What is Purchase Predictor?

It is a retail intelligence platform that gives you the best understanding of when customers are most likely to buy the products that you sell. Purchase Predictor maximises your sales and transforms your decision making, through a deep understanding of customer purchase intent. Purchase Predictor enables you to:

• • Review thousands of your products to identify the one which will grow your sales the most
  • Forecast the size of your customer events and allocate your budget to gain the greatest returns
  • Predict seasonal trends in your categories to uncover new journeys and missions in your market
  • Size your sales opportunities to pinpoint your revenue left on the table – “sales headroom”

Who is Purchase Predictor for?

Marketeers, category managers or anyone looking to drive retail growth!

Marketing and Customer Teams

Maximise the return from your marketing spend by using Purchase Predictor to pinpoint when there are the most customers in the market ready to buy the products you sell.

Commercial or Category

Optimise your category sales and gross margin by using Purchase Predictor to prioritise product placement and promotion to achieve greatest category profitability.

The science behind Purchase Predictor

Purchase Predictor makes it easy for retailers to plan promotions more effectively and maximise profits. But behind the simplicity of the platform, there’s a world of complicated data science powering the insights. Processing and analysing millions of data points across 40,000+ products, in more than 10,000 categories, the platform uses machine learning to identify and cluster customer behaviour patterns. Digital fingerprints are also analysed to understand Customer Purchase Intent.

This data is then used to predict future behaviour through a Bayesian statistical modelling system. Purchase Predictor decomposes data in order to identify long-term trends, underlying seasonality and the effects of key marketing dates or events. This enables the platform to forecast behaviour and demand up to three years out, using linear regression analysis to understand areas of growth and decline. The platform’s recommender engine is able to identify the most appropriate activity and focus product categories for each week.

Taking the retailer’s last year of sales data, the engine identifies incremental sales revenue opportunity using headroom analysis based on trends and customer behaviour. Crucially, this is used to provide a monetary value on the identified potential sales opportunity, allowing retailers to make planning decisions backed by statistics.