To Acquire, Or Not To Acquire

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In this edition of "Pacific on Research," we discuss our approach to helping a large US company facing a difficult decision. Should they acquire an otherwise attractive company that had recently suffered bad publicity due to claims about their product? Our client needed to understand the impact this move would have on each of the two brands among consumers, distributors, and retailers. And here’s the rest of the story…

Our client, a large US based manufacturer with several successful global brands, was evaluating the purchase of a similarly sized Northern European manufacturer, with a similar product line and better consumer awareness and distribution in certain international markets, including Europe.

As our client saw it, the upside would be access to new markets and distribution channels, access to new customers, consolidation of manufacturing and marketing operations, and greater overall market share. All pretty good reasons. One concern for them: due to a recent safety/product quality scandal, the target company’s image, and value, had been tarnished. (This makes me think of so many other recent company scandals and the range of impacts they had on their brands: Volkswagen, Mylan, BP,  Wells Fargo, etc.)

So…was this a good thing, acquiring a solid brand at a bargain price, or was the company potentially exposing its own brands to harm? And then of course there were the usual questions we see with every acquisition: will I be cannibalizing my own brands, how will this impact our bottom line revenue and profitability, how will consumers perceive the merger, can we effectively merge the two cultures and find operating efficiencies, etc. Even among the largest companies, we’ve seen these decisions made with emotion and gut feelings, seemingly haphazard or incomplete information, overly optimistic assumptions, or in some cases a premature jump straight to price negotiations (if we can get them down to x, let’s buy it, and then we’ll figure out the rest). Faced with decisions with what seems like a firehose of factors to consider, all of us can retreat to a bit of groupthink, choose a few factors to focus on (which may not be the right ones), and move away from a meta-rational approach, dismissing or ignoring data which seems to contradict our perspective.

In this case, we were very fortunate: we were working with a data driven client, and despite the myriad factors and a race to decide, our client wanted a data driven decision.

We collaborated with our client to create a research plan with four phases, to be executed concurrently:

  • Consumers - We set out to establish brand perception of the target brand compared to leading brands and our client’s current holdings. Were there unexploited opportunities for the target brands? A segmentation would help us identify how our client’s customers differed (or didn’t) from the target brand.
  • Observational/Ethnographic Style Mystery Shops – We sent in 75 of our “shoppers” to observe the way the category was merchandised and displayed, to ask questions of salespeople, and to inquire about the various brands - with emphasis on the target brand.  We tested if the scandal was brought up organically, and if not, what was said about it when the “shoppers” inquired.
  • Trade/Retailers, Analysts/Distributors - We conducted in-depth interviews with industry experts and category subject matter evangelists to learn the drivers for purchase decisions in the wholesale market: 
    • What were the levers for shelf space decisions?
    • How was the target brand was perceived in relation to its competitors?
    • Where did the audience see growth opportunities for the target brand?
    • How would they react to the merger of our client and the target brand?
  • Secondary Research - We leveraged existing proprietary and industry research to determine best practices, past successes and opportunities, and delve into successful marketing and communications strategies.

What we found in a short amount of time surprised us, and helped inform our client’s next move:

  • The acquisition would cannibalize one of our client’s main brands.
  • The target brand’s only notable strength rested on an elusive perception that it was stylish and on-trend, which was due to several celebrity endorsements which had been granted free of charge. However, these endorsements were waning and the amount of money required to re-energize the brand in this manner was prohibitive and not in line with our client’s mission or business strategy, which had always been substance over style.
  • Finally, we found that a key distribution channel for our client was adamantly opposed to this particular brand and had pledged to never carry it again, going so far as to train their salespeople on this opposition and regularly telling customers about these issues.  This distribution channel was very important to our client, and our client feared an association with the target brand could impact their existing brands. Ouch!

In short, we advised our client of the significant negatives associated with acquisition, as well as some positives. Our client decided acquisition was the wrong decision.

Their decision was turned out to be the right one moving forward—the damaged brand went on to have their market share shrink by 25%, and while the target brand continues to maintain more of a presence in the areas where it has traditionally been strongest, the brand has flatlined in terms of growth and expansion.

This was a challenging and rewarding project for Pacific. A chance to support our client on an issue of critical importance, and provide them with sufficient insight in hand to make a smart and informed decision.

