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Analytics for Customer Experience Management

Andrea_Fabrizi

Today in most part of the world, mobile devices have reached the full penetration of the population. The mobile expansion era is ending; Telecommunication are becoming a mature market, where maintaining customers and increasing their expenditure is a mandatory strategy for any Telco CEO.

 

In a mature market, indeed, the strategy of continuous growth by acquiring new customers is expensive: advertising costs, campaign management expenses, discounting, and equipment subsidies are extremely costly. Acquiring a new customer costs 5-8 times more than maintaining an existing one. On the contrary, account maintenance costs decline over the lifetime of the relationship and long term customers tend to be less inclined to switch, less price sensitive and are more likely to buy ancillary products. So one of the pillar of any Telco strategy must be the increase of customer loyalty.

 

analytics2.jpgBut customer loyalty doesn’t mean only good services; it implies all the aspects of the interaction between subscribers and operators during the whole customer lifecycle: from promotion of new services and tariff plans to the use of self-care web site, from the interaction with the Customer Care to the billing and charging, and obviously the quality of the service perceived. This means that the increase of customer loyalty is not a simple task or an activity owned or demanded to a single department (f.e. Network). It is a more complex activity which involves the whole company. It is a new way to re-think Telecommunication operators around their most valuable asset: their customers.

 

All the activities targeting the increase of the customer loyalty are grouped under the name of Customer Experience Management (CEM).

 

CEM is an operative model that Operators have to adopt to be more efficient in preserving and increasing their customer base. It is a shift from Network and IT centric operational model (OSS and BSS) to a new Customer-centric model, where all company functions are focused in maintaining and acquiring customers. Service providers that will be able to measure and manage the Customer Experience will be the most successful.

Unfortunately for Operators, Customer Experience is an extremely complex task to monitor and manage, as it is influenced by many diverse factors that can happen on different channels and that depends by individual characteristics (f.e. the same event can be perceived differently by two or more users).

 

Consequentially, to manage the Customer Experience to elements play a crucial role:

 

  • The collection and the analysis of any customer-operator interactions, to transform them in an appropriate measure (QOE KPI). These values are used to monitor to the quality of the user interaction for each subscriber and for each interaction and channel
  • Combine all customer-operator interactions into analytic models which provide a comprehensive and global picture of the subscriber experience (Loyalty score). These value are used to monitor and predict the churn propensity of each subscriber. Analytic models are used also to identify the best or more efficient action to avoid to lose subscribers with high churn propensity score

In summary, if service providers want to realize strong customer relationships, they must be extremely focused on data management and analytics. In the figure below is shown the overall CEM lifecycle.

 

 

As discussed before, Customer Experience Management isn’t a single process but a combination of processes, so to fully implement CEM, all operator-subscriber interaction processes should be transformed in data and analytic driven.

 

In the table below are shown the key processes and related analytic cases needed to implement a CEM-based company:

 

Telco department

Use of Analytic (Use cases name)

Marketing

  • To better understand customer preferences and behaviors (Customer preferences)
  • To propose personalized offers (Next Best Offer) and predict the likelihood of the offers being accepted (Product analysis and Performance optimization)
  • To propose ancillary services and contents (Recommendation systems)
  • To Predict the likelihood of a customer churning (Churn Management)
  • To manage Service provider perception in the market (Brand protection)
  • To make intelligence on OTT and 3P services (Network Usage Analytics)
  • To optimize partner value (Dealer Management)
  • To Measure performance improvement and customer satisfaction (CEM KPI monitor and Churn management)

 

Security and Corporate

  • To manage revenue protection (Revenue Assurance and Fraud)

 

Network

  • To prevent network fault (Service and Network Assurance and Survival models) and to manage in real time network congestion (Policy Server)
  • Network Performance analysis and traffic usage (Performance analysis and Network Usage Analytics)
  • To monitor in real time customer QoE (Actionable User Experience and Customer Experience Assurance)
  • To identify the root cause of poor QoE (Automatic QoE degradation root cause detection)

 

Customer Care

  • To manage customer self-care (Mobile Experience Personalization)
  • To optimize first call resolution rate (Call Center optimization) and analyze subscriber’s sentiment (Sentiment analysis)
  • To personalize the real time interaction in assisted and unassisted customer care (Call Center optimization)

 

All

  • To Model and improve customer experience related processes (processes automation)

 

Andrea Fabrizi
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"There are three kinds of lies: lies, damned lies, and statistics." (Mark Twain)
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About the Author

Andrea_Fabrizi

I’m the Director of the Big Data and Analytics solution family for the Telecommunication market. My responsibilities include defining the strategic direction of our products, go-to-market, securing the business, manage Profit & Loss and turn around with success the Big Data Analytics in Telecommunication Industry. 20+ years of valuable experience in: building new business, strategy, delivery, partner channel, alliance, sales, marketing, product and people management in Big Data analytics and in Telecommunication

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