How amazing would it be if you knew every meal you cooked would fit your tastes? McCormick & Company, a major player in the flavor industry, is reinventing traditional FMCG business models through its data-driven, customer-focused offerings. While the company generally manufactures and distributes spices, seasonings, and other products over 125 countries and territories (Amazon Web Services, n.d.), a shift has occurred from a product-centered company to a business model in which the entire customer value is achieved through a comprehensive consumer journey.

McCormick is continually moving towards innovative solutions to reach customers relative to competitors or FMCG companies in other sectors. The expected sales target of $5bn by the end of 2019 will come from e-commerce, innovation through platforms, and acquisitions of other companies (Nunes, 2017); evidently, digitization is driving the company’s growth. In 2014, McCormick created a spinoff company named Vivanda, through which a transformative product called FlavorPrint was developed (Nash, 2015).

FlavorPrint

FlavorPrint is ‘a technology that matches people with food they love’ (FlavorPrint, 2017). When users sign up to McCormick’s recipe platform, they are asked to fill out initial questions about their food preferences. Their recipe search behavior on the platform will continuously adapt the user’s ideal taste palate to recommend recipes that fit the user perfectly. FlavorPrint ‘combines sensory science and culinary science’ to ‘offer personalized recommendations for recipes, meals, and eventually wine pairings’ (Amazon Web Services, n.d.). FlavorPrint is able to change a person’s cooking habits by offering exciting alternatives that are customized to the user (while promoting McCormick’s products) (FlavorPrint, 2017).

Value to Consumers

Vivanda’s FlavorPrint follows a number of mass customization (MC) drivers while requiring little to no investment by the consumer, and consumers participate in the service because it offers them significant product utility. The extra costs for consumers are low; the quality of recommendations is high, no financial investment is necessary to use the service, and the effort of signing up to the platform is relatively low (Tsekouras, 2018). Furthermore, the FlavorPrint service works automatically, meaning that the consumer does not have to take any specific action to use the service, other than signing up to the platform. In short, FlavorPrint’s predictive analytics technology has made recipe selection much easier and more likeable, while demanding little time and effort from consumers.

Efficiency Criteria and the Future of Predictive Analytics in Food

In 2013, McCormick initiated a small beta program for its new technology. While a 1% increase in sales is very large in the industry, FlavorPrint quickly grew to 100,000 participants (while still in beta mode) and drove sales up by 4.9% (Amazon Web Services, n.d.). This was a sign that the company needed to ensure scalability for its platform, to allow millions of users to participate.

While financial data and statistics regarding platform usage have not been published, Vivanda has officially spun off from McCormick. In 2016, Vivanda announced a strategic partnership with and investment from German software giant SAP. This collaboration will ‘help our food industry partners to grow profitably by delivering increasingly personalized experiences and outcomes directly to customers’, according to E.J. Kenney, SVP Consumer Products Industry at SAP (SAP, 2016). The partnership indicates that Vivanda has shifted its strategy from focusing on McCormick customers to delivering its service to various players in the food and beverage industry; by targeting a wide range of food and beverage customers, Vivanda’s growth seems inevitable.

Drawbacks

It will be interesting to see what the future will hold for Vivanda and the use of predictive analytics in food. McCormick evidently derives great value from the technology, but one has to wonder if the technology has its criticisms pertaining to a possible lack of understanding of consumer behavior or privacy issues. For example, while the technology takes into account various contextual factors such as consumer budget and nutritional objectives while recommending foods, changing lifestyle situations may prove it difficult for the technology to adapt fully to consumer’s lives.

Conclusion

Although FlavorPrint does not directly offer a new revenue stream, the new possibilities for consumer packaged goods firms to reach customers indicate a potential for significant impact on future sales for Vivanda clients. Customization/personalization lies at the heart of the service, which is why the business model provides companies with a way to target consumers much more directly than through traditional marketing.

Will you use FlavorPrint to find new recipes? Does the company have a bright future? Let me know in the comments!

 

References

Amazon Web Services. (n.d.). AWS Case Study: McCormick. [online] Available at: https://aws.amazon.com/solutions/case-studies/mccormick/ [Accessed 18 Feb. 2018].

FlavorPrint. (2017). FlavorPrint. [online] Available at: https://www.myflavorprint.com/ [Accessed 18 Feb. 2018].

Nash, K. (2015). Tech Spin-off from Spice Maker McCormick Puts CIO in the CEO Seat. [online] WSJ. Available at: https://blogs.wsj.com/cio/2015/04/01/tech-spin-off-from-spice-maker-mccormick-puts-cio-in-the-ceo-seat/ [Accessed 18 Feb. 2018].

Nunes, K. (2017). Innovation central to McCormick’s growth strategy. [online] Food Business News. Available at: http://www.foodbusinessnews.net/articles/news_home/Business_News/2017/04/Innovation_central_to_McCormic.aspx?ID={CD115D1F-0E2B-4AE5-8295-8ED5DD8C1516}&page=1 [Accessed 18 Feb. 2018].

SAP. (2016). SAP and Vivanda Serve Up FlavorPrint Technology. [online] Available at: https://news.sap.com/sap-and-vivanda-serve-up-flavorprint-technology/ [Accessed 18 Feb. 2018].

Tsekouras, D. (2018). CCDC Lecture 3.