It’s a great headline, but not a great goal

Source: Photo by burak kostak from Pexels

“Brand Love” makes a great headline. Here are some examples from a recent Google search:

· The Importance of Brand Love

· Creating a Culture of Brand Love

· How to Build Brand Love and Why it Matters Now

· 3 Essential Ways to Earn Brand Love

· New Research Reveals 6 Ways to Boost Brand Love

“Connecting with products the same way we connect with people” (Blended Collective, 2017) is, if you will, a seductive idea. But how practical is cultivating brand love in reality? Following is a review of some of the academic research on Brand Love in a response from a colleague’s request for “the evidence.”

Brand love has attracted a substantial amount of research attention over the past 15 years. In the academic literature, Carroll and Ahuvia (2006, p. 81) advanced a very influential conceptualization of brand love as a “passionate emotional attachment” to a brand. More broadly, brand love can be characterized by, among other things, “passion-driven behaviors,” “self-brand integration,” and “positive emotional connections” (Batra, Ahuvia, and Bagozzi, 2012). Being loved is widely considered in parts of the professional and academic worlds to be an important asset, especially in terms of loyalty.

The semantics of brand love can be complicated. Is “love” the same thing as “commitment,” “identifying with a brand,” or “thinking about the brand a great deal (in a positive way)”? How does love relate to trust in or satisfaction with a brand? All these variables have been studied as well and this can be hard to disentangle. They are all likely to be positively correlated. For example, Carroll and Ahuvia (2006) point out that by its nature, brand love requires satisfaction and awareness as pre-requisites.

Overall, I am skeptical of cultivating brand love as a marketing goal for three reasons:

· Love is rare

· Love is complicated

· Love (at scale) is expensive

Love is Rare

To love and be loved is wonderful, but as in life itself, love is relatively rare.

Absolute numbers are hard to come by. Batra, Ahuvia, and Bagozzi (2012) report that 89% of their respondents put at least one brand in the “love” category. Romaniuk (2013) does the obvious math here: the correct denominator is not how many people say they love one brand, but what proportion of a person’s brands are loved. I run a thought experiment with students where I ask them to write down the names of brands they love. It’s usually a short list.

A couple of studies do shed light on this issue. Albert, Merunka, and Vallete-Florence (2008) is a fascinating study of 843 French consumers discussing brands. The authors first asked respondents to identify 1 to 3 brands to discuss. Out of 2,340 brands identified (2.78 brands per respondent), 365 are ones where the respondent either agreed or completely agreed they loved their brand. So, of brands a consumer already recalled and felt comfortable discussing with a researcher, only 16% are loved or completely loved. The number of brands loved as a proportion of total universe of brands purchased is likely much lower.

Rossiter (2012) asked 300 university students in Germany to rate seven brands per category for four different product categories: laundry detergent, coffee, computers, and fashion clothing. Here is the percentage of respondents reporting loving at least one brand out of seven in the category:

· Laundry detergent: 17%

· Coffee: 18%

· Computers: 26%

· Fashion clothing: 45%

A different way of expressing this result: for each of the four categories, the majority — usually the vast majority — of respondents had zero loved brands.

Even within a customer base, where people presumably are positively disposed since they bought you at some point, love is relatively rare. Rossiter shows the percentage of brand users who love, like, or have neutral/negative views of a brand for eight brands. The percentage of users loving the brand they use ranges from 6 to 27%. This compares to the 49 to 60% of users report liking the brand they use and the 16 to 40% of users who report either neutral or negative feelings towards the brand they use!

The reality is that relatively few brands are loved, and the likelihood is that relatively few of your customers love you. Betting on achieving brand love among a broad population is likely a bad bet.

Love is Complicated

For example, Rossiter finds that for loved brands, both self-reported usage share and self-reported positive word-of-mouth were approximately twice that for brands that were merely liked. Ahuvia and his colleagues (Batra, Ahuvia, and Bagozzi, 2012; Carroll and Ahuvia, 2006; Bagozzi, Batra, and Ahuvia, 2017) uniformly find positive effects, often explaining large amounts of variance in the outcomes. Other studies finding these effects include Bairrada, Coelho, and Cohelo (2018), and Wallace, Buil, and deChernatony (2017).

There are two caveats to this work. First, all of it is based on self-reported surveys. This is likely to overestimate the size of the effects. (NB, this is not solely an issue for brand love research: it is true for many topics addressed solely by surveys.) There is plenty of evidence that repurchase intention may not equal repurchase behavior (e.g., Chandon et al., 2005; Oliver, 1999; Seiders et al., 2005). Broader studies of the effects of attitudinal relationship variables (Watson et al. 2015, studying trust, commitment, satisfaction; Khamitov et al., 2019, studying love, identification, self-brand connection, attachment, trust) find weaker, often much weaker, results relating relationship attitudes to behavioral loyalty and performance. (The latter study also finds no fewer than nine significant interactions, meaning the association between brand relationship and performance is very complicated.)

