March 2015 Feature

The Best Scorecard Is Built on a Solid Foundation of Smart Channel Data Management

by Hobart Swan

Ross BrownThe first two articles in our 3-part series on channel data management focused on why CDM has become so popular, and how to set up data management programs that deliver valid, actionable business intelligence.

But at the end of the day, the reason for collecting channel data is to be able to accurately assess the contribution each of your partners makes to your overall profitability. Channel data management is, therefore, the foundation on which partner scorecarding should be built.

In this final article we conclude our conversation with Ross Brown, Senior Principal at The Spur Group, by focusing on scorecarding. Ross explains many of the nuances of effective scorecarding. He ends with advice for partners that may ruffle some vendor feathers in the short term. But his advice can also help those same vendors make sure that the partners they choose to work with are positioned for maximum return on investment.

CCI: It’s interesting to contrast what the scorecarding process has looked like historically with where we are today. The old model is more like an Excel-based scorecard created by getting a few smart people from a channel sales organization huddled in a room. They bang out the scorecard, which then lives on somebody’s computer—walled off from the rest of the organization.

What should scorecarding look like today with all of the data collection and analysis tools we’ve been discussing now available?

BROWN: A good scorecard contains a mix of leading and lagging indicators and individual and trend activities. For example, a discreet metric would be something like, “We have a target to hit ‘x’ number of lighthouse wins for a new product this year. We want to be in this many customers.” You break that target up across different geographies and assign goals to people who go off and hit their numbers.

Another scorecard metric might be, “We want our partner net satisfaction go up three points.” This is a trend metric because you have to do a lot of things right for that number to go up. It’s also a leading indicator because if you’ve designed your SATs measurements correctly, you know that the things that drive SAT are the things that will drive revenue and performance in the future.

Lighthouse wins, on the other hand, are lagging indicators because they look to the past: did you hit that number of lighthouse wins or not?

CCI: What do you think is driving today’s increased interest in partner scorecarding?

BROWN: Everyone wants a scorecard because they’re watching other business units put scorecards in place. They’re seeing revenue or budget flow to these business units because scorecarding gives these units an accountability matrix that they can manage the business towards.

There’s also a sense in the channel of, “We’ve got to have a scorecard to be seen by management as having the same level of controls as these other business units do.”

But here’s the thing: you can’t get a valid scorecard unless you’ve done the work creating the right partner data model. You need to be able to state with confidence that your data model correlates to reality and predicts how any given partner will perform. Your organization may come to rely on the conclusions you draw from your scorecarding, so you better make sure the data model is correct.

The difficulty in doing this gets back to what I call data awareness. Most channel professionals come from sales and marketing backgrounds, not data backgrounds. Most IT people, on the other hand, have little understanding of the complexity inside a partner organization.

A lot of our work at The Spur Group is helping companies bridge this gap and create partner data models that deliver accurate scorecards.

CCI: What sort of response do you get from companies when you talk to them about the value of channel data management? Do they realize its role in choosing partners?

BROWN: First of all, channel professionals typically underestimate by an order of magnitude how much it costs to put a valid scorecarding system in place. It is not a $50,000 project that involves, as you said, putting a bunch of smart people in a room.

It’s about doing the deep research to create a robust channel data model, and then integrating that data into a valid scorecarding system. That can cost in the millions of dollars, but the result is worth from tens to hundreds of millions—depending on the size of your company.

It needs to be a companywide priority to create an enterprise-worthy partner data model. You need to understand who is going to own all the data structures. You need to make sure it’s accurate. And you have to be ready to use it to design scorecards that drive behavior within the vendor organization.

Much of what we see in the channel is not so much an appreciation of the work involved in creating good scorecarding. It’s more of what I call the “technology industry rain dance.” Vendors see other vendors doing scorecarding and having some measure of success. So they think it comes from the rain dance, not the underlying weather forecast… they don’t understand that success comes from the data modeling work it took to create the scorecard in the first place.

