in advance. Even weather predictions have become accurate enough to know when significant weather events are going to happen in a particular locale. In the context of a machine learning (ML) based business monitoring system, events are useful in two ways: Understanding why an incident occurred for the purpose of root cause analysis (e.g., the increase in app crashes occurred right after a new version release indicates that a bug in the new version caused the errors).  To improve the accuracy of the ML based monitoring. By taking into account the expected influence of an event on the metrics being monitored, you can avoid false positives, reduce false negatives, and improve forecasting accuracy. What is an Influencing Event? An influencing event is an event that has predictable and measurable impact on a metric behavior when it occurs. For example, Cyber Monday is an influencing event on metrics that measure revenues for many e-commerce companies in the U.S. The impact of that event is almost universally a dramatic increase in revenues during the event.  If a machine learning business monitoring system does not consider the influence of such an event on the revenue metrics of an e-commerce company, the spike in revenues would appear to be an anomaly, and a false positive alert might be sent. On the other hand, when the influence of the event is accounted for, it can help identify real anomalies.  For example, if this year’s revenue on Cyber Monday is lower than the expectation learned by the system, an alert highlighting a drop in expected revenues will be sent, ideally in real time, so remediation actions can be taken to bring it back to the expected levels of revenue. An influencing event can impact the baseline of a metric before, during and after the event takes place. To understand the logic of that statement, consider this example: Christmas is an annual event. Depending on the metrics you are monitoring, this event has multiple effects, both good and bad, that happen before Christmas Day, on Christmas Day, and after Christmas Day has passed. For merchants measuring revenue from sales, the days before Christmas are spike days. Christmas Day itself is a slow sales day for those merchants who are open for business. The days immediately following Christmas Day can see spiking sales again as shoppers look for post-holiday bargains. For rideshare companies, there can be an uptick in riders before the holiday as people socialize and get out and about, but Christmas Day is a drop day as people tend to stay at home that day. Sample Patterns in a Real Business Scenario There is a computer gaming company that occasionally runs events (called “quests”) to stimulate interest in the game. Quests happen multiple times per month at irregular intervals and each quest spans several days. For example, a quest might run for five days and be done, and the next one starts in ten days, and the one after that starts 15 days after the second quest ends. An object of the game is to collect “coins” and the total coin count is one of the metrics the company measures. During a quest, the coin count has an interesting pattern: high on the first few days of the quest, then a smaller spike, and then returning to a normal steady pattern at the end of the quest. It looks something like this: The gaming company wants to monitor the coin counts during a quest to learn if there is anything unusual happening with the game. For example, if coin counts are down considerably from the normal usage pattern, it could mean that gamers are having trouble logging into the game. That would certainly be something to look into and remedy as soon as possible. This is why anomaly detection and alerting are so important. In the scheme of machine learning and anomaly detection, these quests are influencing events that occur at irregular times. We can’t apply a seasonality model to the machine learning process because the quests aren’t seasonal; nor are they completely random.  They are irregular, but important, nonetheless. If the machine learning took place without consideration for the influencing events, the forecast of the coin counts compared to the actual coin counts would look something like the graph below.  The shaded area represents the forecasted (baseline) range and the solid line is the actual data. It's a very inaccurate forecast, to say the least. There are many false positives in the timeline, and if a real issue with the game occurred during this period, it would not be detected as an anomaly.   