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:
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.
Written by Anodot
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