Retail Analytics

Business Analytics

Whether you’re an e-commerce business, high street retailer, or a blend of the two, knowing with certainty what customers prefer and what convinces them to buy is the key to prosperity and possibly even survival.

The way data are collected and what you can extract and reliably deduce from that information are fundamentally different in online and physical retailing.

On-line Retail

E-commerce generates large datasets that potentially tell you how every customer prefers to shop, what they prefer to buy, and (potentially) where they haven’t found what they wanted. Data can be segmented and aggregated in imaginative ways that were never possible in the past. The intelligence you gain from this analysis can inform website design, help you develop more appealing product offers and target marketing campaigns at individual needs, motivations and emotional triggers.

How we use statistical analysis in e-commerce:

  • Devising A/B split testing protocols. What factors you can test, sample sizes and accounting for bias and confounding.
  • Conversion rate optimisation. Identifying factors that prevent sales reaching completion and using statistics to drive targeted improvements to site design, navigation, and online checkout.
  • Returns planning to ensure returned goods are ready for resale as quickly as possible.
  • Optimising stock levels held at stores and warehouses.
  • Using qualitative data analysis and user testing to improve e-commerce conversion rates.

Physical Retail

High street retailers on the other hand rely on surveys and physical experiments with store layouts and displays. But what should you observe, how should you collect data, and which types of data analysis tools will tell you what you need to know?

Loyalty cards have added vast amounts of customer data to the mix providing individual-level data to high street stores. Turning those data into commercially useful knowledge is a key area where a skilled statistical consultant can be your guide.

How we use statistical analysis in physical retailing:

  • Missed opportunity modelling to show lost sales resulting from items being out of stock.
  • Stock ordering predictions based on multiple factors including: season, store space, business growth and regional variations.
  • Combining retailers’ data with external demographic, geographic and environmental data to predict demand and identify profitable locations for new outlets.
  • Organisation, mining and analysis of loyalty card data.


Solutions that are secure enough to build business plans, e-commerce stores and retail spaces around can be challenging. That’s where we can help.

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