We are working with a client on a long-term experiential marketing program in the spirits industry. This program is a bit different than is typical: it is a sales program rather than consumer-facing one whereby brand ambassadors are charged with increasing sales in assigned markets and accounts. They go in to accounts and work with staff to increase volume and variety of the brand, or if the account doesn’t currently offer it, they work with staff to get it on the menu.
There is a great deal of management with this experiential marketing program and as a result, there is a great deal of data. We have three sources:
- Sales figures for the brand ambassador’s territory and the accounts not included in his or her territory
- Key performance indicators (or KPIs) that are submitted by brand ambassadors, and
- An account survey where brand ambassadors note key metrics each time they visit an account.
As our client’s analytics partner, we were charged with finding a way to make all of this data mean something beyond sales figures and beyond how many times the brand appeared on the menu.
Our solution was to create a predictive model that identified those KPIs with the greatest impact on account buy-in. Results from that analysis identified seven KPIs as having the strongest effect on buy-in (our outcome variable). From there, we used results from the account survey to provide guidance to the brand ambassadors on where to focus to improve sales. And to further validate the impact of those seven KPIs, we refer to the account sales data and reports.
How is this actionable? We focused on two elements of training that the model showed to be strong predictors of success. We were able to show once bartenders are properly trained, they maintain proficiency, which in turn has a positive effect on future distribution. Additionally, accounts with higher buy-in also had up-to-date and properly branded glassware.
When we reviewed the account survey data, we found one brand ambassador who reported lower than average numbers on each of these KPIs. Sales data indicated the brand ambassador’s accounts decreased in distribution so far this year, while other brand ambassadors’ accounts in the same market saw an increase in sales.
Our recommendation to our client was to have this brand ambassador focus more attention on training and we will monitor the sales data for these accounts for a change in sales (we would expect to see an increase).
By looking at the purpose of each data source, we were able to map a path for how the data could be connected, used, and interpreted in a way that would provide guidance to the brand ambassadors in the field and their experiential marketing.
By reducing the number of metrics to just those that matter, we are able to give our client and their staff something that can have a visible and meaningful impact on their desired outcome.