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To Label Me is to Negate Me (Sometimes): The case for occasion-based segmentation

Posted by Peter Cronin

Wed, Aug 29, 2018

beer

One of my favorite lunchtime routines is to walk from my office over to the Trillium Brewing Company in nearby Seaport to grab a 4-pack of their current small-batch, limited-time, freshly brewed double IPA.

As far as Trillium knows, I’m an “Epicure”—a beer drinker characterized by my ardor and appreciation for craft beer.

During the summer months, I occasionally stop at BJ’s Wholesale Club to get a 30-pack of Corona (along with a couple of limes) because I like to have something to offer guests when hosting a cookout. In these instances, I’m looking for value, but not necessarily the cheapest option because quality and image are still important to me. BJ’s might consider me your average “Cost-aware Enthusiast.”

Every year on my birthday, which typically coincides with the start of March Madness, I stop at my local beer store to buy a six-pack of Samuel Smith’s Oatmeal Stout. They probably consider me a “Sports Oriented” beer drinker.

So, who am I? A beer snob, a deal-seeking but conscientious host, or a sports fan?

The answers are “all of the above” and “it depends.”  

In some categories (like beer) the same person may experience a variety of needs in any given time and make different choices based on those needs. Segmenting people by their dominant motivation/need risks majorly oversimplifying reality.

To understand opportunities for growth in categories like this, a better alternative is occasion-based segmentation. Rather than segmenting people into groups, occasion-based segmentation considers multiple use occasions instead of just one. As you can see from my example, I’m more apt to purchase one type of beer over another based on the occasion (e.g., time/day, who I’m with, what I’m doing).

Occasion-based segmentation is particularly successful when anchored in the psychology of habits. When a behavior is rewarding, we tend to repeat it. The more we repeat it, it eventually becomes a habit. For many people, drinking beer is habitual. Take my backyard BBQ, for example. Throughout the summer, I repeat the cycle of having friends and family over, eating good food, drinking Corona with lime, and feeling relaxed, restive and connected. This occasion has all the key components of a habit: my craving (motivation) to host triggers a routine of good food and drink that results in feeling connected (reward). Feeling connected makes me to do it again.

When we ask people about their occasions at CMB, we also ask what motivates these choices and to describe the rewards—including the emotional and functional outcomes. These inquiries become the base of the segmentation. 

Segmenting your market by usage occasion can be a powerful source of insight about your consumers. By linking brands to occasions and understanding the psychological needs and emotions that drive choices, marketers can position their brands to be the preferred choice. They can tailor messaging to each occasion to build engagement, preference and loyalty.  

Brand managers at The Boston Beer Company, AB InBev, MillerCoors, etc., should be less concerned about whether I’m a “High Impacter,” a “Macho Male,” a “Trend Follower,” or a “Chameleon.” Classifying me attitudinally will dramatically underestimate the complexity of my buying habits. 

Instead, understanding the core types of beer drinking occasions (and the driving psychological needs and emotions of each), how much volume each occasion represents, and which groups of people over-index on them, can enable marketers to make informed decisions on where and how to focus their messaging, promotions, and product development efforts.

Peter is a brand guy who is fascinated with understanding how others see the world, and an equal opportunity beer drinker who refuses to be labeled.

Learn more about segmentation and market strategy:

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Topics: market strategy and segmentation, research design, quantitative research, brand health and positioning

The Anchoring Effect—Avoiding Bias in Market Research

Posted by Hannah Russell

Thu, Feb 08, 2018

anchor.jpeg

Consider the following questions:

  1. Did Thomas Edison patent more or fewer than 7,000 inventions?
  2. To the best of your ability, estimate the number of inventions patented by Edison.

Unless you retained your 7th grade social studies knowledge, you’d probably have a tough time answering. But based on the context given in question 1, you may guess somewhere in the several thousand range. Why is that?

As Nobel Prize winning author Daniel Kahneman explains, this is an example of the anchoring effect—a cognitive bias in which humans tend to rely on the first piece of information offered (the “anchor”) when making decisions. The detail (e.g. number) we automatically “anchor” to then influences subsequent decisions.

So, in the Edison example, according to the anchoring effect, the “7,000” in the first question impacted your answer to the second question.

Consider then, if instead the first question had been: “Did Thomas Edison patent more or fewer than 100 inventions?” Your answer to the second question would likely be a lot less than what it had been in the first scenario.

Our perceptions are often influenced by the stimuli we are exposed to—both consciously and subconsciously. Sometimes, though, this information is useless in helping us make correct judgements (such as the number 7,000 in the example above). Even if we’re aware of an external influence, it can be hard to discount.

As market researchers, we have an obligation to manage and mitigate this type of bias to preserve the integrity of our data.

When creating a survey, we try to avoid anchoring respondents in a particular number (or other pieces of information) and are careful in the way that we order questions. If we’re exposing respondents to various numbers (which is often the case in pricing research), we rely heavily on analytical techniques that ensure randomization and exposure to multiple scenarios.

