Location-Based Marketing: Three Barriers to Success
As I wrote in my previous blog, Location-Based Marketing (LBM) is an area of innovation enabling marketers to tap the tremendous mobile opportunity. However, implementing LB…

As I wrote in my previous blog, Location-Based Marketing (LBM) is an area of innovation enabling marketers to tap the tremendous mobile opportunity. However, implementing LBM is not without its challenges and considerations. Here are my top three:
- Privacy: It's by far the most critical of considerations when deploying an LBM service. Location-based services gather a wide variety of data. The data collected could contain sensitive personal information, such as places widely visited, place of work and place of residence – things that consumers may not want to be disseminated widely by a marketer without their permission.
- Data De-identification: To address privacy, many marketers will deploy de-identification technology, cleansing data of personally identifiable information. It certainly helps to address privacy concerns, but it is not without its limitations. From a data scientist perspective, de-identification approaches can significantly reduce the utility of data for downstream analytic purposes. This means that while data can certainly be used to understand aggregate behavior and trends, you lose a lot of the individual targeting, thus reducing relevance to the individual consumer.
- Data and Infrastructure Bottlenecks: Marketers are collecting location and activity data from multiple sources – geo-fencing, mobile, GPS, WiFi, NFC (Near Field Communications) – and combining it with other data sources. All of this data is Big. The volumes of data have to be cleansed, harmonized and connected to the CRM record before any data can be used as an input for analytics. And while most marketing organizations have data stores, analytic processes and decision support systems, they are grossly inadequate to address Big Data and were not built for real-time analytics. This results in bottlenecks and latency. For LBM to be successful, you need to do near real-time analysis, because this data has a short lifespan.
Work is underway today to address these challenges and considerations. The promise of LBM is great, and while many retailers and consumer goods companies have projects in the works, it is still early days.
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