Most FMCG companies are not lacking in data from a variety of sources. What they do lack is the ability to create actionable insights from this data to create real economic value. Supermarket retailers are increasingly accessing sophisticated methods to analyze their customers´ customer journeys and identify purchasing triggers that will drive spend and increase loyalty, often at the expense of the FMCG brands they stock.
A seismic shift in the FMCG industry
Change has come quickly to the FMCG industry over the past 5 years. Mid-sized and smaller FMCG manufacturers have begun to find their place in a sector driven by economic uncertainty, more flexible production methods, extensive distribution options and of course ever more demanding consumers who now have even more choice from niche and ethical FMCG brands.
Regardless of size, FMCG companies all have to deal with a bewildering increase in complexity in the retail world, as hyper-connected consumers operate and spend in new and growing channels, particularly e-commerce.
The new reality for FMCG companies is forcing them to fight for brand awareness in retail arenas that didn´t exist just a few years ago. Take Amazon. The online retail giant has just started selling its own brand “Happy Belly” snack products, “Wickedly Prime” ready-made sauces and oils to its German customer base. US online startups such as Brandless are also attacking the traditional FMCG brands` value proposition by selling all their products, from tomato soup to multi-surface cleaner, at $3 apiece.
How Big Data can help FMCG companies to increase their ROI.
Big Data allows FMCG product managers and brand managers to “fail fast and learn faster” by allowing them to visualize patterns of causality and business value from various transactional and behavioral data sources. These unique insights allow managers to be more relevant and responsive to customer needs, a real competitive advantage in such a crowded consumer market.
Manufacturers and FMCG companies are already using Big Data to realize significant savings in inventory and supply costs. Procter and Gamble created its “Business Sphere” back in 2012. Designed to utilize data from multiple sources to simplify business decision-making processes, the Business Sphere has allowed brand owners to concentrate on the job of innovating for their customers. The company claims “one supply chain example leveraged supply chain sufficiency models to bring together multiple data points, analytics, and visualizations. This resulted in an inventory reduction of 25% and savings of tens of millions of dollars.”
As I mentioned in my previous post, other major FMCG players such as Coca-Cola and Pepsi, are increasingly using data insights to develop innovative products, improve targeting and to increase revenue per customer.
Case study: Kim, the Maggi Cooking-Bot
The first interesting use cases from leading FMCG brands, aimed to improve the customer experience and in fact to establish a direct relationship with affine target groups, are starting to appear.
An excellent example of this is the innovative Maggi Chatbot called “Kim”, developed by a cross-functional team at Nestlé Deutschland, using agile working techniques. Kim uses artificial intelligence to allow existing and potential Maggi customers to connect directly with her via Facebook Messenger. Customers can interact with Kim to find inspiration about what to cook, how to order the ingredients for a specific dish via the German supermarket chain REWE-Online and about possible allergies or dietary requirements that may affect the outcome of the dish.
Kim also avails of sophisticated machine learning techniques to understand more about the context of what each individual customer says, allowing her to create truly personalized exchanges over time that lock customers into seeing Maggi branded products as integral to their daily cooking experience.
FMCG companies are in a unique position to create huge opportunities for driving more personalized and customer-centric services for their product portfolios while boosting revenue and efficiency. Those who fail to find creative and efficient ways to optimize customer engagement and influence the consumers’ path to purchase by aligning technology with the wealth of data that drives it, risk being left behind.
About the author:
Cecilia Floridi is a Customer Management expert based in Düsseldorf, Germany. She is also the Managing Director of DataLab GmbH.
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- How Big Data Is Empowering AI and Machine Learning at Scale
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- How P&G Got Hooked on Analytics: ‘High Value Problems People Were Willing to Pay For’