Channel Management Is More Than Just A Good Algorithm
The Next Wave of channel management
We've spent quite a bit of time here talking about how using data is essential to taking your channel business to the next level.
A fundamental flaw of using channels is partners always have a different agenda from you. This means there is a always a drift, either slight or big, from your objectives. The goal of channel management is to minimize that drift and create revenue acceleration for your company.
With the shift to the cloud, vendors ability to track information on partner performance, their customers, and the solutions they consume has never been better. We've also seen step-change improvement in systems that make measuring partner performance around lead management, deal registration and marketing effort possible. Analytics engines can now assimilate data from multiple sources, manage complex models, and create bots to derive insights.
The era of channel management big data is here. What more could we ask for?
Big Data isn't enough
At The Spur Group we've completed many projects that boil down to becoming a data-driven channel organization. During that time, we've seen what works and what doesn't.
In the latest issue of the MIT Sloan Management Review they have a great article on why big data isn't enough. While the article isn't specifically about channel management, the warning is appropriate.
Algorithms are powerful but they aren't perfect. Too often they take a casual relationship and can exaggerate it's importance. They lack the natural ability to filter on the important data points and sometimes look at too much information to draw clear conclusions. They also tend to magnify inherent biases that are build into your data set.
The bottom line is you need to be cautious when jumping into the big data pool.
It's all about having a hypothesis
The key is how you use big data and modelling for channel management. It is critical that you formulate a hypothesis and use big data to test that hypothesis.
For example, we have a hypothesis that a partner's revenue growth is always a function of improving some mix of a partner's sales velocity (contribution), product mix (capability), market presence (coverage) and competitive share (commitment). We call this the 4Cs model.
Once you have a hypothesis you can use big data techniques to test your thinking and drive your analysis.
Let's look at our example again. We use big data to find out what lever to use with each partner to have the greatest impact. This informs specific business plans with each managed partner and creates a structure to developing programs for unmanaged partners.
The important takeaway is get more from your data by having a well thought out hypothesis.