Trade promotions are the lifeblood of the CPG industry. To increase the sales of products, trade promotions are often planned with different tactics like price reductions, discounts and deals, coupons, cashbacks etc. A promotion planned is defined as “Effective” only when the returns of the promotion are positive and it is well executed at retailers. To term a promotion as “Effective Promotion” or to calculate the effectiveness of promotions, different measures can be used like incremental units over total units or incremental revenue generated over total spend or simply ROI of the promotion. To reach to these measures, we basically need the sales data from retailers i.e. Point of Sales (POS) data.
The POS data is nothing but sales data from different retailers. This tells us how consumers are spending at retailers for different products. The data from POS terminals is tagged with a coupon code or discount codes from which a trade promotion can be easily identified.
The POS data needs to be harmonized so that meaningful data can be derived out. The data harmonization efforts are specifically targeted to gather raw POS data from multiple disparate sources (ex. IRI Nielsen etc.), cleaning the data, removing the incorrect or misleading data points and then finally creating a single truth as a whole.
Once POS data is harmonized, the planned promotion data can be cross verified on the actual grounds of POS data which can lead us to the final conclusion of whether the promotion is executed well or not. Basically POS helps us the check if we have met expected results.
If a planner plans a promotion for new year, then it is expected that the promotion should be executed from the first week.
The POS data received for the new year period can show us whether the products sales are increased or not. If the sales of a product is increased, we can get a positive return on spend. In turn, the planned promotion becomes effective.
This analysis of POS data can also become a ground for planning future promotions. To explain this we can consider the same example. In the same example above, if the promotion is executed in the 2nd week of the year, we can expect a lesser return after analyzing the POS data. This will also help the planner to plan promotions in a better way.