In any business, competitors are always looking to grab your customers, and many customers are on the lookout for a better deal.
Customer attrition rates range from 7% to 40% annually in various industries. Slowing this customer "churn" rate by as little as 1% can add millions of dollars to any sizable company's bottom line.
As it is already known customer acquisition is 4 to 5 times more expensive than to retain them, an effective customer retention strategy is crucial to a company's success.
Marketing departments are traditionally focused on acquiring new customers than retaining existing ones. But after the economic recession, when finding the new customer is especially challenging, customer retention has become a major corporate priority.
While it is the fact that churn will always exist, ensuring that it is managed effectively is a key, to assure profit margins & sustainable business growth.
By predicting which customers are likely to leave, companies can reduce the rate of churn by offering customers new incentives or packages to stay. Apart from that, it gives the best strategy for them in terms of cost and effort by decreasing the total cost of retention and increasing the effectiveness of campaigns.
There are multiple data mining techniques which can be used for churn prediction.
Just to step back, Data mining is a process of extraction and analysis of patterns, relationships and useful information from massive databases. It usually involves four classes of tasks which revolves around classification, clustering, regression and association rules.
Here are the typical steps that are taken to address customer churn & retention:
- Define “Customer churn” as it varies from industry to industry
- Create a single 360-degree customer view
- Collect every Customer touch point data – billing data, transaction records, demographic details, Call center records, Credit history
- Understand the customer behavior
- Map the entire customer journey
- Profiling & segmenting them on various attributes
- Customer value analysis or Life Time Value
- Identify the customers with the highest chances to churn
- Predictive Churn model
- Typical techniques used are Regression, decision trees, survival analysis
- Discover what are major reasons for churn
- Product or Service-related issues
- Demographic constraints
- Better deals from competitors
- Setup targeted retention campaigns for high-value Customers who are likely to leave
- Customized promotions
- Next best offer strategy
- Location-based real-time offers
- Measure the campaign's effectiveness for continuous improvement
- What is the retention rate or how much Churn % has come down
Today social media analytics including Speech analytics is becoming a key aspect to analyze Customer sentiments which helps in finding out the reasons for customer churn. This involves capturing & analyzing unstructured data from customer touch points like customer support call notes, chats, email exchanges & scraping customer comments from Facebook, Twitter, blogs etc.
It is very important to go beyond just predicting customers who are likely to leave & identify the reasons for churn & effectively drive the targeted campaigns to retain those customers.