Voices: the CMB Blog

Hello Dr. Jay,

I enjoyed your recent post on predictive analytics that mentioned Bayesian Networks.

Could you explain Bayesian Networks in the context of survey research? I believe a Bayes Net says something about probability distribution for a given data set, but I am curious about how we can use Bayesian Networks to prioritize drivers, e.g. drivers of NPS or drivers of a customer satisfaction metric.

-Al

Dear Al,

Driver modeling is an interesting challenge. There are 2 possible reasons why folks do driver modeling. The first is to prioritize a set of attributes that a company might address to improve a key metric (like NPS). In this case, a simple importance ranking is all you need. The second reason is to determine the incremental change in your dependent variable (DV) as you improve any given independent variable by X. In this case, we’re looking for a set of coefficients that can be used to predict the dependent variable.

Why do I distinguish between these two things? Much of our customer experience and brand ratings work is confounded by multi-collinearity. What often happens in driver modeling is that 2 attributes that are highly correlated with each other might end up with 2 very different scores—one highly positive and the other 0, or worse yet, negative. In the case of getting a model to accurately predict the DV, I really don’t care about the magnitude of the coefficient or even the sign. I just need a robust equation to predict the value. In fact, this is seldom the case. Most clients would want these highly correlated attributes to yield the same importance score.

So, if we’re not interested in an equation to predict our DV, but do want importances, Bayes Nets can be a useful tool. There are a variety of useful outputs that come from Bayes Nets. Mutual information and Node Force are two such items. Mutual information is essentially the reduction in uncertainty about one variable given what we know about the value of another. We can think of Node Force as a correlation between any 2 items in the network. The more certain the relationship (higher correlation), the greater the Node Force.

The one thing that is relatively unique to Bayes Nets is the ability to see if the attributes are directly connected to your key measure or if they are moderated through another attribute. This information is often useful in understanding possible changes to other measures in the network. So, if the main goal is to help your client understand the structure in your data and what items are most important, Bayes Nets is quite useful.

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

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.

Dear Dr. Jay,

How can my organization create a Key Performance Indicator (KPI) that’s really useful?

-Meeta R., Seattle

Dear Meeta,

A key performance indicator (KPI) is often used to communicate to senior management how well the company is doing, with a single metric. It could be based on a single attribute in the questionnaire, e.g., the top two boxes of intent to continue using the brand. Another popular KPI is the Net Promoter Score (NPS), based on likelihood to recommend, where we take the percentage of customers who are promoters and subtract the percentage who are detractors.

Over the years, likelihood to continue, overall satisfaction, and likelihood to recommend have all been candidates for inclusion in creating a KPI. We find these measures are often highly correlated with each other.  This suggests that while any one measure might be a decent KPI, there is a unique piece of each that is not captured by the others. Likelihood to continue and likelihood to recommend both have a behavioral dimension to them, while overall satisfaction is most likely purely attitudinal. 

There are a few key things to consider in selecting (or creating) a KPI: 

1. The number should be easy to explain and compute. 

2. It must be tied to some key business outcome, such as increased revenue.

3. Finally, it should be fairly responsive to future changes.

In the third consideration, a balance of behavioral and attitudinal measures comes into play. If you’re trying to predict future purchases, past purchases are a good measure to use. For example, if my past 10 credit card transactions were with my Visa card, there’s a very good probability that my next transaction will be made with that same card. Even if I have a bad experience on the 11th purchase with my Visa card, the prediction for the 12th purchase would still be Visa. However, if I include some attitudinal component in my KPI, I can change the prediction of the model much faster.

So what is the best attitudinal measure? Most likely, it’s something that measures the emotional bond one feels for the product, something that asks: is this a brand you prefer above all others? When this bond breaks, future behavior is likely to change.

A final word of caution—you don’t need to include everything that moves. As your mentor used to say, keep it simple, stupid (KISS). Or better yet, keep it stupid simple—senior management will get that.

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

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.

Watch our recent webinar to learn about the decision-focused emotional measurement approach we call EMPACT℠: Emotional Impact Analysis. Put away the brain scans and learn how we use emotion to inform a range of business challenges, including marketing, customer experience, customer loyalty, and product development.

“How do I love thee? Let me count the ways.” “You’re my favorite brand ever.” “You’ve taken such good care of me over the years we’ve been together.” “I can see myself spending the rest of my life with you.” How many of your customers would say such things about you?

Loyalty is a behavior, and behaviors often have underlying attitudes that drive them. We might continue to purchase the same product over and over for a variety of reasons. Don’t get me wrong: repeat business is almost always a good thing. But, if it comes at a negative margin, it may not be a good thing. If you frequently incentivize your customers, you might be buying loyalty (deal loyalty), but are you making money doing it? If your deal loyals are promoting you, are they promoting the deal or your brand? In a perfect world, we not only create a behavioral commitment, but also an emotional bond with the brand and, ultimately, the company.

