Mastering cost analytics is about understanding the fiber rollout cost, not just the average per kilometre or household cost. When running fiber build analysis, it’s common to pick up voluminous data points with significant variances between similar situations. To help us focus on the correct data set, we use a network infrastructure pathfinding simulation (NIPS) that provides a highly accurate and automated low-level design from which rollout costs can be assessed. By taking existing infrastructure and architectural guidelines into account, we can run an optimization for speed, coverage, or budgets. Which makes the prioritization of build areas within certain restrictions possible. Understanding the fiber rollout cost in detail is the first step toward a value-based approach. Not knowing the cost implications of a planned build will lead to suboptimal resource allocation and cost overruns.

Analytical insights enable us to identify profitable areas with an above-average take rate, lower churn, and higher average revenue per user (ARPU). However due to the lack of public information on both taking and churn rates we must work with approximations and develop our own model taking into consideration both internal factors (age, education, or income) and external data (take rate, product mix or churn rate) to arrive at a specific hypothesis. Once the observations and hypothesis have been concluded we can then build a statistical model which can be stress-tested to confirm the findings. Not knowing the revenue side of the equation will result in stranded assets. Building fiber in areas with low take rates or areas with high operational costs will prolong ROI.

Now that the cost, revenue, and feasibility have been assessed the results need to be presented to strategic planners in a non-static view. At this stage, we need to not only assess the one-dimensional approach of comparing the cost of a rollout in conjunction with the attractiveness and building restrictions. We also need to consider competitor moves, changes in regulation, and improvements in technology. From these scenarios different factors can be calculated, compared, and selected. Simulations act as great support material when securing funding for rollout projects and answering executive questions.

Building up various models, understanding the correlation between factors and gaining insights into strategic options is not a one-off. There is a continuous effort needed to add factors where needed, deal with changes observed in the field and change requirements from decision-makers. In short, we must manage continuous improvements to increase our scope in terms of modelling and hypotheses to be validated, features and visualization to make the data consumable, and capabilities and technologies that allow for more insights.