Prescriptive analysis in trade marketing

Merchandising in the financial market

The areas of Trade and Promotion have great difficulty in measuring the success of a campaign, mainly because of the management of the many regions and partners involved. In this scenario that we described, the challenge was even more significant because it brought together two giants from the financial segment.

Decision-making process of our client suffered delays between the actions taken and their impact on the behaviour of the final consumer. Despite the high value invested advertising in different channels, measuring results required a lot of adjustment and consolidation work because they came from more than seven different sources. There was a lot of reliance on a time-consuming manual process with a high possibility of human error to generate information to guide decision-making.

Trade Marketing Strategy

The goal was to create promotions to change the consumer’s purchases habit, and this requires a broad set of techniques that must be applied, regardless of the product or service. Promotions that aim to promote the use of the debit card are even worse, given the few distribution channels available. 

This scenario makes old techniques of measuring results counterproductive , based on descriptive analysis at the end of the promotion when it is already too late to act on it and change the course of the action.

Decision-making in real-time

The focus of our work was to establish communication with possible participants in a relevant way. We had a lot of data from past campaigns, in addition to what would be generated in the current campaign. To do so, we used Machine Learning and Business Analytics techniques to create a robot with functions of unifying and automating data interpretation and analysis processes. After a month of “listening” to the campaign without interference, the algorithm was able to identify cause and effect patterns between the actions of the different channels involved. From there, our solution could suggest actions, predicting what would be the impact on the point of sale (which in this case was digital)


Want to know the results?

Want to know how we do it?

Methodology of merchandising strategy analysis

•Communication rules of different channels/areas
• Goals (KPIS) for each channel/area
• Data extraction and sharing process
•Existing database
• Available Martech tools

• KPIs integration
• historical data
• Media Performance
• Registration Journey
• Behavior after registration

• Demographic and behavioural enrichment engines
• Social Media Monitoring
• Data Integration

• Media Performance
• • Registration journey in promotion
• • Change of behaviour (use of the product) after registration in the promotion


• Change of behaviour (use of the product) after registration in the promotion:

Unify the data
Unifying communication rules of different channels and stakeholders
Automate the process of updating, crossing, and analyzing data.
Dashboard – Real-time access to the information generated by data crossing. Monitor campaign performance without relying on human interaction.
Identification of best conversion channels + investment recommendation by the performance – Correction of media investment strategy

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