D.I.A.L: Gaining insight from customer data

Starcount has a specific four-step approach to delivering data-driven business transformation. It’s called DIAL: Data, Insight, Action, Loyalty/Learning. In this new series we explain more about the process and how it can help improve customer understanding and transform business performance.

D.I.A.L.

The second stage in Starcount’s D.I.A.L. model is both vital and complex: turning the client’s (newly cleaned) data into game-changing customer insight.

Defining the problem

The first, crucial step in the customer insight process is to get to grips with the problem that we’re trying to solve. Our data scientists spend time with our clients to understand their specific challenges: what obstacles are they facing? What’s affecting their bottom line? How engaged are their customers? The Starcount team will work with the client to draw out some hypotheses about their business and customers. These ideas then become the starting point for the deep dive into their data.

Next, our scientists will start to get to grips with the data itself, using our expertise, experience and unique algorithms to uncover the revealing in-depth picture of different customers (such as how old they are, how frequently they shop and how much they spend). Interrogating the data, we identify key patterns and relationships predicting and explaining behaviour. Our work can challenge existing assumptions and also qualify and quantify existing knowledge. We can also predict the seemingly irrational.

Visualising the insight

Data visualisation is a core part of Starcount’s D.I.A.L. model. Whether our data scientists are building customer segments or looking at a forecasting model, visualising the data not only makes it easier and faster to pick out helpful trends and solutions, but it also enables us to articulate those key patterns to clients, and to work with them to turn the insight into action.

A test and learn philosophy

Trial and error is not only unavoidable when delivering strong customer insight, it’s an essential part of the process. Our team will never spend too long pursuing one particular avenue if it’s not yielding results. Instead, they look for the fastest and most effective solution for the problem at hand – and we encourage that same agility in our clients.

How does this work in practice?

Segmenting for greater relevance

The vast majority of retailers will have a specific idea of what their customers look like. However, for many of Starcount’s clients, our insight has revealed that their customer base is actually very different from the image they’ve long maintained. A segmentation is the best way to separate different customer groups in order to gain a deeper understanding of their behaviours and to communicate with them in a more relevant way.

Segmentations have the power to reveal hidden customer groups who offer remarkable opportunities for growth. For example, our data scientists recently analysed the transaction data of a popular high street retailer in order to discover which of their customers were most valuable to them. A surprising insight emerged: 15% of their customers were responsible for 75% of ongoing sales. In other words, rather than focusing on the 85% of customers who purchase fewer items, by tightening targeting around this valuable 15% of customers, the retailer could double their revenue.

Building a customer-centric ranging tool

Starcount’s data science team are adept at allowing our clients to find new and effective ways to understand customer behaviour and empowering them to adapt their business strategy accordingly, transforming everything from store locations to marketing communications. Recently, we applied this know-how to the product ranging of a particular retail client.
Historically, ranging decisions at this retailer had been based on financial criteria, rather than driven by customer behaviours. Starcount tested a broad range of product evaluation metrics, assessing and comparing them to identify the most meaningful and discriminating measures when considering a range through a customer lens. Each product was scored against the final set of metrics; this enabled us to understand the relative performance of the product in the range, along with its importance to customers.

Starcount’s scoring method and the accompanying insights we provide gives category managers the power to select a customer-centric range and avoid delisting products that drive valuable customer behaviours – turning insight into action.

This article was written with contributions from Starcount’s Data Science team, including Sarah Blom, Darius Ansari and Josh Eversham.

Part 1: Getting your data fit for purpose

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