Anyone can track purchases, subscriptions and licenses. But how do you measure things that produce no revenue at all?
Most companies have some mechanisms for handling non-revenue activity. But they tend to be tightly coupled systems, where information flows in defined pathways from one system (e.g., website forms) to another (lead management or SFA).
Once the data is in the appropriate system, marketing automation can kick in, nurturing the customer or prospect through the next phase of the relationship.
The problem is getting the information in the first place. Only proven indicators tend to be collected.
The answer is in technology.
Many marketers still haven’t grasped the changes in the tech landscape. Web services have finally come into their own, making it much easier for companies to use each other’s APIs. Service oriented architecture (SOA) has become mainstream, making it much easier for companies to share information internally.
The details are far beyond the scope of this column, but the important point is that customer activity data can be accessed and used by any system, regardless of where it resides. (Your IT director may disagree, but give it time.)
Since there is far more data on activity than revenue, you will make your IT director happy by concentrating on aggregated data—for example, who did an activity or how many times they did it, versus every detail of that activity. And you should focus on event brokering (events trigger distinct processes and actions, but aren’t necessarily stored for future use).
Don’t assume you will have complete freedom to use every detail of every action of every customer. Pick out key activities to start and expand over time.
Beyond web behavior, which is already widely used, the most obvious activities are user generated content, such as product reviews, forum postings, and blog comments.
Another indicator of customer engagement comes from Facebook applications. (You have at least one, right?) Usage information is an important indicator of churn, particularly for subscription businesses, and is not always readily available for marketing applications. Customers’ social graphs (essentially who they are connected to via social networks) can help you find mavens and connectors.
What can you do with this information?
For starters, you can include referral value of customers in lifetime value estimates. And you can sharpen their predictive models dramatically. In addition, you can more accurately predict churn, while improving lifetime value estimates.
That’s not all. You can more clearly identify key customers and treat this segment very differently from less active customers. Finally, activity data will help you test your effectiveness, both for short-term promotions like coupons and long-term loyalty programs.
First, get yourself some good software and a great data analyst. Then work on understanding the causal relationships between activities, financial incentives, and the resulting behavior. Tie appropriate incentives to specific, incremental, value-creating behavior.
Start small and get some easy wins—for example, website visits and the number of review or forum postings in the last 90 days. Once you know all this, you can figure out what generates lift and start on the road to thinking beyond transactions.
Michael Greenberg is president of Loyalty Lab, a San Francisco-based developer of best customer management and loyalty solutions for the retail, travel, CPG, and consumer services industries.