Trade Promotion Optimization
Leverage previous sales to predict future promotion performance with machine learning
Optimize promotions with machine learning
Trade promotions are a massive expense for many consumer goods companies and too often this cost is considered part of the “table stakes” to remain competitive in the market. Neal Analytics is challenging that assumption by using machine learning to provide account and promotion managers with the knowledge they need to see what’s working and what isn’t in their promotion calendars.
Our trade promotion management and optimization solution clearly identifies which historical promotions have provided a positive ROI, leveraging that data to recommend future promotions that are the best fit for each product/market/customer, or other defining factors.
The Neal Analytics TPO solution is a rapidly deployable set of models and dashboards that:
- Create a sales baseline to determine what sales would have been if a promotion wasn’t run
- Calculate historical promotion ROI, volume uplift, redemption rate, and other KPIs using cost factors and sell-through data
- Build optimal promotions from a given a set of desired characteristics
For the baseline model, we need to isolate the effect of promotions on sales independent of other factors. To accomplish this, we use an advanced time series forecasting algorithm with additional regression components developed by Facebook and the open source community known as “Prophet” which we custom tune for your unique business factors.
By training this algorithm on historical periods where promotions didn’t occur, we can impute data to accurately predict what sales likely would have been had a promotion not been run. This will help your business understand exactly what value add promotions bring to your bottom line, separating promotion lift from seasonality, product assortment, market growth, and so on.
Interpret performance using post event analysis
With promotions increasingly viewed as a cost of entry it is critical to inform management of the true performance of promotion activities and which ones are growing sales and market share. Many organizations do have some intelligence here, but a promotion with 100% redemption rate may be unprofitable and result in significant forward buying, suppressing future demand. Our visualizations provide an easily interpretable view of historical promotion performance as well as a variety of additional capabilities to analyze by Product Group, Customers, Geographies, etc.
Machine learning for calendar recommendations
Our Trade Promotion Optimization solution extends your capabilities beyond typical TPM solutions by using the performance data generated by this first stage to feed a second machine learning algorithm to recommend the optimal promotions subject to the conditions and restrictions the promotion planners and key account managers require. This can be used to plan for an entire season in a calendar application custom developed to your business specifications.