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Predictive analytics making strides
Real estate agents often rely on direct mail marketing to reach potential new clients entering the home market, either by selling or buying. A broker looking to initiate a direct mail marketing campaign to a ZIP code area can expect to spend $2,500 to $10,000 a month to reach households, according to Inman. The totals vary depending on the population living within an area, but the average population estimate per ZIP code as of May 2015 was 7,515.
The high, fluctuating costs may be lowered in the future if brokers embrace predictive analytics as a new technique to reach households.
What is it?
The definition of predictive analytics, according to Webopedia​, is, "the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends." A combination of data mining, modeling, current trends and historical facts help lead to that future prediction. Brokers must keep in mind, however, predictive analytics does not tell what will happen, but what might happen. With this prediction is also a number of "what-if" scenarios and risk assessment.
How can it help?
Brokers can use this method to create a more effective direct mail marketing campaign, already bolstered by address verification software. This ensures real estate brokers will remain competitive, even against digital marketing. Direct mail is still an effective method to reach potential customers, but it can still be more precise.
Brokers and marketers need to consider predictive analytics because organizations may be throwing money away. Brokers targeting all the households in a neighborhood are wasting resources because the direct mail is also reaching households not looking for the help of a real estate agent. Data will most likely show brokers spending thousands of dollars to reach a base of thousands, even though hundreds of residents are seeking the help of a real estate broker.
Likewise, predictive analytics may be able to help direct marketers send specific mail materials to consumers. For example, marketers may find through data mining a consumer is researching home electronics. Marketers can then send them physical mail to provide more resources on those electronic goods and maybe offer special promotions. It would not make sense for marketers to send information on televisions to someone looking to buy a toaster, for instance.
How does it work?
According to the Harvard Business Review, predictive analytics require good data, otherwise, the method will not work properly. Then, special software can be utilized by direct marketers to find those hard-to-see trends and make assumptions. Mailers can include consumer habits and industry trends to stay one-step ahead and send out information before a consumer may even realize they need it.
Direct marketers are already implementing more technology into physical mail. For example, physical mail may include QR bar codes for consumers to scan. Predictive analytics may be able to tell marketers if consumers will scan those codes.
"Predictive analytics require good data."
Future use looks bright
Predictive analytics is still making strides within the direct marketing sector. Yet, according to Inman contributor Rick Frascona, predictive analytics will continue to get more powerful as and accurate as data is increasingly available.
From marketers to real-estate brokers, combining direct mail software with predictive analytics will help increase their impact and deliver goods and services to those who most want them while also saving resources.
