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Unlocking Growth in CPG with AI and Advanced Data Analytics - I

Updated: Mar 25



In the battle for market share in the consumer packaged goods (CPG) market, large players have been challenged recently by nimble niche competitors, as well as by traditional and online retailers that use their data advantage and direct consumer connections to push private-label or alternative brands. But these companies have multiple ways to counterattack, one of which is to use artificial intelligence (AI) and advanced analytics to transform their own data into valuable insights.


To understand the value, impact, and challenges of adopting AI and advanced analytics for CPG companies, we conducted a joint study with Google in which we interviewed executives at 25 medium- and large-size fast-moving-consumer-goods players and 5 niche brands, as well as approximately 100 industry experts worldwide. (See the sidebar.) We found that by using AI and advanced analytics at scale, CPG companies can generate more than 10% revenue growth through more predictive demand forecasting, more relevant ­local assortments, personalized consumer services and experiences, optimized ­marketing and promotion ROI, and faster innovation cycles.


PUTTING AI AND ADVANCED ANALYTICS TO WORK


CPG companies can increasingly access vast amounts of information, from traditional enterprise data (via their finance and operations departments) to consumer data (especially online behavior) to partner data (typically by way of panels, retailers, insight partners, and others)—even data generated from sensors and Internet of Things (IoT) applications. So far, however, they have neither treated this data as a strategic asset to be protected and nurtured nor applied it in ways that would have a concrete impact on their business.

By using AI and advanced analytics techniques, brands can generate actionable ­insights from such data. The most obvious application for AI and advanced analytics techniques is making predictions, such as the level of demand for a new product, the estimated impact of a marketing campaign, or the emergence of a new consumer trend.

We have identified about 30 applications that brands can use to harness AI and advanced analytics to boost their business. They touch all functions in a CPG organization, from marketing and insights to operations, sales, and support. They can also be used to power new, innovative services such as personalized assistants and recommendation engines.

Out of these 30, we singled out 10 primary applications that represent most of the AI and advanced analytics opportunity for CPGs. When deployed at scale, they can result in more than 10% growth in sales.


Four of the ten were cited most often by those in our study and are considered “must-haves” in all CPG categories:

1. Demand forecasting for existing and new products by SKU and region

2. ROI measurement for predicting the impact on sales of advertising and promotional spending

3. Data-powered sales activation for identifying the right retail outlets/points of sale at which to activate the applications and the right set of sales actions to take at the point-of-sale level to maximize market share

4. Optimized product assortments at the individual store level

The other six applications are considered sector-dependent, as impact and implementation complexity can differ widely among sectors such as beauty, food and beverage, and consumer health care:

5. Trend predictions for product development

6. R&D and testing acceleration (in silico)

7. Dynamic, localized, personalized pricing and promotions

8. Precision marketing

9. Personalized consumer engagement

10. AI-powered diagnostic and recommendation services


Notably, these applications include trend predictions, which are most relevant to sectors characterized by short time to market, such as beauty, and by dynamic pricing and promotions, which, to implement effectively, require frequent negotiation with retailers (something that is rare in food and beverage, for example).


Dr.Ella Burcu Keskin

#burcukeskin #keepgoing #datascience #future


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