Customer Data & Analytics Blog

How a data-driven approach predicts your buyer

Albert McKeon | 1 minute read


Sure, many people are cautious about what they reveal online, going to great lengths to leave a light digital footprint. But many others don't mind that companies collect large amounts of data about them. They've accepted that data sharing is the cost of doing ecommerce.

But that cost also carries big expectations. In return for revealing who they are, where they live
and what they like to buy, consumers won't tolerate clumsy marketing. They know companies
have great insight about their habits, tastes, socio-economic status and many other aspects of
their lives, so they expect to be wooed accordingly. Generalized marketing that applies to just
about anyone or, even worse, incorrect attempts at personalization will fall flat.

It's only natural for companies to feel overwhelmed by all the data they collect. How can they
possibly personalize marketing and sales messages when they can't get their data house in
order? Some data is dirty – lacking complete, correct information – and it has to be coalesced
alongside clean data to be properly analyzed. But that's where customer analytics technology
serves as a bridge between consumer and company.

A customer data and analytics platform collects data from all sorts of sources: social media,
CRM, the web, predictive analytics tools, to name a few. It then curates, analyzes and acts on
that data, tapping elements of artificial intelligence to provide a segmented view of customers
in real time.

That means instead of being paralyzed by the depth and breadth of the data you collect, your
business can actually see, in the moment, which customers are inclined to take a deal offered
with a service plan, or which ones will leap at certain merchandise. It's only with a data-driven
approach that you'll be able to predict who is your next buyer.

Albert McKeon is a Zylotech contributing writer.

If you liked this post, check out our other blog post on three reasons marketers should be enthusiastic about machine learning.

Topics: Customer Analytics