Why Financial Services Firms Need Less Art and More Science in Their B-to-B Marketing

Jason Brown

Given how adept financial services firms are at data-based B-to-C marketing—as evidenced by the barrage of credit card offers, mortgage pitches and other marketing promotions that many consumers receive each month—you might think that the financial services industry is just as savvy at applying sales and marketing analytics throughout the business. Yet financial firms’ successes with analytics in the B-to-B space haven’t been as frequent or impactful.

With the help of the Economist Intelligence Unit, ZS recently completed a cross-industry study to assess the state of analytics implementation and integration, and found that companies’ results were underwhelming. Among 448 respondents, some of whom hail from leading financial services organizations, 70% rate sales and marketing analytics as “very” or “extremely” important to their competitive advantage, but just 2% report that they’ve managed to generate a “broad, positive impact” from their analytics investments thus far.

Jason Brown, a principal at ZS and leader of the firm’s financial services practice, talks about the big difference in analytics between the B-to-C and B-to-B sides of the industry, and how firms can generate momentum by focusing on internal applications first.

Q: Are the study’s results in line with your experience in the financial services industry?

A: It depends. On the B-to-C side, some financial services companies are very, very good at analytics, but on the B-to-B side, and with services that are sold through an intermediary like an insurance agent or a financial advisor, those companies have been a little slower to fully explore and make good use of analytics. Also, I think it’s fair to say that marketing and sales, in general, are a little behind areas like underwriting, finance or fraud.

Data availability is always a problem. When you have multiple steps in your distribution, you lose a little bit of transparency and it’s a little harder to understand how decisions get made.

Talent is an issue, as well. There are a lot of people who are really good at math and statistics, and a lot of people who are good at business and who really understand these complex distribution systems, but the intersection of those two is a pretty small group and that’s really what you need to be effective in the space.

Even when a firm has that kind of talent in place, it’s tough to retain people. They get recruited away to other firms or even to other departments in the same firm. A really sharp, effective analyst for a big asset manager has many possible career paths right now.

Q: Is technology a limiting factor?

A: I don’t think so. Firms have plenty of technology, and sometimes more than they need. The catch is that it’s not organized around a central vision for the entire institution. We’ve seen a couple of firms that have multiple analytics suites and multiple reporting platforms. Different departments simply went out and made their own agreements with vendors. When you have many fragmented analytics pods like that, it takes away some of the horsepower you might get from a more concentrated effort.

At the more mature financial firms, what’s often a limiting factor is the core data infrastructure. You’ll come across some really old legacy systems. Just getting the data out can be a problem. Once it’s out, they’ve got more than enough tools to deal with it.


READ REPORT: Broken links: Why analytics investments have yet to pay off

Q: Do cultural considerations hold some companies back

A: They’re definitely a factor. Insurance companies are, by definition, risk-averse. Even some of the wealth management and advisor firms can be hesitant to run tests against a control group so they can start measuring impact. They think, Even if the test works, it means I haven’t treated the control group of customers or intermediaries “fairly” because I haven’t allowed them to participate. Companies are more willing to do the basic diagnostic and reporting because they’re safe. You’re just studying the data, so nothing’s at risk.

That’s not a bad start, but it’s really just the first step. Eventually, we see companies starting to move toward the “orchestrator rep” model, which is already getting good traction in the pharmaceutical industry and resonates really well with a lot of financial firms. Many of them already have a dashboard that reps can access showing all marketing activities and interactions with specific customers.

Right now it’s primarily a reporting function. At some point, companies will use that information to shape future sales and marketing efforts based on previous customer interactions. All of the information will go to a single person who owns the customer relationship and can tailor specific steps for each customer, but companies aren’t quite there yet.

Q: What advice would you give to firms trying to improve?

A: A safe and easy way to get better at this is to apply analytics to your own sales and service people. It’s a little easier and more data-rich than the end customer—especially if you have a two-step or three-step distribution process—so you can be more willing to experiment. In many ways, these applications can be easier than running programs on customers, where there’s a big regulatory burden regarding the language of consumer-facing marketing and promotions messages, and you can’t make small, quick changes to see what’s working better.

We worked with one firm on a pilot test to get its insurance agents to earn an internal qualification faster. The firm sent targeted emails to a subset of associates and it measured what happened next. The emails had a 60% open rate—higher than for similar email campaigns—and associates who received the emails had a 20-percentage-point bump in qualification rates. That translated to an additional $543 in business for each associate over a four-week period. Extrapolated to the whole population of agents, it added up to $1.2 million in added sales for the year from something that was a very easy, inexpensive program.

Q: Any other ways that companies can move faster?

A: I think speed isn’t necessarily the objective. There’s a natural aversion to analytics on the B-to-B side, but it’s for understandable reasons. Think about a B-to-C company—say, a bank sending out all kinds of offers to its customers: If one out of 10 hits, that’s a phenomenal success rate, but if you’re selling to a financial advisor who manages $100 million in assets, you can’t do that kind of blanket marketing. You can’t offer discounts and teaser deals. You only get one shot, maybe two, so you have to be pretty sharp to do it well on the B-to-B side, and a bit of caution is probably warranted to make sure you don’t negatively impact the customer experience.

That said, there are people who see the marketing and sales of B-to-B products as more art than science. There’s definitely room for more science. 

For more analysis and insights from the study, read “Broken links: Why analytics investments have yet to pay off.

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About the Expert

Jason Brown is a principal in ZS’s Boston office and the leader of the firm’s financial services practice. He has extensive project experience in go-to-market strategy and implementation, market research, sales process design, compensation design, and sales force sizing and deployment, and has worked with clients in industries including insurance, banking and asset management.

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