Say Hello to Channel Data Management and Goodbye to Gut Instinct
by Hobart Swan
This is the era of data — using complex information-processing tools to extract knowledge and insight from very large data sets to drive insights. And while not every profession engages in this kind of data analysis, many are affected by the promise data analysis offers. Channel marketing is one such profession.
To better understand how we professionals can profit from channel marketing data sets, Channel Management Insights, recently had a conversation with Ross Brown. Brown is Senior Principal at The Spur Group, a Seattle-based consultancy that specializes in developing partner programs for Microsoft and Dell, managing messaging and partner conferences for Cisco and Juniper Networks, and providing recruitment insight and strategies for Autodesk and VMware.
Formerly Vice President of Worldwide Partner Strategy at Microsoft, Brown has held leadership roles at Citrix, IBM, eEye Digital Security, and worked with more than 50 technology firms in a consulting capacity. In the process, he has become a leading expert on channel data management (CDM).
In part 1 of this 3-part series, Brown makes a clear distinction between what is commonly known as Big Data and the data that channel marketers typically manage in the course of their daily work. Channel data sets are much smaller, Brown says, but no less filled with the promise of new insights on what makes the channel tick.
People have been collecting data about the channel for some time. Why is it only recently that companies have become so interested in channel data management?
One reason is the plummeting cost of data storage. Now even smaller channel companies can afford to store the amount of data needed to do useful analysis. We’re also seeing an increase in data analysis tools that are within reach of marketing professionals—not data-analysis experts. And these tools are specifically designed to help channel marketers map out the relationships behind the data.
Channel relationships are complex. In direct sales, it’s a simple transaction between buyer and seller. But in the channel, transactions are much more multi-dimensional.
That’s right. A vendor might work with hundreds or even thousands of partners. Each partner has multiple touch points with each vendor—from taking the lead and readiness at the front end all the way through to incentive payments on the back end. What that means is that non-channel practitioners—CFOs, CEOs, CMOs, and heads of sales—have come to rely on what I’ll call an “anecdote-driven” understanding of the channel. They clearly see the limitations of this approach and have long pushed for more “science” and less “art.” That need has propelled channel data management into the foreground.
Good data analysis would give you the power to make less emotion-based decisions, instead of relying on interpretations by CAMs and sales people.
Channel professionals have always had to rely on their gut instincts to make sense of the channel. CDM allows them to use the their brains more.
When some people hear the phrase “channel data management,” they immediately think of point-of-sale data. That makes sense, because when all is said and done, it all comes down to products and services sold. Do you think analyzing POS data is the way to understand what’s going on in the channel?
It’s certainly valuable to be able to do POS analysis. At The Spur Group we do something called “reach, yield, frequency” analysis. “Reach” refers to how many partners the vendor engaged with in the last time period, whether that’s monthly, quarterly, or yearly. “Yield” is the average value of the transaction over that period. And “frequency” is how many transactions a typical partner did over that period.
This kind of behavioral data can help vendors figure out which partners are providing good value—and which ones they need to engage with more to increase sales.
Having said that, let me also say that historically POS has also been a horrible source of data because it’s the truth that allows a lie to exist: It is an absolutely true statement of who transacted a deal, but it provides no information about who actually created or sold that deal.
The reason many vendors do POS data analysis is to identify their most valuable partners. But there is no way to determine which partner is actually creating business for you based on POS data alone.
From what you’ve written about POS data analysis, you seem to go a step further and assert that relying on POS alone can be dangerous to a company.
It’s true. If you looked only at POS data alone, you might be tempted to increase your MDF and incentives to partners that are booking a lot of sales. What POS data won’t show is that these same partners are soaking up demand generated by the diverse set of partners that make up your channel. Looking at the POS data only, you think you’ve identified these strong players driving lots of revenue just by selling on price. So what do you do? You stop funding demand creation and that can cause your entire channel to collapse.
So relying only on POS data can lead to real problems. What data can help create a more accurate picture of the channel?
You need to balance POS data with demand-creation behavior data. You can look at marketing ROI and MDF studies to understand which partners are doing what activities to create opportunities, and use deal registration to see specifically which partners created which opportunity.
Support calls are very good indicators—especially pre-sales support calls. I create this ratio that looks at support calls per amount of revenue. If a partner did 18 support calls and generated $18 million then, on average, they’ve sold $1 million for every support call. If you find partners with very low revenue per support call, they may be actively supporting and deploying your solution—but no longer selling it. That’s good information too. You need to talk to these partners and figure out why they got out of the product business.
With the complexity of technology solutions, partners are getting more certifications. Can you speak to the insight to be gained from certification data?
If you see an increase in certifications, one of the big questions is: “Does having more partners certified in my products lead to more revenue since it gives me more capacity?” Understanding the impact of increased certifications on sales can lead to capacity planning. You can use that data to figure out how many more partners at which level of certification you need in order to hit next year’s revenue goals. And that is powerful insight.
That’s a great question. I wish I had a great answer. I’m still trying to figure it out. I joke that tweets are emotional (here’s something that upsets me), Facebook posts are celebrational (here’s something that was a lot of fun), and LinkedIn posts are aspirational (something I’d like to be doing more of).
The problem is that none of these posts give any indication of what that partner is actually doing with his or her daily time. But what we do look at is the information partners put on their websites.
On their public websites?
That’s right. The Spur Group’s Partner InSite technology is all about using this public data to generate insights. We assess the kinds of visible information they provide on their website, then analyze the data to figure out how they describe their businesses relative to vendor, industry, offer, solution sets, services. We’ve found that that helps our clients zero in on the right partners for them to work with. Then you can use the MDF and deal registration systems to ask, “Do we have the right activities with these partners?” and use POS data to ask, “Did it result in revenue?”
It creates a continuum: you go from public data, to leading indicator data between deal registration and marketing activities, to post-activity data in point-of-sale. That’s an example of having a process view—of using a combination of data sets to create a holistic view of channel activities rather than using just, say, POS data to get a snapshot view.
We began this discussion by talking about the value (or lack thereof) that comes from looking at individual data sets. You’ve made the point that it’s better to look at more than one. An even greater challenge seems to be correctly combining all the data. Is that a fair statement?
I can talk about this in more depth, but at The Spur Group we have created what we call the “Four Best Practices in Channel Analysis.” The first best practice is “integrate everything you can.” So you are absolutely right: it’s all about creating a single data model that correlates all of your data sets to give you the most accurate, useful insights into your channel business.