What is Customer Success and Why it's Important to Your Retention Strategies
Your customers are the foundation of your business. As a business leader, you already understand that returning customers are much more cost-effective than the efforts required to bring in new business.
But if you're really serious about improving customer retention rates, your business needs to understand and adapt to customer journeys. To do this, you'll need to develop a strategy for using data to understand what customers need to deliver their desired results.
Achieving customer success requires a holistic integration of your retention efforts and loyalty programs. This holistic view is achieved by defining what "success" means, and by taking a step back from individual retention strategies to examine how your entire ecosystem is serving your customers. When organizations adopt data-driven technologies that support retention, they're able to use predictive modeling to deploy marketing strategies that support an optimized customer experience on a targeted, one-to-one level.
Here's a look at how customer success supports retention strategies, and how your business can shift its mindset from short-term initiatives to an emphasis on a holistic, data-driven model.
Customer Support vs. Customer Success
When comparing customer support and customer success, the first thing to understand is that you aren't facing an either/or situation. Customer support can't optimize your retention rates on its own, but it still plays an important role in optimizing user experiences for existing customers.
Customer support —which includes multi-channel communications to address customer concerns and improve satisfaction — also offers valuable metrics to inform your company's data-driven strategy. Data gathered from artificial intelligence and machine learning tools, for example, can measure the speed and quality of customer service, and provide insights for strategic changes that elevate overall satisfaction rates.
But customer support is inherently reactive, meaning it waits until problems arise to trigger a strategic response. Successful customer retention strategies require a more proactive approach. This is where understanding customer journeys comes into play. Marketers need intelligent tools and data-driven insights to predict risk factors for churn, examining the full customer journey to recommend actions that increase retention rates.
The rise of subscription-based models across a number of industries is a case-in-point. Churn is a fact of life for any subscription service, but a data-driven approach can help analyze this churn to identify strategies for improved customer retention. For example, of the 5.6 percent churn rate experienced by both B2B and B2C companies, 1.39 percent of that churn is involuntary, meaning that payment or other technical issues caused subscriptions to lapse.
Even within the voluntary churn experienced by businesses, AI and machine learning can identify risk factors that signal a likeliness to churn, and trigger proactive marketing strategies aimed at addressing dissatisfaction and retaining the customer relationship.
Using KPIs to Measure Customer Success
In order to track and leverage the key performance indicators (KPIs) that tell the story of customer success, you need an analytics tool equipped with AI and predictive modeling. Your customer retention rate will tell you how well your business is building long-lasting relationships, but other KPIs are needed to understand the full picture.
Average lifetime value (LTV) is an important KPI to follow. Instead of telling you how many repeat customers you have, LTV tells you how much revenue your company can expect from a customer. The total revenue of each customer relationship is a reliable reflection of your customer success.
Starbucks, for example, reports an LTV of more than $14,000 per customer. That's not bad for slinging cups of coffee—and it shows how the brand has solidified its revenue streams by using AI-powered analytics to create customer success.
Other KPIs can provide even more insight to drive data-driven business decision-making. Overall revenue, as well as metrics to define customer health and product usage, can help your business acquire the knowledge necessary to identify churn risk factors and improve retention rates.
How to Put Your Data to Work
The first step of any data-driven process is implementing the right tools, including AI-driven analytics and predictive modeling, to generate insights and put that information to work. No matter how high-quality and comprehensive your customer data might be, remember that this value can't be realized without the right tools to identify actionable insights.
These tools have the capability of quantifying the tendencies of your customers and visualizing the results in a way that is easy to understand and draw conclusions from. Marketers also need to develop a strategic plan that uses those insights to shape engagement efforts. For example, event-triggered email marketing or more responsive customer service that will resonate with your audience.
Your strategy should emphasize onboarding and education to help customers understand the value of a relationship with your brand, and how to take advantage. You should also use marketing automation tools to power loyalty programs, which lowers customer acquisition costs, decreases churn, and increases average lifetime value.
If your retention rates are low, for example, consider a rewards program to encourage repeat purchases. Or, if your average lifetime value is falling short of expectations, consider progressive rewards and savings programs that offer better deals and savings the more customers spend.
AI-powered analytics helps businesses generate deep insights that support improved customer success. AI solutions like Ki can also be used to automate certain processes in strategy development, allowing your business to focus on implementation and action.
By using AI to power data-driven marketing strategies, businesses can develop a customer success strategy that provides a better front-end user experience, while optimizing internal resources to increase retention rates — and ROI.
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