Four Steps for Driving Customer Loyalty With Data Science

Four Steps for Driving Customer Loyalty With Data Science

Four Steps for Driving Customer Loyalty With Data Science

For most companies, the cost of acquiring a new customer is far more than the cost of retaining an existing customer—often 5-10 times more expensive. Moreover, 61% of small business owners have reported that more than half of their annual revenue comes from repeat buyers.

Those impressive numbers confirm the 80/20 rule to be true (20% of customers bring 80% of the business); and, thanks to the substantial amount of customer data businesses now have available, many businesses are shifting their primary focus to these customers.

If a business is to retain current customers, then its marketers must truly understand repeat customers' needs to improve their overall experience with the brand and gain their long-term loyalty.

For most marketers, it is no longer a challenge to collect customer data: Analytical technologies have given us the tools to understand a customer's actions at every point of interaction with a brand. But many marketers are still struggling to transform that analytical data into relevant information that can help improve customer loyalty.

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Fortunately, there are steps marketers can take to harness data and keep churn rates at a minimum.

Companies have been using data science as a secret weapon to generate quickly actionable information that improves customer retention. If you're interested in significantly decreasing your customer churn rate, here's how to use the power of data science to define a process that will help your brand keep the customers you've already worked so hard to obtain.

Nowadays, subscription and recurring-revenue business models are everywhere. And no wonder: how customers want to access and pay for goods and services is changing, so companies are in turn changing their pricing models.

The first step in setting up a data-driven approach to increasing customer loyalty is to define the model used by your organization. More than likely, your model will fall under one of two varieties: a subscription model or a non-subscription model. (Netflix and Spotify are examples of a subscription model; Uber and eBay are examples of a non-subscription model.)

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Your business model has an impact on the difficulty of determining "churn rate," which can have different definitions; however, for most businesses, it's a matter of whether a customer will become a "churner"—i.e., no longer a customer. Churn could also refer to the loss of contracts, MRR (monthly recurring revenue), contract value, and bookings.

Churn is frequently expressed as a rate, a ratio, or a whole number: for example, "We have a churn rate of 10%," and, "We churned five customers."

Identifying churners is straightforward in a subscription model: A customer churns when she requests a cancellation of her subscription.



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