Predictive Models Fuel Customer Engagement

5 Predictive Analytics Models that Fuel Customer Engagement

As marketers, we are constantly put in the positon of trying to anticipate a customer’s next move. It is an important part of modern day marketing strategy and when it comes to ways that help us stay ahead, or at least in step, with customers, predictive analytics are our friend.

Using historical and current data, and statistical algorithms we can create predictive models which identify the likelihood of different customer engagement outcomes. And with artificial intelligence, these models can be perfected-learning overtime.

A Variety of Predictive Models Inform Different Customer Engagement Strategies

Models can help us qualify and prioritize customers based on future lifetime value or determine what product or offer is the most appropriate for each customer, at a given point in time. There are a variety of predictive models. We’ve listed some of the most common and why they’re effective.

  1. Churn Prevention. Churn prevention models encourage better customer engagement and loyalty because they identify churn warning signs as they arise. These models are created from abandonment traits — a predetermined set of variables that (historically) indicate that a customer is about to disengage or from our brand. By creating a churn model, we can easily anticipate which customers are at risk of leaving and stage an appropriate intervention to keep them both satisfied and engaged.
  2. Customer Lifetime Value. Customer lifetime value (CLV) models are fashioned from a variety of behavioral, demographic, and psychographic variables to help us predict someone’s propensity to be a high-value customer. Essentially, this model infers a customer’s future value from their current level of engagement and defining characteristics. It then evaluates how much revenue they are likely to contribute long-term. CLV models can become more complex than just measuring purchase over time. Variables can also be drawn from certain behaviors like social shares and referrals, where the value of the customer extends to their ability to convert others.
  3. Next-Best Action. Next-best action models help us understand what a given customer is likely to do next. They help encourage certain actions and can be programmed to be ready with a relevant offer. Next-best action is determined by evaluating a customer’s expectations, needs, and interests and our objective for that customer.
  4. Product Propensity. Product propensity models look at a customer’s purchase activity to determine new audiences for a particular product or service. By discovering correlations in a customer’s purchasing activity and leveraging predictive analytics, we can target new audiences with a high propensity to purchase the product or service thereby driving revenues for that product or category.
  5. Cross-sell and Upsell. Cross-sell and upsell models focus on what’s in a customer’s shopping cart. Amazon’s “you might also be interested in…” and “often bought together…” prompts are good examples of using insight gained from cross-sell and upsell analytics to inform a bundled pricing strategy (selling multiple goods as a single unit so that the sale of one item buoys the sale of another).

Keeping the Big Picture in Mind

With all predictive analytics models, it’s important to understand how each model interacts to form a bigger picture. The critical factor, as always, is being able to adapt our strategy to meet changing customer needs and expectations. Predictive analytics models provide an accurate, reliable and adaptable strategic guide and tool — but it’s still up to us to follow through on the insights found within the data with creative communications. At the end of the day, any model we use should be done so with the overall goal of increasing revenue and lifetime value of the customer.

To find out more about how predictive analytics can fuel customer engagement download our eBook, The Value of Context, Cross-Channel and Real-Time Capabilities: Advancing Marketing’s Productivity.

About VeraCentra: Marketers everywhere want to use data to implement more modern customer engagement strategies. But there can be many obstacles standing in the way of success. That’s where we come in. VeraCentra provides easy-access Customer Data Hubs. We represent best of breed Cross Channel Engagement Platforms (so marketers get the right fit) and offer the Marketing and Data Services that guarantee speed to value and quick win ROI from technology investments. We deliver these solutions with an unmatched wholehearted approach bringing personalized support, care, and service to every client. That’s why many of our client relationships span more than a decade.