What is Business Analytics? Business analytics (BA) is the process of evaluating data in order to gauge business performance and to extract insights that may facilitate strategic planning. It aims to identify the factors that directly impact business performance, such as ie. revenue, user engagement, and technical availability. BA takes data from all business levels, from product and marketing, to operations and finance. Where analytics at the IT layer has a more direct causal relationship, at the business layer metrics are interdependent and their behavior regularly fluctuates – making business analytics an especially complex process. In this article we'll explore how the integration of AI in business analytics is critical as the volume and complexity of data continues to grow, challenging traditional methods of data analysis using BI dashboards. Why Business Analytics Matters? Regardless of size or type, organizations need to collect and evaluate data to understand how their business performs. Critical decisions, such as changing pricing structures, or developing additional products and features, follow an understanding of the numbers and their financial impact. According to Harvard Business School, 60 percent of businesses use BA to boost operational efficiency. For digital companies, this goes hand in hand with user experience. A smoothly functioning website or app is often a prerequisite for visitors agreeing to pay for goods. The study also says 57 percent of businesses leverage BA to drive change and strategy, helping identify hidden opportunities and detecting performance gaps that would be hard to grasp on intuition alone. In 52 percent of businesses, BA facilitates monitoring revenue, although the metrics involved aren’t always limited to financial data. The concept is to collect data from all business units and analyze their impact on financial performance. [CTA id="aa4483ba-9bbe-4bd5-8fc6-a2293a6f22cc"][/CTA] The Evolution of Data Analytics Until late 1960, business analytics relied on handwritten or typed business reports, and people used some form of a calculator to carry out statistical ascertaining. The motivation was gaining visibility into the company's activities and profitability by measuring, tracking, and recording quantifiable values, such as time and cost, and understanding how they relate. Computers made this a lot easier. With the onset of SQL and relational databases, collecting and analyzing statistical data moved to the next level. It was still only the beginning of modern data analytics. Data warehouses and data mining allowed for more data to undergo statistical analysis. Companies started to use the 'slice and dice' technique in which they break down large data sets into smaller segments to get a deeper understanding of specific points of interest. At this time, analysts still worked with historical data. Real-time data only entered the stage at the break of the millennium. When it became possible to analyze processes while they were happening, business analytics took on a much more significant role in digital business. Analytics could now be used as an operational tool and not merely as intelligence to back up strategies. Once again, though, the amount of data became unmanageable. The need to collect data from various sources presented additional challenges. Big data was born and, together with cloud computing, enabled businesses to scale. AI in Business Analytics Not too long ago, agile, interactive dashboards were the business analyst’s dream come true. But for growing enterprises, data analysis needs are outgrowing the capabilities of KPI dashboards. When the data analyst wants to investigate why a given anomaly occurs, they have to look at KPIs across data silos and manually identify relationships between them. Finding the root cause of an underlying issue can take a significant amount of time when analysts have to wade through dashboards as they work through a process of elimination. Using AI in business analytics allows organizations to utilize machine learning algorithms to identify trends and extract insights from complex data sets across multiple sources. AI analytics probes deeper into data and correlates simultaneous anomalies, revealing critical insight into business operations. Business analytics powered by AI can autonomously learn and adapt to changing behavioral patterns of metrics and is therefore significantly more precise in detecting anomalies and deviations. That means a significant reduction in false positives and meaningless alert storms and the surfacing of only the most business critical incidents. Unlike traditional BI tools, by detecting business incidents in real-time and identifying the root cause, AI business analytics helps you remedy problems faster and capture opportunities sooner. Benefits of Anodot’s AI-driven Business Analytics Using AI in business analytics solutions like Anodot, autonomously learn the behavior of 100% of your data and correlates metrics in real-time. Anodot monitors all metrics at scale, enabling operators to achieve complete visibility over the total of services, processes, partners, customers, and business KPIs. Leveraging Anodot’s AI capabilities, you can significantly cut both TTD and TTR and protect your revenue streams from disruption. Anodot's autonomous monitoring platform learns the behavior patterns of all backend and frontend customer experience data and correlates between metrics to create context and visibility. You can discover suspicious spikes or drops in engagement metrics or other user-experience-related parameters and act in real-time. In this example, an eCommerce customer was alerted to an unusual drop in approval rates for purchases paid for with PayPal. Monitoring user experience also helps you identify opportunities to optimize and implement them in your business strategy. Anodot allows you to take your business analytics to the top level. Take the next step towards fully autonomous AI in business analytics monitoring.

