It is critical to note that businesses can benefit from utilizing all unstructured customer data available in contact centers. It can help them identify their high-risk customers, allowing them to take preventative measures that could prevent loss in revenue.
Structured data is mainly used in traditional customer retention strategies since it’s easier for their models to interpret and be trained with. This is not only a constraint but also the foundation of a myopic retention strategy.
While organized customer data can tell companies how many customers are likely to cancel, unstructured data can reveal customers’ needs, wants, fears, expectations, and reasons for withdrawing. Without these insights, retention efforts will fall short, and brands may fall far behind with rising competition and customer expectations.
Here are a few ways unstructured data can enhance customer retention strategies.
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Carrying Out a Large-Scale Churn Analysis
The most effective proactive customer retention program is providing customers with a frictionless customer experience that matches their expectations. Customer intent cannot be identified at scale using the current feedback process, which involves low-volume questionnaires. However, agents record all of their encounters in call centers in text format. This can amount to millions of rows of data per year, depending on the company’s size. These unstructured agent notes can help understand the intent and mood of customers at scale.
Early Detection of Churn
Unstructured textual data analysis can be quite valuable for detecting churn. Businesses can extract sentiment, effort, intent, and consumer risk signs from call center text notes using text analytics technologies. Simultaneously, AI and ML can aid in the discovery of complicated behavior patterns that contribute to churn. Because these churn indicators are derived from consumer interactions in near real-time, they help in the early detection of potential churn trends. Early detection allows businesses to take the steps necessary to retain customers at scale before it’s too late.
Detecting Addressable Churn Risk
Not every customer is the same, and not every churn is the same. Setting unrealistically high retention targets is a bad idea. For instance, if a customer moves to an unserviceable area, the churn cannot be addressed. However, customer dissatisfaction-related churn is preventable and should be handled with proactive resolution strategies before it becomes a costly retention issue.
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Models can readily forecast actionable risk scenarios by evaluating prior consumer interactions. Then retention teams can focus their time and resources on proactively resolving such customer difficulties, maximizing the impact of their retention efforts.
Targeted Marketing with Customer Segmentation
The data gleaned from agent notes has shown to be highly accurate and valuable. Additional consumer data, including demographics, transactional history, and surveys, are used to bolster these insights. This unified customer record opens up new opportunities for customer segmentation and offers greater insight into each customer’s behavior. Businesses can segment consumers depending on their propensity to churn once the usual behavioral patterns of each consumer have been detected. After these segments have been developed, marketing teams can efficiently conduct targeted marketing by developing relevant outreach initiatives that are highly effective in reducing churn.
Reducing Customer Effort
Churn can be reduced by improving the customer experience. Improving the customer experience requires reducing customer effort. By tracking calls at call centers, one can learn about the call drivers and what causes customers to come again. Businesses can detect process gaps by repeat call mapping the overall call flow, resulting in the customer exerting effort to remedy their concerns, resulting in dissatisfaction, which leads to churn.
Competitive Intelligence
While trying to remedy their concerns, dissatisfied customers frequently expose competitor names, which agents record in-text notes. Such information can be easily extracted using Natural Language Processing, revealing detailed information on competitors, their demographic strength, and offers. Businesses can use the information to serve their consumers better and remain ahead of the competition.
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