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Mobile Ecosystem Transformation: From CSP to DSP

Text mining will play a pivotal role in the transition.

Somnath De, Saibal Samaddar and Upasana MukherjeeBy (l-r) Somnath De, Saibal Samaddar and Upasana Mukherjee

The ubiquitous availability of high-speed Internet and increasing smartphone ownership are at the forefront of the new era of digital transformation. The entire mobile ecosystem is experiencing significant growth, including the following projections:

  • There will be 5.8 billion smartphones users by 2020 as compared to the 2.6 billion at the end of 2015 [1].
  • Data traffic will grow by a compound annual growth rate (CAGR) of 49 percent by 2020 [2].
  • Mobile Internet penetration will reach 60 percent by 2020 from 44 percent in 2015 [2].
mobile ecosystem, communications service providers to digital service providers

Evolutionary journey of the telecommunication industry: from a “smartphone era” to a “hyper-connected world.”
Photo Courtesy of 123rf.com | everythingpossible

However, despite the growth in the overall mobile ecosystem, communications service providers (CSPs) across the world are facing saturated markets, experiencing slow subscriber growth and stagnated revenue as their core services are being increasingly commoditized by new Internet giants and over-the-top (OTT) content players. Operators are constantly striving to keep up with the rapidly changing preferences of customers who rely more on recommendations from friends, colleagues, acquaintances and online customer opinions across various social media sites than normal corporate marketing messages. This requires a paradigm shift in how CSPs engage with their customers. The evolutionary journey of the telecommunication industry from a “Smartphone era” to a “hyper-connected world” is illustrated in Figure 1. The new business models of individual telcos will be the key enablers in this path-breaking journey.

Text mining, mobile ecosystem, digital transformation, communications service providers (CSP), machine learning, natural language toolkit

Figure 1: Road ahead for the communications industry.

As CSPs transition from a conventional service provider to digital service providers (DSPs) who provide the functional platform that enables the customers to go to the market (e.g., using self-service mobile payment apps to pay utility bills generated by smart meters), they need to move toward a sustainable business model. It will also be imperative for them to prioritize business requirements in order to identify the key use cases that will reap significant benefits while at the same time being relatively easier to implement. Following are some of the use cases that can leverage enormous amounts of unstructured text data from social media, call center data, browsing histories, log files, network analyzer, etc. to derive actionable insights:

  • real-time congestion and customer offload management;
  • real-time personalized offers based on browsing history, device, location, live interactions;
  • product/service innovation, e.g., payment banks, mobile money, etc., and product/service pricing;
  • preventive action on network failure;
  • Capital expenditure and operating expenses (CAPEX/OPEX) optimization using network function
  • virtualization (NFV) and software-defined networking (SDN); and
  • call center, workforce and inventory optimization.

Thus, moving forward, operators need to move toward a disruptive business model and chalk out a business strategy for an enterprise-wide digital transformation with focus on the following three key areas:

  1. digital customer journey map, which succinctly portrays the rapidly changing expectations and experiences of customers across multiple channels/touchpoints;
  2. enterprise-wide digital adoption, e.g., virtual office, virtual stores and virtual retailers; and
  3. CAPEX/OPEX optimization: Operator CAPEX is on the rise, so this is the key area that could differentiate a successful DSP from an unsuccessful one.

Operators can efficiently plan their network design by segmenting customers according to their daily travel plans. This will help them evaluate and update their geographical networks to optimize the network spend and provide better customer service. This would significantly increase their customer engagement, reduce the cost of network deployment and reduce CAPEX.

Re-defining the Digital Customer Journey

The key to increasing customer engagement in the age of digital transformation is obtaining a comprehensive view of a customer by creating a digital customer journey map that helps operators pitch personalized product/services to their customers. The first step in developing a customer journey map is developing a “persona” for the digital customer to map his or her expectations and experiences at each stage of his or her life cycle.

Insights generated from this digital persona about specific customer traits, positive/negative experiences, browsing history, online behavioral patterns/trends can be incorporated into cross sell-up sell/customer retention models to identify the various digital touchpoints (website, email, social media, mobile) through which contextual offers can be pitched to more profitable customers. This will also enable the operators to optimize the marketing spend on campaigns.

