Using Customer Loyalty Analytics To Drive Retention for Your Business

Using Customer Loyalty Analytics To Drive Retention for Your Business

Customer loyalty analytics can help brands understand and drive retention using data and loyalty metrics

According to Invesp, brands have a 60%–70% chance of selling to existing customers compared to a mere 5%–20% chance of converting new prospects. Despite these odds, most companies still spend more on acquisition efforts than on retention. Knowing how to retain customers allows businesses to maximize opportunities, no matter the size of their current customer base. One way brands can increase retention is by leveraging customer loyalty analytics. Let’s take a closer look.

Customer Loyalty Analytics

Customer loyalty analytics examine data behind the reasons a customer stays loyal to a company. While no standalone data set contains the answer, correlating several different metrics can give brands a more complete picture of customer loyalty. These metrics include:

  • Customer Retention Rate: What is the percentage of converted customers who remain in relationships with the brand after a certain period of time?
  • Repeat Purchase Rate: What is the ratio of customers who made purchases within a certain period compared to customers overall?
  • Customer Satisfaction Score: How satisfied are customers with the company’s products or services?
  • Customer Effort Score: How much effort must customers exert to get what they want, e.g., purchase a product online, find an answer to a product question, or resolve an issue with customer service?
  • Reviews and ratings: What are customers saying about the company’s products and services? What is the average rating for a brand’s service, customer support, or convenience?
  • Net Promoter Score: How likely are loyal customers to promote the brand’s products or services to others? How does the company’s NPS change over time?
  • Behavior Patterns: What can be learned from customers’ behavior on websites, mobile apps, or in-store visits? Are there specific trends and patterns that point to critical loyalty-building moments?

Customer loyalty analytics use these metrics, along with others, to help brands identify different factors that influence the sense of loyalty that underpins customer behavior. Analytics also equip brands with insights to transform user experiences and nurture valued customers, thereby improving retention.

Driving Retention

Below are a few ways brands can leverage customer loyalty analytics to make data-driven decisions that improve retention among converted customers.

Understand Loyalty Patterns

Customer data analysis highlights patterns in behavior. While examining the journeys of loyal customers, brands can use analytics to determine critical moments that lead to retention. For instance, a personalized follow-up from the support team could serve as the catalyst for an uptick in repeat purchases. Or, an engaging social media campaign may encourage converted customers to recommend a brand to friends. By using customer loyalty analytics, teams can capitalize on such moments and engineer similar loyalty patterns in lookalike audiences.

Improve CX

Customer loyalty analytics uncover loyalty patterns in customers’ journeys and also identify recurring pain points. Brands can obtain key insights to find areas of friction that cause customers frustration (for example, slow customer service response or a buggy checkout process). Addressing these concerns helps improve CX and keeps existing customers happy.

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Increase Customer Lifetime Value

Customer loyalty analytics can also help brands learn how to bring value consistently to loyal customers. Analytics can pinpoint the best upsell and cross-sell opportunities that meet converted customers’ expectations. As loyal customers find a brand that anticipates their needs, their continued patronage will increase customer lifetime value and drive revenue.

Reward Repeat Customers

Brands can use analytics to amplify loyalty-building actions such as launching a loyalty program or rewards system. Teams can further analyze customer data to learn which works best for a particular audience. For example, retailers can encourage senior customers to make repeat purchases with in-store rewards while stimulating younger shoppers to do the same by offering mobile game incentives. Analytics arms brands with insights about how to reward and reinforce loyalty behaviors among converted customers.

Encourage Reviews and Ratings

Humans tend to trust human value judgments, especially when it comes to qualitative assessments of a brand’s non-tangible characteristics like trustworthiness, user-friendliness, or worth. Ask loyal customers to give honest feedback and reviews on channels that customer loyalty analytics recognize as important. For example, if a brand targeting Millennials scores high on convenience, that brand can encourage users to rate products or services for user-friendliness and share reviews on the platforms most popular with Millennial shoppers. This will target audiences with the highest potential for loyalty and retention right from the beginning of their journeys.

In summary, customer loyalty analytics provides brands with insight into customer behavior through data and metrics that indicate loyalty. These metrics include NPS, customer retention score, customer satisfaction score, reviews/ratings, and more. Brands can use customer loyalty analytics to drive retention by understanding loyalty patterns, improving customer experiences, and increasing customer lifetime value.

Improve Retention With Treasure Data

Treasure Data Customer Data Cloud is an integrated suite of cloud-based customer data platform solutions. Treasure Data provides insight by collecting and centralizing customer data, unifying profiles, and analyzing journeys to spotlight hidden trends in customer behavior including loyalty-building interactions.

Treasure Data’s enterprise-grade customer insights platform is trusted by Fortune 500 and Global 2000 companies around the world. See what you can do with Treasure Data:

  • Collect and centralize customer data from all sources
  • Unify customer profiles using online + offline data
  • Analyze customer journeys
  • Apply multi-touch attribution models to determine which channels and touchpoints influence customer conversion and retention
  • Derive actionable customer insights using machine learning techniques
  • Personalize customer experience at all customer journey stages
  • And more

To learn more about how you can use customer loyalty analytics with Treasure Data’s CDP to drive retention and loyalty, consult an expert today. Want to learn more? Request a demo, call 1.866.899.5386, or contact us for more information.

Kellie de Leon
Kellie de Leon
Kellie de Leon is the Senior Director of Content Marketing at Treasure Data. She is a marketer, writer, and speaker who is passionate about delivering relevant and valuable experiences for customers throughout the buyer’s journey to drive business growth. Connect with her on LinkedIn.
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