CCI: You’re exactly right. Otherwise the credibility of that data—and therefore your credibility as a channel organization—can be undermined.

BROWN: If the data isn’t good most execs can smell it right away. C-level people usually have their own metrics that give them a strong sense of what’s going on in the channel. The best way to discredit your channel organization is to go in with data that the exec above you or diagonally above you in a different organization knows to be wrong. And once your credibility goes out the door, and it’s hard to get it invited back in again.

CCI: So good scorecarding needs to be built on the foundation of a sound data model. At the end of the day, vendor organizations can use that scorecarding to arrange their partners into tiers.

As we said, in some vendor organizations, partners earn their way up the tiers through the “totem pole”: sell ‘x’ amount, that gets you into the gold tier; sell “y” amount to get into the platinum tier.”

BROWN: Right. And this model is fine as long as the way the vendor assigns partners to tiers makes sense. The model of “The more you sell the more you get from us,” seems pretty straightforward. But the way it often plays out in the real world is that your top tier gets filled with partners who sell on price and capture demand created by other partners. It doesn’t take long for the partners who are creating that demand to realize this—and stop working to create that demand.

That’s why the model needs to be more like, “We don’t really care what you, as a partner, transact. What we care about is how much you create. Participate in our deal registration program so we can measure the things that really matter.”

CCI: So then it’s not about POs coming in, but POs being created.

BROWN: Exactly. When you shift your thinking that way and measure the right things, you can actually start leveling your channel program appropriately. That other model is fine for certain products that are specialty commodity-oriented, where it makes sense to get more by selling more. But even then, you just need to be careful that the way you set up your structure doesn’t create a hollowed out channel filled with partners who depend on others to create demand.

CCI: Collecting and analyzing data to create sound scorecarding makes perfect business sense from a vendor standpoint. But isn’t there a danger of creating a ranking system in which the partner doesn’t fully comprehend the logic you’re using to put them in one tier versus another?

After spending years to get into the gold tier, I would resent it if all of a sudden a new channel chief comes in and says, “We’re going to assign categories based on an extensive data analysis. Some of the ways we measure your performance will be transparent; some won’t. At the end of the process, we’re going to assign you to a category based on that analysis, not on how you’ve earned your way through the program so far.”

BROWN: You’re right. It really must be about transparency and clarity—and about understanding something very basic about your partners: what success looks like in their eyes.

The simple fact of it is that a partner’s value can be very different than what you as a vendor want to do in any given year. If there’s one piece of advice I have for partners, it is this: If you organize your partner business around creation of value for the vendor, you’re always going to be limited in your growth. Instead, you need to organize yourself in terms of the values you create for your customers—and then work with partners who are aligned with those values.

It’s a perfectly reasonable thing to say, “Hey, Mr. or Ms. Vendor, what are the things you want to accomplish this year?” and then figure out which of those goals line up with your business. I think too many partners say to themselves, “I’m going to realign my business to align with what the vendor wants to accomplish this year.”

Frankly, I think that anyone who designs their partner business around what a vendor is focused on from year to year is always going to be, at best, sub-optimized. It works the other way too. Vendors that invest in partner organizations that aren’t fully aligned with their goals may find that their investments don’t deliver the expected returns.


Seven Rules for Using Analysis Well

The Spur Group’s Four Best Practices in Channel Analysis provide insight into how vendors can best set up their data. The group’s Seven Rules for Using Analysis Well help companies understand what they need to think about when analyzing the data.

  1. Analysis is as good as the data under it. Capture the data correctly the first time and get as close to the actual data as you can.
  2. Know the right time to go macro (trends across partners) and when to go detailed at the partner-rep level. The first is for creating strategy; the second for taking action.
  3.  Align your times and normalize for seasonality.
  4. Look for leading and lagging indicators between quarters.
  5. Be consistent in your measurements: If you change taxonomy, map it!
  6. Drive internal alignment on indexes and common metrics that drive alignment on investments.
  7. Use the data to drive change, not just to illuminate problems.