Why are we dubious about deep learning?
The prospect of deep learning gives those of us in the industry something to get really excited about, and something to be nervous about, at the same time.
Business owners think they have a great understanding of their customers, but a new survey shows they're completely clueless.
Thanks to increasingly sophisticated analytics tools, virutally every business owner now has access to an array of information about his or her customers: Where they live, what language they speak, their gender, and even their income levels. You know what pages on your website they look at, and through your sales analytics, you know what products they buy.
Sure enough, an upcoming survey from Yesmail Interactive shows that 53 per cent of marketers say they have an excellent understanding of their customers' past purchase behaviour, and 42 per cent say they have an excellent understanding of their customers' demographic information.
In theory, that information can be used to develop an insightful picture of your customers, which can in turn be used to customise product and service offerings. The problem: This information doesn't seem to be particularly useful in its current form when it comes to truly understanding how customers behave, and businesses don't really realise it.
Mike Fisher, president of Yesmail Interactive, says "When it comes to deeper insights that can be used to build one-on-one relationships with customers, most marketers don't have access to the necessary data."
He points to additional data points from the survey, which note:
"The research suggests that 80 per cent of B2C companies rely on little more than transaction and basic profile data for segmentation," says Fisher. "Yet most marketers think they know their customers sufficiently well, revealing a disconnect between self-perception and reality. The survey shows that most companies have a very basic targeting strategy, which limits their ability to truly get to know their customers."
In response, Fisher offers some tips on getting at a deeper and more valuable understanding of customer data. That starts with looking less at what products you've been selling and more at how numerous data sources interact to create a fuller look at your customers and their behaviour.
"Brands should start thinking about the customer journey and response-based engagement that moves away from transactional information to relational data. They need to build a complete picture of their customer based on a relationship developed over time through a series of interactions across multiple channels. Instead of bombarding customers with non-targeted messages, businesses should be striving to be personal and relevant," says Fisher.
How do you do this?
"Research has consistently shown that relevance drives revenue," says Fisher. Customer messaging should be hyper-targeted to customers using up-to-the-minute data and information like "behaviour and life stage triggers, web browsing and online behavior history, and likelihood-to-purchase scores."
And businesses aren't the only ones that benefit from this relationship, he adds. When business messaging is more appropriate, consumers are happier with the relationship, too. (Which ultimately should lead them to buy more from you.)
Yesmail's full report on the subject is forthcoming in a few weeks.
The prospect of deep learning gives those of us in the industry something to get really excited about, and something to be nervous about, at the same time.
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In this bonus last episode of this new podcast series, BrandHook MD, Pip Stocks, talks with former ANZ group general manager of marketing, Louise Eyres, talks about the importance of thinking like a customer and using intuition to solve customer painpoints.
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