Location Intelligence, Spatial Data Analysis | Pitney Bowes
X marks the spot
Savvy use of location-based data can help locate new business opportunities
It’s not enough to know who your ideal customer is. You need to know where he is, too. Fortunately, location analytics can help. “When business practices such as marketing efforts are combined with location-based data, the sum is greater than the parts,” says Richard Rollins, director of global product marketing for location intelligence at Pitney Bowes. “Together, they give better insights into customers — which in turn helps businesses make better decisions.”
Marketers have long understood that knowing where a person lives can provide a wealth of demographic information. This is due to homophily—the “birds of a feather flock together” principle. Social scientists have found that people tend to live near people who are similar to them, which means that companies can learn about everything from credit scores to preferred vehicles for each of the 74,134 census tracts in the United States.
“Knowing someone’s spatial location has the potential to unlock a lot of valuable information about them,” says Eric Bradlow, co-director of the Wharton Customer Analytics Initiative at the University of Pennsylvania. “Analytics can help you understand what that information really means and how you can use it to tailor messages to particular groups of people.” An insurance company, for example, might use location data to promote one message in an area where most people rent their homes and another a few blocks away where most homes are owner-occupied.
Thanks to the ubiquity of new technologies, such market segmentation is becoming more sophisticated: Now companies can examine information on an individual basis, as well as in aggregate. “Companies are increasingly interested in how location analytics can help them monetize their customer base,” says Rollins. “They have vast volumes of transaction data. Analytics can help them better understand where their target customers are located, how they travel and what places they frequent.”
Location intelligence can also draw on past patterns to create predictive models of future behavior. These anticipatory models can have many applications. One common use in the insurance industry is catastrophic event management, or CAM. “We have so much data on past events such as hurricanes and floods that we can use to predict the potential effects of future disasters,” says Joe Francica, managing director of geospatial solutions at Pitney Bowes. “There’s always risk, but these models can help you understand what kinds of catastrophic events you are most likely to face.”
The possible applications for outcome modeling tools can benefit any situation that has a spatial dimension. Cell phone companies, for example, can use location intelligence to understand not only where their customers are, but also their travel patterns. Analyzing trends in call location and volume can help companies determine where need is increasing so they can locate new cell phone towers optimally. Analytics tied to past customer behaviors can help predict which customers are most likely to upgrade or suspend services, so companies can tailor messages accordingly.
Businesses can also use predictive location analytics to play out if-then situations to optimize decisions. For example, a company that wants to open a new branch could model the impact on the overall trade area, or examine what the impact on market opportunity might be if a competitor moved nearby.
Human beings have a tendency to look for patterns. However, it can be hard to spot the underlying logic in a massive spreadsheet of numbers. “I always say, you can look at reams and reams of spreadsheets, or you can look at one map,” says Francica. When data is layered onto a map, patterns tend to become immediately and intuitively visible. Heat maps, for example, let you see the “hot zones” where your customers tend to congregate, or where your sales volume is the greatest. You can use that information to direct more customer-service resources to those areas or build up outreach efforts in the “cold zones.”
Data visualization tools can also make it easier to look at how geographic trends evolve. Longitudinal analysis allows you to measure key metrics over time; when combined with location analysis, you can visualize how time and space interact to create patterns. For example, you may spot seasonal variations or trends that can be traced back to particular local events.
Put simply, the potential for location analytics is enormous. “The challenge for us now is that we have become spatial thinkers,” says Francica. “So many of the questions we ask about customers, trends, patterns and risk are actually spatial queries — even if we don’t realize that at first. Location analytics allow us to answer those questions efficiently and intuitively.”
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