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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:

Data Unavailability

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.

Usage of TPM Systems and Spreadsheets

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.

Lack of Sales Forecasting

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.

With organizations being unable to measure the effectiveness of trade promotions due to lack of sufficient data, inefficient data harmonization, and predictive intelligence, there is a shocking lack of data-backed optimization of upcoming trade promotions – either only minor instinct driven tweaks are made or nothing is done. Again, decision-makers resort to learnings from past experiences or partial intelligence.

How Big Data and AI can Help?

Both challenges discussed above can be addressed by leveraging Big Data and AI effectively. Infact, any trade promotion optimization solution should be equipped with high quality big data and AI-Powered Analytics in order to drive real business results. An ideal trade promotion software should be able to effectively harness the data from your past trade promotions, measure effectiveness and provide meaningful recommendations from the analysis.

This in-depth and goal-driven analysis can be effectively performed only when the TPO software is integrated with AI technologies like machine learning, advanced analytics etc.

Here’s why:

Holistic view of reality with high-quality Big Data

An effective Trade Promotion Optimization solution should capture big data from all relevant and influential data sources in the right format. As the solution is fed with raw data from a variety of consumer and market touch points and data sources, it becomes a solid foundation to generate powerful analysis that provides a comprehensive view of reality.

These sources include both internal data, including market research, past promotions, primary sales, secondary sales, marketing campaigns, POS etc. as well as external data, which includes syndicated data, social, weather, TRP, digital analytics, events, competitor campaigns etc.

Integrating more number of data sources helps increase the accuracy of your overall trade promotions strategy.

Harnessing real-time and unstructured data for meaningful insights with AI

AI-powered analytics enable businesses to perform powerful analysis upon the big data integrated and generate recommendations for the right promotions to be run.

It also identifies key trends and insights in your current trade promotions, which can then be leveraged to measure the effectiveness of trade promotion, optimize the spend and forecast sales for the next quarter/year.

Leveraging AI also allows you insights into what-if scenario and comparison analysis. It offers a forward-thinking strategy and predicts outcomes for different actions. For example, if you wanted to know the outcome of promoting 3 brands for B2G1 and 2 other brands for a BOGO in New York for a time period of 2 months, the software should tell you what the expected ROI and sales uplift can be.

User Adoption With AI Chatbots

Most Trade promotion softwares do not appeal to the end users due to the complicated navigation flows in the solutions. Integrating AI chatbots into the TPO solution helps users access critical promotion related data and KPIs via chat and drives adoption.

AI and Big Data come together to generate important benefits for each step of trade promotions, including improved promotion forecasting and planning, promotion effectiveness and measurement and finally, enabling companies to optimize trade promotions to drive critical business results.

About the Author

Hemanth Kumar handles Acuvate’s Analytics & Database services. He was previously heading the Innovation lab & Tech readiness department at Acuvate. His current role demands providing Delivery Oversight, ensuring team’s readiness and managing the service portfolio of Analytics and Database Practice. Hemanth has consulted various Manufacturing, FMCG and BFSI clients on large scale Data related initiatives. Hemanth has been a part of Acuvate for more than 5 years now.

 

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