Modeling Customer Attrition in Telco IndustryIntroductionThe global telecom industry continues to traverse through signifi...
Advanced ApproachOne challenge with the conventional approach illustrated in Figure 1 is that it does not incorporate a co...
Once the unstructured data from customer conversations is classified and structured, it could then be usedfor predictive m...
The information within the table is then used to build a predictive model with ‘intent to cancel’ as thedependent variable...
ImplementationThe analysis in Figure 5 clearly shows that certain queries are correlated strongly to an ‘intent to attrite...
ConclusionIt is an established fact that cost of new customer acquisitions always tends to be higher when compared tothe c...
About 247-Inc247-Inc is a predictive interactions solutions provider that guarantees measurable business results across th...
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Modeling Customer Attrition in Telco Industry

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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.

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Modeling Customer Attrition in Telco Industry

  1. 1. Modeling Customer Attrition in Telco IndustryIntroductionThe global telecom industry continues to traverse through significant change. Today, being driven by thedemands from the customers, the Telco’s have been reshaping the communication model to serve thembetter. The prevailing competitive, regulatory and recessionary environment unrelenting, Telco’s areimplementing new business models and redefining their business strategy to ensure continued growththrough 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 withincreasing Customer Retention, building Customer Loyalty and reducing churn whilst reducing cost ofoperations. This white paper provides insights on how Telco’s can combine its unstructured data gatheredfrom customer conversations and structured data gathered from its CRM solutions to model customerattrition and arrive at an customer segment who are more likely to respond to retention programs.Conventional ApproachTypical 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’ likelihoodto 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 thestatistical model2. Whether or not each customer remained loyal (and for how long) – this is the response we hope to predictA predictive model is built using this data where the customer attributes used to predict attrition. By applyingthe 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 thehigh-risk groups (Figure 1)
  2. 2. Advanced ApproachOne challenge with the conventional approach illustrated in Figure 1 is that it does not incorporate a coresource of data inputs – ‘Customer Intelligence’, the customer’s interactions with the company and itsservice/support organization. This source of unstructured data i.e. conversations of customers with thecompany, is a strong leading indicator of future customer behavior. Statistical models based on text miningunstructured data show that there is a strong correlation between certain customer queries and anexpressed intent to attrite.Figure 2 illustrates the results of a text mining based categorization of customer queries that occurred duringchat 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 isbased on web chat interactions, a similar query categorization can be created for transcribed voice data aswell.
  3. 3. Once the unstructured data from customer conversations is classified and structured, it could then be usedfor predictive modeling. A structured table (shown in Figure 3) is built with each row representing a customerconversation. The conversation is classified based on major query types including the customer’s expressedintent to cancel.
  4. 4. The information within the table is then used to build a predictive model with ‘intent to cancel’ as thedependent variable and the other ‘query categories’ as predictor variables. The model can be built usingstatistical methods such as logistic regression (Figure 4). Results of this analysis for the wireless carrier showsthat 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 bemerged with the structured CRM data to develop a similar scoring model that uses both CRM attributes andcustomer queries (Figure6) as predictor variables. The structure of the scoring model can be similar to theillustration in Figure 4.
  5. 5. ImplementationThe 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 isimportant to first be able to capture the query data for each customer contact. To enable this, the followingapproach is recommended –1. Data Collection – In the case of chat and email all the contacts can easily be mined and the customerrecords can be tagged for these queries. In the case of voice calls, customer service representatives can beasked to tag calls that contain queries that are strongly correlated to attrition, through a typical dispositioncoding mechanism.2. Scoring – Like any typical attrition model, on a weekly/monthly basis customers can be scored using thepredictive model. In this case the scoring would be based on a model developed using both the CRM andcustomer interaction data resulting in greater predictability.3. Retention Programs – Like all retention programs retention campaigns can be targeted toward customerprofiles that are likely to attrite. However, the scoring model is better able to discriminate high risk groupsfrom low risk groups (Figure 7).
  6. 6. ConclusionIt is an established fact that cost of new customer acquisitions always tends to be higher when compared tothe cost associated with retaining a customer. Given the prevailing competitive, regulatory and recessionaryenvironment, a logical direction for a Telco’s is to target its customer retention programs to that group whoare likely to respond positively. The established statistical tools can help model customer attrition and arriveat customer segments that are more likely to respond to retention programs.
  7. 7. About 247-Inc247-Inc is a predictive interactions solutions provider that guarantees measurable business results across thecustomer lifecycle. With its patented “predictive interactions” SaaS platform coupled with “24/7Outperformance” framework, 247-Inc promises to improve sales by 25% or more, improve telecom customerexperience by 10% or more and reduce contact center costs by 20% or more for its clients. Today, 247-Inc isthe no. 1 partner in contact center operations for 90% of its clients.

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