Data Analytics
Cohort analysis: track how customer behavior changes over time
Totals hide what is happening underneath. Cohort analysis groups customers by when they started and follows each group …
- Blog
- Data Analytics
Data Analytics
Hands-on analytics techniques including cohort analysis, funnel optimization, forecasting methods, and metric design for SaaS and e-commerce.
10 posts
All posts
Data Analytics
Customer acquisition cost: beyond the basic formula
Spend divided by new customers is a starting point, not an answer. How to calculate fully loaded and channel-level CAC,…
Data Analytics
E-commerce funnel analysis: from visit to purchase
Most visitors to an online store leave without buying. A funnel shows where. This guide covers the five stages, the for…
Data Analytics
Spot data quality issues before they cost you
Most bad numbers come from five predictable problems: missing values, duplicates, stale data, wrong values and changed …
Data Analytics
Revenue forecasting for subscription businesses
Subscription revenue should be easy to forecast because most of next month's revenue is already on the books. Most fore…
Data Analytics
Data warehouses: what they are and when you need one
A data warehouse is a database built for analysis rather than for running an app. Here is what that means in practice, …
Data Analytics
Customer segmentation: RFM, behavioral and practical methods
A practical guide to customer segmentation for small and mid-size businesses: RFM scoring step by step, behavioral and …
Data Analytics
Time series patterns in business data: trends, seasons and anomalies
Revenue, orders and signups all mix a long-term trend, repeating seasonal cycles and the occasional anomaly. Here is ho…
Data Analytics
Why real-time analytics is often overkill, and what you need instead
Streaming data pipelines are expensive to build and run, and most business decisions do not need them. A plain framewor…
Data Analytics
Data governance best practices for growing companies
Governance is a set of clear answers: who owns each dataset, what each metric means, how good the data has to be, who c…