How Retailers Can Use AI Analytics to Increase Revenue

Powerful computing advances in recent years have enabled retailers to use artificial intelligence (AI) to help them realize their full potential. AI analytics uses machine learning to find differences and make assumptions automatically.

A study by Business Insider suggests that as much as 85 percent of customer interactions will be controlled without a human by as soon as 2020.

This is no more apparent than in the e-commerce and retail sectors. In fact, business intelligence is increasingly being replaced by AI analytics due to its exponentially enhanced capabilities at managing big data.

What Are the Benefits of AI Analytics?

Poor quality data can lead to a crisis in information trust and business value, such as financial and operational performance. Analytics solutions that use AI technologies to automate manual tasks and learn data trends are helping businesses ensure data quality remains consistent, providing a smarter way to spot anomalies. Results are also in real time, preventing incidents from escalating into brand crises and, on the flip side, revealing market opportunities early.

AI has already been making waves in the retail industry by automating tasks such as inventory search and floor maintenance. And the market opportunity for AI in retail will only grow from here. In fact, a study by Juniper Research suggests that by 2022, the budget for AI technologies in the global retail industry will grow to $7.3 billion annually. This makes finding innovative tech solutions a top priority.

Gain Powerful Insights

Tackling cart abandonment is one of the most challenging parts of running an e-commerce business. In fact, with a staggering 69 percent of online carts being abandoned by users, it’s critical that businesses gain a deeper understanding of their consumer intentions.

Without the right tools, data analysts struggle to pinpoint which areas need improving or resolving. Data blind spots prevent analysts from progressing, and constantly changing variables mean staff can never be on top of their game. By monitoring data with the scale, granularity and speed of machine learning, fewer questions need to be asked. System errors can be identified quicker and revenue dips can be more easily mitigated.

AI analytics, such as anomaly detection, provide e-commerce businesses with the data they require, freeing teams from having to monitor dashboards in search of changes. Exchanging rule-based alerts for machine learning models dramatically reduces false positives. For solutions that correlate anomalies, this can minimize the alerts and the noise, and enable teams to more quickly pinpoint the root cause.

Below is a real-time anomaly alert from an AI analytics platform showing a drop in repeat buyers. This drop is abnormal considering the holiday season and the fact that a promotional e-newsletter was just sent to this audience.

This anomaly didn’t drop far enough to bypass a static threshold, so it would have gone unnoticed. But this machine learning anomaly detection solution identified this as anomalous based on the metric’s historical behavior and seasonality (marked as the shaded blue “baseline”) and correlated it to the related event (see the green flag, “Newsletter: Black Friday Sale”). In this case, there was a glitch in one of the e-newsletter’s promo codes. That system was able to detect the anomaly and alert the product and marketing teams to a potential glitch.

These alerts are essential to identifying system faults that can cause dents in revenue. This is one of many use cases in which retailers, pure-play e-commerce companies, and other data-driven businesses can benefit from real-time AI analytics.

Obtain a Competitive Advantage

Many retailers that started off as simplistic brick-and-mortar stores have now grown to have complex business structures that blend web, in-app and in-store commerce. With information coming in from multiple different sources, getting a comprehensive view of the business has become more difficult.

AI analytics allows companies to gain a competitive advantage by integrating data and breaking down silos. Retailers can track and find incidents using real-time metrics extracted from both internal and external sources. Instead of waiting weeks to find and fix glitches, they can easily correlate related anomalies, which can help teams more easily identify the root cause.

Conclusion

Static business intelligence systems are increasingly failing to meet the needs of organizations with large and complex data.

The power of AI analytics allows retailers to gain valuable insights, such as causes of cart abandonment and other user interactions.

AI analytics provides a wealth of benefits for retail businesses as well as other data-driven companies. It uncovers anomalies in live data streams, consequently saving data analysts valuable time, pinpointing root causes, preventing revenue drops and improving business outcomes.

Amit Levi is vice president of product and marketing at Anodot, an AI platform that monitors business data, detects anomalies and forecasts business performance in real time.