In the competitive business world of today retention of existing customers is the best marketing strategy to survive with a competitive edge. Churn management is the term that has been widely used to define change of customer loyalty and for customer turnover. In more detail, using analytics to predict churn management is the concept of statistically detecting and forecasting customers who are intending to move to a competitor service provider. When such customers are identified, they are segmented based on the reasoning for their intent of changing service provider and thus a focused effort with proactive marketing campaigns for retention. Customer retention has become a very important decision in business strategic decisions. Similar to a product lifecycle there is also a customer acquisition lifecycle. After the initial aggressive targeting and acquisition of customers the business comes at a point when the number of customers that can be acquired for a particular business reaches its peak. Thus finding and retaining new customers becomes increasingly difficult and costly. At this stage of the lifecycle it becomes imperative to secure and retain the most valuable existing customers or customers with the highest CLV (Customer lifetime value) than trying to win new ones. In addition, it has been a common practice that finding new customers can prove to be a more expensive business strategy than retaining existing ones.
Telecom companies can increase profitability by creating a predictive modeling for identifying potential churn candidates and non-revenue earning customers; and can increase revenue and profitability by targeted campaigning and promotional offers which will not only retain these customers but also convert the non-revenue earning customers to profitable revenue earning customers. With competition being on the rampant rise and new player emerging in the market like never before the ARPU (Average revenue per user) is reducing. Every player is concentrating thus on what % of the market share they can capture. A single mistake of not retaining and avoiding customer churn can cost the telecom company dearly.
Analytics can thus be used as a very important tool to identify and predict
- Which customers are most likely to churn?
- How can we motivate and persuade them to stay?
- What is the reason for their churn?
- Which customers will stay (churn retention)?
- How much cost will be involved in retaining the customer?
- What is the profitability of the customer?
- What is the ROI (Returns on Investment) for spending done on the customer retention?
Predictive analytics for measuring churn is developed focusing on predicting the probability of the customers to churn out in future. This takes into consideration different aspects of consumer behavior, market conditions, reasons to churn, including past historical data of people those who have changed their service providers in past. Thus a model that is built on past experience and present inputs on an ever evolving market segmentation model will generate the probability factors and rate the customers according to the marketing strategy of the telecom service provider. Then based on the customer segmentation done and the value associated with the customer they are then lured with incentives to change their decision.
For example: If a customer has a history of high billing for a long distance calling and further investigation reveals that he is calling a number from the same service provider in another location then he can be offered a same service provider calling pack or his tariff plan can be changed accordingly to suit his needs.
For example: -If many customers have churned from a particular area due to problems in reception quality and frequently not reachable errors based on the service assurance and ticketing data a comparative analysis can be used to develop an algorithm of these customer tickets vs the customers churned from that area. The focus that thus be directed to the technical department in that area and thus issues can be fixed on time to avoid further churn
The customer churn can be categorized broadly under the below areas
- Churn due to usage tariff/ billing rate plans, value for money
- Churn due to network/call service quality
- Churn due to change in preferences of services/ products not available with current service provider
- Churn due to dissatisfaction on other issues (related to billing, customer service, compatibility with current instruments used iphone, cell phone or handset)
- Ordering, provisioning and fulfillment issues (Turnaround time for conversion)
Customer churn is just like an epidemic of swine flu. The Telecom companies can only do a proactive prevention to defend against the same. But once it strikes it keeps on taking a toll and then it takes a large amount of time, effort and cost to regain what is lost in the epidemic and a huge effort goes into the reactive response to the churn. 1 customer dissatisfied affects 4 others and thus the multiplicity spreads on, thus no need to do the math. Thus ‘Prevention is better than cure’ not just applies on Human Health but also in telecom industry for a long run.
There can be two ways to manage customer churn
- Proactive Analysis and Customer Management
- Reactive Analysis and Customer retention
Proactive Analysis and Customer Management
It is a very well known fact that retaining a profitable customer over a long period of time is more profitable than acquiring a new customer with higher returns for the short run
The areas in which a proactive analysis can help are
- Analysis of customer segmentation and the segment usage patterns
- Analysis of competitor offerings for the same customer segmentation
- Analysis of network and call quality monitoring systems
- Primary research of the customer feedback on the current products/ services
- Analysis of the customer complains and tickets in the CSR systems/ Call centers
- Billing and default analysis
- Enabling a better focused and segmented campaign management system
- Cross selling and strengthening customer’s belief in value for money from the product/ services
Reactive Analysis and Customer retention
The reactive analysis and churn prevention plan needs to be in place in order to prevent any further spread of the churn due to a seeding customer
- Identify the churn pattern based on the customer segmentation done according to region, profiling and customer lifetime value
- Identify the reason for the churn and trace it back to the customer usage pattern, pattern of tickets and complains by the customer throughout the lifecycle and do a root cause analysis
- Based on analogy algorithms predict the customers with highest risk due to the recent churn
- Device a response plan for the spread of churn and retention of the existing customers
- Create a rapid action force and alert mechanism in place to look into identifying any anomalies in the consumer behavior.
Industry research shows that a customer who cancels his account with one telecom service provider has only 20% probability of returning to the same service provider in case the churn is due to poor services. In case the customer churn is due to hopping nature and volatility of the customer choices and mindsets that is around 38% probability of return in case he has had a good experience earlier. In case of a bad experience the probability dips to only 24% of the return.
A customer once lost not only takes with it the customer lifetime value and revenue it can provide to the telecom service provider but also takes along a portion of trust and customer loyalty to the service provider. This is coupled with seeding and spreading of dissatisfaction to its contacts having on an average a 25% chance to be influenced. The only one conclusion that the telecom companies can make out of it is that Churn is Good only while making Butter and not for customers. If they wish to retain their customers they need to Redefine their Strategy towards customer churn management.
*** More detailed report is present separately
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