Agentic AI in RGM: Making revenue growth smarter!

Did you know that, as per Mckinsey, CPG companies spend ~20% of their revenue in trade promotion but in the US 72% lost money, and most businesses are gambling with half their marketing budgets? That is going to burn holes in pockets! 

Let's talk about Agentic Revenue Growth Management (RGM), which helps companies turn top-line growth into real profit. It includes five essential elements directly impacting your bottom line: optimal pricing, price pack architecture, assortment mix management, promotions management and trade promotions management. 

Despite substantial investments to optimize capabilities, many organizations still leave significant value untapped in today's volatile commercial landscape. 

Why Traditional RGM Approaches Fall Short 

With inflation soaring and economic uncertainty becoming the new normal, a robust RGM strategy is essential. Simply relying on traditional methods with static spreadsheet-based modelling and limited integration capabilities would be less effective in today's dynamic market.  

Bain & Co. pointed out that roughly 50% of promotions cannibalize non-promotional sales, and about 80% of promotional investments fail to contribute to the category growth, effectively wasting money for retailers and manufacturers. 

Then what's the next logical step for sustainable growth?  It’s use of Agentic AI! 

Agentic AI in Revenue Growth Management 

Unlike conventional AI implementations that merely analyze data and generate insights, Agentic AI possesses autonomous decision-making capabilities, continuous learning mechanisms, and self-optimizing algorithms that evolve with your business. 

Agentic AI in Revenue Growth Management goes beyond simple data analysis; it understands and anticipates market fluctuations and recommends optimizing strategies across all elements of RGM. This transition from reactive to proactive adaptation is essential for maintaining that competitive advantage. 

Agentic AI use cases in RGM 

Let's talk about the pricing element of RGM! 

Currently, pricing recommendations are based on static models and require human intervention, often leading to delays and slower response time. But, Agentic AI leverages swarm of specialized agents to manage pricing dynamically and autonomously. 
 
Take instance of a CPG company. By using pricing agents across multiple avenues, the company enables real-time, SKU level price optimization. The agent continuously ingests external data like competitor prices and internal metrics like cost structures, historical sales, inventory levels to execute changes in pricing. 
 
In case, competitor reduces price on a particular SKU, the agent would autonomously calculate the optimum response using the price elasticity and competitive intelligence. It then executes the price changes through API integrations, thereby reducing the human intervention. This not only ensures speed but also captures the optimal value for the organization. 
 
Most importantly, Agentic AI has a feedback loop, which improves the its future decision, by continuously evolving the pricing mechanism, making it a dynamic tool. 

Now think of Market Mix Modelling (MMM), which is used for budget allocations, but currently suffers from: 

Agentic AI turns MMM into an intelligent budget allocating system. IT not only forecasts, but it also independently monitors campaign’s performance, adapts to shifting consumer behaviours and self-executes the decision to adjust the spend across marketing channels with guardrails set by the user. 

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