Making sense of a mass of retail data
The challenge for Shopitize has been to make sense of this large-scale and complex retail data, which includes data on user demographics (collected during the registration process), in-store transactions, and retail outlets.
“We previously relied on a mixed bag of spreadsheets, data aggregation, and visualisation tools to integrate and present the retail reports with brand owners,” says Graham Halling, commercial director. “It sometimes took days to crunch through the data and create an analytical profile of a brand that we could present to one of our brand partners—and by that time the data was getting ancient. If the brand partner wanted to see a different slice of the data, we had to go back and start again.”
Roger Pubill, campaign performance manager, was equally frustrated by this labored approach to business intelligence. “To create a report, we used to extract all the data from our back-end server,” he says. “For someone with limited IT experience it was a nightmare. There would be dozens of spread sheets lying on the table, and we raced around trying to make sense of the data for a report needed the next day.”
The visualisation of the data was key: the retail intelligence contained in the reports presented to brand partners had to be clear, concise, and immediately understandable. Dr. Alexey Andriyanenko, co-founder and managing director of Shopitize explains, “If I turned up at a client with a 150 page report, packed with spreadsheets and dull graphs showing shopping trends over time, I’m not sure they’d even offer me a coffee. Being able to give them the results in a format they can cascade around the business, without me having to come in each time and explain it all over again, would be a powerful differentiator for Shopitize.”