Few business quotes are repeated as often as Peter Drucker’s famous words, "If you can’t measure it, you can’t manage it." In today’s digital age, however, perhaps Drucker got it backwards. As many managers are realizing while wrestling with big data, "If you can’t manage it, you can’t measure it."
Improving data management is particularly important in channel analytics. Most of the big data hype focuses on the potential to find growth opportunities by mining mountains of consumer data. For many B2B companies, tremendous opportunity exists to glean actionable insights by integrating and analyzing information provided by both distribution partners and customers. Industry leaders are tackling this additional complexity and improving their ability to:
- Identify high-performing partners who are in the best position to drive future channel growth
- Quantify the impact of partner incentives and support to optimize channel program ROI
- Share insights with channel partners to enable cross-selling or up-selling
I recently hosted a webinar on this topic with Dan Hawtof of Channelinsight in which we shared best practices and an approach to building a “channel analytics engine.” Click here to view the recording.
Kicked off with enthusiasm and anticipation, channel analytics projects too often fizzle after months of delays due to data gaps and quality issues. Integrating data from disparate IT systems and dozens (in some cases hundreds or even thousands) of channel partners proves overwhelming. Sales leaders are left frustrated, believing that their company is "different"—unable to adopt best practices because of insurmountable IT issues, skill gaps or counterproductive channel dynamics.
Often these challenges arise when a company has not taken time to establish a comprehensive approach to channel data management before diving into analysis. As my colleague Bhargav Mantha pointed out in a recent post, improving data management is not solely an IT issue. Building a channel analytics engine requires new processes and a clear governance model that defines how channel data is gathered, cleaned, organized, analyzed and distributed.
Here are a few pointers that might be helpful as you embark on any channel analytics initiative:
1. Define your objectives.
To quote another famous author, Lewis Carroll, "If you don’t know where you are going, any road will get you there." When starting a channel analytics initiative, idefine what questions you hope to answer and what information you need … and, perhaps more important, don’t need.
2. Build a blueprint.
In construction, you don’t start pounding nails without first sketching out what you are building. Take the time to develop a comprehensive vision of the processes, technology and skills that must be in place to build a scalable channel analytics capability.
3. Walk before you run.
Often the best way to start is by building a proof of concept that demonstrates the value of integrating partner and customer data for a limited number of partners in a defined geography. To accomplish this, select an area where you have the data you need and partners who are eager to help.
4. Focus on usability and storytelling.
Analytics initiatives often fail when IT dumps a bunch of Excel files on the channel sales team’s laptops and expects magic to happen. Use powerful analytics and reporting tools (e.g., QlikView, MicroStrategy, SAS, Cognos) to improve data visualization and present insights in a narrative that is easy to understand and act upon.
5. Enable your channel operations team.
Many companies believe they will be unable to effectively analyze channel data without first hiring analytics experts. Often easier said than done. At a recent big data conference, one panelist complained, "Data scientists are like unicorns. Everyone knows what they look like, but no one has actually seen one." To succeed and scale, your channel analytics initiative usually must be supported by the team you already have in place.
This last point is important. In an upcoming post, I will elaborate on the changing role of channel operations and the key skills organizations must develop to harness the power of big data in the channel.