Can brand profitability be saved from cannibalization?

Topics: Innovation

A successful trade promotion event on one promoted group from a category and brand perspective might be extremely detrimental to the category and or brand. Accurately analyzing the cannibalistic impact on both category and brand requires a sophisticated centralized database that accurately houses individual SKU/Promoted Group baselines, shipment data, spending data and pertinent competitive data. This standard trade promotion optimization feature allows for comparative visibility into performance.

Too valuable to ignore
  1. Elimination of manually intensive error prone data compilation/management

  2. Informed accurate decision making ability

  3. Increased effectiveness/efficiency of the trade investment……. leading to a quantified return

  4. One version of the truth for internal dialogue regarding strategic brand initiatives

  5. Comprehensive category view fostering true collaborative joint business planning with your trading partners

  6. Resulting in a quantified mutual return of the significant trade spend

The results are all too often “How many hours……how many errors.” Most CPG organizations today are barely able to provide a top-line view of the incremental profit/volume that their trade promotion investment returns on individual promotion events. This calculation is normally the result of compiling data on spreadsheets from 3-5 different intelligence silos in the organization.

The inability to have access to the necessary data to conduct accurate cannibalization analysis at the category/brand level has been the key deterrent in accomplishing productive joint business planning between the retailer and manufacturer.

The past is about to change

With the recent advent of “Big Data” being managed in a cost effective cloud environment, coupled with the power of optimization modeling, it is now possible to accurately identify the impact of cannibalization created by trade promotion at the category and brand level. A best in class Trade Promotion Optimization (TPO) solution combines shipment, trade spending COG’s, POS data, competitive data and consumer/shopper marketing initiatives in one real-time data base.

In addition, it provides the user with:

  • accurate baselines (at SKU level)
  • lift coefficients (both historical and predictive)
  • real-time promotional KPI analysis

This rich historical intelligence enables the user to evaluate the impact of cannibalization on non-promoted items at the category and brand level and have a roll-up view of the total incremental volume/profit at the desired hierarchy level for the promotion period.

Now what?

Once you are able to evaluate the impact of cannibalization on non-promoted items, protecting the brand profitability requires informed planning. Predictive optimization modeling, utilizing the power of constraint based modeling, can develop an optimal customer/category promotion plan for volume/profit or revenue.

 What to read next: Breathing New Life into Trade Marketing with Optimization


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