Why decision driven analytics is key to driving customer engagement in fuel retail
Peter Baudains, Head of Solutions and Analytics Innovation at The ai Corporation (ai), writes about the importance of using data analytics to improve customer engagement.
The pandemic and resulting national lockdowns have created a challenging operating environment for fuel retailers, with less people travelling and sales decreasing. As a result, many retailers are turning to data analytics and associated technology to reduce costs and improve customer service.
Some of the most interesting – and potentially more beneficial – projects focus on using data analytics to improve customer engagement. Helping to retain and understand existing and new customers better. Identifying individual customer’s needs, and then adapting service levels to meet those needs and expectations. Indeed, it is becoming widely accepted that a data driven business can understand their customers better and tailor their services in a much more systematic way.
For example, by analysing the data which a retailer holds on an individual’s purchases – the types of product an individual buys, the extras they might add to their basket, the regularity of fill-ups or the location of their favoured store or forecourt – retailers can personalise their marketing efforts, which may involve not sending a customer a particular type of email or loyalty reward, because they understand what an individual is likely to buy, when and where. They can time and tailor their approach. They can send customers useful information or have one of their team call a customer, who might be thinking of buying from a competitor.
In the fuel card world, this might involve identifying novel and flexible pricing and rebate structures, which are based around the operational needs of the customer and derived from analysis on customer usage patterns. Another analysis might investigate the routes typically taken by a fleet and to provide incentives to fill up at locations which fit those patterns. This type of approach benefits all the involved parties: the driver, because it is convenient; the fleet manager, because it is cheaper; and the retailer, because it locks customer usage into their network.
Insights are only as good as the questions you ask
Despite the benefits, the continued existence of the data-value gap tells us that many data analytics projects are still destined to fail. And while many organisations continue to improve two important competencies which remain central to successful data and analytics projects (data governance and analytics skills), a third element to this trifecta is often overlooked.
No matter how advanced your data analysis function is, your data will not provide you with a ready-made solution unless you ask it the right questions. Many businesses forget that any analysis needs to be decision driven. In a recent article, experts explain how some approaches that are data-driven can focus on using available data to answer the wrong question.
A much better approach is to start with the question that needs answering; find the data that enables the question to be answered and then design an analytics approach that is directed at evaluating the available alternatives.
