There's a blind spot in almost every manufacturing company: the moment the product leaves their warehouse and enters the wholesaler's.
From that moment on, what happens? Who sells it? To whom? In which geographic area? How often? Which SKUs move and which sit still?
The honest answer, in most cases, is: we don't know for sure.
You have sell-in data, that is, how much the wholesaler purchases. But sell-out data, meaning how much and what the wholesaler's salespeople actually offer to end customers, is almost always unknown territory. And a big part of the commercial game is played out in that unknown territory.
The real value of a reseller incentive program
When you think about a reseller incentive program, the first thing that comes to mind is the reward: what the top sellers receive, which thresholds to reward, how to structure targets.
That's the most visible outcome. But it isn't the most important one.
The real competitive advantage of a structured incentive program is something else: the creation of a sales database that normally doesn't exist.
To get wholesalers' salespeople to take part in an incentive program, you have to register them. Name, role, area of responsibility, wholesaler they belong to. For the first time, you have a precise list of the people who, every day, either bring your product to end customers or don't.
To assign them rewards, you have to measure what they sell. And to measure what they sell, you have to collect sales data. Data you didn't have before, that the wholesaler rarely shares spontaneously, and that now enters your system in a structured way.
The salesperson database: why it's so valuable
Imagine having a network of five hundred salespeople spread across the entire country, all employees of wholesalers and distributors who technically don't work for you.
Without an incentive program, these salespeople are invisible. You know they exist, but you don't know who they are, where they operate, what they offer, or how effectively.
With an incentive program, you have:
A complete census of the indirect network. Every salesperson is registered, with their profile, their area, their reference wholesaler. For the first time you have a real map of your actual sales force, not just the nominally direct one.
Individual performance data. Who sells the most, who's stagnating, who actively promotes the premium lines and who sticks to routine products. This information is the basis for any commercial decision: where to train, where to push, where to intervene with the area manager.
Real geographic segmentation. Not the estimated potential of an area, but actual sales data by zone, by wholesaler, by salesperson. Knowing that in a certain region your premium SKUs are never offered isn't an intuition: it's data on which you can build an action plan.
From incentive to sales opportunities: the role of AI
A sales database on its own is just an archive. The value emerges when that data is analyzed to find the patterns that aren't visible to the naked eye.
This is where AI comes in.
A salesperson at a wholesaler in the Northeast regularly buys three SKUs but has never ordered the premium line. Yet their total purchase volume suggests that the customer base they serve could sustain it. That's an upselling opportunity that doesn't exist without the data.
A geographic area that grew 15% the previous year has slowed to 3% over the last two quarters, while neighboring areas are stable. That's an anomaly signal that calls for investigation: has a competitor made an aggressive move? Has a wholesaler shifted priorities? Has a key salesperson left?
Three salespeople out of thirty, in different geographic areas, have premium-line sell-out rates significantly above average. Those three salespeople know something the others don't. It's worth understanding what it is and replicating it.
These analyses, on a monthly basis, would be impossible to do manually across a database of hundreds of salespeople. With the right tools, they become automatic.
How to structure a reseller incentive program that generates useful data
The program must be designed from the outset with a dual objective: incentivizing sales and collecting structured data.
This means the participation mechanics must include the individual registration of each salesperson, not just the wholesaler as an entity. Points must be assigned by SKU and not by aggregate revenue, so that you have product-mix data for each salesperson. Sales uploads must be periodic and verifiable, creating a historical series to work with over time.
The more granular the data, the more useful the subsequent analyses. A program that rewards only total revenue per wholesaler generates no insight. A program that rewards by SKU, by salesperson and by period generates a commercial map that's worth far more than the cost of the rewards handed out.
The virtuous circle: data, analysis, intervention, results
The model that works is the continuous-loop model.
The incentive program generates the sales data. AI analyzes that data and identifies the opportunities: who is below target, who has untapped upselling potential, where the anomalies are concentrated. The area manager intervenes with the right information, at the right time, on the right salesperson. Sales improve. The program generates new data. The cycle starts again.
The Salestack AI platform is built around this logic: collecting sales data from the indirect network, real-time analysis, identifying opportunities, monitoring progress toward targets. The incentive program stops being just a rewarding tool and becomes the engine of a commercial information system that the manufacturing company never had before.
The question I'll leave you with
How many of your wholesalers' salespeople do you know by name right now? And of those you know, do you know what they're selling this week?
If the answer is "very few" or "I don't know," you're managing your indirect network practically blind. A well-structured incentive program doesn't just solve the motivation problem: it solves the information problem.
Gianluca Testa
Founder Salestack
Host of the Business Garage Podcast