Organizations using Amazon Web Services (AWS) cloud traditionally leveraged Reserved Instances (RI) to realize cost savings by committing to the use of a specific instance type and operating system within the AWS region. Nearly 2 years ago, AWS rolled out a new program called Savings Plans, which give companies a new way to reduce costs by making an advanced commitment of a one-year or three-year fixed term. Based on first impressions the immediate understanding was that saving money on your AWS would be significantly simpler and easier, due to the lowering of the customer’s required commitment. The reality is the complete opposite. With Amazon’s Saving plans, it is significantly harder to manage your spending and lower your costs on AWS Plans, especially if you only rely on Amazon’s tools. 1. What are Savings Plans? To understand why the new Saving Plans significantly complicate cloud cost management, it is necessary to briefly review the two savings plan options. EC2 Compute Saving Plan The EC2 Savings plan is just a Standard Reserved Instance without the requirement of having to commit to an operating system up front. Since changing an operating system is not routine, this has very little added value. Compute Saving Plan With this product Amazon has clearly introduced a new line. The customer no longer has to commit to the type of Compute he is going to use. You no longer have to commit to the type of machine, its size or even the region where the machine would run, these are all significant advantages. In addition, Amazon no longer requires a commitment to the service that will use Compute. It does not have to be EC2, which means that when purchasing Compute Saving Plans, using Compute in EMR, ECS EKS clusters or Fargate can also be considered a guarantee and you will receive a discount. In RI Convertible, to get a discount on a different server type, rather than the original server for which we purchased the RI an RI change operation was required. With the new Compute Plan, it is not necessary to make the change and the discount is automatically applied to the different types of servers. The bottom line is that you commit to the hourly cost of computing time, however, you choose whether the commitment is for one or three years and how you want to pay i.e. prepayment, partial payment, or daily payment. At this stage, it sounds like Compute Saving Plans would simplify and lower your costs, as the commitment is more flexible. However, as we stated above, the reality is much more complex. 2. Are Amazon’s Saving Plan Recommendations Right for Me? Let’s start with the most trivial yet critical question, how do I know the optimal computing time for me? Amazon offers you recommendations of what your computing time costs should be and what they feel you should commit to buying from them. It’s interesting that Amazon offers these recommendations considering they don’t share usage data with their users. So what is this recommendation based on? Amazon is recommending to their users to commit to spend hundreds of thousands of dollars a month without any real data or usage information to help users make an educated investment decision. Usually when people commit to future usage they do so based on past usage data. The one thing that Amazon does allow you to do is choose a time period on which their recommendation will be based on. For example, based on usage over the last 30 days of a sample account, Amazon recommended a spend of $ 0.39 per computing hour. The IT manager can simply accept Amazon’s recommendation, but with no ability to check the data the resulting purchase could cost the company a significant amount of additional and unnecessary money. In the example above, there was significant usage over the last 30 days, however a couple of weeks prior to this, there may have been a significant change, such as a reduction in server volume and/or a RI acquisition and therefore the recommendation here should have been particularly lower. This is even truer if Saving Plans had already been purchased and had earned an actual discount. 3. How do I know which savings plan is best for my company? On this large and significant vacuum Anodot for Cloud Cost can provide a lot of value. Using Anodot, you can see your average hourly cost per day for the last 30 days. Since the Saving Plan estimate does not include the Compute hours already receiving an RI discount, Anodot only displays the cost of Compute on-demand. It is also critical for a user who has already purchased and is utilizing Saving Plans to know how this impacts his costs before making any additional commitments. Anodot shows the actual cost of each individual computing hour over the last 30 days to enable educated decisions that can impact significant multi-year financial commitments. Anodot utilizes its unique algorithm and analyses all your data to deliver customized recommendations on what will be the optimal computing time cost that you should actually commit to. It is important to note that when purchasing a Compute Saving Plans, it is not possible to know at the time of purchase what your exact discount will be. The actual amount of the discount can be only estimated in all cases other than RI. This uncertainty is due to an additional complexity that exists in Compute Saving Plans. Each type of server receives a different discount, so in practice the discounts that you receive depends on the type of server you actually run and if Amazon’s algorithm chooses to provide that type of server with the Saving Plan discounts offered.

We’re excited to announce that we've teamed up with Rivery to offer our data pipeline and monitoring solutions in one. Our one-click Data Kits streamline the analytics process, saving teams valuable time so they can act on business incidents 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.

Anodot’s customers to gain access to advanced cloud cost management and optimization capabilities that can be added to their existing Anodot monitoring services.

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.

With the pandemic, supply chain blockages, and Amazon looming large, what do online retailers have planned for the holidays? Will shoppers buy as much online as they did in 2020? We surveyed thousands of U.S. eCommerce companies and consumers – read the report to learn what trends will dominate holiday shopping in 2021.

