Business-to-business marketers have been a bit slow in adopting the newest statistical modeling techniques to drive their marketing programs, and with good reason. For one thing, B-to-B files tend to be relatively small. When your entire target universe is made up of a mere 10,000 prospective machine tool manufacturers, it’s easy to put off the expense and effort of building a regression model.
Another barrier has been the availability and quality of the data itself. Business marketers often use multiple channels, and coordinating data sources can be a strain. Worse, those marketing through distributors may be cut off from end-user data entirely. For example, a manufacturer may know where the product was shipped, but not how much was paid. Even if customer data is available, it can be hard to keep clean. According to D&B, a CEO changes every minute, and a new business files for bankruptcy every eight minutes. If your data is inaccurate, why bother to model it?
But things are changing rapidly. As B-to-B marketers gain sophistication and experience — and as they face competitive pressures — modeling is cropping up as a useful tool for a variety of applications. Models are being used for both acquisition and retention, with significant results. Just think: B-to-B accounts tend to represent much greater value than a consumer household, so it’s worth doing almost anything that will help gain a larger share of their budgets.
Acquisition Modeling
On the acquisition front, the most common application for statistical modeling is profiling current customers and using the profiles to identify high-potential “look-alikes” among prospect universes. This technique can work for companies of all types, but is most common among marketers whose products are of interest to a broad variety of business market segments. Sprint, for example, considers every business in the country a potential customer for its phone services. But targeting still makes their marketing dollars go farther, and improves the mood of their sales force. So when the Kansas City corporation pulls in prospect lists from compiled files such as D&B and InfoUSA, they apply a model to rank the names based on their similarity to Sprint’s best current customers and their dissimilarity to past non-responding accounts.
According to Tim Hand, group manager of CRM analytics at Sprint, the model’s value lies in refining the quality of the cold names coming in. “The model allows us to prioritize the leads,” he says. “In our experience, a rep will only make calls on about five out of a typical batch of leads we give them, so it’s critical that these names be as productive as possible. We used to select based just on SIC. With the model, we get much more detail, including creditworthiness, which is a key variable for us. Instead of giving each rep 100 leads in his or her territory, we’ll be sending 20.”
The reps have given Hand’s group positive feedback on the new leads — a gratifying if rare experience for a marketing team. But Hand also wisely set up a control group to confirm the power of the model beyond simply the anecdotal input from sales. “We took three or four months of leads and sales results, and compared the sales activity in the control group accounts against the accounts given to the sales teams. The model predicted the sales results successfully,” Hand observes.
Another example of modeling for acquisition comes from Abacus, whose B-to-B Alliance cooperative database contains 1.3 billion transactions from more than 350 participating B-to-B mailers. Abacus clients are primarily catalogers, in such categories as office supplies, advertising specialties (like logo merchandise), and seminars or training courses. These mail order marketers live and die by new customer acquisition, and, unlike the industrial side of the B-to-B world, have a long history of cooperation with competitors to exchange names. Abacus’ service allows mailers to select among millions of names based not only on the category purchased, but down to the individual transaction level.
Abacus’ modeling approach is similar to that used in the Sprint example — but the volumes are simply gigantic. With its two full-time statisticians and five technicians, Abacus builds somewhere around 2,100 models a year for its clients, seeking to identify names with transactional behavior similar to that of the client’s best customers. The models consider 60 or 80 variables, and typically narrow them down to the most powerful 15 or 20. No surprise, the variables net out to the typical direct marketing drivers, like lifetime dollars spent, recency of the names on Abacus’ master file and purchase frequency. After that, the variables are likely to reflect the product category (for example, seminars) and then the specific offer.
Modeling for acquisition is most effective when the results can be applied to large files. Another large source of names — one that does not require that you contribute your own files — is MeritBase, the cooperative database run by MeritDirect. With over 200 million gross records from 1,000 different lists, the best way to improve your results is with a model based on the outcome of a previous campaign vs. a profile against your best customers.
