Using advanced analytics to help transform decisions | KPMG | UK

Using advanced analytics to help transform decisions on store locations and stock replenishment

Using advanced analytics to help transform decisions

KPMG's data scientists knew that applying a bigger, richer mix of data to the problem could deliver the insight this business needed.

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Using advanced analytics

Growth takes businesses to new places and presents new challenges. As one expanding food retailer pushed out with new store openings in unfamiliar territory, they wanted to identify which potential sites could deliver the best profit potential. They needed someone to bring a fresh eye to the challenge. 

Enter KPMG. Our data scientists knew that applying a bigger, richer mix of data to the problem could deliver the insight the business needed. We collected more than 11,500 data signals for each potential site – from data on hourly footfall and site accessibility, to the profile of surrounding businesses and even weather for each location. With sophisticated analysis, we generated a unique thumbprint for each potential site, predicting weekly revenue for six months, one year and two years after opening. Our forecasts according to reporting comparisons, are proving more accurate than previous methods for 80% of new stores, helping our client make better, more profitable decisions on where to invest.

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Fast decisions can be well considered.

Fast decisions can be well considered.

Grasp what’s important quickly, with trusted analytics.

We’re now applying the same fresh thinking to the challenge of demand planning. How can individual retail stores get better at predicting what customers will want and making sure it’s in stock? KPMG’s analysis goes beyond conventional forecasting, which relies on historic patterns to predict the future, and draws on a much wider range of signals to predict sudden spikes or dips in demand. For retailers, the spikes represent opportunities to sell more, and the dips the danger of costly over-stocking. 

Our model lets businesses accurately predict demand for products for any two-hour slot on any day, and replenish shelves accordingly. Used across a portfolio of stores, this has the potential to add hundreds of thousands of pounds to the bottom line. For customers, it would mean their favourite products can always be waiting for them whenever they drop in. That sounds like something you’d call a win-win situation.

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