Ultimately, we can’t avoid priming effects altogether—there is no such thing has 100% unbiased data. But, we need to keep these psychological biases in mind when designing, implementing and presenting data. By recognizing the downstream consequences of something like the anchoring effect, we’re better positioned to find truthful and actionable insights for clients.

Hannah Russell is a Project Manager at CMB who indeed retained her seventh grade social studies knowledge. Thomas Edison accumulated 2,232 patents worldwide, 1,093 of which were in the US.”

Topics: research design, consumer insights, consumer psychology

Begin with the End—Lessons Learned

Posted by Caitlin Dailey

Fri, Feb 02, 2018

Dollarphotoclub_91556333.jpg

A former colleague of mine had a post-it note on his wall that read: You have as many hours in the day as Beyoncé. Inspiring words, but for those of us in professional services (rather than entertainment) it can feel like the to-do lists never end.

I recently watched a webinar on productivity given by MIT Sloan School of Management Senior Lecturer Robert Pozen. There was a lot of useful information in the webinar, but one piece of advice really resonated: begin with the end.

“Beginning with the end” means letting your desired outcome drive the planning and execution of your task. If you are cognizant of what your end-goal is, it will make tackling projects of any scope a lot easier—whether that’s writing an email or the final report of a multi-phased segmentation study.

At CMB, we always begin with the end in mind. When kicking off a project, we meet with key client stakeholders to align on business and research objectives. We leverage our proprietary Business Decision tools to identify what the desired business objectives are, and use that information to inform our research objectives and design. This preliminary decision-focused conversation ensures the research solution, story, and results are actionable and will deliver meaningful outcomes with true business impact.

Once the project is kicked off, no time should be wasted—consider building out a narrative and recording tentative conclusions as soon as data starts coming in. It can be tremendously helpful to have a mid-field check of the data to revise those conclusions, and then do a final revision once you have all the data. The story might not change much during this time, but writing and revising your conclusions prior to the close of an initiative can make delivering the final report less stressful.

Particularly in market research, there’s pressure to deliver results faster than ever. When you start with the end in mind, you can be building out the story in an iterative process, rather than scrambling to at the end. Since unearthing a clear and meaningful story is one of the most important pieces of a project, you’re only helping yourself (and your colleagues) by beginning with the end.

Other ways to improve productivity

As I mentioned, there were loads of other useful tips from Pozen’s webinar on how to increase productivity:

  • Write down your daily goals: Rome wasn’t built in a day, so jot down objectives you can realistically accomplish today.
  • Don’t exhaust your schedule: Avoid scheduling every minute of your day. Having a calendar filled with meetings may look productive, but it’s important to include “thinking time” for yourself.
  • Include work and non-work tasks: Your list should include routine essentials like going to the gym or having dinner with your family. This will help maintain a healthy work/life balance and will give you time to “recharge”.
  • Manage your inbox: If you’re in the zone, don’t feel pressured to stop and respond to each email immediately (unless it’s urgent, of course). Instead, set aside time a little later to respond to all emails.
  • Let go of perfectionism: Do you reread an email 5 times before you hit send? Scan through a deck repeatedly? Chances are, it were ready to go after the second review, so save your mental energy for something else and move on.
  • Quit procrastinating: One of the biggest hurdles to getting things done is simply starting them.

I’m now being mindful of how I can incorporate these practices into my life to maximize my productivity, and in turn, hope to tip the scale of my work/life balance in favor of a more stress-free work week. I hope you can too!

Caitlin Dailey is a Senior Project Manager on the Financial Services, Insurance, and Healthcare team at CMB and is looking forward to trying out these tactics to help get her out of the office a little earlier in 2018.

Topics: business decisions, research design, methodology

Don't Over-Generalize My Generation

Posted by Reed Guerino

Wed, Apr 12, 2017

group in park

I’m sure you’ve heard that Millennials are entitled narcissists (or mold-breaking visionaries) and Gen Z expect instant gratification (or they have the most integrity of any generation yet). Of the companies pouring millions of research dollars into generational research, who’s getting it right? Well maybe nobody.

In fact, we can’t even agree on where one generation begins and the other ends. Millennials are generally considered those born between 1980 and 2000, but there’s disagreement over the exact years—some say it’s as loose as the mid-1970s to the mid-2000s while others say strictly between 1980 and 2000.  When you’re comparing mid-1970 to 1980 and 2000 to mid-2000, it’s not a huge discrepancy. However, the point is that there is a discrepancy. And with growing interest in the emerging generation (Gen Z, “Post-Millennials”, "iGeneration", "Plurals"), once again we face an arbitrary age designation and battle over who best understands these future consumers.

