Welcome to the Customer Data Revolution
SAN FRANCISCO — Once upon a time, companies worked under the premise that they controlled their customers -- or so they thought. But those days are long gone, according to Dr. Andreas S. Weigend, former chief scientist at online retailer Amazon.com. And Weigend, as part of his presentation at Predictive Analytics World here this week, was perfectly clear about identifying the driving force behind the transition.
"Underlying this shift in the last 20 to 30 years is the fact that communication costs have basically dropped to zero," Weigend told attendees. While it may have become virtually free for a company to communicate with a customer, time is still money. If customers' time is being taken up, Weigend said, they consider that a cost. Companies that ignore those costs -- attention costs -- end up losing those customers.
Weigend went on to explain that no matter what today's consumers decide to share -- time, information, or money -- they only do so as part of an exchange that guarantees them a benefit. "The consumer data revolution is about shifting expectations," Weigend told the crowd. People willing to give up their data, he said, "want to get back something in return." In the past, Web-based content had a limited value to customers -- all they could do was look at it. Now, they're able to access it, contribute to it, edit it, and share it -- often with whomever they please. Companies can forget about "owning" their customers, Weigend said, adding that companies hardly own their own products anymore due to the amount of information that can be found online.
For all these reasons, Weigend said, prediction models are what he trusts now. In order to predict a customer's aptitude to buy, the organization must move from being assumption-rich to being data-rich, and from a focus on short-term customer relationships to long-term ones. Realizing customer value, he added, now means going where the customer is. From his days at Amazon.com -- a company at the forefront of predictive modeling -- Weigend shared three key pieces of knowledge about customers:
- To learn about customers, it's crucial to sniff the digital exhaust: There are so many data sources on the Web to be utilized -- do something about it.
- Individuals love to talk about themselves: It's time companies capitalize on this -- but in a way, of course, that places the utmost importance on customer trust.
- Individuals are apt to reveal relationships with others: A person's social interactions reveal far more than that person's passport ever could.
These three factors led Amazon.com to begin combining implicit data with explicit data, Weigend said. The company, in other words, used predictive analytics to combine disparate customer information -- the number of clicks on a product, for example, combined with information captured in a customer survey. The aggregate data is beyond powerful, he said -- provided the combination is relevant.
As difficult as relevance can be to achieve, Weigend said, there are five steps that reliably generate the benefits of the customer data revolution -- five steps summed up in an acronym of Weigend's creation: PHAME.
- Problem to identify: Go beyond assumptions. Usually the problem isn't what you think it is.
- Hypothesis to form: You have to go to outside of your norm to locate the reason that people buy from you.
- Action to take: Develop a solution specific to the identified problem and focused on proving the hypothesis.
- Metrics to put in place: Weigend said people are surprised by how much of the customer interaction they can actually measure. Go beyond sales data. Start thinking about clickthroughs and social media.
- Experiments to test: The alternative -- failing to experiment -- can be even more expensive, he said.
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