General Fuller gas stations (GF) return up to 40% clients due to personal fuel and goods prices

Previously, GF could not form and notify separate groups of clients and the notification process was based on ordinary SMS.
Business scale and background: The company General Fueller has 25 gas stations. The rising price of gasoline and the fierce competition in the gas station market lead to the need to find new ways to compete for customers and improve profit margin.
Tasks: Ensuring systematic clients return and increased sales of related products through personalized fuel and goods prices
Solution: Integration to the Megainsight platform, which allows to automatically identify the consumption model of each client with the ability to form individual price offers for fuel and goods, both in automatic and manual mode.
Results: 783% - ROI from automation of the process of forming personal offers for clients
One of the our main task is not to look for additional income only in new clients or products, but to work efficiently with our loyal clients, retain them, find optimal solutions to increase sales to them. But clients are all different, with different wallets and different life needs and behaviors. Hence, the task is being built - to learn to recognize them and point by point, individually offer them such conditions under which their loyalty and, accordingly, the frequency of purchases or the amount of one-time purchases will grow.
Sergey Balsin
Member of the board
Key benefits:
According to the internal report of customer and the data of Megainsight

Ensuring customer return

Increase in average bill
+4 USD

Increase in marches per ton of fuel
For the analysis, we took the indicates from December 2020 to May 2021 inclusive minus the cost of connecting and using the Megainsight platform.
Key cases of platform application in customer:
Case 1. Who is lost...
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 lost clients or for clients whose demand is falling at the moment. In addition, the most demanded clients were identified among the lost clients by the monthly number of 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 offer was sent to the application with a personal price for gasoline. The conversion of such coupons ranges from 5 to 15% depending on the month.
Case 2. Who didn't buy...
As in case №1, the first step was to form target groups by consumption parameters. However, this time was determine the target groups of clients who doesn't buy a goods. It was hypothesized that the price-oriented clients buys lower quality store or coffee shop related products, or uses cheaper offers from competitors. For such client, a coupon was created for those goods that they did not buy with a special personal price reduced by 20-30%. As a result, the conversion for certain product groups reached 30%, and the number of daily bills also increased by an average of 25%. At the same time, for those who previously bought these goods, the price has not changed.
Case 3. Who likes to buy goods
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%.
What's become available due to Megainsight:
Data collection and customer segmentation
Creation of a single place for storing and processing all client data, followed by deduplication and normalization. Convenient interface for forming target customer groups by consumption parameters for a task or hypothesis, depending on the needs of the company.
Target offers and prices
Flexible functionality for creating holding shares in the form of coupons that can be linked both to a specific group of clients and individually to each client, depending on recommendations from machine intelligence.
Improve customer service quality
Providing gas station operators with recommendations on the goods that need to be offered to the client during his identification made it possible to standardize the client service within the entire network.
Branded mobile app for clients
A completely updated mobile application that allows to conduct personal communication with the client and increase his brand loyalty through gamification and personal discounts.
Conversion control
The ability to track key business metrics and their dynamics of changes for each target group of customers formed in the platform.
Hierarchical pricing
Transparent ROI analysis for each price coupon, which allows you to form a client group among those who used it / did not use it for further impact and increase the conversion to sale.
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