Customer retention refers to a company’s ability to keep an active base of current customers. Customers often build expectations for a service or product through the buyer’s journey. This expectation determines the customer experience (CX) after interacting with a particular brand.
Today, customer experience is exceptionally critical to the success of brands. As per Gartner, every year in the USA, nearly 35.3 billion USD is lost by businesses due to customer churn triggered by CX issues. Customers are willing to pay for great CX. As a result, CX-driven brands undergo YoY growth of more than 1.5 times.
Now, an effective CX strategy should not only keep customers happy but also be capable of predicting if they will remain with the brand or abandon it down the line. This is where the importance of predictive analytics for customer retention lies.
This article explores the prediction of customer retention analysis in the light of customer experience.
Why predict customer retention?
CEO of Reforge, Mr. Brian Balfour said, “If your retention is poor then nothing else matters.”
Customer retention management is not only about how effective a company’s strategies are; it has a direct impact on the revenues too. Predicting customer retention, therefore, comes with multifarious uses, like –
- Increasing revenue – Retaining customers can positively impact the overall profits. Losing the existing customer base puts massive pressure on the marketing and sales team to acquire new clients. Also, it deprives the company of returning customers. Prediction helps improve any robust product produces loyal buyers. According to a study by Emarsys loyal customers are likely to spend 67% more on products and services.
- Enhancing cost-effectiveness – It is more profitable and straightforward to retain customers rather than get new clients. Getting new clients requires targeted ad campaigns and several other marketing and promotional endeavors. Hence, predicting retention trends can reduce the risk of current customers abandoning the brand by coming up with solutions for customer issues.
- Facilitating sustenance and growth – The growth of a business depends entirely upon the business strategies. Prediction can provide valuable insights to help build decisiveness in the company while devising plans for smooth operation and increased profit. This way, a company can be free from the uncertainty of a sudden loss of customers.
Benefits of using analytics for predicting customer retention
Leading organizations are investing more and more in emerging technologies for CX and customer retention management. Almost 641 billion USD is estimated to be spent on CX technologies, like predictive analysis, in 2022.
- Better customer retention – For any business gaining new customers is imperative to growth in ROI. However, this process incorporates a higher cost of acquisition. To make the process less expensive, the prediction of customer retention can ensure profit from the existing customer base via suitable customer retention.
- Recognizing profitable customers – Prediction helps identify profit-building customers. These clients are the ones who often spend the most resulting in a long-term profit for the company.
- Improved segmentation – Diverse preferences among customers require segmentation to identify them distinctly. Predictive analysis can help a company segment customers and focus on only the target audience to maximize returns.
- Price optimization – Predicting demand in the market can help a company avoid stocking inventory which can be expensive otherwise. It also helps a firm optimize pricing strategies and include instruments, such as discounts, promotions, segment-based pricing.
- Asset utilization – Prediction also helps optimize processes and performance to find new revenue opportunities in a competitive market by increasing asset utilization, collaboration, and control.
How to predict customer retention?
- Predictive statistical models: Use both qualitative and quantitative prediction models to forecast customer retention from the latest customer data. An example could be the Probit Regression method which estimates the probability of occurrence for a particular trait into a binary classification.
- Determine customer churn period: The churn period is the period after which a customer is considered lost. Defining the customer churn period is necessary for better customer segmentation and analysis of market trends. Follow this advice from the director at Zynga, Bing Gordon, “Stop churn with Coming soons.”
- Churned customer profiles: Create a comprehensive customer profile to highlight the characteristics of inactive customers. Use historical data which contains customer feedback and focuses on buying behavior to improve your CX strategy.
Useful strategies to keep in mind
The words of Walt Disney best describe the basics of an outstanding CX, “Do what you do so well that they will want to see it again and bring their friends.”
To maximize the benefits of predictive analytics for customer retention, here are some effective pointers –
- Analytics and data accuracy must be the #1 priority – As far as predictive analytics is concerned, focus on data sets that represent all the customer segments in a balanced fashion. This way, you can avoid inaccurate or biased data to obtain systematic and precise results.
- Segmentation of customers – The customer base of a company is consisted of people with various preferences and buying choices. Therefore, segmenting customers based on their buying patterns becomes a determining step if one strives to retain more customers.
- Set specific goals – Setting clear goals with your prediction analysis, like reaching a particular turnover by the year’s end or introducing new products or services, can help you achieve a loyal customer base. Follow a step-by-step strategy to achieve a time-bound goal.
- Leverage the power of qualitative insights – Qualitative analysis can help bypass the errors of quantitative one. Feedback from an open-ended market can get used to the company’s advantage to derive insights from the customer’s experience, thus improving CX’s strategy to retain more and more customers.
- Get professional help – A professional can always bring out the best information from prediction analysis. They can help you find the possible solutions you are looking to integrate with your business operations.
With the accessibility of cutting-edge predictive analytics techniques, designing a great customer experience can be streamlined considerably today. However, it is critical to consider the entire buyer’s journey instead of focusing on individual experiences to make a real difference in customer retention.
