How AI and Big Data Can Help Consumer Goods Companies Run Profitable Trade Promotions?
The Consumer Goods industry( CPG) is both diverse and complex, making profitable trade promotion optimization a herculean task for CPG companies. The 5Ps of marketing involved in the CPG industry are varied and complicated, making it virtually impossible to drive 360-degree data driven decision making.
A report by Nielsen Holdings confirms that 40% of CPG trade promotion spending doesn’t drive the desired results while 59% of trade promotions globally don’t break even. Yet another study by Booz Allen Hamilton reveals that most manufacturers lose nearly one-third of the money they put into trade promotions.
However, optimizing trade promotions can be game changers for the CPG industry. Economic Times Retail reveals that a 15% improvement on Trade Promotion ROI will improve the top line by 10% and operating margins by 3-5%.
Organizations want to run profitable trade promotions, but aren’t quite sure how to get the momentum going. There are two key challengers in particular to the current approach adopted by businesses in optimizing their trade promotions:
One of the key challenges in the approach in current usage is that businesses have access to only limited sources of data. This forces business leaders to rely entirely on historical data from syndicated providers. Most CPG companies rely on syndicated data brokers like Nielsen and IRI to determine their promotion strategies. While this data is critical to the overall puzzle, it is still only a piece, and depending entirely on it will not offer a comprehensive view of reality.
Further, the data is also available only in silos, therefore not generating an overall picture that would offer meaningful insights. Your syndicated data is in one place, POS data resides in a different system, past promotion data is stored at some other place. Being forced to access data from multiple systems complicates data consumption and is particularly hard to integrate into everyday workflows, thereby greatly diminishing the efficiency of your decision-making.
Decision makers continue to use an endless number of custom-built spreadsheets to collect and analyze promotional data. Spreadsheets not only require manual compilation of promotion data but also don’t offer the desired flexibility to consume and visualize data.
Some companies also use Trade Promotion Management (TPM) systems for optimizing trade spend. TPM systems are actually built for the purpose of managing and controlling trade promotion activities
TPM systems lack advanced analytics and optimization capabilities which are crucial for profitably optimizing spend and measuring trade promotion performance
This results in inefficient and non-actionable decision-making, forcing executives to rely on gut-based decision making or decisions based on partial intelligence or past experiences. Since they are unable to measure the performance of trade promotions effectively, they are also rendered unable to optimize future spends profitably.
A large part of devising an effective trade promotion strategy revolves around forecasting and making accurate predictions about impact. Being able to answer critical questions such as, “what is the impact if X happens” or “What will be the impact of my ROI and sales uplift if I run BOGO promotion for a cash cow” is a significant element of the trade promotion optimization.
However several trade promotion management tools and software do not offer this type of in-depth analysis and instead offer only generic analysis which doesn’t enable an accurate data-driven decision-making.
