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Machine Learning & AI / Case Study
High Street Health Food
A long established high street health food retailer with hundreds of stores and employees faced intricate and complex challenges for both its business and IT. A major focus was the promotions strategy and mechanics, which had grown organically into a difficult to manage state of affairs. It was not clear what the effect of each promotion was on the company’s bottom line.
The business wanted an investigation into the role of promotions and how this could be managed as the business migrated to a new retail ERP solution. It was known that the existing bespoke promotion modelling tool would not sit alongside the new ERP software. The client wanted to embrace retail best practices and so a way to address all this was needed.
What we did
Dae.mn embarked on a twenty day discovery at the client’s offices. The aim of this was to get to a cost and effort estimation to implement an interim promotion modelling tool that would work with the new Oracle Retail platform.
The Discovery consisted of
- Understanding ways of working for promotions and modelling.
- Documenting the findings and processes.
- Understanding the business’s technology and solutions.
- Proposing a high level solution with a high level cost.
- Presenting the solution.
Outcome and results
We had a view of what a promotional modelling tool could look like as team members had developed and delivered this kind of tool before. What soon became clear was the requirement from the client was not for a modelling tool, but for a forecast and planning tool - more akin to a WSSI (Weekly, Sales, Stock, Intake) So, we turned our attention to developing a high level cost and time estimate for a WSSI.
Changes to to overall direction of the business meant that the work was put on hold. However, we did continue the consultancy around Retail Best Practice which led to a collaboration to develop a model to improve the accuracy of sales forecasting. The impact of this was a significant improvement from a +/- 30% inaccuracy to a +/- 5% inaccuracy.
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