In the retailing industry,
Improve the audience segmentation
to reduce media acquisition costs
keywords: Media ROI, Data exploration, Data Impact, Data Science
For one of the leading French retailers, we had two challenges, a quantitative and a qualitative one:
- The qualitative challenge was to get customer insights to think out how to improve the quality of the engagement with the customers.
- The quantitative challenge was to ensure a positive ROI for the whole operation.
In other words, the idea was to lead an On+Off DMP project that could help our client retailer define and activate a new customer segmentation to improve engagement of clients and leads while reducing acquisition costs in programmatic buying.
The four most important challenges in this project were to:
- Implement a tool to extract and digitize CRM data;
- Implement a scoring tool of the different segments that could help prioritize some client segments based on some criteria to define (socio-demo classifications for instance);
- Enrich the analysis, based on other relevant data, such as sales data (average basket, etc) or their digital performance (history of click rates, etc) to meet the first qualitative challenge;
- Classify the cookies targeted by DSPs in order to exclude some of them or limit bidding on others to meet the second quantitative challenge.
- The clic rate increased by 50% 50%
- The acquisition costs were reduced by 30% 30%
- The repetition decreased by 50% 50%
A better data exploration can lead to an improved ROI
After the project completion, the outcome demonstrated the need for a better data exploration to find efficiencies in the media buying process.
- the click rate increase by 50%
- the acquisition costs were reduced by 30%
- Increased time spent on website x2, due to the exploitation of the customer insights gained and the transformation into more relevant creatives.
Discover another use cases from Havas Village in the retail sector
The challenge for MFG Labs
The client asked MFG Labs to make sense of the numerous and very diverse available datasets, including: their products database, sales data, websites logs, forums discussions and meteorological data.
MFG decided to explore the characteristics of this client’s stores. The overall goal of the data exploration was to explore differences and similarities within the network of stores and find ways to uncover business opportunities.
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