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Business Case

FlavorPrint: Personalizing your recipes through your tastes

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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.

Articles

Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions

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Recommendations: who has not seen them? Whenever you go online, different recommendations appear for you: with similar products, with different products, based on your past purchases, or based on what other people viewed. But did you know that all these types of recommendations have different names and different effects?

Li and Karahanna (2015) review 40 empirical studies between 1990 and 2013, that focussed on the understanding of online recommendation systems (RS). An RS is basically a web-based technology, that has the ability to advise and offer a certain product that would satisfy the individual users’ needs.

Based on past literature, three stages in this so called recommendation process have been found. Stage 1 involves the understanding of the consumer (including the collection of consumer data and creating a consumer profile), as well as the delivery of recommendations to this consumer (which are match making approach and the recommendation system presentation). This is followed by a personalized recommendation (stage 2). In stage 3, the impact of the recommendation system is assessed. Stage 3 ‘flows’ back to Stage 1 in the form of feedback.

I think especially the recommendation system presentation and its effect are particularly interesting. Multiple types of RS are discussed within academic literature, such as content-based, visual, collaborative-based and social-network based recommendations. According to Li and Karahanna (2015), these types often overlap in practice, creating hybrid RS.

The content-based recommendation takes into account a consumer’s preferences, as well as his past search and purchase behaviour. A collaborative-based recommendation system does the same, but also takes into account other customers (Other customers also bought…). One main difference is that for the latter, much more data are needed, since you need data on not only one, but more customers.

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An example of recommendations on Amazon 

While collaborative recommendations have as a disadvantage that new products do not have such links yet and some customers have atypical behaviour, collaborative RS are often used when it comes to alternative-based and cross-sell recommendations. The latter means that recommended items are generated across multiple, different categories, whereas the first is are mostly based on multiple customer ratings and purchases. The algorithms used for alternative-based recommendations are further based on a bunch of different customers’ clickstream data to detect preferences.

An example of a content-based recommendation is the visual recommendation. While content-based recommendations take past behaviour into account, visual recommendations do not. As expected, this type of RE shows products that are similar to another product a consumer has viewed.

So, which recommendation do you think is most effective?

That is up to you to find out (if you are still looking for a thesis topic)! While a lot of research has been done on the types of RS, limited empirical research exists on which strategies to implement to optimally use the different types of recommendation systems.

Based on some other papers and past theses I have read, I think that the visual recommendation works the least well – it will not increase the sales of one consumer, but I believe it rather shows alternatives to something they were thinking of purchasing (black dress 1, 2 or 3: that is the question). Further, while it might be nice to know what other consumers bought or viewed, I often find it irrelevant to myself. I’d rather shop-the-look if a complete outfit is shown on a model for example. However, with products other than clothes (such as books or videos) it might be different. Hence, go ahead and pick a nice thesis topic regarding these recommendations in different product categories!

Li, S., & Karahanna, E. (2015, February). Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions. Journal of Association for Information Systems, 72-107.

 

Articles

Online Display Advertising: Targeting and Obtrusiveness

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We all have been in situations where you are browsing the internet and the advertising is targeted on the content on the website and they are shown to you. Hereby, advertising is targeted. And what is maybe even more intrusive, that the advertising pops up. It definitely gets your attention. However, are you more willing to click on the advertising and buy the product? This is what Goldfarb & Tucker (2011) researched. They study the effect of targeted display advertising and obtrusiveness on sales, and what the effect is when these two are combined.

This question is interesting. Because even with all these new techniques, display advertising success drops. People avoid online display advertising because they infer them in their browsing goals (Drèze & Hussherr 2003). Do obtrusive advertising works exactly in the opposite way and affects the effectiveness of advertising negatively?

To find out, this study uses data from a large randomized field experiment on 2,892 web advertising campaigns. For every campaign on average 852 surveys were distributed. Where the half of them were to consumers who have seen the advertising and the other half were on the website without the advertisement on it.

The main results of this study are that targeting the advertising improves the effectiveness of online display advertising and obtrusiveness does also. However, when these two techniques are combined the effectiveness decreases. This is because privacy concerns temper the appreciation of formativeness in targeted advertising. So, for advertising in categories where privacy matters more, the effect is tempered more than in categories where privacy matters less.

The strength of this paper is the fact that it, in contrast to earlier research, propose that obtrusive advertising is not very effective in the contextually targeted situations. Earlier research study the effect of the obtrusiveness on advertising recall, which is of course positive. By adding the privacy concerns and the feelings of manipulation to the fact that advertising can be perceived as useful makes advertising perceived as intrusive, and therefore result the effectiveness negatively.

This paper shows a reason for the unexpected success of search advertising, where the advertising is highly targeted on the context (the advertising is based on search keywords) but is absolutely not obtrusive or attractive. For managers, this means that in choosing the right way to advertise they must not only consider whether to target their audience with contextually targeted advertising, but also consider the negative influence if these advertisements are obtrusive. Economically, 5.3 percent of advertising spending could be cut, without affecting the effectiveness of advertising. This, solely because the wrong combination of advertising content and format is used.

In short terms, either choose to reach your audience with targeted advertising, or with obtrusive advertising. But don’t combine the two.

Drèze, X. & Hussherr, F.X., 2003. Internet advertising: Is anybody watching? Journal of Interactive Marketing, 17(4), pp.8–23.

Goldfarb, A. & Tucker, C., 2011. Online Display Advertising: Targeting and Obtrusiveness. Marketing Science, 30(3), pp.389–404.