The global telecom industry continues to traverse through significant change. Today, being driven by the demands from the customers, the Telco’s have been reshaping the communication model to serve them better. The prevailing competitive, regulatory and recessionary environment unrelenting, Telco’s are implementing new business models and redefining their business strategy to ensure continued growth through new customer acquisitions and profitability through launch of new innovative services.

1. Modeling Customer Attrition in Telco Industry
Introduction
The global telecom industry continues to traverse through significant change. Today, being driven by the
demands from the customers, the Telco’s have been reshaping the communication model to serve them
better. The prevailing competitive, regulatory and recessionary environment unrelenting, Telco’s are
implementing new business models and redefining their business strategy to ensure continued growth
through new customer acquisitions and profitability through launch of new innovative services.
While being innovative is an ongoing process, the big challenge today for Telco’s has everything to do with
increasing Customer Retention, building Customer Loyalty and reducing churn whilst reducing cost of
operations. This white paper provides insights on how Telco’s can combine its unstructured data gathered
from customer conversations and structured data gathered from its CRM solutions to model customer
attrition and arrive at an customer segment who are more likely to respond to retention programs.
Conventional Approach
Typical attrition models take historical behavior of customers to segment them based on their “propensity”
to attrite. The end result of these attrition models is simply a statistical prediction of a customers’ likelihood
to remain loyal and/or leave. This likelihood is estimated by using historical customer data that includes-
1. Observable attributes of customer at a given point in time – these are the predictor variables in the
statistical model
2. Whether or not each customer remained loyal (and for how long) – this is the response we hope to predict
A predictive model is built using this data where the customer attributes used to predict attrition. By applying
the model to current customers, a prediction of future loyalty is obtained. Based on the prediction,
customers can be separated into high-risk and low-risk groups. Retention campaigns can be targeted to the
high-risk groups (Figure 1)

2. Advanced Approach
One challenge with the conventional approach illustrated in Figure 1 is that it does not incorporate a core
source of data inputs – ‘Customer Intelligence’, the customer’s interactions with the company and its
service/support organization. This source of unstructured data i.e. conversations of customers with the
company, is a strong leading indicator of future customer behavior. Statistical models based on text mining
unstructured data show that there is a strong correlation between certain customer queries and an
expressed intent to attrite.
Figure 2 illustrates the results of a text mining based categorization of customer queries that occurred during
chat interactions with the service/support representatives of a wireless carrier. The queries classified as
‘Attrition’ are conversations where the customer expresses ‘an intent to cancel/leave’. Though this analysis is
based on web chat interactions, a similar query categorization can be created for transcribed voice data as
well.

3. Once the unstructured data from customer conversations is classified and structured, it could then be used
for predictive modeling. A structured table (shown in Figure 3) is built with each row representing a customer
conversation. The conversation is classified based on major query types including the customer’s expressed
intent to cancel.

4. The information within the table is then used to build a predictive model with ‘intent to cancel’ as the
dependent variable and the other ‘query categories’ as predictor variables. The model can be built using
statistical methods such as logistic regression (Figure 4). Results of this analysis for the wireless carrier shows
that certain problem categories were clearly significant predictors of an ‘intent to attrite’
(Figure 5)
Once the key queries predicting an ’intent to cancel’ are identified based on the model, this data can then be
merged with the structured CRM data to develop a similar scoring model that uses both CRM attributes and
customer queries (Figure6) as predictor variables. The structure of the scoring model can be similar to the
illustration in Figure 4.

5. Implementation
The analysis in Figure 5 clearly shows that certain queries are correlated strongly to an ‘intent to attrite’.
However, this information is useful only if it can be acted upon to save future attrition. To enable action it is
important to first be able to capture the query data for each customer contact. To enable this, the following
approach is recommended –
1. Data Collection – In the case of chat and email all the contacts can easily be mined and the customer
records can be tagged for these queries. In the case of voice calls, customer service representatives can be
asked to tag calls that contain queries that are strongly correlated to attrition, through a typical disposition
coding mechanism.
2. Scoring – Like any typical attrition model, on a weekly/monthly basis customers can be scored using the
predictive model. In this case the scoring would be based on a model developed using both the CRM and
customer interaction data resulting in greater predictability.
3. Retention Programs – Like all retention programs retention campaigns can be targeted toward customer
profiles that are likely to attrite. However, the scoring model is better able to discriminate high risk groups
from low risk groups (Figure 7).

6. Conclusion
It is an established fact that cost of new customer acquisitions always tends to be higher when compared to
the cost associated with retaining a customer. Given the prevailing competitive, regulatory and recessionary
environment, a logical direction for a Telco’s is to target its customer retention programs to that group who
are likely to respond positively. The established statistical tools can help model customer attrition and arrive
at customer segments that are more likely to respond to retention programs.

7. About 247-Inc
247-Inc is a predictive interactions solutions provider that guarantees measurable business results across the
customer lifecycle. With its patented “predictive interactions” SaaS platform coupled with “24/7
Outperformance” framework, 247-Inc promises to improve sales by 25% or more, improve telecom customer
experience by 10% or more and reduce contact center costs by 20% or more for its clients. Today, 247-Inc is
the no. 1 partner in contact center operations for 90% of its clients.