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Jay Weiner, PhD

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A User's Guide to the “Perfect” Segmentation

Posted by Jay Weiner, PhD

Mon, Jul 22, 2019

iStock-628987676 (1)

A really good segmentation benefits many users. The product development team needs to design products and services for key target segments. The marketing team needs to develop targeted communications. The data scientists need to score the database for targeting current customers. The salesforce needs to develop personalized pitches.  Last, but not least, the finance department uses segmentation to help allocate the resources of the firm. With so many interested parties, it’s easy to see why getting buy in up front is critical to the success of any segmentation.

A "perfect" segmentation solution would offer insights for each user to help them execute the strategic plan.  What does this mean from an analytical perspective?  It means we have differentiation on needs for the product development folks, attitudes for the marketing folks and a predictive scoring model for the internal database team.  That sounds easy enough, but in practice it is difficult.  Attitudes are not always predictive of behaviors.  For example, I’m concerned about the environment.  I have solar panels on my roof.  You’d think I would drive a zero emissions vehicle (ZEV) and yet I drive a 400HP V8 high octane burning gas powered car.  I don’t feel too bad about that since I don’t really drive much.  That said, my next car could be the Volkswagen I.D. Buzz, an all-electric nostalgic take on the original VW van, but I digress.

Segmentation is not a property of the market.  It is an activity.  It’s usually helpful to evaluate several potential segmentation schemes to see how well they deliver the key objectives.  We do this by prioritizing the objectives.  Getting nice differentiation on attitudes to help create more effective marketing campaigns might be more important than getting a high accuracy on scoring the database.

My colleague, Brant Cruz recently listed leveraging existing data sources as one of the keys to successful segmentation.  This is often one of the biggest challenges we face in segmentation.  How well can we classify the customer database?  What’s in the database?  Most often it’s behavioral data like month spend, products purchased, points redeemed.  These data are the most accurate representation of what happened and when it happened.  What they don’t help explain is why it happened and in some cases who did it.  For example, many families subscribe to streaming music and video services.  If you don’t remember to log in, then the behavior is correct for the family, but not necessarily attributable to a specific user.

Appending demographic and attitudinal data to the database can help provide the links.  When such data are available, we have to verify the source of those data.  Many companies offer the ability to populate demographic and potentially attitudinal data. If this is the source of the append, then is it an actual value for the specific customer or is it a proxy for that customer based on nearest neighbor values.  In either case, we would still need to determine the age of the appended data.  How often do these values get updated?  Are some values missing?  For example, if you have recently signed up for an account, then your 90-day behavioral data elements won’t get populated for some period of time.  This means that I would need to either remove these respondents from my file or build a unique model for new customers.  How well we can accurately predict the segments is contingent in part on how accurate our data are.

The most accurate solution would be to simple segment using only information in the database.  If our ultimate goal is to help the client with prospecting for new business, a segmentation of customers is not likely to be too helpful.  This means that I need to collect primary data and ask surrogates for the values in the database.  A concurrent sample of customers would help with any need calibrate the survey responses for over/under statement.

When we start to mix database values with primary survey data, we typically do two things.  First, we dilute the differences in attitudes and needs.  Second, we reduce the accuracy of scoring the database.  There are ways to improve the scoring accuracy.  We can provide a list of attributes that could be appended to the database to increase the correct classification.  Sometimes, the data scientists may be able to identify additions variables in the database that were not provided up front.  Other times, it’s simply a matter of figuring out how to collect these values and have them appended to the database.

One part of the evaluation is to determine how many segments to have. Just because you have a segment, doesn’t mean you have to target that segment.   You should have at least one more segment than you intend to target.  Why?  This lets you identify an opportunity that you have left in the market for your competitors.  Just because there are segments of folks interested in zero-emission vehicles, or self-driving cars does not mean you need to make them.  Most companies can only afford to target a small number of segments.  Database segmentations with targeted digital campaigns are often easy to execute with a larger number of segments.

How long can you expect your solution to last?  Typically, segmentation schemes last as long as there are no major changes in the market.  Changes can come from technological innovations.  ZEV and self-driving cars have changed the auto industry.  Shifts in the size of the segments over time are just one indication that the segmentation could use refreshing.

Dr. Jay is CMB’s Chief Methodologist and VP of Advanced Analytics and is always up for tackling your most pressing questions. Submit yours and he could answer it in his next blog!

Ask a Question!

