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Data Blindspot: Why FMCG Brands Need to Capture Data to Ride the AI Revolution    

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ChatGPT system was fed and trained based on 300 billion words.  

This data deluge is what fuels Generative AI’s brilliance.  

The big truth is that Gen AI is only as good as the data that powers it.  

Data, the lifeblood of AI, holds the key to unlocking explosive growth and value creation. By ingesting sufficient quality data, it allows the AI algorithm to learn, adapt and guide decisions that may otherwise have been made based on intuition, silo-ed datasets and a person’s experience.  

The FMCG Data Blindspot Problem

The big problem haunting FMCG brands is that customer data (who they are, and what they buy) is not consistently captured and with any of the data captured it is often unstructured and scattered across unconnected systems, hindering valuable insights. The data is siloed across distinct brands and channels making it difficult to use the data. Much of the data has also not been labelled systematically for it to be made useful.  

Are You Collecting Zero-Party Data?

Data must also be trustworthy. Globally, brands are making a fundamental shift towards collecting zeroparty data because this is voluntarily contributed by the consumer, in return for some benefits and therefore the information is more accurate and is explicit instead of implied. 

Today, this customer data can be collected in both the online (e.g., website and social media) and the offline space through powerful tools like dialog automation / chatbots, digitizing Spend and Win campaigns, loyalty and AI-powered receipts verification technology that allow for purchase data collection.  

Here are several key elements for a comprehensive data strategy for FMCG brands:  

1. Data Collection: Implement robust data collection methods across all touchpoints, from social media interactions to loyalty programs and purchase history. A unified customer data foundation is the key to powering an effective AI strategy.  

Research from Deloitte showed that companies that compiled their data from marketing, sales and service into a single unified view of their customer with the use of a customer data platform are better able to easily implement AI-enabled personalization journeys. 

For FMCG brands, many are doing an excellent job on data collection on the online front, but a large chunk of the required data for personalization is still originating in the offline world. It is therefore imperative for any successful FMCG data collection strategy to have strong coverage among offline channels such as modern trade channels. Do you have a system in place for this?  

2. Data Strategy: Develop a comprehensive data strategy that centralizes customer information from various sources. This ensures data quality, accessibility, and facilitates a unified view of the customer.  

3. Data Security: Prioritize data security by implementing robust security policies, encryption and access controls to protect customer privacy. Customers will no longer volunteer information if they do not trust your system.  

According to a recent Harvard Business Review article, “the data that generative AI uses needs to be well-curated for accuracy, recency, and uniqueness if generative AI models employing it are to be highly useful.” 

Elevating the Customer Experience and Personalization using AI

With AI being able to analyze vast pools of granular customer data, including purchase data, you can now create cross-selling or up-selling opportunities through personalized recommendations with Generative AI.  

A 2024 report from Marigold found that 91% of those surveyed wanted brands to treat shoppers like an individual. Plus, it’s easier to sell to existing customers rather than getting new shoppers. This means going beyond transactional strategies that are generic to showing your ability to understand a customer at a 1-to-1 level and being able to scale it through automation.  

For example, if you’re a diaper brand, you can upsell products like diaper rash cream or upsell your customers to get a bigger volume of diapers to save more. You’ll be able to influence shopping lists through AI driven by flows of purchase data.  

You could even drive premiumization and retention if you were able to collect signals on when a user might churn, or if they had the potential to upgrade to more premium SKUs.  

The Massive Opportunity for FMCG Brands

You’re likely to be a multi-brand FMCG company, so why not gather data from each of your brand, unify them into one platform while using generative AI to enable you to cross-sell within the portfolio of brands that you own based on basket size data and shopper preferences and interest areas.  

There are new emerging channels where shoppers convert, such as chatbots and messaging apps. 

These tools are already integrated with generative AI to personalize interactions with shoppers just like the chatbot of Mercari who acts like a personal shopper to customers. With LLama3 embedded in WhatsApp, shopper search behavior and purchase decisions may also begin to shift to these channels. 

A global study in 2023 discovered that nearly two thirds of the survey participants said they are open to purchasing new products or services that are recommended to them by generative AI.  

