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Nicole Battaglia

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How L.L. Bean Weathers Customer Loyalty

Posted by Nicole Battaglia

Wed, Apr 11, 2018

 LL Bean Boots_cropped

Sorry outdoor apparel fans, L.L. Bean isn’t accepting your beat-up duck boots anymore. The Maine-based outdoor retailer recently ended its flagship Lifetime Return Policy.

 L.L. Bean founder Leon Leonwood Bean introduced this policy over 100 years ago to prove their commitment to quality products and ensure customer satisfaction. And since then, generations of Bean-loving customers have enjoyed the forgiving policy.

But not everyone’s been so kind. A growing number of customers have taken advantage of L.L. Bean’s generosity by treating it more like a free exchange policy. According to the Associated Press, the company has lost $250 million on returned items that cannot be salvaged or resoled in the last five years alone!

From a financial perspective, this move makes sense. But the loyal Bean boot enthusiast and market researcher in me is curious about potential branding implications—will this alienate lifelong customers who might view this as L.L. Bean as “breaking its promise”?

For more than 100 years, L.L. Bean has built its brand image around “designing products that make it easier for families of all kinds to spend time outside together”. Enduring Northeast winters as a kid, I can vouch for the quality of their products—they are truly second to none. L.L. Bean isn’t ‘cheap’, but I don’t balk at their prices because I know I’m getting something proven to withstand harsh winters.

But, my loyalty for L.L. Bean runs deeper than the quality of my boots. Growing up in a Bean-loving home, I have a strong emotional connection to the brand.  I have memories of flipping through the catalog (back when that was the popular way to shop) and getting excited about when it was time to order a new backpack and matching lunchbox—monogrammed, of course.

When I’m home for the holidays, I head out to the local L.L. Bean store to make my holiday gift purchases. In 2015, L.L. Bean featured a golden retriever puppy on the cover of its holiday catalogue. As someone who grew up with goldens, this ad resonated with me on an emotional level.

I also strongly identify with other L.L. Bean enthusiasts. Most kids I grew up with had the monogrammed backpacks, and when I went to college, everyone wore Bean boots. My image of the typical customer is clear, relatable and socially desirable—the three aspects of social and self-identity that drive purchase and loyalty.

 When it comes to analyzing a brand’s performance, it’s critical to look at the complete picture and account for the identity, emotional, and functional benefits it provides. For me, the functional benefits (e.g. keeps my feet dry during a Nor’easter) L.L. Bean provides me are undeniably important; however, the emotional and identity benefits ultimately rank higher.

 I can’t speak for every customer, but the move to end their Lifetime Return Policy won’t keep me from shopping at L.L. Bean. Yes, it’s a shame the retailer had to rescind its signature guarantee—one that underscores their commitment to the quality of their products. 

But, it’s a powerful lesson for brands in an increasingly disrupted age: the strength of the benefits you provide your customer—social, emotional, and functional—can mean the difference between weathering the storm and keeping and growing your customers.

Nicole Battaglia is a Sr. Associate Researcher who isn’t pleased she’s had to wear her Bean boots into April this year.

Topics: customer experience and loyalty, Identity, AffinID, emotion, BrandFx

Spring into Data Cleaning

Posted by Nicole Battaglia

Tue, Apr 04, 2017

scrubbing.jpegWhen someone hears “spring cleaning” they probably think of organizing their garage, purging clothes from their closet, and decluttering their workspace. For many, spring is a chance to refresh and rejuvenate after a long winter (fortunately ours in Boston was pretty mild).

This may be my inner market researcher talking, but when I think of spring cleaning, the first that comes to mind is data cleaning. Like cleaning and organizing your home, data cleaning is a detailed and lengthy process that is relevant to researchers and their clients.

Data cleaning is an arduous task. Each completed questionnaire must be checked to ensure that it's been answered correctly, clearly, truthfully, and consistently. Here’s what we typically clean:

  • We’ll look at each open-ended response in a survey to make sure respondents’ answers are coherent and appropriate. Sometimes respondents will curse, other times they'll write outrageously irrelevant answers like what they’re having for dinner, so we monitor these closely. We do the same for open-ended numeric responsesthere’s always that one respondent who enters ‘50’ when asked how many siblings they have.
  • We also check for outliers in open-ended numeric responses. Whether it’s false data or an exceptional respondent (e.g. Bill Gates), outliers can skew our data and lead us to draw the wrong conclusions and make more recommendations to clients. For example, I worked on a survey that asked respondents how many cars they own.  Anyone who provided a number that was three standard deviations above the mean was set as an outlier because their answers would’ve significantly impacted our interpretation of the average car ownershipthe reality is the average household owns two cars, not six.
  • Straightliners are respondents who answer a battery of questions on the same scale with the same response. Because of this, sometimes we’ll see someone who strongly agrees or disagrees with two completely opposing statements—making it difficult to trust these answers reflect the respondent’s real opinion.
  • We often insert a Red Herring Fail into our questionnaires to help identify and weed out distracted respondents. A Red Herring Fail is a 10-point scale question usually placed around the halfway mark of a questionnaire that simply asks respondents to select the number “3” on the scale. If they select a number other than “3”, we flag them for removal.
  • If there’s incentive to participate in a questionnaire, someone may feel inclined to participate more than once. So to ensure our completed surveys are from unique individuals, we check for duplicate IP addresses and respondent IDs.

There are a lot of variables that can skew our data, so our cleaning process is thorough and thoughtful. And while the process may be cumbersome, here’s why we clean data: 

  • Impression on the clientFollowing a detailed data cleaning processes helps show that your team is cautious, thoughtful, and able to accurately dissect and digest large amounts of data. This demonstration of thoroughness and competency goes a long way to building trust in the researcher/client relationship because the client will see their researchers are working to present the best data possible.
  • Helps tell a better storyWe pride ourselves on storytelling–using insights from data and turning them into strong deliverablesto help our clients make strategic business decisions. If we didn’t have accurate and clean data, we wouldn’t be able to tell a good story!
  • Overall, ensures high quality and precise dataAt CMB typically two or more researchers are working on the same data file to mitigate the chance of error. The data undergoes such scrutiny so that any issues or mistakes can be noted and rectified, ensuring the integrity of the report.

The benefits of taking the time to clean our data far outweigh the risks of skipping it. Data cleaning keeps false or unrepresentative information from influencing our analyses or recommendations to a client and ensures our sample accurately reflects the population of interest.

So this spring, while you’re finally putting away those holiday decorations, remember that data cleaning is an essential step in maintaining the integrity of your work.

Nicole Battaglia is an Associate Researcher at CMB who prefers cleaning data over cleaning her bedroom.

Topics: data collection, quantitative research