Word-of-mouth findings are similarly complicated. In a fascinating field study relating net promoter scores to actual word-of-mouth on social media, Raasens and Haans (2017) find that NPS categories do affect the positivity or negativity of word-of-mouth, but have no effect on volume of word-of-mouth. (Interestingly, while promoters were more likely to give positive recommendations, the larger size of the passive group meant they generated as many total positive recommendations as the promoters.) In their field study with two companies, Kumar et al. (2007) found less than half of customers who intended to refer other customers to a company actually did.

Second, Rossiter makes a more subtle point about measurement. As is typical in survey research, the vast majority of brand love studies use numerical scales, e.g., “I love this brand!” on a five-point scale where 1=strongly disagree and 5=strongly agree. As with any interval scale, statistically we assume the distance between 1 and 2 is the same as the distance between 2 and 3, etc., and that each point is representative of the phenomenon as a whole.

However, “love” as most people would see it is a 5 on this scale. If I give you a 3 (=not sure), I’m pretty clearly not in love with you. Yet, to the earlier rarity point, it’s likely most responses to the survey are not 5, which means we are running a lot of statistics on responses that are not love. Further, the distance between love (5) and like (4) may be different from the distance between like and neutrality.

Rossiter argues we should be treating each response as a separate binary state, e.g., what predicts a love score vs. a like score vs. a neutral score. For example, many foundational studies of customer delight treated delight as the extreme value on a satisfaction scale (e.g., five on a 1 to 5 scale) and looked at what predicted fives (see Barnes and Krallman, 2019 for a brief review of measurement in customer delight). As it is, these studies may well be taking a five-point attachment or liking scale and showing liking a brand improves repeat purchase and word-of-mouth, which is (I hope) not very surprising.

I find Rossiter’s logic compelling from a practical point of view. Ahuvia, Bagozzi, and Batra (2014) vigorously argue that Rossiter’s approach is an overly simplistic view of brand love. From an academic perspective, I agree. Ahuvia and colleagues’ conceptualization of brand love is sophisticated and well done. There’s a reason it is academically influential. But if there is one thing we know in the marketplace of ideas, it’s that simplistic often beats sophisticated. When the average manager is thinking about building “brand love,” I would be very surprised if they are seeing that as a combination of six underlying factors. Much more likely they imagine a big Valentine from the customer to the company. You can blame the manager for this, but I doubt that is practically helpful.

The most important pieces of evidence on the relationship between brand love and performance from my perspective are the following:

First, see Park et al. (2010), who in one of their studies look at the impact of “brand attachment” on actual sales. Brand attachment is conceived of as a combination of “brand-self connection” and “prominence.” The former represents the degree to which [brand] “is part of who you are” and the respondent feels “personally connected” to [brand]. The latter represents the degree to which thoughts about the brand naturally and automatically come to mind. The study compares this to the impact of “brand attitude strength,” which combines valence (whether a brand is seen as good or bad, liked or disliked) and the strength of that attitude (confidence or certainty in that attitude).

Park and colleagues find that brand attachment better predicts actual purchase than does brand attitude strength, but that the coefficients for both are much lower than for the coefficients predicting self-reported measures of purchase share and brand need share. Further, they find that using brand-self connection alone to predict outcomes is inferior to including the prominence of a brand in a consumer’s thoughts.

The thing is, if you are thinking about one brand, you’re less likely to think about another brand at the same time (Alba and Chattophadyay 1986). Prominence is likely rare due to limits of working memory. You can only think of a few brands a lot of the time. Betting that your brand is the one consumers think about all the time is likely a bad bet.

Second, hot off the presses (published online October 20, 2020), is Nguyen and Feng (2020). This is a comprehensive 12-year study of the effects of brand love for 152 corporate brands on various corporate performance measures. They show that brand love in year 1 has significant positive effects on Return on Assets, Return on Equity, and Market Capitalization in years 3 and 4 (not year 2). They show this effect is independent of positive effects from awareness, perceived product quality, and purchase consideration. Finally, they show that brand love is significantly associated with “joy” and “passion” in social media posts.

However, they measure brand love using a single continuous 1–5 scale that is quite similar to Rossiter’s scale, anchored on “hate” and “love.” They show this scale correlates well with the more sophisticated Carroll and Ahuvia (2006) scale. The mean of the scale is 3.13. This highlights the issue I raised earlier about whether this is a measure of love or liking. Statistically, Nguyen and Feng (2020) demonstrate that each one point move from hate to dislike to neutrality to liking to love is good for performance. (And it takes a while.) They do not demonstrate the return on getting to that final point is high. How you judge their result depends on how you feel about whether brand love is that final point or a continuum.

In sum, do I believe a high score on a brand love scale probably creates some statistically significant positive effects on marketing outcomes? Yes. Do I believe the effects are likely to be straightforward and large? No.

