Although he has taken a fair amount of criticism for it, one of basketball coach Phil Jackson’s greatest strengths is knowing how to treat his top performers. As head coach of the six-time world champion Chicago Bulls, Jackson had what came to be known as the “Jordan Rules.” Simply stated, it meant Michael Jordan was the team’s key player and thus was treated differently than role players. In L.A., the same applied to Shaq and Kobe. It may not seem “fair,” but the results speak for themselves.
The same basic principle applies to sales reps working for technology channel partners. There are the superstars, the ones who consistently perform at high levels for the distributor or VAR day in and day out, year in and year out. There are the role players, who regularly do good work, even if it isn’t at superstar level. Then there are those who may perform well on a short-term basis (usually when there’s a SPIFF involved) but otherwise are non-factors.
For a technology vendor, it makes just as much sense to treat each group of tech reps differently as it does for professional coaches and athletes. The problem is it’s usually a lot tougher to figure out which rep belongs in each group. After all, professional athletes have their stats, accomplishments and failures publicized every day. Rep information is far more difficult to find – not to mention far more difficult to understand.
Assessing the Lifetime Value
It’s not impossible, though. Through a process known as Partner Rep Lifetime Value Modeling, you can identify and segment sales reps based on data that determines the Net Present Value (NPV) of the rep’s future profit contribution. Scores are derived from a statistical model that can be used in several ways, including:
- Estimating all future profits generated by a given partner rep
- Identifying rep segments for targeted communications campaigns
- Providing enhanced reward and recognition offers
- Identifying high-value and at-risk representatives (franchise players v. trade bait)
- Measuring partner rep loyalty and engagement
Lifetime Value not only predicts who the best reps will be over time, but also identifies those whose level of engagement is waning and may elect to go “free agent.” Once their Lifetime Value has been assessed, vendors can become less product-centric and more customer-focused; directing their investments in channel partner programs to the areas where they will do the most good.
Data Collection
Determining the Lifetime Value of your reps begins with data. Most channel partners already have a plethora of rep data at their fingertips, including sales and transactional data like date of sales, amount of sales, profitability, hire date or first sale date. However, partners tend to not share this information with vendors if for no other reason than fear of a vendor move to a direct sales model. The most effective way to gather sales transaction data, by partner rep, is through a non-cash channel incentive program.
In this approach, transactional data gathered via the incentive program is used to create the model. Sales data for each rep should include Recency, Frequency, Monetary Value, and Tenure — or the length of time a rep has been claiming sales — which are all combined to yield a Rep Lifetime Value score. Data may be in the form of individual sales claims or partner allocation – either will yield appropriate data.
Once this information is collected, the Lifetime Value model can be built. The model incorporates a regression algorithm to determine how additional data gathered correlates with high Lifetime Value scores. You can consider behavioral or demographic data, including channel partner program tier, years of service, and type of industry to which they sell, as well as any training or certification activity, vendor Web site visits, and orders for collateral material. Any data that can be obtained and linked to an individual rep can be tested for correlation to Lifetime Value. For example, if the data shows that all of your highest lifetime value Reps use your web-based referral system and have taken three unique training courses, these two elements would show high correlation scores. Thus, you would develop calls to action around these behaviors.
With this information, it becomes clear what correlates with high value, and rep incentives can be designed to encourage high-value activity.