Topics: advanced analytics, market strategy and segmentation

My New Driving Tribe

Posted by Jay Weiner, PhD

Thu, Aug 16, 2018

ambiguous driver

The kind of car you drive says a lot about who you are—at least according to other peoples’ perceptions. And the more people identify with that perception, the more likely they are to use that brand, or in this case, drive that car.

This is important for marketers responsible for effectively communicating their brand to the target market. How is their typical customer perceived? Does that customer image align with their brand?

I’m not much of a joiner so I never thought much about what my car says about me. That is, until I joined a new tribe of car owners.

When I was recently debating buying a new car, my colleague said she wouldn’t speak to me again if I bought this certain brand. I did anyway. Even though she might be unhappy that I now drive this particular car, she still speaks to me (must need some analysis done).

In a self-funded study, we found the typical owner of this brand is viewed as: wealthy, confident, fun, stuck up, young, snobby, arrogant, and cool, among others. In reading this description, you can probably tell it’s not a Buick. 

But this brand’s marketing team might have a strong interest to figure out how best to play up the positive characteristics (cool) and play down or negate the negative dimensions (arrogant/stuck up). My kids still don’t think I’m cool.

word cloud 2Why does this brand generate such a view? It over-indexes on dimensions that might not seem approachable (i.e., worldly, trendsetting) and under-indexes on dimensions that might resonate with a wider audience (i.e., responsible, genuine, relaxed). This perception reflects a very specific view of the typical driver and risks alienating a potentially huge customer base.

Maybe that’s okay if you’re a niche product with a narrow target market, but then again, maybe not.

over under indexing

What does it all mean? Interestingly enough, I’ve seen a change in the way my fellow motorists treat me on the road. Take that with a grain of salt as we typically refer to drivers around here as MASSH@^%$ (editor made me type that despite my STET comment on the first draft).

In my previous domestic brand car, when I put my turn signal on, folks tended to let me change lanes. Now, they race up to box me out. At red lights, other drivers want to race. While my car does have some serious horse power, I don’t drive it that way. I guess if folks don’t see me as kind and caring based on the car I drive, why should I expect them to be kind and caring to me on the road?

Marketers beyond the car industry should be thinking about this, too. How are you communicating who your brand’s typical customer is? Is that image relatable? Is it desirable? Your messaging should clearly and effectively communicate who your customer is in the best light—whatever that means for your brand.

Dr. Jay is CMB’s Chief Methodologist and VP of Advanced Analytics and is always up for tackling your most pressing questions. Submit yours and he could answer it in his next blog!

Ask a Question!

 

Topics: Dear Dr. Jay, AffinID

Dear Dr. Jay: How Long Will My Segmentation Last?

Posted by Jay Weiner, PhD

Tue, Sep 29, 2015

Hi Dr. Jay,

How many segments should we have in an optimal solution, and how long can I expect my segmentation solution to last?

-Katie M.


Hi Katie,

Dear Dr. Jay, CMB, SegmentationYou’re not the only one who’s been asking about segmentation lately. Here’s my philosophy: you should always have at least one more segment than you intend to target. Why? An extra segment gives you the chance to identify an opportunity that you left in the market for your competitors. The car industry is a good example. If you’re old (like me), you remember GM’s product line in the 70s and 80s: “gas-guzzling land yachts.” Had GM bothered to segment the market, it might have identified a growing segment of consumers that were interested in more fuel efficient cars. Remember: just because you have a segment, doesn’t mean you have to target that segment. GM probably didn’t see this particular segment as viable until Toyota, Datsun (now Nissan), and Honda shipped small economy cars in greater numbers to the U.S. market. By that time, GM had shown up too late to the party with a competitive response.

As for how long a segmentation solution lasts? Segmentation schemes typically last as long as there are no major changes in the market. Why? Because segmentation requires strategic research that affects the full spectrum of marketing activities, including all 4 P’s of marketing (product, price, promotion, and place/distribution). One of the greatest catalysts for change comes from technological innovations. In the case of the car industry, those innovations include hybrid, electric, and driverless cars, as well as new competitors, like Tesla and Google. Tesla stands to change the market around distribution because its distribution strategy is unlike any other auto manufacturer. Many of its locations are in or near major shopping malls—not along the traditional auto mile where most dealers compete. While we often see other manufacturers display vehicles in the mall, potential customers would still have to go to a dealer’s lot to actually make a purchase, but Tesla removes this obstacle. This makes Telsa visible to potential customers who are not necessarily looking to purchase a car—a segment many traditional companies ignore.

