How artificial intelligence fuels revenue growth management

Artificial intelligence assistants can scan all channels, markets, competitor actions, retailer actions, and self-assess to find opportunities

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Generative AI

Companies operating in the Consumer Packaged Goods (CPG) segment work under a unique ecosystem. They have to strike a balance between maintaining top-line revenue growth and managing sustainable profit margins. The reason why the task becomes difficult is because they have to do so while managing a dynamic set of operations. Moreover, a shift in consumer preferences, advances in data & analytics, channel shifts, and pandemic-led disruptions have created new challenges for retail and CPG companies.


However, in the current market scenario, they also have the opportunity to upgrade RGM, thereby creating equilibrium between growth and efficiency. They can increase the use of complex and action-oriented analytics across the product catalogue, optimize value capture approaches, use automated technology, and partner with retailers for shared value creation.

Why upgrading RGM is important for CPG companies?

Competitiveness has considerably intensified within the CPG industry. Today, there are limited avenues in terms of customer touchpoints vis-a-vis the year-ago period. The demand pockets have also changed dramatically and so has the New Normal’s supply chain management. CPG businesses today need to unlock as much efficiency from as many avenues as possible.


For those who cannot determine whether they should upgrade their RGM or not, there are a few questions that will help them overcome this quandary. Is your inflation getting outpaced by net revenue realization? Are your capabilities increasing faster than your competitors and retailers? Is the RGM beyond silo and addresses online business as well? Has it been integrated to your overall business strategy and at scale? Are you sufficiently leveraging data and analytics to grow revenue?

If the answer to a majority of these questions is ‘Yes’, then you don’t need to upgrade your RGM. If it is ‘No’, then it is time to think otherwise. Depending on the answers, companies must commit one or more paths towards RGM differentiation today to help their business triumph in the market. Leading data analytics solution providers help you to lay out and execute RGM roadmap for both strategic RGM and tactical implementations seamlessly.

Role of artificial intelligence in revenue growth


Over the last decade, a lot of CPG companies have implemented & tried to bring in new data and technologies for revenue growth management. This has enabled them to gain knowledge on what, how, and why shoppers buy and consume. Organisations are further bringing in MACRO data and other studies like segmentation into the toolkit. While RGM is important to operate effectively in the market, companies no longer have a competitive edge.

For sustainable revenues and growth, CPG companies must adopt AI. Its importance in terms of advanced capabilities in pricing, promotions, assortments, and trade investment will only increase as the competition intensifies within the CPG industry.

Artificial intelligence and ML provide companies with the scalable capability to utilize the power of data and navigate complexity. AI lets CPGs and retailers access customer insights and predict future actions based on the past behaviours. AI uses predictive analysis to help understand the desires, motivations, and actions across physical and digital channels. It allows retailers and suppliers to improve functions such as executing hyper-personalized campaigns and trade promotions efforts.


Artificial intelligence brings in key aspects such as

  • The ability to add in a variety of data sources.
  • Quick feedback loop. It creates a learning mechanism to update the model/recommendations on the basis of the ever-changing market dynamics (such as consumer preferences)
  • Speed to market. For instance, one of the modules within RGM can help identify & recommend price tiers, better trade investments, and so forth. It can also predict future out-of-stock incidences more accurately, thereby helping to optimize supply chains.

Such solutions provide swift and actionable insights that lead to better conversion/engagement rates with customers. It further leverages predictive algorithms for guided decision making, scenario planning, and simulation to drive prepared outcomes.


How artificial intelligence supports RGM:

The most critical function of AI in RGM is that it converts plain data into the famous ‘So What’ or the relevant implication/suggestion. It helps in shifting the output from ‘Insights’ to Recommendations.

These are some of its other advantages:

  • Unified Data View: AI helps leaders understand the consumer and drive efficacy with a unified data view. It reveals how actions impact Key Performance Indicators (KPIs) across the business, and not only within each function. The algorithmic recommendations enable one to look beyond interim improvements and suggest actions that achieve end-goals.

AI unifies data scattered across multiple channels and sources (both structured and unstructured). It can detect and classify relevant information on consumers relating to individual household information, scan cart-level data at point-of-sale, social sentiment, purchase behaviour across channels, travel patterns and dwell time in various venues to gain deep insights of the consumer path to purchase. AI technology enables teams to comb through massive data, analyse, and decode customer shopping behaviour on micro parameters. This also helps CPG companies create a 360-degree customer view.

  • Granular Predictive Models: Predictive AI models built on unified data are very granular and micro-segmented models. This makes them capable of large-scale analysis with tailored objectives and limitations at all levels. They can learn from history and can also predict probable future outcomes. Such models can also estimate baseline and raise forecasts combining a diverse set of influencing factors. They leverage deep learning to recognize shopping patterns and complex interactions. For instance, switching between brands within a category, or switching between channels, or even between shopping occasions. They can identify interrelated patternsbetween trade and other actions in the market.

AI provides minutest insights to understand each customer by sifting through massive structured and unstructured datasets from the first- and third-party sources. It helps spot micro-segments and emerging demand spaces (eg. new occasions, sub-segments, servicing opportunities, etc.) to build new business models, optimize the product, pricing, promotion, and marketing activities.

  • Forestall & Recommend Actions: AI assistants can scan all channels, markets, competitor actions, retailer actions, and self-assess to find opportunities and threats. Next, predictive models can evaluate complex interactions to explore numerous possibilities and suggest the best action to people based on their roles, owing to each market and the account relationship.

AI also helps to redefine businesses by automating manual, repetitive, and high-volume processes. Its learning capabilities allow it to self-optimize over time and reduce the work volume of employees. The technology’s deployment not only boosts employee productivity but also unlocks greater ROI for businesses.

  • Growth Hacking Through Quick Test And Learn: AI systems evolve themselves by learning from experiences. Event analysis in RGM takes a futuristic approach towards learning about consumer behaviour. AI models bridge the gap between plan, execution, and results with the process of continuous learning using ‘recommend’, ‘act’, ‘measure’, and ‘learn’ methodology. RGM or related teams can conduct well-designed experiments in choicest markets, analyse the outcome, and roll out smart strategies across the business.
  • Ongoing Feedback:It creates a loop mechanism for continuous learning model and recommendation improvements. The feedback loop ensures that the model keeps on updating itself without much human intervention.

It also helps in creating hyper-curated experiences. Because AI analyzes massive unstructured data such as photos, audio, video, etc., this helps in creating most relevant and personalized messaging and offers, and value-added services. This is while basing on consumer preferences in real-time.

AI-led RGM fosters sustainable success

Leveraging AI to thoroughly understand the consumer and reinvent relevance, CPG companies can develop a powerful capability. It can help them retain and expand their user-base, reduce costs, stand-out competitively, and drive new opportunities. Also, AI can improve their ever-evolving standards of performance by optimizing interactions and transactions - paving the way for never-ending growth.

Pricing and trade spend within Revenue Growth Management are some of the most powerful yet complex functions. If done well, they can help organizations win over not just their customers, but the market as well. So, prefer a solution provider that has a proven track record of RGM toolkit deployment and its subsequent scaling.

By Imran Saeed, Director, Analytics at Absolutdata