Profitability Analysis by Product/Service Line

Transparency is key. Keboola allows you to expose the allocation drivers and results clearly. For example, you can produce a report that shows “Customer Support costs ($1M) allocated to products based on number of support tickets: Product A had 500 tickets (50% of total) so got $500k of the cost.” By agreeing on drivers with department heads (e.g., operations, support), you get buy-in that it’s a fair basis. During implementation, you’d likely run the model in parallel with old methods to reconcile differences, explaining why some products show different margins (often revealing where old methods were oversimplified). Once reconciled and understood, trust builds. The beauty of Keboola is that it can store documentation or metadata, so each metric or allocated cost can have an explanation available for users. Also, it’s repeatable and consistent – unlike ad-hoc analyses that might change, this will do it the same way each time (unless deliberately changed), which gives people confidence in the stability of the method.

The level of detail is up to you. Some companies choose to allocate only certain major pools (like supply chain costs, support, marketing) and not every overhead line. The aim is to get a meaningful view, not perfect precision. You could, for instance, exclude truly fixed corporate overhead that doesn’t relate to products (like CEO salary) so as not to muddy the waters – that’s a choice. Or allocate it separately as a “below the line” item so product P&Ls can be viewed with or without it. Keboola’s flexibility lets you do that toggling. The usefulness of granular allocation usually comes when looking at many products – it helps identify ones that use disproportionate resources. If your indirects are complex, you might start with a simpler allocation and refine over time as you gather more driver data. Remember, even a directional insight (e.g., Product X likely barely breaks even after distribution costs) is better than none. And because the system handles the heavy calc, you can afford to refine as needed.

Yes. Profitability analysis applies to services (like consulting projects, maintenance contracts) and subscriptions (SaaS, memberships) too. The data might differ – e.g., for services, pulling in timesheet data to allocate labor cost per project, or for subscriptions, allocating customer support and cloud infrastructure costs per product or customer. Keboola can integrate those sources just as well. The model is set up to define “entities” of profitability – whether that’s a SKU, a service line, a project, or a customer segment – and attribute revenue and costs to them. In fact, for many subscription businesses, analyzing profitability per customer or cohort is crucial (lifetime value vs cost to serve), which is essentially the same exercise but at the customer dimension. Our use case can be adapted to that: calculating gross margin per customer, factoring in their support usage, etc. So, whether product, service, or customer, the platform can crunch it as long as we feed it the right data. The finance team can then slice and dice to get the insights they need to maximize profitability across whatever dimension matters.