3 Ways to Win Customer Loyalty With Predictive Data Analytics


To win and retain customers in 2017, companies will need to fine-tune their competencies in using data and predictive analytics to develop actionable insights.  That’s where predictive data analytics comes in to play.

Predictive Data Analytics Advantages

The good news is that most companies have access to an extraordinary amount of data about their customers. The bad news is that very few make the most of that data when it comes to customer acquisition and retention. This is likely because companies are still playing catch-up in the long evolution of tools and techniques for implementing a data-driven customer retention program.

Looking Back

In the not-so-distant past, companies only had the ability analyze small samples of customers at a single point in time. Today, those same companies can constantly keep a finger on the pulse of virtually all customer interactions at any time. As early adopters of analytics, specifically predictive analytics, become proficient with emerging tools, the gap between loyalty leaders and laggards will undoubtedly widen.

To set the stage for successfully implementing predictive models across an organization, it is critically important to ensure that everyone understands the context, interactions, and goals that will generate actionable insights.

Prioritize Engagement Metrics

Collecting engagement data throughout your customers’ buying journeys can give you a better picture of how your customers behave and how to best interact with them. The goal of this exercise is not necessarily to learn who your customers are, but rather, to learn when they are ready to interact with your brand.

To further illustrate the importance of engagement, a customer experience report performed by The Tempkin Group noted that, when compared to customer engagement laggards, customer engagement leaders have 19.5% more customers likely to recommend them, 19.2% more customers unlikely to switch brands, and 18.4% more customers likely to make additional purchases. In other words, customer engagement could be worth 100s of millions of dollars to your company.

Researchers and practitioners in a number of disciplines, including marketing, have made attempts to define engagement, but its definition is far from consistent across these disciplines and researchers.

Engagement Model
To simplify this concept further, engagement exists when your customers exhibit non-opportunistic behavior. This is an important aspect of the customer journey. Customer engagement, when properly measured and optimized, can contribute greatly to customer loyalty.

Collect Data Across All Channels

Earning customer loyalty through data-driven insights requires a palette of information sources and analytical approaches. Companies should collect data from a wide variety of places, including internal sources, such as customer service and procurement, as well as external sources, such as social media, search engines, email, a point of sale, and marketplace research.

Collecting a wide variety of data is not as complex as one might think, but it does require a thoughtful analysis of how one plans to use that data after it is collected. For example, you might use this data to:

  • Streamline operations
  • Competitively priced products and services
  • Inform the product roadmap
  • Shorten time to value
  • Increase customer lifetime value
  • Boost the value of voice of the customer program

The possibilities are nearly limitless. Of utmost importance is your commitment to tie each outcome to a customer satisfaction metric.

Monitor and Course-correct in Real Time

Your customers make decisions about where to spend their time, money, and effort every moment of every day. It is challenging to measure their individual levels of customer loyalty within a single relationship, which is why companies so often succumb to simply defining loyalty as the number of purchases made or a continued pattern of purchase behavior. Instead, consider measuring the predicted attitudes and behaviors expected of a loyal customer, such as:

  • Customer’s likelihood to recommend your products and services to others
  • Continue purchasing your products and services or purchasing other products and services you offer
  • Do your customers believe your products and services are superior to others offered in the marketplace?
  • Is your customer is actively seeking alternative providers to replace you?
  • Will your customer provide your company with opportunities to correct problems before abandoning the relationship altogether?

Summary

The rapid proliferation of customer loyalty programs is a clear indication that retaining current customers has become a high priority. Still, so few organizations harness available data to its greatest potential. There’s no question that predictive data analytics represent the next big wave of innovation. The only question that remains is, how will you respond?


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