Originally Published in AdExchanger. “Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media. Today’s column is written by Jessica Yiu, an audience analyst at PlaceIQ.
The golden rule in real estate is location, location, location. The same mantra has been adopted by the world of digital marketing, albeit with a slightly different meaning, because human behavior, including consumer habits and preferences, is fundamentally shaped by places and contexts.
Put simply, what you buy is predicted by not just who you are, but where you’ve been.
Places are integral to our identities. Even as children, we gain an intuitive understanding of the places that are important to us, such as our home, school, neighborhood park or favorite ice cream shop. As we mature, our connection to significant locations only deepens as we develop enduring associations, more sophisticated representations, and enriched attributes of those places.
Subconsciously, we create place-based mental schemas, keeping track of where we’ve been, how far we are from home and which events occur where.
To make sense of all these different facets of location-based information, they can be organized into “layers” of information that are gathered and filtered through our daily experiences. Suppose you’ve been invited to attend a baseball game at Yankee Stadium but are also expected to attend a meeting in Hoboken, New Jersey, that is scheduled shortly after the game. Your decision on whether to attend the game, as well as what time to leave if you do, is predicated upon your knowledge of the distance between the two venues, the most efficient form of transportation during the time of day, and a contingency plan if something goes awry.
Place-based information goes beyond strictly location to include temporality, modes of transport and patterns of population flow, among other pieces of “layering” information. A place-based understanding of consumer behavior should similarly integrate multiple “layers” of information from different sources and synthesize them into useful models.
A location-focused method for analyzing consumer behavior, which goes beyond a naïve or basic approach to location, should address several objectives:
- Create Clear And Concise Location Categories
Since we are dealing with billions of data points across space and time, it’s important to efficiently map those disparate data points into clear and comprehensive location taxonomies. Each location category must be clearly defined and its constituent elements should share a distinct set of characteristics. For instance, both Motel 6 and the Four Seasons fall under the hotel category but they obviously cater to very different clienteles. In order to differentiate between disparate customer bases, it is important to create salient subcategories – in this case, subcategories for the budget vs. luxury traveller.
- Balance Between Robustness And Precision
Achieving high accuracy sometimes comes at the price of generating stable and robust results. Finding the right balance between these two competing dimensions is a delicate but important task. A key consideration is the unit of measurement for location analysis: Too large and the richness of the data is clouded, but if it’s too small, the results are bound to specifications of the statistical model rather than reflect empirical reality. In other words, hyperlocal data are not necessarily desirable, particularly if they introduce too much noise into the analysis or render it computationally infeasible.
- Account For Time And Population Flow
Places often serve multiple functions, particularly depending on the time of day, day of the week or month of the year. To contextualize locations in an array of temporal dimensions, you must model the “ebbs and flows” of human activity. Moreover, the flow of people through time and space undergoes cyclical and non-cyclical patterns. For instance, the kinds of people who wander into Central Park and their activities vary drastically over the course of time: early morning joggers vs. elderly strollers in the afternoon, family outings in the park on weekends vs. shady characters after midnight. Say a jogging company wanted to leverage location data to target joggers for their next campaign. Knowing who goes to the park at what time would help the company design their advertising campaign to optimize their budget, which is a fruitful starting point for their campaign.
- Consider Heterogeneity In The Same Location Categories
Related to the point above about the interactions between time, population flow and location, it’s important to remember that not all locations are created equal. In addition to accounting for temporal variation, the same types of locations also vary along a wide range of other dimensions, from climate and geography to more abstract factors, such as cultural idiosyncrasies or social development. Imagine targeting an ad campaign to soccer fans in Brazil during the World Cup. Conceivably, the strategy would be different from an ad campaign targeting MLB fans in the US Midwest. Even though both events take place at large stadiums, linguistic differences and culturally distinct rituals associated with soccer vs. baseball fans make these events markedly different experiences. For instance, while hot dogs and Cracker Jack are the noshes of choice at a baseball game, it’s common practice for Italian soccer fans to eat meatball subs – preferably made by their mothers – before the game. Same categories of locations can therefore record very distinct population footprints.
- Create A Holistic Profile Of The Consumer Journey
The ultimate goal of leveraging location data is to better understand the entire consumer journey. In order to do this effectively, multiple data sources should be “layered” on top of the locational data to create the most holistic and in-depth profiles of your targeted consumer base. Suppose Starbucks is interested in knowing the demographics of its customers, such as where they live and work. While those are useful pieces of locational information, advanced methodologies can provide even more useful information, such as places that the consumer visited that day before going to Starbucks and his or her favorite TV shows. And, quite often, analyses involving the crisscrossing of different streams of information can reveal surprising insights.
These insights produced by a location-focused analysis of consumer behavior can offer potentially be both revealing and fruitful. However, as with any type of analysis, there are challenges and caveats. An effective analytical analysis leverages, integrates and layers multiple and highly diverse data sources, while being able to interpret and make sense of the results.