Gen AI boosts revenues, marketing efforts for CPG brands & retailers, Google reports

By Ryan Daily

- Last updated on GMT

Source: Getty Images/ J Studios
Source: Getty Images/ J Studios
CPG companies and retailers are moving beyond generative AI (gen AI) testing to realizing tangible business benefits of the technology, including increased revenues and operational efficiencies, Google shared in a recent report.

“2023 was the year of 'let us experiment and let us try a whole bunch of different things'. And 2024 has been 'let us get serious, let us implement and let us scale.' ... Folks have moved well beyond [from] 'this is a novelty' to ‘Wow, how do I really leverage this in my business.’ At the end of the day, they have woken up to the fact that you either get on board with [AI], or you get beat by the competition,” Paul Tepfenhart, director of global retail strategy and solutions for Google Cloud and author of the report​, told FoodNavigator-USA.

Google finds link between gen AI investment, increased investments

In partnership with the National Research Group, Google Cloud interviewed 376 senior executives from CPG brands and retailers about their progress on deploying gen AI into their organization and the return on investment (ROI) that they are seeing. 

Gen AI is a technology that produces texts, images or other assets by using algorithms and models (i.e., large language models) to respond to a prompt. The technology is used in the food and beverage industry to create marketing assets, speed up product development​ and work around supply-chain issues.

"At the end of the day, they have woken up to the fact that you either get on board with [AI], or you get beat by the competition.” — Paul Tepfenhart, director of global retail strategy for Google Cloud 

Most retailers and CPG companies (66%) claimed to be using some form of generative AI, with 33% saying that they leveraged AI in the last year, while the same percentage said they used AI for more than a year, Google reported.

Among those who use gen AI, 57% of CPG companies and retailers increased annual revenue by 6-10%, 30% saw a more than 10% increase in annual revenues, and 13% grew revenues by 1-5%. Almost half of the survey respondents (44%) said they want to use the gains made with gen AI to improve brand perception, 42% said they want to improve operating profit margins, and 40% for new product development.

CPG companies, retailers find gen AI benefits in marketing, customer service

CPG companies and retailers deploy gen AI capabilities to improve the customer experience and boost marketing efforts, Tepfenhart explained.

Nearly two-thirds (64%) of CPG companies and retailers used gen AI for customer and field service, 59% for sales and marketing and 57% for productivity purposes, Google reported. Additionally, 55% of survey respondents used it for manufacturing and production purposes, 54% for new products and services, and 54% to boost digital commerce and experience.

“When you think about the value chain, enhancing customer service and customer experience is a big bucket. And there is a large part of [Google's] customer base that has started in that space, and that is really an important area to go after, everything from tailoring product suggestions, personalized promotions, [and] even doing call center and customer service [operations],” Tepfenhart explained.

Gen AI is used for “everything from generating campaign ideas all the way through the assets that are necessary for all the channels that you want to take advantage of in marketing from social to email,” Tepfenhart added.

Getting started with gen AI: ‘You do not have to boil the ocean’

CPG companies and retailers seeking to make inroads with AI often face challenges due to siloed data structures and lack of capital to make major AI investments.

However, companies can take a gradual approach to experimenting and deploying gen AI, focusing on the use cases that make the most sense, as "implementing Gen A is not that expensive," he said. 

“A lot of AI projects turn into data projects. Now, the good news is you do not have to boil the ocean and fix all your data problems on day one. ... You have to take a slice for the use cases that you want to execute and ensure that you have rich, holistic data to be able to support that use case, and then you quickly rinse and repeat,” he elaborated.

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