Success Case | Increase in car wash sales by optimizing use during off-peak hours
Global multinational energy company with an annual turnover of 49BN Euros.
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The Challenge
The client wants to develop a data model that predicts the time of lowest utilization of the wash systems at service stations. Using this information, the client wants to encourage its customers to use the wash systems during off-peak periods. This is intended to distribute the use of the washing equipment throughout the days of the week, avoiding peak periods on weekends, and to increase sales.
The Solution
Keepler performed a data exploration of different data sources provided by the customer from its IoT platform:
- Gas station transaction data (customer flow per hour, tickets, washings, …).
- Gas station wash prices
- IoT sensor data from the washing equipment.
After the exploratory analysis, Keepler performed a demand prediction model focused on determining the periods with the lowest number of users. For offer customization, the washing price data was not very relevant as it is not very volatile over time.
The Result
Keepler is a full-stack analytics services company specialized in the design, construction, deployment and operation of advanced public cloud analytics custom-made solutions. We bring to the market the Data Product concept, which is a fully automated, public cloud services-based, tailored software that adds advanced analytics, data engineering, massive data processing, and monitoring features. In addition, we help our customers transition to using public cloud services securely and improve data governance to make the organization more data-centric.
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