predictive analytics 2First used by asset managers to support direct-marketing campaigns, predictive analytics is steadily pushing into all aspects of marketing and sales, with an assist from Big Data. The subject gets lots of attention from asset management marketing and sales teams, and predictive analytics is a popular topic at conferences as new vendors emerge to tout their analytic systems.

But my sense is there have been at least as many failures as successes when asset managers have used analytic techniques to guide wholesaler-driven processes.

In these scenarios, companies face a fundamental obstacle to success: the wholesaler. Wholesalers themselves aren’t a problem; rather, companies make mistakes when their analytic solutions don’t consider how wholesalers can apply insights and change their actions practically. Analytic solutions that are too complex, too fleeting, or too inflexible tend to fail in wholesaler-driven sales processes, as I discussed in an earlier post.

Consider this example: A financial advisor (FA) googles "Best Large-Cap Growth Funds." Meanwhile, a predictive analytic solution considers information about the advisor and the search terms, values the opportunity and then determines whether to bid on a Google ad placement. If this same information were provided to a field-based wholesaler, what would he or she do differently? Maybe place a call or try to schedule an in-person meeting, but there would be no guarantee of near-term contact with the FA. When speaking next with the FA, the wholesaler might address large-cap growth funds—but should he or she do so at the expense of other messages?

It’s a silly example, but it illustrates how the wholesaler channel has unique needs when it comes to predictive analytics. In our experience, there are three areas where predictive analytics applications can be practical and impactful for wholesalers:

1. Measuring general responsiveness to sales activity.

The fundamental questions behind wholesaler territory and activity planning are: Which advisors should I see, and how often should I see them? Understanding the relationship between wholesaler contacts and market share change (typically by segment) can yield powerful insights: It informs contact and meeting planning, it guides territory design, and it can also be used to inform sales goals. This type of analysis is reasonably stable, so wholesalers can rely on its insight for quarterly or semiannual planning.

2. Measuring event-specific responses.

Sometimes asset managers face marketing decisions that link to large budget expenditures. A good example: whether to pay $250,000 for access to Wells Fargo’s fund platform. The question here is simple—is it worth it?—but the analytic approach is often complex and relies on assumptions or proxies. In these cases, predictive analytics can help inform forecasts, identify leverage points and ultimately inform go/no-go decisions. And the complexity is generally acceptable, since the analysis impacts relatively few decision makers.

3. Reacting to FA behavioral triggers.

This is the most challenging application, as it combines analytic complexity, predictive uncertainty and a reliance on wholesalers for action. But we’ve seen situations where behavioral insights can direct broad wholesaler actions. For example, it is sometimes possible to identify advisors whose investment mix is shifting. Rather than asking wholesalers to drop everything and reach out, companies can be more successful by using analytics to inform wholesaler “messaging” on a medium-term horizon (next month, next quarter). Even better, companies can engage wholesalers in the planning process, to bridge the analytic insight with local knowledge.

None of the above examples would qualify as state-of-the-art in the world of predictive analytics, but they would represent significant progress for most asset managers—at least in the wholesale channel. And regardless of the analysis being pursued, asset managers would be wise to heed our general rule of thumb: It is better to collaborate with wholesalers on activity planning than to dictate activities to wholesalers!

Topics: Jason Brown, wholesalers, asset management, asset managers, predictive analytics