If you’ve ever watched customers leave your business or “churn,” you’ve probably felt your ego deflate pretty quickly. The bigger danger of churn, though, is the damage it does to your financial stability and competitive reputation. After all, acquiring a new customer can be five to 25 times more expensive than keeping the ones you’ve got. Predicting which customers are at risk of leaving enables you to target them with good retention strategies. One key to predicting churn accurately across industries is to harvest actionable insights from both operational and survey data.
HOW COMBINING SURVEY DATA WORKS
Companies can bring operational and survey data together by creating a data lake or single source of truth. The data lake aggregates data from across silos and serves as the main location for storing, cleaning, analyzing, and sharing the information. With artificial intelligence and machine learning supporting these processes, professionals can better understand what would improve the customer experience and incentivize people to stick around. This applies to both real-time assistance or notifications as well as longer-term retention strategies. Let’s take a look at three industries where bringing survey and operational information together in this way is most impactful.
In healthcare, customers have one of the highest churn rates of any industry, ranging from 15-20%. That’s partly because customers understand that benefits are increasingly uniform. Margins of differentiation for payers like insurance companies are slim, and the customer experience becomes the attractive wrapping paper around the payer’s overall package.
Let’s say you look at your organizational data and see that one of your care facilities routinely gets less traffic than others despite having the same services and care satisfaction levels. Looking at survey results might reveal that people don’t go to that facility because they lack transportation and are selecting facilities that are accessible on foot. You could then offer Uber rides to patients as a way to give them more freedom of choice and care flexibility.
Importantly, payers can apply combined data to their providers as well as consumers. A crucial area here is identifying and eliminating unnecessary burdens that drive up costs, including administrative and “middle-man” hurdles. But care quality can be addressed, too. Operational and survey data can identify not only which healthcare professionals or facilities are struggling, but also why (e.g., exceptional caseloads, too little time between patients). The payer can see what kind of flexibility they need to provide within their provider contracts.
In telecom, customers can easily hop to another provider. In fact, they can often do so at significant cost savings, even engaging in bidding wars to get carriers to compete on price. Telecom companies fight hard with massive marketing budgets to avoid churn and base most of their strategy on renewal because they know that, if they lose a customer, they’ve probably lost that customer for life.
Because the risk of losing lifetime customer value is so high, telecom companies should be proactive about gathering customer feedback at every juncture, including after customer service calls. They can use software tools to analyze what customers are doing, leaning on the power of big data to extrapolate meaning, predict what customers want to see, and try to build trust in the customer-provider relationship. Companies have to be picky about the enterprise resource planning (ERP) systems they use for this purpose because most ERP systems don’t support the entire organization. Fewer than 5% of telecom companies say their enterprise resource planning system fully meets their data analysis needs.
Consider the influence of a simple outage message. If a telecom company sends a message to customers to advise them preemptively that there will be a service disruption, or if they tell customers they’re already aware of the disruption and are working to fix it, customers feel more at ease because they sense the company has things under control. In the same way, if you use almost no data on your service plan, or if your usage suddenly drops, then the telecom company can use that information to reach out and ask whether you need something, or to invite you to buy something else.
Most retail companies can use survey and operational data to reduce churn. But certain companies, most notably a global coffee brand, provide a strong example of using data-supported customer segmentation exceptionally well. They look at their information to figure out which customers spend the most, what they buy, and how often they buy coffee at specific stores. Then they identify which customers aren’t active, or aren’t buying as much as they used to. They figure out the specific pain points that are causing trouble, such as a lack of parking, slow service, or a dip in the coffee quality. Then they address those pain points and deliberately send those customers targeted email campaigns to try to draw them back or increase their transactions.
In the same way, companies such as Amazon use information from loyalty programs, user log-ins, or even weather forecasts in the buyer’s location to personalize recommendations and predict what people will buy. These companies turn to the data to see exactly when they are losing customers and improve the entire customer journey, and they use AI to personalize and develop the most effective messaging.
In the age of big data, build your customized system to see your unique big picture.
In an increasingly global market where customers can often go elsewhere if they’re not happy, customers don’t just want a good relationship and service—they demand it. Companies can take advantage of the vast data available to prevent churn and improve loyalty. Looking at the data in a segmented way is not effective enough, however. Businesses must get a big picture about pain points and be proactive based on combined organizational and survey information. This strategy applies to virtually every industry, including the major players of healthcare, telecom, and retail. To ensure you’re competitive, build your data system to put all your information in one place now.