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Data Science Breeds Success in Broadband Rollouts
By Gerry Lawler
Building telecommunications infrastructure requires massive up-front investments, and to date, service providers have used very simple methods of determining how and where to build their networks – decisions based only on the number of passings and the cost per foot. I believe this is a significant reason we continue to see the digital divide, with under- and unserved communities across urban, suburban, and rural areas. Unlike other major infrastructure investments for water, electricity, or gas, broadband infrastructure does not have a guaranteed return or take rate; some investments pay off, and others don’t. Making significant investments with such uncertain returns using such simple metrics is not a good way to deploy sustainable, successful projects.
In some ways, the traditional method of broadband buildout selection is understandable. There’s such a fervor at the beginning of a move into a new broadband market, and a drive to validate a business plan and a financial model. Naturally, the provider wants to generate as much interest as possible, but in many ways, this becomes their undoing because they end up promising great things for everyone in an area.
But a successful market entry must be very calculated as to whom and how you validate the investment while also being able to engage the right people with the right message. Successful broadband projects must accurately make financial, competitive, technical, and social decisions and they must get the formula right over a long period of time in an ever-changing marketplace, especially when cost per foot or density is challenging.
Hexvarium uses data science techniques drawn from financial security risk management, global supply chain management and military geospatial targeting to identify, deliver and connect broadband markets. And we literally can get down to the point where we can target individual homes and have a unique message to each home.
We use thousands of factors and sensitivities within the design, construction, demographics, and competitive landscape to achieve pinpoint accuracy on what to build, when to build it and how best to monetize it. For example, we know who’s in the home, what the home is made of, what commercial entities are inside what buildings, what the demand profiles and competitive profiles look like, and the cost profiles of building a connection to each home. So, we know in advance what it will take to be successful in providing a connection to each residence or business.
Data this granular shows just how crude most other assessments are. If we know that two seniors live in a home and they have a $150/month cable bill, for example, we can market to them much more differently than if the residents are a family with two school-age children with a $200/month cable bill, or if there are two professionals who work from home who have gigabit service. Compare this against the typical mail-order marketing campaigns used by major service providers. One path breeds success, while the other is a crapshoot.
I believe that most service providers will eventually come around to using data science modeling when deciding which markets to enter and how to market to each of their residents or businesses, but for now, Hexvarium leads the market with this type of approach.