Revenue diagnostic for a print house
A leading Podgorica print house had a steady inflow of jobs — but an inflow that didn't repeat. The data showed why.
Problem
Clients came once, ran a single job, and never returned. With no system for tracking the client relationship, the loss was invisible — until we measured it.
- €85,000estimated annual leak
- 58%churn rate
- 30,552work orders analyzed
Analysis
We pulled historical work-order data from the internal system and structured it in Python for analysis: every order linked to a client, date, value and job type. In SQL we grouped clients by order frequency and calculated churn at the cohort level — how many clients from each month returned within the next three, six and twelve months. Cross-referencing that with average order value gave us an annual revenue loss estimate by client segment, not just a single total.
System designed from the findings
Based on where the most clients were being lost, we designed:
- A Notion operations system for managing capacity, machines and orders — visibility that didn't exist before
- A HubSpot CRM pipeline with automated client statuses by order stage
- Make.com automation that triggers a client reminder after a period of inactivity, instead of waiting for them to come back on their own
Outcome
A quantified opportunity of ~€85,000 a year — and a system that turns one-off clients into repeat customers. A decision grounded in data instead of assumption.