Cuebook

How to reduce Customer Churn via Anomaly detection?

A few years ago, I helped an Indian payments company build its data platform. The company provided card swipe machines to its customers. Its customers included large retail chains to mom-n-pop stores. The company had a huge feet-on-street team to … Read More

Is Search-driven Analytics really about Search?

Say you want to search “augmented analytics”. You go to Google, enter “augmented analytics” and Search. You get 10 results from different websites on the home screen. Now imagine the following. You go to search engine XXX . You must … Read More

What is Time-lagged Lineage of Metrics?

Say you work for a financial services company in the lending business. You receive loan applications that you process in exactly 2 days. For every 20 applications, you approve one. This means your approval ratio is 5%. Say below is … Read More

Anomaly Detection with Low Quality Data

Say you monitor metrics on a daily basis and have daily anomaly detection jobs. These anomaly detection jobs run every day at 4am. These jobs pull data from your data warehouse. Let’s say your data warehouse is refreshed twice every … Read More

Why Anomaly Detection at Scale is Hard, Expensive and Noisy

Say you work for an online retailer. Your store sells 1000 products. You want to run anomaly detection on daily orders for each of these 1000 products. This means the following: Number of Metrics = 1 (Orders) Number of Dimension … Read More

Root Cause Analysis – a story

Let’s say I recently joined a fictitious online fashion store as an analyst. One morning I receive a message from Bill, our VP of Analytics. Bill: Jack from reverse logistics team called. said he sees lots of return requests being … Read More

Anomaly Detection: Business metrics vs. Technical metrics

In my previous post What is an Anomaly, we looked at why it is not simple to define what is normal for a metric. What’s normal for a metric depends on at least two factors – granularity and the amount … Read More