The Next Big Leap for Marketing Software

I’ve often written about the tendency of marketing automation vendors to endlessly expand the scope of their products. Over all this is probably a good thing for their customers. But at some point, the competitive advantage of adding yet another capability probably approaches nil. If so, then what will be the next really important change in marketing systems?

My guess is it will be a coordination mechanism to tie together all of those different components – resource management, execution, analysis, and so on. Think of each function as a horse: the more horses you rope to your chariot, the harder it is to keep control.

I’m not talking about data integration or marketing planning, which are already part of the existing architectures, or even the much desired (though rarely achieved) goal of centralized customer interaction management. Those are important but too rigid. Few companies will be technically able or politically willing to turn every customer treatment over to one great system in the sky. Rather, I have in mind something lighter-handed: not a rigid harness, but a carrot in front of those horses that gets them voluntarily pulling in the same direction. (Okay, enough with the horse metaphor.)

The initial version of this system will probably be a reporting process that gathers interactions from customer contact systems and relates them to future results. I’m stating that as simply as possible because I don’t think a really sophisticated approach – say, customer lifecycle simulation models – will be accepted. Managers at all levels need to see basic correlations between treatments and behaviors. They can then build their own mental models about how these are connected. I fully expect more sophisticated models to evolve over time, including what-if simulations to predict the results of different approaches and optimization to find the best choices. But today most managers would find such models too theoretical to act on the results. I’m avoiding mention of lifetime value measures for the same reason.

So what, concretely, is involved here and how does it differ from what’s already available? What’s involved is building a unified view of all contacts with individual customers and making these easy to analyze. Most marketing analysis today is still at the program level and rarely attempts to measure anything beyond immediate results. The new system would assemble information on revenues, product margins and service costs as well as promotions. This would give a complete picture of customer profitability at present and over time. Changes over time are really the key, since they alert managers as quickly as possible to problems and opportunities.

The system will store this information at the lowest level possible (preferably, down to individual interactions) and with the greatest detail (specifics about customer demographics, promotion contents, service issues, etc.), so all different kinds of analysis can be conducted on the same base. Although the initial deployments will contain only fragments of the complete data, these fragments will themselves be enough to be useful. The key to success will be making sure that the tools in the initial system are so attractive (that is, so powerful and so easy to use) that managers in all groups want to use them against their own data, even though this means exposing that data to others in the company. (If you’re lucky enough to work in a company where all data is shared voluntarily and enthusiastically – well, congratulations.)

You may feel what I’ve described is really no different from existing marketing databases and data warehouses. I don’t think so: transactions in most marketing databases are limited to promotions and responses, while most data warehouses lack the longitudinal customer view. But even if the technology is already in place, the approach is certainly distinct. It means asking managers to look not just at their own operational concerns, but at how their activities affect results across the company and the entire customer life cycle. More concretely, it allows managers to spot inconsistencies in customer treatments from one department to the next, and to compare the long-term results (and, therefore, return on investment) of treatments in different areas. Comparing return on investment is really a form of optimization, but that’s another term we’re avoiding for the moment.

Finally, and most important of all, assembling and exposing all this information makes it easy to see where customer treatments support over-all business strategies, and where they conflict with them. This is the most important benefit because business strategy is what CEOs and other top-level executives care about—so a system that helps them execute strategy can win their support. That support is what’s ultimately needed for marketing automation to make its next great leap, from one department to the enterprise as a whole.