However, if the ML model were to be told when a quest is going to start – after all, quests are scheduled, not impromptu – the model could learn the pattern of the event. The baseline could learn the pattern and it could be taken into account each time there is another quest. The resulting forecast versus actual looks something like this: You can see the forecast is much more accurate, even with a very complicated pattern. Take note of the small square marker (circled in red) at the bottom left of the graph. This is the indicator that tells the model a quest is starting. When this marker is sent before the start of a quest, the forecast algorithm understands how to treat the data coming in because it has seen this pattern before. In mathematical terms, the influencing event is called a regressor, and it’s critical to incorporate it into the forecast algorithm to ensure better accuracy. The example below shows a real issue that happened during a quest. Because the baseline was accurate, the drop in activity was detected and the issue was quickly fixed to get the game up and running as normal. Challenges of Learning the Impact of Influencing Events You can see just how important it is for accuracy that a model learn the impact of an influencing event. This is far easier said than done. There are some relatively difficult challenges in having the mathematical model accurately and automatically learn the impact of influencing events. The three main challenges are: 1. Being able to automatically identify if a group of historical events has any influence on a given time series To a ML model, it’s not inherently apparent if a group of events – like Black Friday occurring over a period of years, or the gaming company’s quests over the span of a year – has an actual impact on the metrics. The first part of the challenge is to figure out if that group of events does have an impact. The second part is, if the group of events is shown to have an influence, how can occurrences of the events be automatically identified without human intervention?  For example, with the gaming company, it’s measuring many other metrics besides the coin count, so how can you tell if it is indeed the quest that has an influence on the coin count and not something else? And how can this be recognized automatically? 2. If the group does have an influence, being able to identify accurately and robustly the pattern of the influence, both before and after the event date So you’ve determined that the group of events has an influence on the metric’s pattern. An event has a pattern, and the challenge is to learn this pattern robustly and accurately. There are two main factors making it hard: Separating the event effect from the normal pattern: The pattern of the event needs to be separated from the normal pattern of the metric occurring at the same time - e.g., a metric measuring viewership during an event like the Superbowl is composed of the normal viewership pattern and the added viewership due to the Superbowl itself. To accurately and robustly learn the pattern of influence of the event, applications of techniques such as blind source separation are required - and the assumptions behind those techniques require validation during the learning process. Causal and non-causal effects: A complication is that sometimes there is an impact even before the event starts. You can’t assume the impact of an event will start just when the event starts 3. Given a set of many events, automatically group them to multiple groups of events, where each group has a similar influence pattern and a clear mechanism for identifying from the event description to which group it belongs All sorts of groups of events can have an influencing event on a particular metric. Sometimes different events can have an almost identical pattern. If these events can be grouped together, the learning of the pattern and its impact can be faster and easier. Say you are measuring revenue for a rideshare company. This company sees spikes on New Year’s Eve in all major cities and on St. Patrick’s Day in New York and Boston because people like to go out to celebrate these days. The patterns of ridership for these events are almost identical. When you have lots of these types of events with similar patterns, you want to group them because that makes learning about them more accurate. What’s more, the groupings provide more data samples so you can do with less time in history to learn the pattern. Despite the challenges highlighted above, being able to automatically include influencing events in the machine learning model is critically important for two key reasons. First, it reduces the generation of false positive alerts, and second, it enables capturing when a special event you are running is not acting as normal. Consider an e-commerce company whose Black Friday sale event has lower sales than expected. By incorporating this influencing event in the ML model, the merchant can see that sales are off for some reason and can begin investigation of the cause before revenues are significantly impacted.

AI-based cross-domain correlation analysis reduces TTD and TTR of customer impactful incidents, revenue loss and damaged brand reputation

By implementing Anodot’s autonomous monitoring on top of its network, T-mobile Netherlands reduced time to resolution of incidents and progressed to proactive incident and customer experience management

