I’m often asked to provide a rule-of-thumb metric to predict the financial success of a broadband business plan. The two most commonly requested metrics are customer density (how many households are needed per mile of road) or the percentage of customers needed (penetration rate) to make a fiber business plan work.
After having done hundreds of feasibility studies I’ve stopped boiling success down to any simple metric – it’s never that simple. The reality is that there are a number of important variables that have a major impact on operating a successful broadband business plan. Every ISP and every market is different, and one or two variables can have a huge positive or negative impact on a given business plan.
Following are the major variables that can make a difference when building fiber. A similar list can be made for deploying fixed wireless or other technologies.
- Customer penetration rate. An area with low density might still have great financial results if the penetration rates are high enough. I’ve seen expected penetration rates vary from 40% in some large markets to over 90% in markets with no existing broadband. Changing the expected penetration just a few percentage points can have a big impact on cash flow. This is why we think it’s mandatory to do a survey to understand customer interest in fiber broadband.
- Labor rates. The cost of staffing varies widely across the country and between companies. and there are places where labor costs twice as much as in other parts of the country. Labor costs also include taxes and benefits which vary widely by state and between ISPs. The staffing structure of the ISP also comes into play since companies vary between lean and staff heavy.
- Borrowing costs. The interest rates and the term of a loan (15-years versus 25-years) can have a huge impact on a fiber project since the size of the borrowing is usually significant. Things that mitigate borrowing costs such using some equity, getting grants, etc. can have a big positive impact.
- Prices. Broadband prices can have a big impact. We know that most customers will buy the lowest priced broadband that has a reasonable speed. There is a big difference if this primary product is at $50 versus $60.
- Cost of the Network. The metric I’m often asked about is the minimum number of households needed per road mile. While customer density is an important factor, there are many other issues that can have a big impact. The cost of building fiber varies widely across the country due to some of the following:
- The mix of aerial and buried fiber has a giant impact.
- For buried fiber, the type of soil matters, because the presence of rock adds big costs.
- Again labor rates, meaning the cost of construction crews. We’ve also seen projects that took federal money that had to pay prevailing wages for rural construction that killed the project.
- The condition of the poles and the effort and cost needed for make-ready can be a huge factor.
- The difference between building in the power space versus the communications space on poles can be significant.
- Choosing PON versus active Ethernet can have a difference, with Active E having larger fiber bundles needing more splicing.
- One of the biggest impacts is the cost of fiber drops – the two important factors are 1) average distance customers are from the road, and 2) who builds the drops (we’ve seen the labor costs for drops vary by several hundred percent).
- Building in phases versus building as quickly as possible can sometimes make a big difference.
- Customer density is important, but the above factors can matter a lot more. Density can also be a tricky number. Consider two examples of companies that would have the same average density but significantly different costs: Company A has no towns and the rural areas average 10 households per road mile. Company B includes one decent sized town but is surrounded by big farms but still averages 10 households per road mile.
Clients always want me to predict the outcome of a business plan before we undertake the needed business models. I’ve learned to not predict. I’ve worked on projects that look to be far more profitable than I would have expected and looked at others that don’t look feasible for some reason. As an example, I recently finished a business plan model where it turns out that the existing poles in the new market were nearly unusable and the alternative of going underground was impractical because of rock. This one factor made it hard to justify building fiber in a market that otherwise would have passed the sniff test using high-level metrics.