Remember, segmentations are powerful tools—they can help your product development team generate products that appeal to your target segments, allow you to create stronger demand, and charge higher prices—but they won’t last forever.

Dr. Jay Weiner is CMB’s senior methodologist and VP of Advanced Analytics. Jay earned his Ph.D. in Marketing/Research from the University of Texas at Arlington and regularly publishes and presents on topics including conjoint, choice, and pricing.

Got a burning research question? You can send your questions to DearDrJay@cmbinfo.com or submit anonymously here.

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Topics: product development, Dear Dr. Jay, market strategy and segmentation

Dear Dr. Jay: Data Integration

Posted by Jay Weiner, PhD

Wed, Aug 26, 2015

Dear Dr. Jay,

How can I explain the value of data integration to my CMO and other non-research folks?

- Jeff B. 


 

DRJAY-3

Hi Jeff,

Years ago, at a former employer that will remain unnamed, we used to entertain ourselves by playing Buzzword Bingo in meetings. We’d create Bingo cards with 30 or so words that management like to use (“actionable,” for instance). You’d be surprised how fast you could fill a card. If you have attended a conference in the past few years, you know we as market researchers have plenty of new words to play with. Think: big data, integrated data, passive data collection, etc. What do all these new buzzwords really mean to the research community? It boils down to this: we potentially have more data to analyze, and the data might come from multiple sources.

If you only collect primary survey data, then you typically only worry about sample reliability, measurement error, construct validity, and non-response bias. However, with multiple sources of data, we need to worry about all of that plus level of aggregation, impact of missing data, and the accuracy of the data. When we typically get a database of information to append to survey data, we often don’t question the contents of that file. . . but maybe we should.

A client recently sent me a file with more than 100,000 records (ding ding, “big data”). Included in the file were survey data from a number of ad hoc studies conducted over the past two years as well as customer behavioral data (ding ding, “passive data”). And, it was all in one file (ding ding, “integrated data”). BINGO!

I was excited to get this file for a couple of reasons. One, I love to play with really big data sets, and two, I was able to start playing right away. Most of the time, clients send me a bunch of files, and I have to do the integration/merging myself. Because this file was already integrated, I didn’t need to worry about having unique and matching record identifiers in each file.

Why would a client have already integrated these data? Well, if you can add variables to your database and append attitudinal measures, you can improve the value of the modeling you can do. For example, let’s say that I have a Dunkin’ Donuts (DD) rewards card, and every weekday, I stop by a DD close to my office and pick up a large coffee and an apple fritter. I’ve been doing this for quite some time, so the database modelers feel fairly confident that they can compute my lifetime value from this pattern of transactions. However, if the coffee was cold, the fritter was stale, and the server was rude during my most recent transaction, I might decide that McDonald’s coffee is a suitable substitute and stop visiting my local DD store in favor of McDonald’s. How many days without a transaction will it take the DD algorithm to decide that my lifetime value is now $0.00? If we had the ability to append customer experience survey data to the transaction database, maybe the model could be improved to more quickly adapt. Maybe even after 5 days without a purchase, it might send a coupon in an attempt to lure me back, but I digress.

Earlier, I suggested that maybe we should question the contents of the database. When the client sent me the file of 100,000 records, I’m pretty sure that was most (if not all) of the records that had both survey and behavioral measures. Considering the client has millions of account holders, that’s actually a sparse amount of data. Here’s another thing to consider: how well do the two data sources line up in time? Even if 100% of my customer records included overall satisfaction with my company, these data may not be as useful as you might think. For example, overall satisfaction in 2010 and behavior in 2015 may not produce a good model. What if some of the behavioral measures were missing values? If a customer recently signed up for an account, then his/her 90-day behavioral data elements won’t get populated for some time. This means that I would need to either remove these respondents from my file or build unique models for new customers.

The good news is that there is almost always some value to be gained in doing these sorts of analysis. As long as we’re cognizant of the quality of our data, we should be safe in applying the insights.

Got a burning market research question?

Email us! OR Submit anonymously!

Dr. Jay Weiner is CMB’s senior methodologist and VP of Advanced Analytics. Jay earned his Ph.D. in Marketing/Research from the University of Texas at Arlington and regularly publishes and presents on topics, including conjoint, choice, and pricing.

Topics: advanced analytics, big data, Dear Dr. Jay, data integration, passive data