Customer loyalty is the act of repeatedly choosing one company’s products and services over another’s. Loyalty doesn’t happen overnight; rather, it builds up over time as the accumulation of positive interactions with an organization or brand. A poor experience with a product or service doesn’t necessarily strike a blow to loyalty; how the organization handles the problem is the critical factor in ensuring enduring customer loyalty.

Customer Loyalty Pays Dividends

A loyal customer is one that isn’t as easily swayed by price, availability or a sales pitch from a competitor. They’re likely to send other customers your way with a positive recommendation, and many of them are willing to give you valuable insight into other products, services or capabilities they might find helpful. And they’re increasingly taking part in customer loyalty programs offered by a brand—they account for more than 3.3 billion memberships in the United States alone. In retail, members are more likely to buy from a retailer whose program they belong to, and they are more likely to download the retailer’s app. They’re also more likely to follow and engage with a company on social media and recommend it to their family and friends; with 81% of people saying they trust recommendations from family and friends over those from companies, which leads to further growth for the business.

Repeat customers are incredibly valuable because they:

• Spend 67% more on products and services than new customers.
• Refer friends and family; second-time customers refer an average of three people to the business and ten-time repeat customers refer an average of 10 people.
• Are more likely to buy from you than from the competition, even when the competition is selling the same product at a lower price.
• Are 50% more likely to try a new product or service you offer.

The bottom line? Customer loyalty is good for business. Repeat customers convert more frequently, spend more, cost less than acquiring a new customer, have more flexibility when there is a mishap because there is established trust, and are more likely to refer your product or service.

How Customer Loyalty is Measured

As the adage goes, if you can’t measure it, you can’t improve it. The good news is that organizations have a variety of methods at their disposal to measure customer loyalty.

Some of the most common metrics used to measure customer loyalty include:

• Net Promoter Score (NPS): A single survey question asking respondents to rate the likelihood that they would recommend a company, product or service to a friend or colleague.
• Customer Loyalty Index (CLI): A survey that averages the scores from three questions: How likely are you to recommend us to your friends or contacts? How likely are you to buy from us again in the future? How likely are you to try out our other products/services?
• Customer Lifetime Value (CLV): A calculation that typically multiplies customer revenue per year by duration of the relationship in years then subtracts the total costs of acquiring and serving the customer.
• Repeat Purchase Rate: The percentage of customers who return for another purchase.
• LTV: The value of business attributed to the customer during his or her entire relationship with the company.
• Churn Rate: The annual percentage rate at which customers stop doing business with an organization.
• Referrals: The percentage of customers making referrals and the number of referrals being made.
• AI Sentiment Analytics: A predictive indicator of customer satisfaction for each touchpoint, which leads to loyalty.

How to Build Customer Loyalty

Customer expectations are changing faster than ever before in history; consumers sometimes care more about the experience itself than they do about the outcome. The most innovative, disruptive, and high-growth businesses are raising the experience bar for everyone – consider, for example, the so-called “Amazon effect” – how companies like Amazon have redefined convenience and cemented consumers’ expectation of a frictionless buying experience, regardless of the industry. The accelerated adoption of digital channels during the pandemic only increased consumer expectations for a great CX.

“The customer expects so much more than just a seamless digital transaction,” Janet Balis wrote in Harvard Business Review. “Now that companies have their personal data, they want anticipatory, personalized experiences across the entire customer journey.”

To meet these rising expectations, organizations today are increasingly taking an omnichannel approach to serving customers. This means empowering them to reach out on their channel of choice, whether it be voice, email, chat, social or some other method. It means meeting them where they are in their buying journey with highly personalized experiences. It means ensuring that the customer’s experience with the organization is seamless across channels.

Loyalty, after all, is an ongoing relationship. At the core of any mutually beneficial relationship is shared value, and the most effective customer loyalty strategies result in additional value for the customer as well as the business – regardless of where and how the customer is interacting with you.