Many companies track the Net Promoter Score (NPS) as a measure of loyalty. This adds another potential behavior to the mix—advocacy. If we look at a traditional purchase conversion ladder, advocacy or evangelism would be at the top of the pyramid. Promoters are certainly advocates, but are they also evangelists? Is promotion really enough? Don’t you really want to know what they’re saying?

Advocacy is attempting to influence decisions. Evangelism is relaying information about a particular set of beliefs to encourage conversion. Advocacy may encourage lexicographic information processing—buy cheapest, buy fastest, etc. Evangelism should encourage a more holistic view of evaluating the brand. The implication is that beliefs are probably more deeply rooted in brand performance. This creates a bond with the brand that transcends getting a good deal. You want folks that are proud to wear your logo and serve your product as well as folks who would gladly buy other goods/services from you if you want to extend the franchise.

In a recent survey, we found that about 60% of brands promoters love the brand. If they don’t love you, what are they saying about you? On the flip side, over 80% of those that love you are promoters.  Clearly promoters have value to the franchise in helping grow the brand. As a company, you not only want more promoters, you’d like to believe they are, in fact, promoting the brand and the company and not something else.

How can we improve on tracking the NPS score? We find a way to capture the emotional bond of your true loyals. Those customers who love you will clearly go out of their way to buy you, pay more for your product or service, and proudly share your brand with others. Growing this share of your customer base will certainly help you grow both the top line sales and bottom line net profits.

At CMB, we’ve been looking at the Emotional Fingerprints™ brands leave on their customers. We’ve developed a measure of the emotional bond customers have for brands. When we look across different segments of your loyal customers, we can clearly see that those that love you clearly are more bonded to your franchise.

So, even if you forgot the roses this Valentine’s Day, don’t forget to send your favorite customers a forget-me-not. Let them know how much you appreciate their business and their love.

Dr. Jay Weiner is the top digit-head at CMB. Starting next month, he’ll answer your burning market research questions in his new blog series: Dear Dr. Jay. You can send your questions to DearDrJay@cmbinfo.com or submit anonymously here: http://forms.cmbinfo.com/dear-dr.-jay   

NPS, or Net Promoter Score, is all the rage in market research. Most major corporations have a tracking program built around the statistic, and many companies also gauge their customer service and client relationships against this number. But what is NPS? 

At its root, NPS is a measure of advocacy. In terms of data collection, NPS is a single question usually included in a customer satisfaction or brand tracking survey. The question’s scale ranges from 0-10 but is grouped according to the graphic below. In the aggregate, an NPS score is the percentage of Promoters minus the percentage of Detractors.  

We did a recent study in which we took a deeper look at NPS and what how Promoters differ from Detractors. We surveyed customers from a wide array of industries (travel, eCommerce, telecom, etc.), and we uncovered quite a few statistics that might surprise you. 

What if I told you that only a slim majority (53%) of Promoters love the brands they do business with? Remember: this isn’t 50% of all consumers but 50% of Promoters. In fact, only 15% of all consumers use the “L word” at all. This isn’t to say that advocacy isn’t important—word of mouth is an essential part of advertising—but wouldn’t you rather your loudest advocates be your biggest loyalists? If these Promoters found a competitive brand more attractive, are they likely to advocate on that brand’s behalf?  

Here are some more fun facts: 4% of Promoters are only loyal customers during sales or when they have a coupon, and another 5% of Promoters would be happy to purchase from another brand if it were available. Consumers are fickle beasts. 

So, what does all this mean? Are we ready to throw NPS out the window? Certainly not. NPS is great in that provides a clear measure of how many advocates exist in comparison to Detractors. Think of it as a net tally of all the communications in the world. Scores above 0 mean you have more Promoters then Detractors, and negative scores mean the opposite. But for those companies out there that have the traffic on their side, it’s time to ask: is advocacy enough? Advocacy is great—it provides momentum, gets startups off the ground, and fuels growing brands. But love is better—love builds dynasties. 

James Kelley splits his time at CMB as a Project Manager for the Technology/eCommerce team and as a member of the analytics team. He is a self-described data nerd, political junkie, and board game geek. Outside of work, James works on his dissertation in political science which he hopes to complete in 2016.

Check out our new case study and learn how CMB refreshed Reebok’s global brand tracker, which gives the global fitness giant insight into how the brand is performing, its position in the global marketplace, and whether current brand strategies reach their targets.