In an era that used to be characterized by predictable, segment-specific roadmaps, today’s small-to-medium business (SMBs) CMOs need to set their journey maps aside and embrace intelligent platforms that can manage their constantly changing customer relationships.
Once the standard for web-based marketing campaigns, “journey maps” are being displaced by intelligent data platforms that do much more than prescribe responses when customers open emails, click on ads, or request demos.
“The days of customers following linear, predictable paths are over,” says Rob Tarkoff, vice president of customer experience application development at Oracle. Instead, he notes, today’s consumer hops back and forth between websites, mobile apps, chatbots, social networks, retail stores, and service centers—expecting a tailored, personal experience that’s relevant every step of the way.
But most customer relationship management systems aren’t designed to support this amorphous flow of experiences a customer has with a company. Putting the right infrastructure in place can require complex system integrations and personalization rules customized for different channels and departments, Tarkoff says. “And this can get really expensive, really fast,” he says.
Yet the price of poorly timed marketing campaigns “can frustrate customers to the point of defection,” Tarkoff notes. He and other experts urge CMOs to take the following three steps:
CMOs won’t be able to truly understand their customers until they’ve connected each source of customer data, says Joe Fuster, vice president of customer experience marketing at Oracle.
Connecting that data doesn’t mean simply integrating sales, marketing, and customer service applications, he says. It requires building a massive data lake that also includes data from supply chain, dealer service, and finance applications; retail point-of-sale systems; social networks; email exchanges; and other online and offline sources.
“The only things a CMO will ever get from a standalone marketing app is data about when certain offers were sent to customers and how those customers responded,” Fuster says. That data says nothing about the customer’s personal interests, service histories, and other indicators that signal the customer’s propensity to buy and predict what he or she will need in the future, he says.
“Yet all of those ‘other’ data points are the keys to great customer segmentation,” Fuster says.
While having a fully stocked data lake is an essential first step to truly knowing your customers, the second step is to analyze that data in a way that’s useful to your sales, marketing, and service teams. But the volume of data at a CMO’s disposal is staggering—consumers conduct about 3.5 billion searches every day, for example, and they tweet about 460,000 messages every minute.
So it’s the job of artificial intelligence (AI) algorithms—embedded in your company’s interconnected sales, marketing, finance, and other applications—to sift through that data. The goal: Find what constitutes an actual lead, compare it to other leads with similar characteristics, and then figure out the steps that sales reps, call center agents, and even chatbots can take to convert the lead into a sale, says Jon Stanesby, director of AI product strategy at Oracle.
The power of AI is that it can analyze the trajectory of thousands of leads in milliseconds, and then suggest invitations to webinars or price discounts—“whatever it takes to move those leads toward the best outcomes for the business,” Stanesby says.