Profitability Analysis by Product/Service Line

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.