Promotions are a crucial aspect of marketing that helps drive sales, particularly for consumer-packaged-goods (CPG) companies. Its importance is evident because large CPG companies employ up to 20% of their revenues for it. Promotions have become even more relevant in today’s time when consumers have more choices and tend to switch brands more often.

However, many companies do not have adequate capabilities to assess the long-term impact of promotions. A recent study concluded that the lack of data, analytical tools, and a good understanding of changing consumer trends are significant challenges to the growth of CPG companies.

Precision Revenue Growth Management

Revenue growth management (RGM) has dramatically evolved over the years. With a bulk of data and sophisticated analytical tools available now, companies have resorted to precision RGM. Companies use point-of-sale data and retailer loyalty-card data to gain deeper insights into consumer behavior and build promotional campaigns accordingly.

There are four levels in the promotion chain where the first two are fundamental levels, and the last two are higher-level. Most companies reach only the fundamental level since the last two groups require advanced analytics and data. This is where precision RGM comes in.

In precision RGM, companies algorithms that can help them predict specific parameters. These algorithms are periodically tested and updated, which has enabled the brands to progress to level three, which deals with predictive impact simulation. It provides an objective assessment of the impact of specific changes.

In level four, which is the most advanced, companies can monitor the long-term impact of promotions on retailers and households. 

Building Precision RGM Capabilities

It can be seen that companies that invest in precision RGM can reach potential consumers and drive sales even in declining markets.

But the bigger question is - how to build such capabilities?

Companies can start by collecting better and as much data as possible. They can use internal financial data and sales data. Moreover, loyalty-card data and demographic and geographic data can add to the existing data. This extensive data of millions of consumers can be used to develop sophisticated algorithms by RGM analysts, which can then be used to predict the consumption patterns of consumers. The insights derived by these precision RGM methods help the companies enhance their visibility and target potential consumers.

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