Love (at Scale) is Expensive

For a small number of customers, probably yes. You almost certainly have some customers who love you already (e.g., your mother). More seriously, you fit some number of customers perfectly by the intersection of your offering and their lives. You may have picked them up without even knowing it. You may have communities of fans (e.g., sports fans) who provide significant revenue and word-of-mouth. They may be your cushion in a downturn — the last to abandon you. You didn’t have to work too hard to excite these people in the first place — they may have been the early adopters for whom your product was a revelation — and you don’t have to work too hard to keep them excited now. Likely good ROI on this group. But to take one of Sharp’s (2010) points about loyalty, how much more can you sell these people? They’re probably pretty close to the maximum unit consumption and share of wallet they’re willing to give you. They can (and I think likely do) sell on to other customers, but that process will be neither quick nor precise.

So, for brand love to work as a goal, you need to generate love at scale. And that is where the return on investment may begin to be daunting.

Start with what I call any customer’s “relationship capacity.” If they can only love a small number of brands, that means you are competing for a very small number of available slots for that customer. If all slots are currently occupied, you have to displace someone from a slot. That is likely to be hard.

To the extent customer preferences vary, as you grow you are likely to have to attract customers for whom your product is not a perfect fit. Assuming you have the operational flexibility to deliver it, customization and/or product line filling may get you part of the way to love by improving the product experience. But that is likely to be hard.

Love is also likely to unfold in complicated ways. Langner et al. (2016) conducted an interesting qualitative study on how brand love develops. Brand love, they find, is often not at first sight. Rather, a customer’s relationship may build from either neutrality or liking to love over time. It may initially need to overcome feelings of dislike. It may exhibit what they call a “bumpy road” of fluctuating feelings over time. People progress towards brand love based on what they call “formative experiences” involving a brand. Only some of these are related to the actual performance of the product. Many others are personal experiences of some kind that are beyond a firm’s control, e.g., childhood memories, personal relationships, hobbies, or particular events in the customer’s life. Schmid and Huber (2019) also show complicated dynamics in elements of brand love across the relationship life cycle.

Finally, there are likely to be diminishing returns to efforts at improving the relationship. This means that the higher your current score on a relationship, the harder it is to improve. So it may be harder to move a customer’s brand love scale score from 4 to 5 than it is to move from 3 to 4. Bowman and Narayandas (2004) highlight diminishing returns to customer management efforts as an important factor for customer profitability at the B2B vendor they study. Ngobo (1999) finds diminishing returns in the customer satisfaction-loyalty relationship. Generally, the impact of brand on stock market performance may be subject to diminishing returns (Kerin and Sethuraman 1998). Most directly relevant, Nguyen and Feng (2020) find advertising has diminishing returns in achieving brand love. (R&D had straightforward positive returns: better products are good!)

OK. Now what

Work on things that are less rare and expensive.

I’m not going to do a literature review on this — we’re already probably at TLDR — but think about a pyramid of brand metrics for a given consumer.

There’s nothing particularly new here. You could almost think of it as an inverted hierarchy of effects or sales funnel. But imagine this as the number of brands in consumer long-term memory.

There are very few brands I love. On the other hand, there are a lot of brands I can like or trust in a given context. I don’t love them, but they’re known, reliable, and satisfactory. And there’s a bigger universe of brands I’ve heard of, whether I like them or not. Finally, there is the great grey mass of brands that lies outside my memory.

For a given customer, how high can you get in this pyramid at a reasonable marketing expense? Given different expense levels, across a million customer pyramids, is it better to generate X customers who love me or 3X customers who like me or 9x customers who simply recognize me at checkout?

I’d argue that in most cases, it’s a better bet to work on getting into the two middle tiers than the top one. Acres of evidence indicate that liking and trusting brands is good (not doing a literature review), and it’s a less difficult goal to achieve than love. And awareness is a foundation of how we think marketing works (not doing a literature review).

I come away from Nguyen and Feng (2020) impressed with the four Harris Interactive brand items they use:

· Brand Attitude: “You hate the brand” to “You love the brand”

· Familiarity: “Never heard of the brand” to “Extremely familiar”

· Perceived quality: “Unacceptable/poor” to “outstanding/extraordinary”

· Purchase consideration: “Never would purchase” to “absolutely would purchase”

Earlier I noted that Nguyen and Feng (2020) find positive effects of brand attitude (what they call brand love) on performance independent of familiarity, quality, and purchase consideration. They also find an index composed of those three variables also is positively associated with performance independent of brand attitude. The effects are smaller, but they are achieved more quickly. The index is only modestly correlated with the attitude item, suggesting it captures a separate phenomenon.

I find myself thinking that if you measure your brand with those four items, you probably have a pretty good picture of where your brand is in a market.

I hope you have found this helpful. (If you’ve gotten this far, I assume you either have or are steaming mad. We’ll see what the comments say.)

Thanks to Felipe Thomaz of the Said School of Business, Oxford and Charles do Prado, a good, inquisitive practitioner, for inspiring this article.

Bruce Clark is an Associate Professor of Marketing at the D’Amore-McKim School of Business at Northeastern University. He researches, writes, speaks, and consults on managerial decision-making, especially regarding marketing and branding strategy, customer insight, and how managers learn about their markets. He may be found on Twitter @bruceclarkprof and on LinkedIN at https://www.linkedin.com/in/bruceclarkprof/

References

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A practical business professor musing on marketing and management from his not quite ivory tower. Writings do not represent the views of Northeastern University

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