Impact Study

Capex ROI Prediction to maximize value

Our client is the leading leading supplier of fixed and mobile networks for telephony, data and television in the Netherlands, with over 10,000 staff and revenues over €5bn.


To meet growing consumer demand for bandwidth, our client embarked on a 10-year plan to upgrade its fixed fiber-to-the-home network, at a total estimated cost of €3bn. Traditionally, when scheduling such a large-scale infrastructure project, the focus would be primarily on the cost side: the most cost-efficient areas go first. But costs are only half of the equation. Prioritizing areas that deliver the greatest revenue opportunity will accelerate time to value and maximize ROI. But which areas are they?


To answer the question, we developed an AI-powered solution called Smart ROCE (Return on Capital Employed). The solution collects historical sales and usage data and combines them with a wide range of demographic and external data. Then it applies AI-powered algorithms to predict churn and acquisition probabilities, average revenue per location, and therefore market share and revenue evolution. We could then calculate ROCE per location by dividing the increase in Customer Lifetime Value (CLV) by Capex invested, and prioritize accordingly.


By prioritizing the highest yielding localities, we were able to achieve ROI 2.5x higher than average for the project in the first investment year. This yielded an additional €75m per year over par for the first 3 years. Overall, deploying the Smart ROCE solution is estimated to yield 120% growth in investment returns (NPV) across the 10-year project.

Impact stats
Year 1 ROI
First year returns exceeded estimated annual average for the full project by 2.5x.
Additional value years 1-3
Prioritizing high-value locations yielded an additional €75m per year for three years.
Growth in project ROI
Over the project lifecycle, additional value amounted to 120% uplift in total ROI.
Smart ROCE allows network operators to optimize investment schedules to the most granular level through predictive modelling of market response and long-term customer value. By prioritizing Capex to the highest yielding areas, this maximizes value for both customers and operators.
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