Spatial Modeling of IPTV Potential A Case Study: Massillon Cable TV 2006 Location Intelligence Conference Professor Paul Rappoport, Temple University Robert Gessner, President, Massillon Cable TV Dr. Amy Liu, Marketing Systems Group Kevin Babyak, Marketing Systems Group April 3-5, 2006 San Francisco, California

Outline The ProblemThe Problem The ApproachThe Approach Case StudyCase Study Results & ImplicationsResults & Implications

The Problem  How can a local cable provider measure the competitive threat posed by a telephone competitor? Are all service areas equally at risk? What customer segments are at risk?  How can spatial information of a market provide competitive insight? Can advertising be used to effectively challenge competitor’s claims of Internet speed? Where could IPTV be provided

The Problem The “claim” is that a telephone competitor can provide high speed Internet access as part of a package of services. However, current technology is limited by distance – not all households can receive high speed access. Thus this claim could be disputed.

The Approach Nearest Neighbor Hierarchical Clustering Identify groups of households that are spatially close where close is based on 2 criteria: 1. 1.Threshold distance – only points that are closer than the threshold distance are selected for clustering 2. 2.A minimum number of households are required to form a cluster These clusters can then be used to produce a hierarchy of clusters, where higher order clusters satisfy the above two criteria. Cluster become entities for subsequent analyses.

Hierarchical Clusters First order clusters (the smaller circles) can be combined to form higher order clusters (the red ovals) and so forth until an entire market area is evaluated.

Approach   In this application, clusters provide a proxy for the presence of remote terminals or other outside plant that could be used to deliver high speed data or video services.   Cluster attributes include the number of homes passed, average income, penetration rates for DBS, broadband, average spending on video and local and long distance telephone.   Clusters can then be used to segment a market by degree of risk or contestability.

Massillon Cable TV Cable franchise area is defined in this analysis by block groups

Massillon Cable TV The cable franchise has areas of very low to very high levels of income.

Massillon Cable TV There are 5 central offices that coincide with the cable area. High speed Internet requires that a central office be enabled for providing DSL.

Massillon Cable TV This map displays the distribution of households by ZIP+4. The majority of the 32,000 households are clustered in the City of Massillon

Massillon Cable TV This map displays the results of the clustering as well as the location of remote terminals. Remote terminals can be used by a telephone company to extend the reach of DSL

Massillon Cable TV The effective reach of current DSL technology is 3 KM. The circles display the reach of DSL

Massillon Cable TV If selected remote terminals become DSL enabled, households in the higher income areas could receive high speed Internet access and other IPTV services from the telephone company.

Results & Implications   Spatial clustering reduces the complexity of the problem   Clusters represent entities for analyzing competitive activity   Clusters can be evaluated by usage, spending and demographic characteristics   Spatial clustering identifies areas that are contestable   For large systems, this minimizes the type of competitive response   Clusters provide an efficient scaling for targeted marketing

Results & Implications  For Massillon Cable TV, the analysis uncovers areas that are contestable  For Massillon Cable TV, these areas correspond to high income locations. Broadband and video services are strongly correlated with income.  70% of homes passed are potentially at risk

Contact Information Paul Robert Amy Kevin

Citation The citation for the clustering algorithm is: Ned Levine, CrimeStat III: A Spatial Statistics Program for the Analysis of Crime Incident Locations. Ned Levine and Associates, Houston, TX., and the National Institute of Justice, Washington, D.C. November 2004.