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Aligning data and AI to power revenue growth management for brands

By David Hawkings, SVP EMEA at

CPG companies that continue to take the traditional route to trade promotion investments will continue to realise only marginal and unsustainable improvements unless they adopt a broader, strategic consumer-focused strategy.

In the last two years, three major market dynamics have come together to re-awaken interest in Revenue Growth Management (RGM), but the conventional price and market share growth strategies that are used to drive the growth implied are not currently working.

Firstly, it has become clearer that current models are not working.  Even before Covid, according to McKinsey, in North America alone, 72% of trade promotions lost money.  As a result, also according to McKinsey, from 2000 to 2009, economic profit grew 10.4% per year; from 2010 to 2019, it dropped to 3.2% per year, and for the top 30 consumer packaged goods (CPG) manufacturers, margin expansion contributed twice as much as growth to value creation.

Secondly, because consumer behaviour has changed so dramatically as a result of Covid, it is harder than ever for CPGs to create growth.  50% of 25,000 consumers across 22 countries, according to Accenture,  said that the pandemic had caused them to “reimagine” their lives and needs in such a way that they would question their brand loyalty and willingness to spend more.  This leads to brands losing market share to own-label goods during the pandemic and they have yet to recover it.  As’s own research shows, over half (57%) of consumers surveyed traded down from premium to own brand, while nearly 40% plan to stick with those choices post-pandemic.

And thirdly, Artificial Intelligence (AI) has matured from being an aspiration to a reality.  AI technology is now recognised as able to optimise decisions that have become harder and harder to get right, across the supply chain and at scale.  For instance, AI understands how the key levers of price, promotion, assortment, and trade investment work together so that CPGs can analyse customer buying behaviour at segment and location level, and so create a clearer picture of motivations and needs.  This insight is then used to power growth strategies with the objective of increasing overall basket sizes and market share. The goal is to create a unified demand signal that is the engine of RGM.

Looking in detail, once AI has done the deep analysis, CPGs can then rebalance their assortment mix and distribution which will give them a competitive advantage at the shelf. They can also start to close the base price gap in key categories and segments at the shelf, rather than trying to fix it by spending money in trade investment.  And they can start to limit trade promotions to products that they know are desirable to consumers who are willing to switch, to boost ROI through higher sales and incremental volume.

The challenges for CPGs in making a change relate to the heavy influence of current ways of working.  Organisational silos duplicate effort and create conflict because there is no single view of the demand truth.  For instance, it is common for companies to look for price elasticities for the benefit of marketing while the sales teams is creating promotions based on different inputs. And then demand planning teams are using their own analysts, systems, baselines and lift projections independently of Marketing’s projections.

Trade Promotion must therefore be a subset within the larger RGM strategy, where teams can collaborate to balance short- and long-term needs, both strategically and operationally.

The bottom line for CPGs to deliver on revenue growth, they need to operate using a single, shared platform leveraging data and revealing insights that everyone can all agree and act on.  And to find even more profitability, CPGs that deploy the more advanced RGM solutions, should share their forecasts, baselines, and lifts with demand planning teams to align planning decisions and ensure consistency in execution.