The global pandemic has changed B2C markets in many ways. In the U.S. market alone in 2020, consumers spent more than $860 billion with online retailers, driving up sales by 44% over the previous year.eCommerce sales are likely to remain high long after the pandemic subsides, as people have grown accustomed to the convenience of ordering online and having their goods – even groceries – delivered to their door. Despite the growth in online sales, eCommerce companies continue to deal with the perennial problem of shopping cart abandonment, online consumers who quit the checkout process before completing a purchase. According to Dynamic Yield, the average shopping cart abandonment rate globally is 70.05%. Why Shoppers Abandon Carts There are numerous reasons why shoppers might forgo their prospective purchases. Perhaps they were only comparison shopping among products or different e-commerce sites. It could be that the checkout process is too cumbersome, or the store doesn’t accept the forms of payment shoppers prefer. But sometimes system errors or technical issues lead people to leave the purchase funnel or abandon their carts. Perhaps the payment platform isn’t working right, or the product page isn’t loading, or a new customer can’t complete the account creation process. In cases like these, shoppers tend to abandon their cart and leave the merchant’s site in frustration and disappointment—and the merchant may never know why it is losing sales. Why Traditional Monitoring Falls Short Online shopping issues are almost impossible for eCommerce companies to detect manually. There are just too many metrics and dimensions – different products, devices, campaigns, sessions, etc. – to manually detect issues that impact customer experience and revenue. Another challenge with traditional dashboards is the time it takes to realize there’s a problem in the path to purchase. Delays between anomaly occurrence and detection can take hours, sometimes days. That’s a delay eCommerce business can’t afford if they want to remain competitive and reduce customer churn. How to Catch Online Shopping Incidents Quickly The best way to detect incidents in the purchase funnel is to put all business level KPIs and data points into a machine learning system that can learn normal patterns and automatically detect anomalies. For system metrics, it’s important to learn shopping behaviors across every dimension in a very intimate way. For example, the ML platform would need to learn how many times shoppers search for a product, how often it is added to a cart, what the average cart size is, whether the product was on a special promotion, what time of day or day of week the product sells best, etc. There might even be “seasonality” to these metrics, such as the pattern of sales over a week, a month, a year, during special promotions that are seasonal (think “back to school” shopping), and so on. After learning the normal patterns of the various metrics and dimensions, the anomaly detection system can analyze incoming data feeds of all the metrics and quickly identify anomalies in real time. An alert notifies data analysts that something is amiss and needs attention. In the example below, the alert reveals a significant drop (the solid orange line) in the approval rate of payments using the PayPal widget as compared to the typical approval rate (shaded blue area). The drop is correlated to the time when a spike in downtime of the PayPal widget is occurring, indicating a problem with the payment platform. To prevent a loss of sales when such a glitch is happening, the merchant can advise customers to use another form of payment to complete their sale. The next example shows a lengthy drop in purchase completions for a particular product when the shopper is on an Android device. The cause of the problem was a software upgrade for the Android app. Once the anomaly was detected due to a low volume on the purchase metric, the developer was able to correct the app and prevent extended losses in sales. How Correlation Helps with Investigations But why is an anomaly happening? Where to begin to investigate? Correlation analysis helps with these questions. By having related anomalies and events grouped into one alert, you can get to the root cause of conversion issues faster. In the graphic below, the top circle is an anomaly that has been detected. The right circle shows the related events (ex: holidays or promotions) and anomalies that are happening concurrently and grouped in that alert. The left circle identifies the leading dimensions within the monitored metrics. This is real-time insight that helps merchants find and fix the root cause of problems before they impact the bottom line. The image below is a good example of how correlated anomalies can point to the source of a system problem. In this example, a drop in successful orders corresponds to a drop in finished orders, which seems to indicate a problem with the process to finish orders. That would be the first place to investigate if there is anything wrong with the underlying process.   eCommerce businesses can’t afford to wait until the end of the sales day, or week, or quarter to understand if there is a problem in their system that is resulting in cart abandonment. An anomaly detection solution with real time alerts can let you know if problems or errors are occurring that should be investigated promptly to prevent shoppers from leaving items in their carts before the sale can be completed.

