How Predictive Channel Management Solutions Support Smarter Decisions

By Dave Geoghegan, Chief Technical Officer, ChannelEyes

The management of your indirect sales partners presents unique challenges. Motivating and incenting them to achieve your goals is difficult when they have their own set of priorities and objectives. And as channel spaces becomes increasingly crowded and complicated, making smart management decisions about partners is even more crucial.

Predictive channel management solutions are providing vendors with new capabilities to evaluate the channel-related decisions they need to make. For each possible course of action, this technology helps decision makers understand what’s likely to happen.

As a result, channel executives can get reasonably reliable answers to questions like “Will choosing a particular partner be worth the on-boarding investment?” or “How likely will a specific partner reach its revenue goals?” to inform the decisions they make.

With the aid of machine learning and artificial intelligence solutions, vendors are now making more informed decision about the outcome and impact that crucial decisions will have on their business.

Equally important is how vendors can uses these tools to help find “hidden” opportunities for revenue in their customer base. These are products that customers are highly likely to purchase when offered the opportunity. Generally, they fall into three categories.

Consumables

Consumables provide the simplest example. When a customer’s printer is out of ink, it buys more. The key here is predicting when the customer’s printer will be out of ink and signaling to the partner that the opportunity for revenue exists. The complexity arrives when the determination of whether or not the consumable is exhausted is not straightforward. Understanding when similar customers have replaced the consumable helps us understand how likely the prospective customer is to be in need of replacement. This is especially difficult in an indirect model since the partners have access to such a small slice of the information around buying habits.

Upgrades

Upgrades to software and hardware provide a slightly more complex scenario as the need for the new purchase generally comes from desire rather than need. To continue with the above example, the printer still works, but the customer would benefit from a newer model. The key here is understanding the behavior of similar customers and their upgrade behaviors.

If 85 out of 100 similar customers have upgraded in the last 12 months, there seems to be a high likelihood that the other 15 are at least considering the upgrade. The difficulty here is in determining a “similar” customer. Again, access to the entire customer base data is critical here, along with the ability to meaningfully determine how “similar” a customer is to another.

New Products/Services

The purchase of new products and services provides the most complex of the examples, but also the most significant revenue opportunity. Here, customers may be presented with entirely new products which they hadn’t considered before. The ability to provide novel and synergistic recommendations is at the heart of popular consumer recommendation engines like Amazon.com. The key to a recommender like this is that it provides “novel” recommendations. That is, products which you are likely to buy, but not likely to have found on your own.

Mining The Opportunity

The ability to provide this kind of leading information to your partners is a potential gold mine for them. This type of analysis is problematic for the partner due to two key factors. First, a partner sees only a limited slice of the deal flow for the supplier. They typically see a fraction of a percent of the overall transactions and lack the critical information to do such analysis. The second issue is the cost, effort, and expertise necessary to perform such an analysis. Even if a partner wanted to engage in this analysis, it’s the rare partner who could actually do so.

The vital information we need to understand to perform such an analysis includes sales history and partner/customer Firmographics. It is critical here to realize that the partner plays a vital role in this analysis. The partner is the key difference between a general analysis (not that useful in an indirect model) and a specific analysis that is decision-grade.

The result of the analysis is an understanding of each particular customer and what they are likely to buy next. We also have an indication of just how likely that purchase is to be made. This allows us to filter to only the likeliest of products and stack rank them. Partners want only the highest value targets so they can focus their energy appropriately. We also understand that there is a time dimension here, so the recommendations also need to be marked in a way that lets the partner know when the deal is likely to happen.

What Partners Value

The value to the partner of this type of analysis is quite high. While the monitoring of consumables and upgrades has been well understood, the ability to suggest novel product offerings which are complementary has not generally been offered. The key here is understanding the similarities which exist among customers and distributing this knowledge across the partner base. If partner A makes sales of Product P to a customer and partner B has a “similar” customer, there is some likelihood that partner B can also sell that product to their customer. Essentially, it’s like getting new leads into the partner’s current customer base.

In the real world, it takes a system which has predictive capabilities and treats the partner as a critical element in its modeling and predictions. Direct sales offers significantly different challenges and simply adapting an existing direct sales approach generally won’t produce the desired results.

The predictive channel engine at ChannelEyes, helps our customers realize revenue from “hidden” sources. In addition, our partner-facing solution provides the information they need to act on these potential new revenue sources.