Customer Loyalty: Does it Matter?
Today many mobile markets are approaching saturation. The consequence is a radical shift in operators' customer strategies. The purely quantitative growth of the past years will be replaced by the focused acquisition of profitable customers, the offering of relevant data products and the push to maximize profits from existing customer relationships. The reasons for this are clear: It is estimated that extending the length of a customer relationship by one year increases revenue by 45%; that figure jumps to 130% when the relationship is extended by three years. If usage of data services, such as mobile Internet connection, is included, the revenue increase from an individual customer could increase by 240%.
For mobile operators to accomplish this strategic shift away from the sheer number of customers to attracting and retaining the most profitable ones, they must adhere to a systematic customer interaction and insight strategy. Loyalty programs serve as an excellent platform for such a strategy.
Measuring Loyalty
In customer loyalty, there's no substitute for good research and planning. One must start by understanding why customers stay and why they leave. Taking this customer-centric view will enlighten the management on why customers behave the way they do, which allows one to create a profit-generating loyalty strategy. Some companies make this mistake by constantly acquiring new customers with price promotions and then wondering why these same customers flit to the next deal offered by a competitor. The resulting high-churn, low-margin customer base is unlikely to ever be profitable. Keeping the right customers is crucial to effective CRM. Data mining can help a telecom giant uncover the true drivers of customer loyalty.
Loyalty has the following dimensions which can easily be measured by a good business intelligence (BI) tool:
Desired Behavior: The extent to which customers engage in revenue-generating activities.
Commitment: The degree to which customers use the products because they have a voluntary interest in doing so - an emotional bond between the customer and the telecom company.
Depth of Involvement: The extent to which customers use the full range of products/services offered by the telecom company.
Loyalty Model: Dimensions of Loyalty
A good BI tool can help to find out how existing and former customers are behaving within the database. The entire database should be fed into the BI solution. Next, find out the dimensions of loyalty:
- Behavior: Segment the customer base according to the high, medium and low average revenue per unit.
- Commitment: The age of the customer within the network can easily divide them to high, medium and low aged customers.
- Involvement with network: Segment the customers into high, medium and low value based on various products used by the customer.
Once the segmentation is created, applying the model onto the entire database should reveal the correlation between all the three dimensions. The model must be then applied onto the existing and former customers. By applying the model on former customers, the reasons for the customers to leave can be found out. When the same model is applied to existing customers, the customers about to churn can be revealed.
Figure 2
Churn Reduction: Customer Retention
Only if a good data mining model is run onto the customer base can a telecom really discover the likelihood for a particular customer to churn.
There are two types of churn:
Involuntary Churn: Managing involuntary churn is sometimes ignored because inbound calls take precedence. It is found that calling these subscribers early in the delinquency process is highly beneficial, as it accomplishes several tangible and intangible objectives. It gives a chance to resolve possible issues causing subscribers to delay bill payment, collect late payments, stop further delinquency (that could lead to possible churn), up-sell and offer incentives to reward subscriber loyalty.
Voluntary Churn: The first step in increasing subscriber retention is to identify potential churn candidates, based on a variety of selected indicators and characteristics (i.e., usage behavior patterns, service problems, complaint histories, maturing contracts, etc.) As with involuntary churn candidates, calling these subscribers creates an opportunity to identify and resolve possible problems that might cause churn, award incentives for business loyalty, reduce churn and reduce new subscriber acquisition costs.
Business Intelligence: The Key to Customer Retention
These two tasks referred to as "prediction" and "understanding" represent the two most important aspects of data mining in use today. By predicting which customers are likely to churn, the telecommunication company can reduce the rate of churn by offering the customer new incentives to stay. By understanding why customers churn, the telecom can also work on changing their service to satisfy these customers ahead of time. In addition, the chance of the customer churning after action is taken can be assessed by the data mining tool so as to choose the best strategy in terms of cost and effort.
