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.
Ensuring systematic clients return and increased sales of related products through personalized fuel and goods prices
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.
783% - ROI from automation of the process of forming personal offers for clients

Key benefits

According to the internal report of customer and the data of Megainsight. For the analysis, we took the indicates from December 2020 to May 2021 inclusive minus the cost of connecting and using the Megainsight platform.
+4 USD
Net profit increased (25 filling stations)
Increase in marches per ton of fuel
Increase in average bill
ROI increase with Megainsight Platfotm
Net profit increased
Sergey Balsin
Member of the board
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.

Key cases of platform application in customer

Who is lost
By analyzing client consumption patterns, the company formed parameters for creating target groups of lost or declining-demand clients. The most valuable lost clients were identified based on factors like monthly gas station visits and average bill. For this narrow group, the company sent personalized gasoline offers with coupon conversion rates ranging from 5 to 15% depending on the month.
Who didn't buy
The company formed target groups based on consumption parameters to identify clients who didn't purchase certain goods. Hypothesizing that price-oriented clients bought lower quality products from competitors, the company created coupons with personal prices reduced by 20-30% for those goods. This resulted in up to 30% conversion for certain products and a 25% increase in daily bills, without changing prices for those who already purchased the goods.
Who likes to buy goods
The gas station used the automatic calculation of personalized recommendations for clients, creating a list of popular items with reduced prices. Using machine learning algorithms, each client received a unique list of coupons based on their purchase history and consumption patterns. This resulted in personalized offers for each client, leading to up to 40% increase in conversions.

Explore the product that has produced the result

AI will tell you about the dependence of buyers on the price and what kind of
783% ROI
3.5 M USD
ROI increase with personalized and automated promo marketing
Increase in related goods average check
Net profit increased (25 filling stations)
Customer return rate
ROI increase with Megainsight Platfotm
Net profit increased

What's become available due to Megainsight

Data collection and customer segmentation

Conversion control

Hierarchical pricing

Improve customer service quality

Target offers and prices

Branded mobile app for clients

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.
The ability to track key business metrics and their dynamics of changes for each target group of customers formed in the platform.
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.
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.
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.
A completely updated mobile application that allows to conduct personal communication with the client and increase his brand loyalty through gamification and personal discounts.

Case Studies

General Fuller gas stations (GF) return up to 40% clients due to personal fuel and goods prices
ROI increase
Ensuring customer return
Increase in average bill
Increase in marches per ton of fuel
+4 USD
RozaMira gas stations increased fuel realization up to 26% by clients who have falling demand
Increased fuel realization*
Ensuring customer return
Increase in average bill
Increased weekly gain of new clients due to the ability to share coupons to friends
in 2 times
Resource-Oil fuel chain showed explosive growth of the loyalty program participants
Average week new customers growth
Average cross-sell growth
Active customers growth among digitized
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