In the past, digital marketing used the same techniques as print advertising.
Marketers made their best guess about the demographics and keywords relevant to their product, measured a campaign’s success, and refined their approach from there.
Machine learning technology has changed that.
Now companies can use predictive analytics to build intelligent customer profiles, allowing them to maximize revenue by knowing which customers to target and how.
The biggest problem from a digital marketing standpoint isn’t acquiring useful data.
Sales patterns, loyalty programs, website traffic, customer volume, social media activity- all of these result in data that could provide valuable insights.
The real problem is that companies have struggled to find a way to effectively interpret their raw data into marketing direction.
More data has been generated in the last five years than in all of human history before then, yet only 0.5% is ever analyzed and put to work.
Instead, data ends up siloed within different departments of an organization.
Parts of it are used, but the bulk of a company’s data ends up aging out of usefulness in databases.
Deeper Customer Understanding
Predictive analytics can take advantage of that existing store of information by using it to build models of customers based on data, a process called intelligent customer profiling.
Although customer profiles have been a popular marketing tool for years, until now their creation was subjective.
There was no reliable way to know who was buying a product and why, so profiles tended to be limited to demographics and whatever information could be gleaned from focus groups or customer interviews.
Intelligent profiling- sometimes referred to as predictive customer intelligence– is worlds beyond that approach.
It draws on data gathered through the course of normal operations to add refined demographic and psychographic details to the customer profile.
Having a better grasp on customers has major benefits for a company.
For starters, it provides a reality check as to who uses their services the most and why.
Is it the ideal customer from the company’s buyer persona, or is there growing popularity among a completely different demographic?
Customer profiling also serves to connect the data-gathering back end of a business with the action-focused front end.
In other words, predictive analytics is where the business value of data science lies.
It provides concrete suggestions that can steer the marketing team in the most profitable direction.
These are just a few questions intelligent profiling can answer:
- Who is most likely to be a repeat customer?
- What types of customers offer the highest ROI?
- Who will respond to an email?
- Why do consumers choose this company over its competitors?
- What indicators suggest a customer will qualify for a program or loan?
- Is marketing reaching the right sort of customers?
These profiles are powerful tools for the marketing department.
They can help reach full potential revenue on existing customers by revealing what matters most.
Alternately, they can reveal opportunities to target prospective customers, ones that weren’t included by the subjective buyer personas.
Intelligent profiling benefits more than the marketing department.
It guides the sales team in creating the perfect pitch for every profile type, each informed by hard data that reveals what matters most to that customer.
If you’d like to know more about how predictive analytics can empower your sales team, watch this blog.
We’ll be posting the second part of our customer profiling series next month.
Knowledge is power, but only when it’s used.
Intelligent customer profiling is the path from inert data to actionable insights.
Having trouble managing the output from a handful of marketing programs? Concepta can unify your analytics through a single custom dashboard to display all your data in one location.