I hope this post had some value for you, even if it was just a chance to take a break from checking your email. If you’d like to share your feedback, or just say hello, I’m all ears:

Find Your One Thing: Relative Importance Analysis

© Metro-Goldwyn-Mayer Studios Inc/Columbia Pictures All Rights Reserved

© Metro-Goldwyn-Mayer Studios Inc/Columbia Pictures All Rights Reserved

Fans of the 1991 movie City Slickers might remember the crusty old cowboy telling Billy Crystal’s character that if he wanted to find happiness, he should find his “one thing”. In a related tale, our clients often want us to help make their lives easier and frequently ask, "What is the 'one thing' we can do to increase customer loyalty to our brand?" 

Our clients are smart to want to know how to specifically direct their efforts to drive loyalty.  Rather than try to improve their service efforts across the board, they want to make improvements in one or a few areas which will have the most impact on engendering loyalty, thereby increasing sales or improving client retention.  While they shouldn’t ignore poorly performing service areas or let service lag where they are performing well, they do want to get the most bang for their service improvement dollar by concentrating on those service attributes that are most impactful to the bottom line.

Typical survey design sometimes lends itself to methodological concerns about which predictors are actually valid drivers of loyalty. The problem is that predictors are often highly correlated, making the “one thing,” or best predictor, hard to identify. This issue is known as multicollinearity, and generally is best addressed with experimental design using MaxDiff or conjoint where one chooses whether a component is present or absent. However, these design schemes are not always appropriate. As in our example of a survey for the restaurant industry below, service attributes are best measured by degree with rating scales, rather than whether they are present or not (e.g., the restaurant is clean or dirty by some degree as opposed to being clean or not clean).

In 2014, Pacific Market Research conducted a study in the hospitality industry to determine which touch points of service are most impactful or drive overall restaurant loyalty.  In our survey design, restaurant goers were asked to rate the relative importance of 20 attributes of the dining experience in addition to providing ratings on three measures of loyalty to a restaurant establishment. In the resulting analysis, loyalty was the combination of the three loyalty variables and became the dependent variable in our model.

Traditionally, market researchers would look to Stepwise Regression to determine the key drivers of loyalty. Below, one can see the resulting output of the regression model (note - if you are on a mobile device, flip to the landscape mode):

Key Drivers of Total Loyalty

We expect no one is surprised to discover that price and quality of food are the biggest drivers of customer loyalty. However, on their face a few things in this output just don’t make sense. First, it tells us that food portion size and cleanliness of restaurant facility are negative drivers of loyalty. Have you ever heard someone say, “I love going to Joe’s because it is dirty and they don’t give me enough food?” Unless you are a rat on a diet, this doesn’t pass the sniff test (or, in this case, the taste test).

Besides, we know from additional analysis that this is an untrue representation of the data in that portion size is positively correlated with loyalty. Another limitation of Stepwise is we know nothing about 11 of our 20 predictor variables. The model doesn’t tell us anything about location, or cleanliness, or quality of the greeting – which goes against what we know from qualitative feedback to be important aspects of hospitality.  So, while our Stepwise Model isn’t wrong, it is providing results which call the model into question.

These model shortcomings likely arise from multicollinearity between our attributes and some level of model overcorrection. For example, the model struggles to distinguish between attributes that are similar yet distinct such as cleanliness of restaurant and cleanliness of the facility.  In this case, the model chooses only one variable. The model doesn’t need 20 variables to provide an accurate model equation and it doesn’t care about the sniff test; thus variables are left out.

So what are we to do about finding our one thing?

At Pacific Market Research, we often use what is known as the Shapley Value Regression to address these types of issues. The Shapley Value Regression takes all the possible combinations of Linear Regression equations and determines which variables are the most important to the model. Below is an example output (note - if you are on a mobile device, flip to the landscape mode):

Key Drivers of Loyalty

From this chart, we can clearly see that food quality represents 26% of the total importance when determining loyalty – taste is indeed the one thing. Next, it makes sense to concentrate on price and the final farewell in order to drive restaurant loyalty. Greeting, restroom condition and manager’s level of concern are less important factors.

While Shapley Value Regression has provided more plausible and actionable insights, we acknowledge some continued presence of multicollinearity. However, for this example and others, it does the best job of identifying the key drivers of loyalty with rating scale measurements.

Please ask us how we can help you find your one thing to improve loyalty or satisfaction.  We promise not to make you drive cattle to find it. 

Author: Trevor Taylor, Project Specialist/Analyst