Consumer marketing used to just be about “spray and pray” tactics. We looked at our consumer database and came up with our simple segments, from a combination of age, geography and race. It was an art not a science, often running on gut feeling and “experiences”.
We all know the problems with this approach. It’s too generic and ineffective; and it’s entirely inside-out; you don’t get to draw on the customer’s preferences. Reaching your target buyers is a shot in the dark, requiring huge marketing budget and luck.
The Invisible Prospect
With the advent of big data, segmentation and targeting should be more science than art. But big data isn’t linked up yet. For example, I recently wanted to buy a new car. I did my research, spoke to friends, and picked a few I was interested in, and then went to dealers to test-drive them. Then I went online, Googling for these cars, visited Edmunds.com to compare features, prices, reviews, and looked at the manufacturer’s website for details.
There were several contact points there where I could have been tracked and targeted. The dealership got my name and ID for example – but the sales rep wouldn’t share that with his colleagues, let alone the dealer or the car manufacturer. The online data trail is longer. I searched on Google. I talked on social media. I searched on Edmunds.com -. I even went to the manufacturer’s own site. Marketers should have taken advantage of all these interaction points to increase the likelihood of a sale for their brand.
The Way It Should Be
So let’s paint an alternate picture. I start my research and the search trackers detect that someone in my area is looking for that brand of car, through Google and through social network keyword tracking. Then they can track my activities to Edmunds.com where they get my registration details (through a data-sharing scheme), and then to the manufacturer website, where I’m recognized and given a discount offer in return for registering my details.
This online profile can then be automatically combined with offline data from the dealership and other personal data to show that I’m a definite prospect, so that the manufacturer followed up with an offer that I can’t refuse.
Of course, this sort of intelligent targeting may only viable for high value products for now, where buying journeys are long and leave data trails However, it is really only a matter of time when this will be applicable to almost all products.
Leveraging Big Data for targeting should be a key arsenal for marketers. Consumers have access to a lot of information, which enabling them to change how and when they engage with brands and sellers. However, consumers also create and leave behind a long data trail. By leveraging real-time computing to process and analyze this data trail, marketers can also become more effective in how they engage and win customers.
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This post was originally published on The Customer Edge