Until now, many marketing organizations have used a “good enough” approach with regard to location data. Like playing horse shoes or throwing hand grenades, getting close was deemed sufficient. Marketers relied on generic map information, three-digit Forward Sortation Area (FSA) codes and open source data (including street addresses) to get a general sense of where customers were located.
Unfortunately, the level of precision possible with a “good enough” method is significantly limited. For example, it is not possible to segment an FSA geographic region to understand which locations reflect businesses and which are residential.
As companies seek to leverage their data sets even further, they face challenges when attempting to match their data internally. One common problem is that streets may have two equally valid names, such as Main Street and a highway designation. As a result, companies seeking to move to an “enrichment” phase with their data may begin to introduce basic geo-coding for more precise analysis. Geo-coding at this level is generally interpolated (or estimated)—meaning a particular street is mathematically segmented into 10 or 12 locations, even though those locations may not relate precisely to physical addresses. At this stage, third party and other open source data may also be associated with the geo-locations, such as differentiating between business and residential locations.
While beneficial for marketing intelligence, the “good enough” and “enrichment” phases of location data intelligence can only take an organization so far. Gaining precision requires more intense data cleansing and moving beyond interpolated geo-coding to designating unique identifiers to exact geo-locations.
This “premium” level of location data opens the way for a tremendous amount of insight. For example, a retailer can determine the geographic areas that are actually relevant to a particular store’s traffic. Obstacles to store access that would not be accounted for with a “good enough” or “enrichment” approach, such as a river with limited bridge access, can now enter into the analysis, making it possible to target customers more precisely. Similarly, drive times from various locations to a store can be plotted and used to gain insights on the right targets for direct mail campaigns.
At the next level, the aggregation stage, organizations begin to realize benefits by partnering with other organizations using the same unique location identifiers. This enables powerful data sharing and the overlay of data relevant to a precise location. For example, inferences can be drawn through comparative analysis of prior buying habits, demographic and psychographic data and more. Additionally, once coded with unique location identifiers, data sets become marketable to other companies looking to append an organization’s collected information to their own data sets for the same locations.
Finally, at the highest level of location intelligence, uniquely identified location data can be used to enhance real-time decision making. Marketers may track real-time locations of mobile phone users to send push notifications with advertisements or coupons to potential customers when they are near a retail store.
These are just a few of the many ways that location data has the potential to drive context and relevance for marketers and business organizations today and in the future. Now is the time to ask: What location data does my organization have and how are we using it to our advantage?