Do you still find yourself visually monitoring dashboards for anomalies? That leaves catching revenue-related issues to chance. It’s become humanly impossible to catch incidents on streaming data. This is why many eCommerce and data-driven companies have adopted automated anomaly detection. Using machine learning, these solutions can detect incidents in business KPIs as they’re happening, long before your business feels the impact of lost revenue, negative social media shares, embarrassing news articles, or calls from upset customers.

The time series metrics or data points most commonly tracked by eCommerce companies tend to include aspects such as purchases, page views or unique website visitors, failed payment transactions, or abandoned sales carts. Each of these is usually broken down by product category, geographical region and the device, operating system or app used at the time. These are the metrics that are most closely tied to revenue for eCommerce businesses and thus directly impact the bottom line.

Running machine learning anomaly detection on streaming data can play a significant role in your overall revenue. Here’s why:

#1: Complex business big data requires real time detection

Having an anomaly detection that can monitor streaming data requires sophisticated machine learning algorithms which not only instantly and accurately identify anomalies, but are also smart enough to adjust to seasonal behavior and other changes in those metrics. In eCommerce, nothing is ever static: products are constantly added and removed, discounts and rebates from manufacturers and retailers change, competitors attempt to out-maneuver each other with ad buys and social media marketing campaigns, and so on.

With all these moving pieces, the last thing your business needs is to be overwhelmed by alert storms or burdened with setting and then constantly adjusting static thresholds, but traditional business intelligence (BI) tools that rely on traditional anomaly detection leave you no other choice than setting static thresholds or tracking the dashboards yourself.

#2: Automated anomaly detection helps eCommerce companies scale

Automating anomaly detection with machine learning helps eCommerce businesses scale by allowing them to avoid the delays, errors, and costs of manual anomaly detection with traditional BI tools. In the fast-paced world of eCommerce, detection delay is perhaps the worst drawback of manual anomaly detection since hundreds of customers can take advantage of a price glitch in a matter of hours. The faster problematic data is detected, the faster it can be fixed, resulting in less money lost. Less money lost means higher profits, which then can be reinvested in the company, fueling growth.

#3: Manual anomaly detection is neither fast nor scalable

As web development platform company Wix discovered, relying on manual anomaly detection requires prioritizing, since that approach can never scale to thousands or millions of metrics. This forced its analytics team to make, at best, educated guesses about which metrics or data points deserved their limited resources.  Critical evidence of important business incidents, however, can and do occur as anomalies in seemingly insignificant metrics – and not just in the handful which Wix was actively monitoring – which is why you need to monitor them all. The needle can be in any part of the data haystack.

Even with a skilled team of data analysts scrutinizing a few metrics, Wix’s manual anomaly detection approach still couldn’t catch anomalies in real time. Delays between anomaly occurrence and anomaly detection ranged from hours to days, a lag with potentially catastrophic consequences in a price glitch or payment processor API breakage scenario.

Now that they’ve begun using Anodot’s platform, detection has gone from hours to minutes, and the entire organization, not only the BI teams, have a unified platform for discovering important incidents everywhere from sales to R&D.

Right for eCommerce big data

Anodot anomaly detection with machine learning is a must-have for retail success simply because eCommerce occurs in a very fast-paced environment where delays can be outright disastrous. Our real-time anomaly detection system will find the critical signals in your streaming data by monitoring all your metrics – without astronomical headcount or unacceptable delay.

Topics: Big DataeCommerceretail
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