AI-powered Revenue Growth Management

CPG (Consumer Packaged Goods) companies and retailers have unique challenges in managing pricing and promotions while operating in low-margin, high-volume businesses. They operate in a very dynamic competitive environment. Consumer demand and behavior are changing rapidly and so are the pricing pressures. The need for robust revenue growth management is more than ever before when there is increasing penetration of e-commerce, direct to consumer and weakening brand loyalty. 

Revenue Growth Management (RGM)

Revenue growth management (RGM) helps companies with profitable revenue growth by application of analytics to discern the customers’ perception of product value and then sense, predict and shape the customer path-to-purchase by optimizing price, product assortment, promotions, and the channel mix.

Why Artificial Intelligence (AI) for RGM?

Almost all CPG companies & retailers have traditionally been optimizing all or some parts of the revenue growth management. They are using rules-based software tools and most of these rules are defined based on experience and a limited number of variables impacting the revenue. This is the traditional approach to revenue growth management. 

The new or the modern way of running revenue growth management is by leveraging the power of Artificial Intelligence (AI) and automated technologies. In the past couple of years, artificial intelligence has matured significantly. It is now possible to consider all the variables impacting the consumer behavior and yet model the key levers of revenue to optimize for the profitable growth goal. AI is a perfect fit for RGM due to the ability of AI to synthesize large amounts of data and decipher patterns in the data that are not possible via manual or traditional methods. The key reasons for implementing AI for revenue growth management are:

Impact on Revenue

BCG has published a study of the impact of AI on revenue based on the projects completed by BCG to date. BCG found that automating revenue management systems' pricing rules with AI can increase revenues up to 5% in less than nine months. It also found that revenues can increase by another 5% if a company implements targeted selling programs customized to specific customer segments, e.g. bundling components of existing offerings to meet target customers’ needs, converting one-time sales to subscriptions, or improving the effectiveness of targeted promotions and loyalty programs.  

Data Unificationx`

All enterprises have their data scattered in different formats and different systems across retailers and partners. Apart from the first and second-party data, a significant amount of data is available today about the customers on social media and other third-party sources. There is a need to unify and organize this humongous data in a way that a consumer profile can be created by organizing and triangulating all the information on his or her preferences, household information, point of sale (POS) granular scan data, likes and dislikes, media consumption, etc. At Tuzo, we call this consumer graph. AI can help unify this diverse data and create that elusive consumer graph that is foundational to every analysis in Revenue Growth Management.

Granular Predictive Models

With the help of the unified data and the consumer graph, we can use machine learning to create highly relevant predictive models that are granular, accurate, and self-learning. Patterns and trending insights from AI makes business more competitive. A deeper and detailed consumer and store segmentation are now possible by using AI and ML. The predictive models can identify complex patterns in the data and can make predictions at an individual consumer level. This granularity of predictions is a game-changer and source of competitive advantage leading to significant revenue impact.

How AI impacts the 4 key components of RGM

Pricing

AI-driven RGM can drive a more nuanced pricing strategy by understanding how consumers shop in the category. Armed with insights on consumer shopping behavior, companies can define the pricing by SKU, by channel, and by geographic area. A portfolio view allows the companies to maximize profits by reducing the price on a few SKUs while increasing the price on the others. The key to success here is to find the micro customer segments, estimate pricing elasticity and then do a retail segmentation. So far most companies have been relying on blanket discounts which may not be very productive. Modern RGM helps eliminate unproductive discounts by targeting the right consumer with the right offer.

Assortment

Consumer graph on purchase habits, influence, etc is the foundation of RGM. These detailed insights can be used to identify what is consumed where, why, and at what time. With this information, the companies can better align their SKUs, product mix, and pack sizes to consumer needs. New products can be introduced based on white spaces identified that complement the existing portfolio and the loss-making SKUs can be removed if not adding value to the portfolio as a whole.

Promotions

Promotions are the biggest spend for any company and for CPG companies in particular as they spend close to 20% of revenues on different kinds of promotions. It is also a fact that CPG and retails executives are painfully aware that a majority of promotions fail to break even. AI allows CPGs and retailers to gather customer insights in an automated fashion and predict the next actions based on previous patterns or images. AI uses predictive patterns to help understand desires, motivations, and actions across both physical and digital channels. This lets retailers and suppliers enhance many functions, such as executing more targeted and personalized marketing campaigns and improving trade promotion efforts. AI can also automate forecasting of inventory needs, more accurately predict out-of-stock incidences, and ultimately help optimize the supply chain.

Marketing(Advertising) Mix

Granular data, insights, and patterns using AI can be used to make strategic investment decisions across advertising channels Marketing budgets can be allocated to different media based on their impact on sales. Marketing mix modeling (MMM) has been around for a while using traditional statistical methods. However, the process used to take months of data collection & preparation and then building a statistical model. With AI, the MMM process has only accelerated significantly but can also be made highly accurate and scalable for a high number of stores, brands, products, or customer segments. Moreover, the evolution of AI-based marketing mix models accounts for newer, faster-moving digital channels taking an omnichannel view.

Summary:

Revenue growth management has revolutionized the way business is conducted and artificial intelligence is like steroids to RGM driving speed to value. Using AI and modern ways of revenue growth management can bring significant benefits to the topline (up to 10% increase in revenues). 

The chart below summarizes the key differences between the traditional approach and the AI-based approach to revenue growth management.

The use of AI is driving a competitive advantage to the early adopters. The cost of adopting AI for revenue growth management is coming down rapidly especially with no-code platforms like Tuzo. Talk to us to understand revenue growth management and how Tuzo can help you achieve your goals.

It would be great to hear your views on this topic. Please do leave a comment.

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