“Not everything that can be counted counts, and not everything that counts can be counted.”
Einstein’s observation holds true for sales enablement content-related analytics. Imagine the launch of a shiny, new enablement and collaboration platform. Many different roles in sales, marketing and product management are looking forward to the content analytics that the system will provide. Will the reality live up to their expectations?
Let me share with you a few lessons I’ve learned.
Correlation and causation
According to Oxford Dictionaries, a correlation is “a mutual relationship or connection between two or more things.” Causation, on the other hand, is the action of causing something, e.g., “the postulated role of nitrate in the causation of cancer.” Let’s keep in mind that even a strong correlation is not a proof of causation.
View, clicks and downloads are indicators—nothing more, nothing less
These metrics are foundational information, for different target groups—salespeople, their managers and the cross-functional enablement team. The data shows what people view and what they download. That’s all it says. It does not necessarily mean that people use what they download. And it does not say that the downloaded content was helpful. These are very common and widespread misinterpretations.
To better understand these analytics, check out your organization’s key sales initiatives. What are the important products, solutions, services? For which portfolio elements can people earn the biggest commission? Is there a performance management rule that rewards people if they download or indicate that they used certain content, e.g., the latest campaign playbooks? Next, check out the biggest revenue generators in your portfolio and examine the analytics for the related content. It can happen, especially if a sales force is very experienced, that there is only a small correlation between top revenue generators and related content usage. Those experienced people often still share across the “black market” of sales information, which is the informal network of colleagues who know each other personally. Map these insights back to your enablement analytics and you will come to a slightly different conclusion. Even if your enablement platform is completely linked to the CRM system and analytics show people working with recommended content stage per stage, it’s never more than a correlation.
Content ratings and likes—it depends
The biggest challenge for enablement platforms and teams is always to get the salespeople to actually use these social functions. Being a customer at Amazon and being a sales person in complex B2B sales forces are two different things. Just because sales people have an “Amazon” behavior at home, does not mean that they behave the same way at work. Mature sales forces are especially hard to convince that there is value in this activity—value for the entire sales community and over time. What we appreciate with top performers is their strong focus on what matters to their sales success, and to ignore everything that doesn’t create immediate value for them. Rating content is definitely not in this category, especially not when you ask them to go back to the system and rate the content after they have used it. And what does it mean when a rating is given by someone who has not yet used the content? Nothing. To understand ratings and likes, it helps to analyze the percentage of your content that’s rated in the first place. The lower the percentage, the less valuable it is. Then, check which roles are authorized to rate and to like content. If there is no role-based limitation (and that happens more often than you may think!), the value of ratings and likes is precisely zero. On more than one occasion I have discovered that content creators have rated their own content high and their colleagues’ content low. If that’s the case, it’s better to switch off the entire function: the absence of data is better than false data.
Content analytics are only one side of the coin
What content analytics really mean is different in every sales organization, in every culture and in every industry. Imagine a sales force of millennials in San Francisco selling technology, and a mature sales force in the manufacturing industry in Europe. The specific value of content analytics couldn’t be more different in these two cases. For you as a sales enablement leader, it’s essential to define a content analytics framework that defines how to look at the data and what additional elements are necessary to understand the big picture. Additional elements can be dedicated win/loss interviews, campaign reviews, and sounding boards with “early adopter” salespeople and front line sales managers to discuss analytics and learn more about their perspectives and experiences. Approaching the issue in a holistic way like this helps to leverage content analytics and to make the right content decisions—to create value, not noise.