RozaMira gas stations increased fuel realization up to 26% by clients who have falling demand

The company RozaMira has 15 gas stations. Due to the limited local market, the tasks of retaining the existing clients base come to the fore.

Ensuring the return of lost customers at the expense of personal fuel prices, as well as increasing their average check through individual offers for related products.

Integration to the Mega Insight platform and the launch of a new mobile application that allows to form individual price proposals for fuel and goods for clients with advanced analytics by them.

Monitoring of the condition of clients allowed us to give a low price only to customers focused on it and, as a result, to keep sufficiently high prices for the stele. This approach maintains overall margins without falling sales volumes.

A detailed analysis of the consumption model of each client made it possible to form a number of parameters, on the basis of which it became possible to create target groups for clients whose demand is falling at the moment. In addition, the most demanded customers were determined among them by the monthly number of visited thr gas stations, the average bill and other parameters. This allowed us to single out a narrow group, influencing which the Company can get the maximum effect. Then, for the formed group, a coupon was sent to the application with a personal price for gasoline. Conversion for such coupons averaged 20%, and instead of falling demand for these customers, growth began.

As in case № 1, the first step was to form target groups by consumption parameters. However, this time the target groups of clients were determined by those who had never bought coffee drinks. It was hypothesized that a price-driven customer does not buy coffee at a gas station. For such customers, a coupon was created for coffee drinks with a special personal price reduced by 20-30%. As a result, in 30 days such clients managed to sell about 600 cups of coffee. At the same time, for those who previously bought coffee, the price has not changed. Those. RosaMira sold 600 more in a month, and this is in the summer. In autumn and winter, it is assumed that the sales volume will be 2-3 times higher than usual.

In this case, the functionality of automatic calculation of recommendations for each client of the gas station was used. A list of coupons for the most popular items of goods with prices reduced by 10-15% was created in advance. Machine learning algorithms made it possible for each client to form its own unique list of coupons, based on the purchase history of both the client himself and others similar to him in terms of the consumption model. Thus, each client automatically receives a truly personalized set of offers, which allows them to increase conversions by up to 40%.