Direct marketers know that the days of “spray and pray” with flyers and other direct mail initiatives are over. Effective marketing requires highly targeted campaigns directed to very specific customer segments; however, despite the best efforts of an organization to build a valuable database of customers and prospects, address data is often flawed and therefore it becomes an Achilles heel undermining the success of direct marketing campaigns.
The city of Shawinigan in Quebec serves as a recent example of how database flaws can happen. On May 16, 2016, Shawinigan changed addresses as a result of a merger of seven municipalities that took place 14 years earlier. The project affected 70% of the more than 50,000 citizens and involved changes to addresses, street names, door numbers and postal codes. Similarly, each year more than 10% of Canadian addresses are impacted by changes to postal codes or street names.
In addition to external factors such as address changes, many internal factors including sloppy data entry, systems integrations and merged data due to acquisitions can make location data in customer and prospect databases inaccurate. The reality is that customer and prospect address data isn’t static and is vulnerable to inaccuracies, which poses a significant problem for direct marketers who rely on the accuracy of their organization’s databases to achieve the best return on investment for their campaigns.
Fortunately, cloud-based solutions are available that can automatically consolidate, cleanse and validate address data, as well as remove duplicate records and assign unique geocodes to locations in a customer and prospect database
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. Using high-quality cleansing tools can improve database quality by as much as 30% and ensure that mail campaigns are targeted to the right people. Additionally, location intelligence can be used to identify new prospects that were not contained in a company’s database previously.
Once accuracy is achieved for address data, it is possible for a company to move to the next level by integrating additional valuable location information that will be helpful in segmenting customers and prospects. This location information can include data such as age, gender, family size, income, dwelling type, drive time, drive distances and many other variables that can improve the direct marketer’s decision-making process. For example, if a company is marketing replacement windows, location data can be used to eliminate anyone living in an apartment from a mailing list. Similarly, if a high end retailer is opening a new store, location intelligence can help identify affluent residents to target within a certain driving distance from the store.
Using additional attributes of this kind enables an organization to understand which data points and geographic areas are associated with the most sales so that it can tailor marketing campaigns and easily refine programs to ensure that the right people are receiving the right messages. Moreover, some cloud-based location intelligence solutions include easy-to-use visualization tools that make it possible for business users to view location information on a map for greater insight, better collaboration and more precise analysis. Database data can dynamically identify clusters of relevant customers and create boundaries that are more relevant than what can be achieved using census and postal boundaries.
One utility’s experience
A case in point that illustrates the value of location intelligence for direct marketing is the experience of a major Canadian natural gas storage, transmission and distribution company that wanted to expand a home weatherization conservation program. In the past, the program had only been offered to customers living in subsidized housing, leaving a significant segment of its potentially eligible customer base with minimal access to the program benefits. With more than one million customers, the challenge was to only promote the program to the customers most likely to be eligible for it. The utility estimated that only 14% of its customers would be eligible but did not know where these customers were located geographically.
Turning to a location intelligence solution, the utility was able to visualize existing customer addresses and cross-reference those addresses with house size and house age range characteristics. By tying all this information together, the utility was able to pinpoint the exact neighborhoods in which to promote the program, resulting in a 400% increase in the program uptake rate over previous direct marketing attempts to expand the program.
In today’s big data environment, there is a massive amount of information available that is relevant to buyer behavior. Location is tied to almost all of that information and can serve as the backbone for strategic business planning, ensuring that as marketing campaigns are designed they leverage accurate data that will make them more cost-effective and targeted to achieve the desired results.