Agents, of course, play a critical role in these relationships as the face and voice of the organization. Among the relationship-building behaviors proven to impact customer satisfaction and, by extension, customer loyalty:

• Demonstrating ownership
• Active listening
• Empathy
• Rapport
• Expectation setting
• Effective questioning
• Promoting self-service
• Inappropriate action
• Acknowledging loyalty

How AI is Enabling Customer Loyalty

A growing number of contact centers are leveraging AI, sentiment and intelligent feedback to connect social media, surveys and other data points to digital or phone interactions in the contact center to enable a better view of the customer and deliver the type of experiences that drive loyalty. They’re applying AI and analytics to omnichannel data to act on insights and transform customer experiences by:

• Leveraging sentiment analysis to measure emotion in customer and agent interactions.
• Enabling autodiscovery to surface trending topics with little effort.
• Using AI anomaly detection to surface unexpected upward or downward trends in topics, making it easy to spot high value or potentially high volume issues and resolve them quickly.
• Proactively communicating with customers on complex issues.
• Personalizing outreach and preventing churn.
• Uncovering blind spots across the customer journey.
• Coaching agents to drive meaningful conversations.

In practice, it looks something like this:

• Customer Satisfaction Use Case: At a leading travel agency, voice and survey data is siloed. The agency is missing key information due to its inability to tune in to all communication channels.
  • An agent helps a customer book a travel reservation via chat. The reservation is booked, which leads the organization to think the customer is satisfied.
  • The online reservations channel isn’t working, though. Some customers call for help, while others defect and make a reservation with a competitor, resulting in lost revenue and customer dissatisfaction. Web surveys reflect customers’ inability to book a reservation online.

  Solution: Unifying contact center and digital channel survey data enables the travel agency to:

  • Use AI autodiscovery and sentiment to surface topics.
  • Benefit: The agency is now able to turn insight into action, improving the digital channel experience, improving CSAT and creating loyal customers.
• Customer Churn Use Case: Like the travel agent, a telecom’s voice and survey data is siloed, causing the organization to miss key insights.
  • An agent helps a customer open an account, and the analytics dashboard indicates that the “open account” KPI has a low AHT and that script compliance was met. Customer sentiment is positive.
  • In the post-call survey, however, the customer scored the interaction low because the process was tedious, resulting in a negative customer experience, risk to brand loyalty and an increased risk of customer churn. The telecom can’t easily find the call with a low CSAT score or identify the root cause of the dissatisfaction. Supervisors have to manually search/listen to the call for the answer.

  Solution: Unifying voice and survey data enables the telecom to:

  • Identify the agent and interaction quickly: After receiving a low survey score, a workflow queues the interaction to be reviewed by the supervisor automatically. The agent followed procedures, but the account opening process was tedious.
  • Identify trends: Low survey scores are trending on “Open an Account” topics and are correlated to the word “tedious.”
  • Benefit: The telecom is able to prevent customers from abandoning the “open account” process and coach agents to ask probing questions, improving customer advocacy and increasing loyalty.
• Customer Effort Use Case: Similarly, a cable company is suffering from siloed voice and survey data.
  • A customer calls in to pay a bill online. An agent successfully helps the customer, leading the organization to believe that the interaction was positive.
  • The customer had first tried to self-serve on the web, then the mobile app, then tried to pay in the IVR system before finally paying over the phone. After this, the customer canceled the account.

  Solution: Unifying voice and survey data enables the cable provider to:

  • Monitor interactions: The cable company detects high customer effort in web and mobile account services.
  • Identify trends: First Contact Resolution for bill pay scores high in the contact center, but this is clearly a topic that should be handled in self-serve channels.
  • Benefit: The cable company is able to turn insights into action, optimizing its self-service channel, reducing customer effort and improving customer loyalty.

Customers today are willing to give their loyalty to companies that are able to deliver seamlessly connected experiences across channels. It’s an investment worth making: Customers who feel like you know and understand them are more likely to return and refer your products and services to others. AI-powered solutions like NICE Enlighten AI make this a reality by making it easier than ever to use omnichannel data to transform the customer experience.

Learn more in our guide to Customer Experience Analytics.