Is there a More Difficult (or More Important) Time for RGM Decision Making?

It’s more important than ever to make the right decisions for the key levers of revenue growth management (RGM), such as pricing, promotions, assortment, and marketing. 

With current supply chain issues, for example, an accurate prediction can mean the difference between product shortages in some places and overstock in others — a costly mistake that directly affects your bottom line.

Making it worse, there are an increasing number of factors influencing the outcome of revenue growth strategies. Price sensitivity, product substitutability, competitive pressure, COVID-19, and other macroeconomic conditions all affect KPIs.

These factors also make it harder than ever to get predictions right, and 2019 was the last “normal” year to use as a baseline reference. How will you know if your 2-for-$5 promotion will have the same impact in 2022? Will it need to be 2-for-$6 or 2-for-$7 to get the same results?

The truth is that making accurate predictions for demand has never been easy. Internally generated forecasts are iterative, time consuming, and require data from different sources.

[Read more: Strategic Priorities for Brand Innovation and Growth]

Traditional models use only a few data points — often just historical data, seasonality, and input from sales — yielding limited visibility into potential competitive dynamics. An executive we recently talked to, from one of the largest CPG companies in the world, described its accuracy as mostly “lucky or wrong.” 

But, there’s hope.

Self-teaching algorithms can disrupt the RGM toolkit, especially when powered by the deluge of new data sources now available, such as coronavirus trends, real estate price data, and consumer sentiment.

When machine learning and AI are tested against just experience and intuition, they have shown spectacular results.

In addition to improved results, machine learning can save valuable time and expense compared to the traditional iterative process. The most advanced AI solutions can not only help identify the best of considered scenarios, but they can also find the optimal answer without human guidance. Finding the optimal price, the right discount, and the best allocation of marketing budget can literally be a click away. Marketers can then use those insights to optimize decision making.

However, there’s been skepticism about the efficacy of AI/ML, which hasn’t been helped by the many “AI washing” claims by vendors. It’s also been out of reach economically, with in-house resources either limited or simply unavailable.

Will now be the time — and these new models the reason — that brand leaders finally go all-in with AI? 

AI is becoming ever more available, and an increasing number of companies are tapping into its potential for boosting growth and gaining a competitive advantage. AI mitigates uncertainty around key decisions by enabling executives to “look around the corner” and anticipate the impact of various strategies. Dynamic discounting, product recommender systems, and stock replenishment are classic use-cases where AI can optimize and automate decision-making. 

Brand leaders will want to take the plunge sooner than later. Infusing AI/ML into processes is now a trusted and established practice at the most innovative companies. The age of “cognification” is undoubtedly here, and those who embrace and advance the potential of intelligent, self-learning systems can be predicted to win. 

Robert Molnar, Director, WorldQuant Predictive