As a market researcher myself, I'm the first to admit that researchers will be tempted to define and assign attributes to Gen Z early on because of our natural tendency to categorize and bucket into mutually exclusive groups. However, in our need for clean groups with labels, we forget that some groups aren’t mutually exclusive, and different groups (or in this case, generations) might share some overlapping qualities.

What’s more, generations aren’t as homogeneous as we’d like to think. While normally there are overarching behaviors and attributes assigned to each age group, there can be room for variations among the cohorts. For example, we released a report where we found a segmentation of Millennials revealing five distinct personas with various preferences, attitudes, and behaviors. Our self-funded study focused specifically on financial behaviors, but it can serve as a microcosm for the rest of the generation. You can learn more about it here. This research underscores the potential for inaccuracies that can result from defining a generation too narrowly.

There will always be a place for analysis by generation, but we have a lot more data to consider today than ever before. In his 2013 book "Buyographics", Matt Carmichael reaffirms the importance of demographics, but emphasizes analysis shouldn’t stop there. He explains, "Demographics drive consumer behavior, and that's as true today as ever. We just have better means, thanks to more data sources, of measuring those behavioral impacts and targeting around them. All data needs to be considered through a broader lens and put into context."

Cuts by generation alone ignore the impact of geography and make assumptions about how age influences behavior and psychographics. For example, we often find our psychographics (e.g. our attitudes and aspiration), regardless of age, are good indicators of who we are and who we want to be. In fact, these aspirations (e.g. Who do I want to be?) are strong motivators of brand consideration and loyalty. This means if two people from separate generations can identify with the same type of person, they'll likely share an affinity for the brand because of that identification, not their age.

We'll hear a great deal about who Gen Z is in the next few years until they are eclipsed by the next group. But researchers, advertisers, and marketers should take heed against categorizing Gen Z—and the ensuing generations—solely by their date of birth. Without a multi-faceted approach to understanding consumers (considering demographics, psychographics, etc.), we'll continue to yield narrow insights that may result in marketers producing ads that alienate their target audiences.

Reed Guerino is a Data Manager at CMB who is an entitled millennial on the side and is bitter he missed being the “mature and in control” generation by 1-5 years.

Topics: millennials, research design, Consumer Pulse

A New Year’s Resolution: Closing the Gap Between Intent and Action

Posted by Indra Chapman

Wed, Jan 04, 2017

resolutions.jpg

Are you one of the more than 100 million adults in the U.S. who made a New Year’s resolution? Do you resolve to lose weight, exercise more, spend less and save more, or just be a better person?

Top 10 New Year's Resolutions for 2016:

  • Lose Weight
  • Getting Organized
  • Spend less, save more
  • Enjoy Life to the Fullest
  • Staying Fit and Healthy
  • Learn Something Exciting
  • Quit Smoking
  • Help Others in Their Dreams
  • Fall in Love
  • Spend More Time with Family
[Source: StatisticBrain.com]

The actual number varies from year to year, but generally more than four out of 10 of us make some type of resolution for the New Year. And now that we’re a few days into 2017, we’re seeing the impact of those New Year resolutions. Gyms and fitness classes are crowded (Pilates anyone?), and self-improvement and diet book sales are up.

But… (there’s that inevitable but!), despite the best of intentions, within a week, at least a quarter of us have abandoned that resolution, and by the end of the month, more than a third of us have dropped out of the race. In fact, several studies suggest that only 8% of us actually go on to achieve our resolutions. Alas, we see that behavior no longer follows intention.

It’s not so different in market research because we see the same gap between consumer intention and behavior. Sometimes the gap is fairly small, and other times it’s substantial. Consumers (with the best of intentions) tell us what they plan to do, but their follow through is not always consistent. This, as you might imagine, can lead to bad data. [ twitter icon-1.pngTweet this!]

So what does this mean?

To help close the gap and gather more accurate data, ask yourself the following questions when designing your next study:

  • What are the barriers to adoption or the path to behavior? Are there other factors or elements within the customer journey to consider?
  • Are you assessing the non-rational components? Are there social, psychological or economic implications to them following through with that rational selection? After all, consider that many of us know that exercising daily is good for us – but so few of us follow through.
  • Are there other real life factors that you should consider in analysis of the survey? Does the respondent’s financial situation make that preference more aspirational than intentional?

So what are your best practices for closing the gap between consumer intent and action? If you don’t already have a New Year’s resolution (or if you do, add this one!), why not resolve to make every effort to connect consumer intent to behavior in your studies during 2017.

Another great resolution is to become a better marketer!  How?

Register for our upcoming webinar with Dr. Erica Carranza on consumer identity and the power of measuring brand user image to help create meaningful and relevant messaging for your customers and prospects:

Register Now!

Indra Chapman is a Senior Project Manager at CMB, who has resolved to set goals in lieu of new year’s resolutions this year. In the words of Brad Paisley, the first day of the new year “is the first blank page of a 365-page book. Write a good one.”

Topics: data collection, research design