The Secret Sauce for B2B Loyalty Marketing
Properly measuring customer loyalty is often a difficult task in multichannel B2B marketing environment. The first question is often, “Where should we start digging when there are many data silos?” Before embarking on a massive data consolidation project throughout the organization, we suggest defining the problem statements by breaking down what customer loyalty means to you first, as that exercise will narrow down the list of data assets to be dealt with.
Who’s likely to be your valuable customer? What will their value be in next few years? How long will they continue to do business with you? Which ones are in vulnerable positions, and who’s likely to churn in next three months? Wouldn’t it be great if you could identify who’s vulnerable among your valuable customers “before” they actually stop doing business with you?
Marketers often rely on surveys to measure loyalty. Net Promoter Score, for example, is a good way to measure customer loyalty for the brand. But if you want to be proactive about each customer, you will need to know the loyalty score for everyone in your base. And asking “everyone” is too cost-prohibitive and impractical. On top of that, the respondents may not be completely honest about their intentions; especially when it comes to monetary transactions.
That’s where modeling techniques come in. Without asking direct questions, what are the leading indicators of loyalty or churn? What specific behaviors lead to longevity of the relationship or complete attrition? In answering those questions, past behavior is often proven to be a better predictor of future behavior than survey data, as what people say they would do and what they actually do are indeed different.
Modeling is also beneficial, as it fills inevitable data gaps, as well. No matter how much data you may have collected, you will never know everything about everyone in your base. Models are tools that make the most of available data assets, summarizing complex datasets into forms of answers to questions. How loyal is the Company XYZ? The loyalty model score will express that in a numeric form, such as a score between one and 10 for every entity in question. That would be a lot simpler than setting up rules by digging through a long data dictionary.
Our team recently developed a loyalty model for a leading computing service company in the U.S. The purposes of the modeling exercise were two-fold:
- Find a group of customers who are likely to be loyal customers, and
- Find the “vulnerable” segment in the base.
This way, the client can treat “potentially” loyal customers even before they show all of the signs of loyalty. At the opposite end of the spectrum, the client can proactively contact vulnerable customers, if their present or future value (need a customer value model for that) is high. We would call that the “valuable-vulnerable” segment.
We could have built a separate churn model more properly, but that would have required long historical data in forms of time-series variables (processes for those can be time-consuming and costly). To get to the answer fast with minimal data that we had access to, we chose to build one loyalty model, making sure that the bottom scores could be used to measure vulnerability, while the top scores indicate loyalty.
What did we need to build this model? Again, to provide a “usable” answer in the shortest time, we only used the past three years of transaction history, along with some third-party firmographic data. We considered promotion and response-history data, technical support data, non-transactional engagement data and client-initiated activity data, but we pushed them out for future enhancement due to difficulties in data procurement.
To define what “loyal” means in a mathematical term for modeling, we considered multiple options, as that word can mean lots of different things. Depending on the purpose, it could mean high value, frequent buyer, tenured customers, or other measurements of loyalty and levels of engagement. Because we are starting with the basic transaction data, we examined many possible combinations of RFM data.
In doing so, we observed that many indicators of loyalty behave radically differently among different segments, defined by spending level in this instance, which is a clear sign that separate models are required. For other cases, such overarching segments, they can be defined based on region, product line or target groups, too.
So we divided the base into small, medium and large segments, based on annual spending level, then started examining other types of indicators of loyalty for target definition. If we had some survey data, we could have used them to define what “loyal” means. In this case, we mixed the combinations of recency and frequency factors, where each segment ended up with different target definitions. For the first round, we defined the loyal customers with the last transaction date within the past 12 months and total transaction counts within the top 10 to 15 percent range, where the governing idea was to have the target universes that are “not too big” or “not too small.” During this exercise, we concluded that the small segment of big spenders was deemed to be loyal, and we didn’t need a model to further discriminate.
As expected, models built for small- and medium-level spenders were quite different, in terms of usage of data and weight assigned to each variable. For example, even for the same product category purchases, a recency variable (weeks since the last transaction within the category) showed up as a leading indicator for one model, while various bands of categorical spending levels were important factors for the other. Common variables, such as industry classification code (SIC code) also behaved very differently, validating our decision to build separate models for each spending level segment.
The following is the efficiency curve of one of the resultant models:
This is a typical way of measuring the predictive power of the model in terms of “cumulative gains” realized by the exercise. Here, the top model group displays an over four-times gain in terms of loyalty measurement over general population, while the tail-end of the curve indicates “not-so-loyal” or “vulnerable.”
Could this model have been more effective with more colorful sets of input data? Yes. Would it have changed the way marketers would line up their customers in terms of loyalty proxy in a significant way? Not really.
That is why moving quickly with readily usable data is important. Models can improve, but generally speaking, rankings do not shift drastically. In other words, some company that scored three or four on the loyalty scale won’t jump up to the top group just because some new type of data got introduced into the mix.
So what did we recommend after this type of exercise?
- Now that we have proxies of loyalty (not carved in stone, but proxy scores for everyone in the base), marketers can engage “likely to be loyal” customers (generally the top two to three model groups) with special care, more proactively.
- At the bottom end of the curve (generally the bottom three to four model groups), identify “valuable, but vulnerable” customers by combining the loyalty model score with present value — or preferably, a separately developed customer value model score. Then proactively treat those valuable-vulnerable customers to prevent churn.
- Test, test, test. Modeling is an iterative exercise. Set up control groups for a “no-treatment” segment, and continuously measure the effectiveness of prediction. Tweak the models periodically, and enhance them over time by adding other available data.
All of this is just “one” of the many possible ways to create proxies of loyalty. Without a doubt, depending on the business model, immediate challenges, channel usages and available data, the definition of loyalty and subsequent modeling exercises can take dramatically different forms.
Regardless, modeling is useful for maximizing the power of available data. The key takeaway here is that marketers must start small with readily available data assets, and take a gradual approach to improve them over time. In the end, it is not about the most mathematically sound models, but about treating customers properly in the order of importance to your business. For that, some proxy scores in your hand now will be much better that a perfect set of data that may never come.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at email@example.com.