Chapter Six: BONUS: Oatly and How Two Brothers Disrupted the Milk Industry
Overview of the Episode
In Episode 6, I interview Björn Öste, one of the founders of oat milk-company Oatly. While Oatly was founded in the 90s, it started growing rapidly during the mid-10s, mostly due to new milk formula and a new focus on innovative marketing. In 2021, it ran its first Super Bowl commercial, whereafter it did an IPO on Nasdaq at a valuation of $10 Billion.
Björn Öste grew up in Östersund in northern Sweden and spent the beginning of his career funding and exiting a company in the data security business. When his brother came up with the formula to make tasty oat milk, he decided to join the business as a co-founder and investor. The journey at Oatly was hugely successful, and since he left the board, his main entrepreneurial activity today is in Good Idea Drinks. They make functional carbonized drinks to keep blood sugar at stable levels among people with diabetes.
Consumer Behavior Towards Plant-Based Dairy Alternatives
With the rise of more sustainable dairy alternatives, such alternatives need to be price competitive in the store. According to Mr. Gates’ idea on green premiums, one of the main driving factors for consumers when choosing what they buy is the price. However, the price is not the main driving factor for some products when consumers choose their products — as Oatly has shown. Oatly is more expensive than “white” milk, but its target consumers are willing to pay the premium because of its brand value (among other factors). Therefore, it is likely that other factors are at least as important as the price for consumers.
Study: Plant-based Replacement Products in Sweden
Mousel and Tang (2016) studied consumer behavior towards plant-based meat and dairy alternatives in Sweden. Swedish consumers are increasingly reducing their dairy and meat consumption, and the local market on plant-based milk is growing quicker than in neighboring countries. The study’s primary research question is “What are the driving factors and barriers influencing Swedish consumers’ behavior towards plant-based alternatives to meat and dairy?”, whereafter they drew a convenient sample of people at their university.
The study used regression analysis with two-tailed tests to examine how different barriers influence consumers’ behavioral intention (to purchase meat/dairy replacement products). The question is essential, as companies can focus on reducing the barriers which are the most prominent among consumers. Such barriers are not covered in “attitude” or “social norm,” as people may intend to do something, but their actual decision in the grocery store does not reflect that intention. The obstacle that makes consumers not follow their intention, is the barrier (Mousel and Tang, 2016).
Although the sample size was small, the results show barriers for people to purchase such products (Table 1). If the coefficient is positive and statistically significant, it means that the barrier influences the behavior (to buy meat or dairy replacement products). Therefore, the cultural, decision, and information barriers were all statistically significant at an alpha level of 0.05. However, the negative value of the decision barrier means that it does not keep people away from purchasing plant-based replacement products, although they intended to do so. Meanwhile, both the availability- and price-barrier were not statistically significant, meaning that the data provided little or no evidence that they influence the dependent variable.
The results suggest that companies should create products that can overcome the culture barrier, for example, by creating products that are traditionally Swedish foods, such as meatballs. Packaging and marketing should emphasize that the companies are Swedish and that the products are made in Sweden. Regarding the information barrier, the companies need to provide accessible information through websites or social media. Especially the last barrier is one that Oatly has thoroughly addressed, given its extensive focus on social media presence and its strength in providing information about the oat milk and the brand. This study also emphasizes other barriers than just the price that influences consumers’ decision in the stores, which supports Oatly’s success story despite its higher prices.
Study: A Usage Segmentation Approach on Plant-based Food & Beverage in the UK and Ireland
Food and beverage consumption behavior vary within and between different countries. While Mousel and Tang (2016) focused on the Swedish market, Beacom et al. (2021) looked at motivators and barriers among British and Irish consumers. One of the main differences between the two studies is that Beacome et al. (2021) had a larger sample size (n=456) and that they compared plant-based product (PBP) consumers with non-PBP consumers. As the majority of the northern European consumers are still mainly non-PBP consumers, it is essential to understand the barriers that keep them from purchasing PBP. The study used a survey to gather the data.
The authors used binary logistic regression analysis to examine the association between demographic characteristics of the respondents and if they consume PBP or not. The logistic regression model is often used to analyze how socio-demographic variables relate to the outcome variable. It is necessary for the outcome variable to be binary, which is the case in the study (if they consume PBP or not) (Wilson & Lorenz, 2015). The logistic regression results showed that females are almost twice as likely to consume PBPs than males (Table 2). The data also suggests that those living in areas with a more significant population are more likely to consume PBPs. For example, those living in areas of more than 50,000 people are more than four times are likely to consume PBPs than those living in areas of less than 1,500 people.
The results can be used in two ways by PBP brands. First, it may be better to focus their marketing efforts on those more likely to buy the products (such as consumers in larger cities). Second, there may be a risk of market saturation in such areas. Therefore, it can share information about their products (as Mousal and Tang, 2016, suggested) in regions where people are not yet consuming PBPs.
The dominant barriers for not consuming PBPs, identified by non-PBP consumers, are that they do not need to change their diet and are concerned about taste. That is also consistent with other research that has found unfamiliarity with such products to prevent people from trying them (Schösler et al., 2012). Again, the study emphasizes that price is not often the main reason consumers stick with conventional meat- and dairy products. Therefore, brands can focus more on increasing information about their products rather than decreasing prices. It emphasizes how consumers are unpredictably irrational, as behavioral economist Dan Ariely calls it, as the value for consumers is unpredictable (Stiving, 2021). Market research can, however, help brands in finding a way to increase their value proposition to consumers — and increase sales.
Beacom, E., Bogue, J., & Repar, L. (2021). Market-oriented Development of Plant-based Food and Beverage Products: A Usage Segmentation Approach. Journal of Food Products Marketing, 27(4), 204–222. https://doi.org/10.1080/10454446.2021.1955799
Mousel, T., & Tang, X. (2016). Analysis of Consumer Behavior Towards Plant- Based Meat and Dairy Alternatives Market in Sweden. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297183
Schösler, H., Boer, J. de, & Boersema, J. J. (2012). Can we cut out the meat of the dish? Constructing consumer-oriented pathways towards meat substitution. Appetite, 58(1), 39–47. https://doi.org/10.1016/j.appet.2011.09.009
Stiving, M. (2021, November 17). Council Post: Understanding How Consumers Can Be Unpredictably Rational. Forbes. https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2021/11/17/understanding-how-consumers-can-be-unpredictably-rational/
Wilson, J. R., & Lorenz, K. A. (2015). Standard Binary Logistic Regression Model. In J. R. Wilson & K. A. Lorenz (Eds.), Modeling Binary Correlated Responses using SAS, SPSS and R (pp. 25–54). Springer International Publishing.me