Gamers are not shy about reaching into their wallets for premium content and features. They also won’t hesitate to tap the uninstall button at the first sign of trouble. It's not uncommon for a gamer to boot up a hotly anticipated new game or revisit an old favorite only to put it down days or weeks later. The culprit is often gaming monetization issues that get in the way of what would otherwise be a long-term rewarding gaming experience. Not to mention revenue lost when players encounter glitches when trying to make an in-app purchase or subscribe to a game. The solution is for gaming monetization models to enhance rather than distract from the core gaming experience. It's more complicated than it sounds as monetization challenges and opportunities pop up in real-time. The most successful gaming companies use AI and machine learning to monitor revenue streams and quickly see through the complex factors that form real-time opportunities or risks. Gaming Monetization Models 1. Subscriptions Gaming subscriptions first became popular in the advent of multiplayer online (MMO) experiences. Some of the most successful games of all time require subscriptions to maintain access to the game. In recent years, companies have found this model is challenging to scale. The advent of free-to-play gaming experiences has also raised the bar on what is considered worthy of a subscription fee. 2. Microtransactions A live service approach supported by microtransactions, or small in-app purchases, is the most popular gaming model on the planet. It embraces the gaming community's insatiable appetite for content and new experiences within the framework of existing, successful titles. 3. Advertising In-game advertising is extremely popular in the mobile game space. Developers use big data concepts combined with performance marketing to acquire customers and target revenue-generating external ads or ads for in-game purchases. In some cases, the data itself is the revenue stream, with information collected per a user agreement sold to third parties. A Balancing Act: Monetization and the Gaming Experience There are few things more important in any business than having a great relationship with the user community. In gaming, monetization models can make or break that relationship. Even minor monetization decisions can have a massive impact on the user experience and directly translate to churn, reduced player spending, and negative reviews. A complex combination of factors influence how monetization practices and models enhance or detract from a user's experience with a game. Different types of users have varying interests regarding what products or services they are interested in buying. It's essential to tailor the gaming experience to individual users and their behavior in real-time to maximize revenue while at the same time putting an excellent gaming experience ahead of everything else. The highest-performing companies in this space use AI and machine learning to optimize the user experience, which directly translates to better, more consistent revenue streams. Using AI/ML to Improve User Experience and Increase Monetization Online gaming provides companies with an ocean of data with the potential to give intelligence to monetization decisions and directly improve the gaming experience. For example, an AI algorithm could use big data to quickly identify which new users were likely to spend money in the game and immediately identify changes in users’ behavior patterns. In addition to enhancing proactive marketing approaches, AI/ML can optimize the gaming experience and monetization outcomes in real-time. By learning the usage patterns of user segments, these powerful algorithms can immediately identify challenges in patterns due to game mechanics, game economics, or technical issues with payment systems and promotional tools before they impact the gamer or the company’s bottom line. Ensuring a Seamless Gaming Experience for Outfit7 Users Outfit7's gaming portfolio represents one of the most beloved mobile app brands, with Talking Tom and Friends games and video content at the top. With over 350 million active app users, it takes a continuous stream of exciting new content to keep its customers engaged. The challenge they faced was safeguarding the customer experience while also pushing updates to their backend multiple times a day to support add-on purchases and in-game ads, which directly interacted with user gameplay. Their existing monitoring processes and tools couldn't identify and alert performance pitfalls caused by the updates in real-time. The user experience was suffering. With Anodot's Gaming Analytics machine learning solutions, Outfit7 set up alerts based on performance anomalies with customizable significance. Their monitoring and intervention process, powered by machine learning, automatically provided their operators with unique insights and critical metrics, allowing them to stay ahead of performance concerns before they had an impact on customers. AI/ML Empowered Gaming Analytics with Anodot Game developers are no strangers to complex problems. There are multiple operating systems with different versions, devices with unique configurations, graphics engines, and so much more. With all of this on their plate, creating monetization opportunities based on manual user behavior monitoring is not feasible. Anodot’s Autonomous Business Monitoring can observe every single metric, learn its normal behavior on its own, and identify anomalies in real-time. Anodot tracks critical gaming metrics such as spikes and drops in usage, repeat players and purchases, and the number of app-store reviews. Not only does catching and resolving these anomalies drastically enhance the user experience but ultimately it helps companies improve monetization and revenue.

Monitoring application performance, monetization, third-party platforms, and the many other workings of a game is incredibly complex. Learn how Autonomous Business Monitoring can help identify revenue-generating opportunities faster and more accurately.

