A 20+ store retailer found $3m of revenue upside in its own customer data
An apparel and homewares retailer with 20+ stores and an online store built its weekly trading report by hand in Excel, and had no way to act on what its customer data said. We automated the report, saving a full-time role, and segmented every customer to drive targeted actions such as a second-purchase push. Together they are worth an estimated $3m in extra revenue.
- Client
- Apparel and homewares retailer
- Size
- A$60–80m revenue, 20+ stores across Australia and New Zealand, plus ecommerce
- Systems
- Indigo8, SharePoint, Excel
- Timeline
- Testing in production
Result
- $3m estimated revenue uplift from targeted customer actions
- $150k a year saved on weekly reporting, one full-time role
- 1 view of every customer across stores and online
The trail
-
Source
ERP, sales and spreadsheet data from stores and online
-
Definition
One customer matched across stores and online, in agreed segments
-
Ledger/P&L
The weekly trading numbers, built automatically and without errors
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Owner/Action
Targeted actions for each segment, such as a second-purchase push
What was wrong
The weekly trading report was built by hand in Excel, taking hours every week and leaving room for errors. Customer data sat in separate systems, so the business could not see which customers to win back or encourage to buy again. Production and inventory planning also ran on spreadsheets and was updated infrequently.
What we built
We joined the retailer's ERP, sales and spreadsheet data into one customer view and automated the weekly trading report on top of it, saving one full-time role, about $150k a year, and removing manual errors.
We then segmented the full customer base and set up recommendations for each segment, from second-purchase pushes to agent-run marketing, worth an estimated $3m in extra revenue. For example, win-back calls for loyal high-spending customers who have gone quiet, or a second-purchase SMS nudge for first-time buyers.