Retail is one industry where there is significant use of analytics for understanding Customer Behaviour which directly impact the revenue of any Company. Companies are moving into Omni Channel models where they are present online and have an offline presence as well which work in sync. We can see this trend by looking at offerings such as:
- Order Online and Collect at Store or
- Shop at Store and get it delivered at home
- Order online and Try at home
Cases where Anomaly Detection is necessary
Due to such a demand, there are more metrics to monitor that not only tracks the Business performance but also the Customer Behaviour. There can be several blind spots with unknown Business revenue leakages. There are several cases where Anomaly detection is necessary such as to monitor:
- Payment Failures (Monitor Payment mode failures and suggest alternate Payment Methods)
- Shipment Returns (Monitoring the cause of Returns which can be due to the area not deliverable, Item defective, Vendor/Customer Fraud)
- Cart Abandonment (Monitoring Cart Abandonment causes such as Item availability, pricing, Payment issues, etc.)
- SLA Breaches (Monitoring SLA within Business Verticals or with External Vendors to maintain operational efficiency)
- Zero Sales (Monitoring Zero sales due to Listing issues, Ratings, Reviews)
Using AI/ML for Real-time Anomaly Detection
In the Retail Business, there will be various metrics or KPIs to monitor the business performance and to get a sense of how good or bad the overall performance is. The data for these metrics will follow a time-series pattern, which can be used for Time Series Anomaly Detection. Since all the metrics follow time, we can use the time as a common feature to tie various similar behaving metrics together by applying correlation which can help the business to focus on the incident with the list of all impacted metrics. It will be very helpful for finding out the root cause in case they have an incident.
CrunchMetrics can you help you to monitor all the above cases and alert you in real-time so that you can be more cognizant about your Business’s performance and focus on actions to take once the anomaly is detected. Our Anomaly Detection software will give you a 360-degree view of your business.
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Kshitish Sahoo is the product manager at CrunchMetrics. He has more than 7 years of experience in domains such as Energy and Utilities, QSR, Healthcare, CPG, Real Estate, Banking and Insurance and E-Commerce. He has worked on setting the strategy, developing the feature propositions, marketing the product and handling the financial metrics of the product.