Our client, a major FMCG group, wanted a system for viewing their marketing investments to make the teams more accountable, more focussed on the ROI and ensure that they speak a common language.
We conducted ten interviews with the key marketing managers to identify how they plan their actions, the tools and data they use or those that they should use and the ROI calculation methods.
Then we set up a two-day data marathon with eight marketing managers from four different regions. Using gamification principles, we identified what accounts for the success of marketing actions. Together we reached a consensus on the "cost per actual reach in target group" as our common "currency".
Evaluating the diversified methodology - we chose the reach-cost-quality, "actual cost per reach in the target group" as the common currency for evaluating and selecting the different points of contact.
At the end of the data marathon we formed a squad made up of:
- one UX designer;
- two datascientists;
- one client-side business expert;
- two data engineers.
We conducted four sprints with four two-week breaks to gather the feedback from the countries.
The squad started by formulating a view of the availability and quality of the data from all of the advertiser's points of contact and then used the connectors of the Captain Dash platform to retrieve the data from the points of contact. It tested dozens of elements of external data to identify which one made the most sense.
The squad used a Learning machine engine to replay three years of campaign history. This was used to set the minimum and maximum spending thresholds for different types of point of contact.
The Squad delivered a visual management tool for
- viewing all the marketing actions, brand by brand and country by country;
- identifying the ROI of the campaigns on a "cost per actual reach in target group" method;
- performing "what if scenarios" for the allocation of resources;
- measuring the saturation rate of investments by brand, country and channel;
- proposing investment planning matrices based on business objectives.
For this assignment, we allocated
- one UX designer.
- two datascientists
- two data engineers
The client to be allocated a business expert.
The investment was less than €175,000.