Success in today's high-velocity business environments means having the correct information to make the right decisions at the right time. As marketplaces grow more competitive and customer expectations continually rise, the "right time" is often real-time. Every transaction generates a plethora of data. Anomalies within your company's data set can represent opportunities and threats to the business. Real-time detection of anomalies empowers enterprises to make the right decisions to seize revenue opportunities and avoid potential losses. What are the main types of anomalies? There are three commonly accepted types of anomalies in statistics and data science: Global outliers, contextual outliers, and collective outliers. 1. Global outliers represent rare events that likely have never happened before. An example might be if a customer spends no more than $200 per week on e-commerce purchases, suddenly spending $10,000 in a single day. 2. Contextual outliers represent events that fall within the normal range from a global sense but are abnormal in the context of seasonal patterns. If a customer only ever spends $2500 every December on gifts but racks up $2000 in charges in July, it would be considered a contextual anomaly. While that month's spending isn't outside their normal global range, it occurs at an unusual time. 3. Collective outliers represent events that on their own do not fall outside of the standard expected behavior, but when combined, represent an anomaly. A group of customers with a history of order cancellations all canceling their orders at the same time would be a collective outlier. The limitations of manual anomaly detection In the past, when businesses only had a handful of metrics to track across their business, manual monitoring methods were feasible. Now, there are potentially millions of metrics to manage and multiple types of anomalies to consider and evaluate. On top of the inherent complexities, many real-life business anomalies require immediate action. A bad software update could cause a business to lose money every second. And since discovering the problem is the first step in resolving it, eliminating the delay between when the problem occurs and when the problem is detected immediately brings you one crucial step closer to rolling back that update and restoring revenue flow. Manual detection is also insufficient when the anomaly represents an opportunity rather than a problem. For example, an unusual uptick in mobile app installations from a specific geographical area may be due to a successful social media marketing campaign that has gone viral in that region. Given the short lifespan of such surges, your business has a limited time window to capitalize on this popularity and turn all those shares, likes, and tweets into sales. Even when anomalies don't require an immediate response, manual anomaly detection and dashboards can fall tragically short. You can always postpone action on an instant alert, but you can never react to a delayed alert in real-time. [CTA id="3509d260-9c27-437a-a130-ca1595e7941f"][/CTA] The Secrets of Fast and Scalable Autonomous Anomaly Detection If manual anomaly detection is inadequate, then automated anomaly detection must be used to achieve real-time anomaly detection at scale. Incremental machine learning algorithms for anomaly detection have the added benefit of scalability. In anomaly detection, there are two paradigms. In the first, a system detects an anomaly that has already occurred and displays it in a traditional dashboard using batch machine learning algorithms. That's only useful as a retrospective on what's happened in the past to inform future decisions. This has some value with long-term planning, but most online businesses need real-time decision-making to seize opportunities and prevent negative impacts. For example, sudden spikes or dips in purchases could present opportunities for action to generate more sales. The only way to take advantage of real-time trends is to know what's going on at the moment that it's happening. That requires automated, fast, and scalable anomaly detection in real-time. In this example, an ecommerce company was alerted as soon as there was an unusual drop in approval rates for PayPal payments. Fixing the issue quickly prevented a significant loss in revenue. Scaling for Growth Incremental machine learning algorithms are easily scalable, thus making them ideal for the incomprehensibly large data sets of today's businesses. If your company is continuously growing, then scalability is a valid concern. Incremental machine learning algorithms are the best option for companies with more metrics and large data sets. Advanced Anomaly Detection Systems that use advanced anomaly detection are more effective than those that use more straightforward techniques. A Gartner report on advanced anomaly detection explained that "They can detect subtle anomalies that might otherwise escape notice, provide earlier warning of impending problems or more time to capitalize on emerging opportunities, reduce the human effort required to develop a monitoring or measuring application, and reduce the time to solution for implementing complicated anomaly detection systems." Many systems try to find events and data outside the normal range to identify opportunities and threats. Yet, often these systems fail, identifying too many anomalies (false positives) or not enough (false negatives). Applying machine learning to rapidly changing environments means constantly updating and training models incrementally to prevent false positives. Our AI analytics solution offers autonomous analytics with contextualized alerts that minimize false positives. Instead of your data scientists asking many questions and carrying out complex data analysis, Anodot does the work and provides the Autonomous Business Analytics answers to understand why an incident happened. This real customer example illustrates how Anodot immediately detected a drop in completed purchases for an online store and correlated the anomalous events to a specific product and device in order to expedite remediation. AI/ML-based anomaly detection in the real world Scientists already harness the power of AI and machine learning to spot anomalies and the opportunities they present far faster than humans ever could on their own. An AI system developed by NASA's Jet Propulsion Laboratory was able to detect and command an orbital satellite to image a rare volcanic event in Ethiopia – before volcanologists even asked NASA for that satellite to take images of the eruption. When working with thousands or millions of metrics, real-time decision-making requires incremental machine learning algorithms. Whether it's saving your business money or gleaning scientific insights from a brief volcanic eruption, real-time anomaly detection has enormous potential for catching critical deviations in data sets that can have tremendous real-world impacts.

Not everything was gloom and doom. Our own analysis at Anodot confirms that some businesses benefitted from the Facebook outage, while others fretted.