Industries we work in
Analytics problems look different in different businesses. These are the areas where we have the most experience, and the questions we hear most often.
E-commerce and retail
Online stores produce a lot of data and most of it is about behaviour: what people searched for, what they looked at, what they left in the basket. Retail also has the added complication of returns, stock and delivery.
Questions we are often asked
- Which products should we show first, and for whom?
- Where in the checkout do we lose people, and why?
- What are customers saying in reviews and questions that we're not acting on?
- How much of our revenue comes from repeat buyers?
Our experience
We have worked on conversion and experience reviews for a large online home improvement retailer, loyalty and rewards analytics for a fashion platform, and last-mile operations reporting for a global e-commerce company.
Related: Marketing analytics, Customer 360, Data visualization and reporting
Marketplaces and resale
A marketplace has two sets of customers, buyers and sellers, and the health of one depends on the other. Supply quality, shipping times and cancellations matter as much as traffic.
Questions we are often asked
- Which listings are likely to sell fast, and which are likely to be cancelled?
- How do buyer and seller cohorts behave differently?
- How should we rank products so that good items get seen?
- What does a recommendation system need from our catalogue data?
Our experience
We have built product ranking from predicted click-through rate, cross-sell recommendations, cancellation-risk models and buyer and seller cohorts for a luxury resale marketplace.
Related: Data analysis and insights, Customer 360, Product analytics
Software, apps and subscriptions
Software companies live and die by activation and retention. The data to answer those questions usually exists, but it is often scattered across events, backend tables and billing.
Questions we are often asked
- Where do new users get stuck in their first session?
- Which behaviours predict someone staying for six months?
- Is our event tracking telling us the truth?
- Did the last release actually move the metric we built it for?
Our experience
We have audited event tracking across a consumer app, rebuilt backend JSON into a clean warehouse model, and built predictive and market basket models for a B2B events software company.
Related: Analytics tracking and event audits, Product analytics, Data engineering and warehousing
Digital media and advertising
Publishers and ad businesses need frequent, accurate reporting for clients, and pricing decisions that balance fill rate against revenue.
Questions we are often asked
- What is the right minimum price for each ad slot?
- Can weekly client reports be produced without someone assembling them by hand?
- How do we show clients what is working?
Our experience
We have set minimum prices for ad slots across publisher sites and built an R Shiny tool that automated client reporting, saving over 200 hours a year.
Related: Data analysis and insights, Data visualization and reporting, Marketing analytics
Working in a different field?
Many of the problems are the same. Tell us about yours and we will say whether we're the right people.