Anodot is built to deliver value fast with seamless integration, simple on-boarding and ongoing use, and completely autonomous monitoring and correlation out-of-the-box

Though invisible to most users, APIs are the backbone of modern web applications. Developers love them because they facilitate complex integrations between systems and services. The business loves them because integrating disparate systems to create new products and services drives innovation and growth. The challenge with this transformative connectivity is the dependencies that exist between systems. API failure can result in performance degradation, data anomalies, or even system-wide outages. That's why API monitoring is emerging as one of the industry's primary concerns in 2021 and beyond. The Challenges of Monitoring APIs APIs are rapidly evolving as adoption continues to grow across industries, but companies are still facing challenges in adopting strategies to monitor and maintain this critical technology. Common issues include performance, balancing response time and reliability constraints, and data quality. While APIs are designed to solve complex problems, new complexities can manifest themselves in the management and monitoring of the APIs. Here are some of the most significant pain points: The Black Box In software development, a 'black box' refers to software whose inner workings are kept private and unexposed to the interfaces it services. One of the primary benefits of API-oriented architectures like microservices is that two systems or services can exchange data without either side understanding the inner workings of the other. However, this can create challenges when issues emerge in testing or production. The difficulty is often in determining whether a case is associated with one of the APIs or the applications they are servicing. Multiple, Siloed Data Sources Another strength of APIs is integrating disparate applications and sources of data to form a new application that adds value in its own right for users or the business. From the API's perspective, call sequencing, input parameters, and parameter combinations all play a role in how that data will be processed and passed into an application. A complex dance is involved to ensure the data from all of these different sources are processed correctly. It takes an equally sophisticated monitoring framework to observe these interactions. Overhead For many applications, response time is a critical factor for the user experience. As a result, some monitoring solutions can impact API performance and degrade the user experience. Overhead concerns are not limited to software performance. Operations teams are often the most overburdened members of the staff in terms of workload. Performing analytical tasks to understand the information coming from monitoring tools can exacerbate this problem, especially if false positives get out of control. Lack of Context/Actionable Data The work of APIs is inherently technical in nature. As a result, it can be challenging to relate data on API availability and performance back to value streams within IT and the business.   API Monitoring Best Practices While the best practices for API monitoring sometimes vary by industry or software categories, there are a handful of strategies that all organizations should practice. Continuous Monitoring The DevSecOps world brought continuous monitoring to cybersecurity with processes and dedicated tools to constantly assess software systems for vulnerabilities. Organizations should consider their APIs as critical as software vulnerabilities and infrastructure availability. Assess APIs 24/7, 365 days a year, with multi-step calls that simulate internal and external interfacing. Push monitoring beyond availability and performance API failure and response time degradations can have a massive impact on the user experience and the business. But what about data and functional correctness? Even if APIs are available and responsive, it doesn't mean they are performing correctly. Data anomalies can have a tremendous impact on the quality of an application and the reputation of a business. Consumability Data generated by an API monitoring tool must be consumable by human operators and systems configured for an automated response. In addition, data should be aggregated and visualized, preferably with actionable insights to reduce resolution times and minimize operator burden when problems occur. If monitoring tools are too difficult to use, operators won't have time to take advantage of all of the benefits. Contextual Awareness for Business For monitoring to deliver value to end-users and the business, it requires context. There needs to be an established baseline of normal behavior and usage patterns and an understanding of how things like seasonality or holidays impact that behavior. This type of information empowers developers and system architects to optimize APIs for the peaks and valleys that businesses deal with every year. API Monitoring with Anodot The exponential growth of web services in the last decade was driven mainly by the proliferation of APIs. Today, they form the backbone of digital transformation and modern application development. For this reason, API monitoring is just as critical as keeping tabs on servers and infrastructure. Autonomous API Monitoring is a game-changer for businesses that need to go further than monitoring API availability and push towards continuously improving their users' experience. The system is simple to set up, with built-in connectors that allow application code to send events directly to a web application. It can learn the expected behavior of all critical application metrics within minutes and immediately start monitoring for anomalous behavior at every endpoint, including latency, response time, error rates, and activity limits. A fintech client using Anodot’s Autonomous Business Monitoring platform observed a spike in activity with an external API partner that the system leveraged for payouts, indicating a potential for fraud, churn, or a compromised account. Because Anodot automatically monitors all API data in real time, the incident was picked up instantly and the customer was alerted. As a result, the customer could intervene and forward it to their fraud team for investigation before it was too late to prevent further damage.   It isn't sufficient for API monitoring solutions to identify and alert performance degradations and data anomalies. By this point, the damage to critical systems and the business are already happening, with breaches to Service Level Agreements being one of the principal concerns. Machine learning empowers API monitoring solutions to identify and understand normal behavior across the application stack so operators can address issues with functionality, performance, correctness, and speed before they impact critical systems.