Let’s illustrate how the digital customer journey can be re-defined by operators with the help of a scenario:

Mr. Sharma has two postpaid connections, and he is an active user of Internet and mobile banking. His wife also has a postpaid connection in her name. She has been browsing websites on international holiday destinations and actively liking Facebook travel/tourism pages.

While a CSP pitches international roaming packages to his wife based on her browsing history, a DSP creates an exhaustive, comprehensive view of customer, i.e., the persona of Mr. Sharma, by incorporating household, account, transactions, social media feeds, positive/negative sentiments, etc. to determine from Mrs. Sharma’s web browsing history that the entire family is going on an international vacation and identifies personalized cross-sell up-sell opportunities, e.g.,  selling travel insurance through mobile banking apps. A successful DSP will also incorporate insights based on customer-customer relationships and household information in their product recommendations.

Mr. Chopra works in the same department as Mr. Sharma and is also likely to take an international vacation. Here the DSP can cross-sell international roaming offers, international data pack offers and travel insurance to Mr. Chopra as well.

Analytics maturity level of CSPs, communications service providers

Figure 2: Analytics maturity level of CSPs.

Technology Approach

Following are some of the key tools and technologies that will be used in the transition from a CSP to a DSP in the next few years:

Natural language toolkit. NLTK provides easy-to-use interfaces for building Python programs to work with human language data. It provides more than 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization and stemming, as well as tagging, parsing and semantic reasoning.

Machine learning. Machine learning algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than solely obeying explicitly programmed instructions.

Machine learning algorithms can be divided into two main groups: supervised learners that are used to construct predictive models and unsupervised learners that are used to build descriptive models. While a supervised learning algorithm aims to unearth the relationship of other variables with the target variable thereby predicting a target of interest, in an unsupervised learning algorithm there is no target to learn and no single variable is more important than others.

As CSPs are increasingly adopting an enterprise-wide digital transformation strategy, they are moving toward maturity level 3.0, i.e., content analytics that includes sentiment analytics using NLP, text analytics and artificial intelligence (AI). While organizations in maturity level 2.0 use data-driven insights from their conventional analytical models to take business decisions, a transition to level 3.0 requires incorporation of insights from the vast amounts of unstructured internal/external data in the existing analytical models to understand the interrelationships between key drivers better, identify key pain points and get an integrated view of their business.

This will enable them to arrive at more efficient and sustainable business decisions compared to level 2.0. Moving beyond level 3.0, organizations can use the text data for cognitive analytics and robotic process automation (RPA), e.g., extracting competitors’ pricing data from their websites.

Conclusion

The path to becoming a successful digital service provider from a communication service provider is fraught with the increasing challenge from OTT players and new startups such as Uber, Netflix, Spotify, Airbnb, Skype, which with their disruptive platform-based business models are putting CSPs under tremendous pressure by targeting their core services and consuming the bandwidth. It will be imperative for the operators to respond to the challenge by launching innovative services, particularly in the areas of mobile money and machine-to-machine (M2M) services, e.g., launching new voice calling, messaging apps and own content/video platforms. NLP, and text analytics, machine learning and RPA will prove to be the key pillars to this disruptive transformation in the next few years.

This, along with building a sustainable partner ecosystem by providing data as a service and opening up alternate monetization avenues, will significantly improve both the top line along with the bottom line and enable the operator to maintain a competitive advantage over other players in the long run.

Somnath De is a director in the Data & Analytics practice of KPMG, India, where he leads the D&A initiatives. He has more than 18 years of consulting experience and has undertaken multiple data science engagements for leading organizations across multiple industry sectors.

Saibal Samaddar (saibalsamaddar@kpmg.com) is an associate director in the Data & Analytics practice of KPMG, India. He has more than 10 years of consulting experience in data and analytics while working for various telecom clients. He holds a master’s degree in electrical engineering.

Upasana Mukherjee is a consultant in the Data & Analytics practice of KPMG, India. She holds a postgraduate diploma in management from Indian Institute of Management, Indore, and has worked in multiple advanced analytics and data science engagements spanning multiple industries.

Editor’s note:

An earlier version of this article was first published on AIM.

REFERENCES

  1. GSMA Intelligence, 2016, “The Mobile Economy.”
  2. China Academy of Information and Communications Technology (CAICT), GSMA Intelligence, 2016, “Mobile Operators: The Digital Transformation Opportunity,” June 2016.

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