The process works as follows: The client mails a likely sample from the MeritBase, pulled on the criteria typically used in B-to-B list selection, like list source, size of company and recency. Responders and non-responders are then analyzed, a process that includes pass-along attribution of orders from a company where one person was mailed but the order was placed by someone else. A quarter of the names are held out for validation and the rest are modeled. Each record in the MeritBase is then assigned a decile score from that client’s model, and when the client takes names for a new campaign, MeritDirect charges only for the names mailed.
Retention Modeling
When it comes to retention, business marketers are applying modeling across the entire go-to-market process. Consider IBM’s experience with propensity modeling. IBM uses analytics to identify the target accounts most likely to be interested in certain product sets, such as hardware, software and services. The worldwide small and medium-size business marketing teams have had particular success with this approach, says Theresa Kushner, director of market data and analytics.
Kushner’s team builds models for the inside sales teams, known as telecoverage reps. The models score current customers, based on past purchase, for propensity to buy by product category. The team then develops proposed phone dialogues, known as “reason to call” scripts, designed to suggest certain product offerings. The reps then contact the high-propensity accounts to pitch such offerings as a software product upgrade or a set of new printers.
“Our objective is to make the telecoverage reps more productive,” says Kushner. “We want them to be in regular contact with their top accounts and have meaningful conversations with them. These scripts give them a good reason to call, and ensure that they are covering the market opportunity overall and not simply calling the easiest and most obvious accounts every day. We now feed 26 models per year to the sales team in North America alone.”
Propensity modeling qualifies as a classic example of data-driven cross-selling and upselling. But IBM also uses statistical models for other retention purposes. Among them:
- Channel management
IBM regularly models its customer base to support decisions about whether accounts should be covered by face-to-face sales, telecoverage, the e-channel or resellers.
- Account penetration
IBM models to identify opportunities to introduce new products into current accounts.
- Account reactivation
In the Asia-Pacific region, Kushner’s team modeled dormant accounts against best accounts and sent the high-value names to the telecoverage reps to call. The revenue generated in the dormant accounts showed a three- or four-times lift from business as usual.
- Reseller channel support
IBM helps its business partners with marketing programs to help them promote IBM products to their customers.
One of the reasons retention modeling has become so varied is the availability of new tools to support current customer analysis. For example, B-to-B has in the past few years seen the arrival of a Prizm-like tool from Ruf Strategic Solutions that identifies 114 cluster profiles that can be applied to each company location. The profiles attempt to define purchase propensity based on such characteristics as sales per employee, company growth rate, consumption of raw materials, whether the site is a headquarters or a branch location, and the wealth, employment and crime levels of the geographic area.
Clusters can be an inexpensive and fruitful way to begin statistical work on a customer file. President Kurt Ruf recommends that marketers begin with a “data audit,” appending profiles to their current customers.
“These clusters help you tease out patterns and hidden dynamics,” says Ruf. “The economic indicators like labor, finance and crime will affect a target company’s buying behavior.”
Future Trends
What’s the future of B-to-B statistical modeling? A number of promising experiments suggest that the future is bright.
Consider these examples:
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Loyalty Matrix, a data analytics firm in San Francisco, is helping a client identify which of its 17 e-newsletters is most effective in influencing sales results by matching customer sales levels with newsletter subscriber behavior.
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According to Anne Milley, director of analytical strategy at software firm SAS, companies are beginning to use text-mining software to identify hidden market opportunities. SAS has helped HP get control over its myriad marketing campaigns with Impaq Express, a Web-based database that gives campaign managers and analysts instant information on target audiences for the 30-odd campaigns HP runs each week.
B-to-B may have been late to the modeling game, but the payoff for participating is likely to be huge.
Ruth P. Stevens ([email protected]) consults on customer acquisition and retention, and teaches marketing to graduate students at Columbia Business School in New York. She is the author of “The DMA Lead Generation Handbook” and a forthcoming book from South-Western on B-to-B event and trade show marketing.
