The old saying that the customer is king is more than a cliched phrase. It is crucial for companies to keep a healthy relationship with their customers in order to grow market share and revenue. Marketing products and attracting new customers are just one part of the story, another equally important part is keeping current customers by your side.
Customers terminating a relationship with a business is usually referred to as customer churn. It is estimated that recruiting a new customer costs up to five times more than retaining an existing one. A good company will recognize this and invest time and resources into retaining their customers. According to a Harvard Business School report, a 5% increase in customer retention rate on average will result in 25% to 95% increase in profits. No further justification is needed to make the case for building an attentive customer care team.
Great customer care teams understand a customer’s experiences and approach them with skillful communication and professional expertise, but how can we really understand what a customer’s experience is, especially when the size of data gets massive? The answer of course is data science.As a result, an increasing number of businesses rely on data science to do the ‘understanding customer experience’ part first, before humans get involved to take action.
Data science can be implemented to optimize every step of the customer care process:
Taking care of customers when they are confused about the products or run into issues while using it. The answer of some frequently asked questions can be straightforward, so why not leave them to the model and let the tech support staff only focus on more complicated ones. Natural Language Processing can find a position here to intake customer’s questions, identify keywords, and reply with the pre-designed templates. This type of implementation is becoming increasingly common with the rise of chat bots.
Create a model that can detect sentiment of customer reviews on the service. Text mining and sentiment analysis are usually applied here to discover detailed reasons for discontent and even group them into several common themes so actions can be taken accordingly.
Teach a model to segment unhappy customers into groups by the most possible reasons that plague them. Each group should have its own retention strategy and follow-up steps to address particular concerns of each group.
Machine learning algorithms can be applied to predict in advance which customers are at risk of leaving, so customer service specialists can take extra care of these high-risk customers. The point of this strategy is to obtain perspective even before the decision to cancel is made on the customer’s side and form a stronger bond with them.
All these methods produce insights from the massive customer information collected and serve as a way to boost profits. Such insights save human efforts and try to make the most out of the marketing budgets. Millions of companies have acknowledged the capabilities of data science in improving their relationship with customers just as we have at Homes.com. We are excited to share how we used data science to reduce our customer churn.