How much is your company losing by reacting to problems after they’ve had a negative impact on your bottom line?  How many customers churn in the time it takes you to notice complaints to your call center?  Proactive business monitoring allows you to detect incidents before they have a negative impact on your company’s revenue and reputation. There are tremendous opportunities for forward-thinking companies embracing AI to monitor and learn data as it streams in order to detect anomalies.   Left behind will be organizations still relying on the reactive approach of traditional solutions that can’t keep up with the volume and complexity of today’s data.  It’s one reason global advisory firm Gartner predicts the decline of BI dashboards and a move to proactive solutions that use AI-driven technology.  Business Benefits of Proactive Business Monitoring 1. Reduce Operational Costs Reduce operational overhead by proactively solving issues that affect revenue and customers. By speeding up time to detection, your teams will spend less time finding and fixing incidents and more time on activities that drive business value.  2. Improve Customer Experience According to Salesforce, 80% of consumers say customer experience is just as essential as the product or service itself. The best way to improve the customer journey is to make it seamless and free of errors. Proactive monitoring ensures your team gets an alert that something is wrong before your customer notices.  3. Protect Revenue The revenue ecosystem for most organizations today is complex and fragmented, with billions of daily events across segments, products and payment providers. AI monitoring can catch payment issues and missing revenue in real time.  How Traditional BI Dashboards Fall Short of Proactive Monitoring Businesses in today’s data-driven world are tracking thousands of metrics and KPIs and often billions of events each day. Conventional business intelligence (BI) solutions aren’t equipped to deliver the real-time insight needed to catch costly errors.  The following challenges are reasons businesses are ditching BI dashboards in favor of a more automated approach.   1. Business Insight Latency Analyzing data in traditional dashboards is reactive and slow by nature. Retrospective analysis uses historical data to understand what happened in the past in order to spot trends. To get meaningful insights, analysts need to drill down to find answers, which is a time-consuming process. 2. Manual Analysis To ensure the business is alerted to any critical incidents, dashboard analysts have to manually scan for issues and trends. This error-prone process doesn’t provide the automated insight organizations need to stay ahead of problems before they become too costly and damaging.  3. Alert Fatigue The static thresholds of traditional BI platforms result in false positives, which lead to alert storms and false negatives, which pose the risk of missing incidents entirely. They are a drain on resources and reduce the time that could be spent improving efficiencies and delivering business value. [CTA id="dd9d689f-6a6f-4d73-b463-675c736a0bd1"][/CTA] The Essentials of Proactive Business Monitoring The most competitive companies today are using AI-powered business monitoring and anomaly detection to become proactive in everything they do. Gone are the days of finding out about a problem after the customer does or losing tens of thousands after an issue was detected in a payment gateway.  If proactive insight aligns with your business goals, make sure you include the following essentials:  1. Real-Time Detection To find and fix business incidents as they’re happening you’ll need a solution that detects anomalies in real-time. AI and ML-driven business monitoring learns normal patterns of data and can quickly detect incidents as they are happening.  2. Correlation Analysis The summarizing nature of dashboard tools can prevent businesses from understanding the root cause and importance of each incident. AI solutions that group and correlate anomalies ensure that your team gets the most important insights first and eliminates alert storms.  3. Holistic Visibility It’s important that business monitoring solutions integrate data from all available data sources and aggregate into one centralized analytics platform. Breaking down silos will give you comprehensive monitoring and correlation for fast and proactive detection.  Use Cases for Proactive Business Monitoring Customer Experience Monitoring  Monitoring metrics across the customer journey is critical to optimizing how customers interact with your business . Proactive solutions alert you to issues in conversion rates, login issues, subscribers and other critical KPIs to optimize the customer experience.  Payments and Revenue Monitoring  Issues with payment platforms, card transactions or online checkouts can cost businesses hundreds of thousands in revenue if not detected early. Proactive anomaly detection finds the root cause of the issue right away for fast resolution. If your organization is ready to step up your monitoring game and become more proactive, talk to us about how we can help get you there.