Trust International: Selling More, or Earning More?

Trust sells computer accessories, audio and mobile power through distributors and retailers across Europe. Every one of them reports sales and stock back, and every one does it differently: its own file, its own columns, its own product codes, its own periods.

So the first job was not analysis. It was plumbing. We automated the intake from each source and standardised it into one model, so a unit sold in Germany and a unit sold in Spain finally meant the same thing. Only then could the real questions be asked.

Trust does not simply sell a product at a price. Each distributor has a contract, and the contract keeps working after the invoice is sent. There are volume rebates that rise with each tier and apply to every unit once a tier is reached. There is price protection: when Trust lowers a price, it compensates the distributor for the stock still on its shelves. There are marketing contributions. And part of the rebate is already deducted on the invoice, on account.

At the end of every quarter all of this is settled. Sometimes Trust owes the distributor money, sometimes the distributor owes Trust. The rules were all in the contracts, yet nobody could work out the number in advance, so nobody knew the real profit on a sale until months later.

We turned the contract terms into a formula that calculates the settlement continuously, from the same data as the sales. Try it on one illustrative contract: change the volume, or drop the price mid-quarter, and watch the margin on the invoice drift away from the margin that is real.

Sales teams steered on what they could see: units and turnover. So they focused on the products and categories that moved in the largest quantities, and pushed those hardest. It is a sensible strategy when volume is the only number you have.

With the settlement in the data, every product had a real profit, close to real time. Rank the same range by units and then by real profit below. The products the teams pushed hardest sit at the top of one list and near the bottom of the other.

Powerbanks were the clearest example. The range ran from small single-port models to large, heavy ones with four ports, and the big models got the most attention: the best placement, the most promotions, the most time from the sales teams. Unsurprisingly, they also sold the most.

But how much of that was the product, and how much was the push? Sales data alone cannot say, because the two always arrive together. With statistics we could pull them apart: by comparing models, periods and channels with more and less sales attention, we estimated what each feature, the number of USB ports, the weight and the size, added to demand on its own.

The answer cut straight through the sales strategy. The models with two USB ports and a medium weight outperformed all the big, many-port models, and by a wide margin, even though they were pushed far less. Switch the chart below from what was sold to what each model would sell with the same push behind it.

Once the dashboards were live, the conversation in the sales teams changed with them. The goal moved from selling the most units to making the most profit, because profit was now something they could see, per product and per distributor, while there was still time to act on it.

• Sales teams in every country work from one source, instead of a collection of files per distributor.
• The quarterly settlement is calculated continuously, so the real margin on a sale is known when the sale is made, not months later.
• Product and category choices rest on what a product earns, not only on what it moves.
• Range decisions, like which powerbank to build and push, rest on what customers actually buy, corrected for how hard each product was sold.

• Automated intake of sales and stock data from every distributor and retailer.
• One standardised data model across sources, countries and product codes.
• A settlement formula built from the contract terms: rebate tiers, price protection, contributions and advances.
• Statistical analysis that separates the effect of product features from the effect of sales push.
• Power BI dashboards on top, for the sales teams in each country.