Data volumes are booming, right alongside customers’ soaring expectations for more personalized engagement across your entire organization. You want to improve how you convert, segment, and continually delight customers, but how and where do you start?
The answer lies in the external and internal data about your customers and their interactions. Too much data frequently paralyzes decision making, but new analytical techniques transform that data into usable insight that enables you to truly treat each customer as an individual, at a scale that matches your business. And with more personalized approaches come better business outcomes.
One way to accelerate data-driven marketing strategies is machine learning – an approach to predictive analytics where computers automatically learn by example and continuously adapt to shifting customer behaviors. An intelligent layer that sits on top of existing customer-facing solutions, such as CRM and marketing automation systems, and machine learning extracts patterns from complex data to help you determine the next action to best engage with each individual customer.
Here are five areas where machine learning can optimize decision making, taking the guesswork out of common marketing challenges.
Refining Customer Segmentation
Most current segmentation approaches rely on a limited number of variables about customers and their behavior, built on personas that remain largely static. In reality, there are many different data points that inform a customer decision and that indicate how you should respond. For example, segmenting your audience into technical and marketing personas may initially seem like a good idea, but where does someone like a technical CMO fall? Machine learning takes into account changing, disparate data points and automatically updates individual segmentation – all the way down to each customer interaction.
Maximizing Prospect Conversion
Chances are you have databases full of potential prospects, with your sights set on many more. Machine learning allows you to allocate resources more effectively by identifying which leads are most likely to convert. Extend that a step further, and machine learning can identify the right salesperson to work a deal. Oftentimes the right rep may not be the one you consider your best salesperson; however, analysis of countless characteristics about both the individual rep and individual customer indicates a perfect match for that specific interaction.
Increasing Revenue Per Customer
Instead of pursuing one-size-fits-all marketing campaigns, it’s essential to identify the specific behavioral patterns that lead to good outcomes. By analyzing past outcomes, machine learning can help you find ways to influence the customer path to create and recreate those experiences that drive higher revenue per customer. It’s like A/B testing on steroids, giving you the ability to get the most relevant message to the right audience at the right time.
Improving Customer Satisfaction and Advocacy
Since metrics like CSAT and NPS straddle the worlds of marketing and support, proactive support can be an influential factor in creating happy, advocating customers. Take a business offering a free trial, for example, which faces an overwhelming number of support tickets from non-paying customers. By automatically uncovering contextual patterns (e.g. billing issues) in existing data, machine learning can predict and head off potential issues, efficiently prioritizing those customers who are having trouble paying. Support can focus on users ready to convert, giving those customers a great experience while also growing revenue; likewise, marketing can engage in a way that maximizes customers’ propensity to become brand advocates.
Forecasting Customer Lifetime Value
Traditional approaches to identify the best paying customers are inherently historical and based on past spending. Machine learning flips that on its head, predicting who will be high value before they actually reach the required spend limits, and possibly even before they spend a penny. With the ability to predict customers lifetime values as early as possible in the lifecycle and bring those high potential customers into a VIP program, you can influence and accelerate their spending.
It’s time to step away from classic segmentation and static, rules-based approaches to market to your audience. In today’s big data world, you may be tempted to focus your efforts on analyzing broad swathes of customer data to see the forest through the trees, so to speak. But to achieve a level of personalization that wins the hearts of customers, you actually need to narrow your focus. What counts is the individual tree and every leaf, branch, and root that is part of the tree.
With the help of machine learning, you can put a lens on individual customer behavior and past outcomes to drive the desired outcome for every interaction. And in doing so, you will be taking advantage of data in a way that helps you be more flexible and relevant when engaging with your customers.
Edited by Maurice Nagle