For telecommunications carriers considering the addition of a triple-play component to their networks, the lure can often seem more like Pandora’s box than low-hanging fruit. While the triple play comes with the promise of increased profits and customer loyalty, it also possesses a certain level of risk. The additional network complexities can tax resources, increase customer churn, and ultimately drag down profits.
Too often, carriers make decisions about which new technologies to embrace or which new markets to enter without analyzing all the location data available to them. Location is ingrained in almost every operation and opportunity, from a customer’s address to the demographic makeup of a wire center to the location of a central office. But this information is rarely accessed, let alone maximized. Carriers that ignore location run the risk of failing to capitalize fully on available revenue sources.
One way to mitigate this risk is to develop a strategy that incorporates location intelligence. Location intelligence enables organizations to realize relationships between a geographic area, demographics, and operational data to make more informed decisions about which services to offer and where.
In an extremely dynamic industry where convergence, consolidation, and competition meet, service providers today must make decisions not by chance, feel, or competitive pressure, but based on substantive answers to a key set of “where” questions:
• Where are our network assets?
• Where are our current customers?
• Where are our potential consumer and commercial customers-and how do I reach them?
• Where do our competitors do business?
• Where are shared network boundaries, operating centers, regulatory boundaries, and franchise areas (and how can we leverage them)?
• Where am I vulnerable to competition?
• Where is the competition vulnerable?
One of the most critical areas where service providers gain a competitive advantage with location intelligence is through the demographic analysis of market areas. While many may be familiar with some of the basic geographies that demographic data is often analyzed within-such as census tracts, ZIP codes, and metropolitan statistical areas-service providers can also analyze the demographics within industry-specific boundaries. Market areas such as wire centers, rate centers, RF propagation areas for wireless broadband, node boundaries, or even buffer zones around a fiber route can be evaluated and ranked based upon the characteristics of the potential customers that would be served within this boundary. This enables the service provider to make more informed decisions regarding the introduction of new services.
For example, a service provider that is deploying an FTTH network has the ability to analyze each wire center to ensure that only the wire centers with the demographic makeup most likely to purchase its triple-play offering are deployed. As part of this analysis, the service provider can evaluate both the consumer demographics and business demographics of each area. Similarly, a broadband-over-power-line (BPL) provider can evaluate each substation prior to deploying triple-play services to prioritize markets, estimate capacity, and even tailor marketing efforts.
Central to this ability to evaluate market areas is the array of demographic data available for analysis. There are two broad categories of demographics: Census-based estimates and projections and lifestyle segmentation both offer the service provider a glimpse into the types of consumers who reside in an area.
Census-based estimates and projections offer the service provider a basic look at a given market area. Characteristics such as population density, income, education, and household size and the identification of high-growth areas are all available for analysis. Service providers can use a multilayered approach, analyzing markets to pinpoint the areas of high growth that have the highest median income or to identify the most densely populated neighborhoods that have the highest financial asset profile.
With age-by-income and age-by-sex analysis, service providers can tailor their triple-play offering to the demographic strengths of a given area. For example, a service provider deploying IPTV services recently used this data to help determine the video programming it would offer. As a result, the service provider’s initial sales exceeded its predictions.
Occupation and employment data are also available for analysis. This U.S. Census-derived data typically offer information on total employment, employment by occupation, and commuting patterns. Often, service providers will look to identify areas in which there are concentrations of employees from high-tech industries who are more likely to be high-bandwidth broadband users. From a marketing perspective, because a majority of service providers’ customers spend much of their day away from home, it is important to learn how to market to consumers during the day-where they work, during their commute, and as they travel. At the same time, demographic data on employment and occupation can be crucial in a business-to-business marketing environment.
Analysis of housing characteristic data can enable service providers to gain additional insight into their potential customers. Information such as housing value, housing unit data (which defines the type and use of homes in an area such as seasonal versus year-round), and multidwelling data (which identifies multidwelling units within each market area) can help determine the viability and profitability of offering triple-play services in each area evaluated.
Service providers can take demographic analysis to the next level of sophistication by performing customer lifestyle segmentation. There are three basic methodologies available to conduct lifestyle segmentation: segmentation of the service providers’ own customer data, household-level segmentation, and neighborhood segmentation.
Service providers can purchase data-mining tools that will enable them to identify similarities within their customer data and attempt to segment their customers. This approach can be costly and time-consuming and is more difficult to tie to potential customer geographies. Service providers can also look to analyze household-level segmentation provided by third-party data collectors or mailing list providers. However, household-level segmentation suffers from the highly volatile nature of attempting to demographically identify more than 100 million U.S. households. Often, the details of household-level data are imputed or extrapolated from other sources, which dilutes the perceived value of actual household-specific demographic information.
Neighborhood segmentation tends to offer the most stable, statistically viable geographic area in today’s mobile society. Neighborhood segmentation combines geodemographic inputs such as census demographics, street layers, land-use information, retail business locations, mall locations and size, measures of commercial activity, settlement context measures, survey response data, lifestyle data, healthcare data, actual consumer spending information, and highly influential auto ownership data with neural-net and hierarchical clustering algorithms that group more than 208,000 U.S. neighborhoods into clusters.
Neighborhood or geodemographic segmentation or clustering involves a disciplined look at a mix of geography and demography. Sometimes the geography drives the demography, as when the “place” attracts a certain type of people. For example, retirement centers attract retired persons. Similarly, some “ethnic neighborhoods” attract recent immigrants with similar ancestries. In contrast, sometimes the demography drives the geography as when the “people” transform the “place.” For example, as youthful, high-tech workers and young families moved into Austin, TX, they demanded, created, and responded to a unique array of services, entertainment locales, and retail environments. Ultimately, however, neither demography nor geography is destiny.
Service providers can use neighborhood segmentation analysis to identify areas where consumers are most likely to purchase triple-play services; determine the characteristics of a carrier’s most productive customers; identify geographic concentrations of the most productive customers; determine where existing customers could take advantage of new, more profitable services; and determine where high-usage customers are located.
Location intelligence is now a “must-have” for carriers of every kind. The notion of “where” has become a critical factor in providers’ decision-making as they strive to compete, save, serve, and grow in today’s increasingly competitive marketplace. As the communications market crowds with seemingly limitless new capabilities, customers’ attention spans can absorb only so much. New infrastructure and new services, however, need to pay for themselves quickly and efficiently. Service providers need to be able to focus new services and capabilities where they can achieve maximum results. Incorporating demographic analysis into the very core of network and service expansion analysis will assist carriers in making deployment decisions that go straight to the bottom line.
Chris Cherry is director, communications industry strategy, at MapInfo (www.mapinfo.com), a company that helps customers across a wide variety of industries take advantage of location intelligence.