Similarly, SKALE’s WhatsApp chatbot allows brands to capture data, and offers an AI-powered recommendation engine to shoppers, built based on your internal knowledge base of your products and market insights. Such are the opportunities available for FMCG brands looking to take part in the ongoing AI revolution.  

Most shoppers aren’t aware of the complete breadth of your product range so letting AI do this for you automatically is a valuable outcome. 

A straightforward way to collect purchase data is through receipts. Instead of having people throw away their receipts, you can incentivize them with rewards like points or promo codes to share their purchasing habits with you.  

Purchase Data is an Untapped Goldmine

You can use technologies that will automate the process like an AI-powered receipts verification platform provided by companies like SKALE as it will give you real-time insights into customer preferences. More importantly, it extracts and processes receipt data information to give you insights on channel, timebelt, basket size, SKU bought and more.  

Globally, we see more brands drive premiumization as a means for margin uplift. However, to scale this profitably and within the right segment of your existing shoppers, you need basket size data to best identify which of your shoppers are ready to be upgraded to your premium category products.  

Not only that, but you can also use it to give scheduled reminders for them to buy your products, particularly when you are already aware of when they usually buy something. For example, you discover that a shopper tends to do their grocery shopping at 5pm on Saturdays. Then use it. After all, we humans are creatures of habit.  

You get to know who your buyer is, and you now have direct access to them, enabling you to play on a level field alongside your retailers. All of these are customer-centric activities which eventually will translate to customer loyalty. 

Data-Driven Opportunities in the Health Industry

Suppose you’re a company in the health industry producing vitamins, healthy milks (e.g., oat and almond milk) and supplements. You can’t be doing mass wide promotions for each of your products.  

But through the power of data and generative AI, you can now personalize recommendations to each of your shoppers or do promotional bundles that give real value for money. Your shoppers will surely take delight in this given their busy lives in an inflation-weary environment. Imagine AI algorithms suggesting to your team what and how much your customer will buy based on who they are, or on other factors such as location and even weather. 

For example, Carrefour has a chatbot based on ChatGPT called Hopla. Customers can use it to help them with their daily shopping, i.e., choose products for their basket based on their budget, food constraints or menu ideas. FMCG brands like yours can easily adopt this idea, for example, recommending healthier snacks to health-conscious consumers.  

AI can deal with massive information and make connections. With machine learning, AI can suggest shopper segments such as VIP, value-sensitive, or casual customers. This gives you the ability to deliver targeted sales activities and promotions to them. 

This bodes well because traditional trade marketing has proven to be mostly unprofitable.

But when powered by AI, they can create memorable ones that truly deepen the connection between the brand and customer.  

Innovate Faster: Gain a Competitive Edge with AI-powered Market Research

Part of delighting shoppers lies in innovation. 

In the FMCG space, it’s hard to make the products stand out. Every diaper, toothpaste, baby food looks the same.  

Through the power of machine learning algorithms, you can now analyze historical product-related data to help mine for patterns or designs to reduce product failures in the market. You can use survey data collected by chatbots as well as purchase data to spot trends and opportunities.  

The Hershey company is one of the largest FMCG brands that relies heavily on consumer insights coming from regular qualitative and quantitative surveys of their consumers.  

Lynn Hemans, VP of consumer intelligence and analytics said, “We find starting with consumer needs leads to more successful launches- product or packaging.”  

It reduced churn for them and helped improve their retail relationships. With this, the potential to use machine learning for the Hershey company is staggering!  

Inaction is not an option.  

Start to capture data consistently to feed your AI data infrastructure.  

Remember that it will take time for AI to learn from your data inputs and during this time, it is important to be capturing as much customer and purchase data as you can.  

Major FMCG brands like Nestle and Unilever are taking the lead in this revolution.  

Will you be joining them?  

Yuet Whey Siah​
Yuet Whey Siah​

Yuet Whey Siah is the Founder and CEO of SKALE, an Enterprise Marketing Solution used by FMCG brands and shopping malls to capture customer data, drive higher sales, and increase customer lifetime value. She is a frequent speaker at digital marketing and marketing technology forums in the region.​

The FMCG Marketer's Guide to First-party Data Collection

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