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Current analytics and marketing practices are falling short in combating churn and declining revenue per user. Big Data, accessible in real time, presents new ways to reverse the slide and boost profitability. The benefits of Big Data and next-generation analytics, however, are more expansive than improved customer relationship management. Mobile carriers are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams, next-generation analytics will become integral to high performance.
Industry
Today’s customers have a seemingly endless supply of information at their fingertips. Smartphones, for example, enable much faster access to brand, product and price-comparison information. As a result, companies in multiple industries are having difficulty attracting and retaining customers.
Accenture’s 2011 global consumer survey (1) shows that switching, or churn, is particularly high in the communications industry, which includes Internet, home phone, cable TV and mobile communications. Consumers in emerging markets are more likely to switch brands than those in more mature markets.
The big problem for mobile carriers is the vast amount of detail about new behaviors is largely opaque to them. With voice usage, they could track call length, who’s calling whom and more, but the data sphere remains an unknown terrain. Visibility into customer behavior is declining as the data sphere eclipses the voice sphere. More traditional mobile services, such as mobile voice and texting, are being joined by Skype, WhatsApp, ICQ, Facebook, Google+ and Twitter. Skype, for example, reported 207 billion minutes of voice and video conversations in 2010 between 170 million users.
Billions of apps are downloaded each year. Seventy-nine percent of consumers use smartphones to assist with shopping, 95 percent of mobile searches are used for finding local information and 88 percent use that information on the same day. (2) Due to the growing number of data applications, with details hidden to operators, increasingly large chunks of the picture are missing.
Fortunately, opportunities are appearing due to the plethora of Big Data and advanced analytics. New tools provide detailed clickstream usage in real time by location. It’s as if an immense sphere of data has suddenly been removed from a dark shadow and exposed in full detail to the light of day.
The Big Data gold rush
The proliferation of smart devices and popularity of social networking is generating unprecedented amounts of data, both structured and unstructured, whether it be text, audio or video. More data means more opportunities for operators to gain insight about customers, and hence new ways to serve customers, and to offer well-tailored products and services.
An operator can now know within 15 minutes that a subscriber has downloaded a specific application such as Skype, and that the subscriber’s usage pattern will likely change. Telecommunication companies possess information on close to 100 percent of mobile users in a region and can know which searches subscribers are making on Yahoo, Google, MSN and other services.
Operators hold a treasure trove of customer-behavior data, and there is gold in Big Data. Next-generation analytics can help mobile operators mine and refine the value of this new economic asset. The race is on to collect as many details as possible, and mobile network operators are in a prime position to know the most.
Mobile carriers have vast resources of data, essentially everything that passes through their pipes. The assets are considerably larger than what tablet manufacturers, retailers or content providers possess. New data-collection technologies are able to extract virtually 100 percent of mobile network data from a copy of traffic across an industry standard interface.
As Google and Facebook have proven, shareholders place a high value on information companies. Rather than remaining as infrastructure companies or utilities, a mobile operator’s strategic plan might include ways to monetize information assets. Leading operators will be thinking broadly about new products and services they might provide.
Operators have an opportunity to dominate the ecosystem, but they must act quickly or lose out to aggressive competitors, whether they are familiar industry players or innovative companies from other industries. Manufacturers, retailers, media and other companies will be battling with mobile network operators to win the hearts and minds of customers.
Next-generation insight
- Know immediately if a subscriber changes their SIM or swaps between multiple devices
- Know when a customer is tethering activities and whether content is being viewed on a smartphone or laptop
- Know where your customers call on Skype
- Know within minutes when your subscribers access a competitor’s website
- Know which apps your customers download, when they use them and how often
- Know the impact video and games have on your network
There is gold in Big Data. Next-generation analytics can help operators mine and refine the value of this new economic asset.
Evolution of customer analytics
Earlier analytics were essentially descriptive, relying on transactional data, weeks or months old, and involving relatively low amounts of volume. Current approaches include call detail records (CDRs) along with transactional data, and the volume of data has been growing year after year.
Next-generation analytics add clickstreams and locations to the mix, and the volume of data is growing exponentially. Nextgeneration systems provide the ability to capture new information from the web, geographic locations, social communities, smart devices and more. Because the wealth of data includes real-time input, the predictive analysis can also be in real time.
Increasing volumes of complex data, combined with the desire to make quicker decisions, have spurred the pace of evolution to next-generation customer analytics.
Mobile operators now have the opportunity to analyze, store and report combined records for data as well as for voice. Call detail records (CDRs), real-time clickstream data and much more can now be stored in the data warehouse.
The fast rise of smart devices—including smartphones, tablets and machine-to-machine communication—has resulted in data volumes doubling every 18 months. Fortunately, data storage costs are increasingly affordable.
The ability to systematically analyze data, both structured and unstructured, represents a new world of exploration open to mobile network operators. New tools provide great detail about mobile usage—whether voice or data-related—for each customer. Emerging insights will lead to next-generation customer service, as well as next-generation products and services, and entirely new business services.
Profiting from a 360-degree view of customers
New models and frameworks (Figure 4) are emerging to help operators surmount the limitations of current-generation analytics. Advanced tools enable operators to develop inventive strategies to win the intensifying battle for customers.
What follows are several examples of how next-generation analytics are leading to smarter business decisions being made in real time.
Improved agility to respond to competitive threats:
Accenture’s micro-segmentation and predictive analytics techniques allow operators to respond flexibly to market movements, changes in customer behaviors and actions that competitors take. The techniques draw data from real-time feeds and combine this information with historical data to generate real-time insights. As a complementary tool to strategic segmentation, micro-segmentation uses a smaller set of recent data to detect emerging "hot spots," such as current interests, new location patterns and reactions to competitor promotions. As a result, marketers are better able to detect emerging opportunities and present the right offer at the right time.
Leaders can realize significant benefits in terms of retention. A European mobile operator identified a micro segment of "promotional churners" by tracking a sudden surge of activity on a competitor’s website through mobile clickstream analysis. The operator was able to immediately launch a counter promotion to successfully prevent churn.
A Southeast Asian operator with a high pre-pay business was able to track consumers based on both SIM and device data. The analytics identified the customer segment engaging in "rate plan arbitrage" by identifying subscribers switching SIM cards and was able to offer a competitive plan to mitigate the behavior.
Richer insights from social media: Using web data extraction techniques and text mining, social media analytics can help detect sentiment and reputation, and identify key influencers and online sources from unstructured data. Collection techniques such as web-crawling macros and query scripts extract data from sites such as Facebook and Twitter.
Content categorization and text mining-based segmentation are used to generate insights such as likes and dislikes, top phrases, sentiment and social segments. By combining social characteristics with existing mobile behavioral knowledge, network operators are able to create extended and deeper insights to engage and retain customers via social and other channels.
Using analytics to differentiate the user experience: Data from Internet protocol television (IPTV) set-top boxes can help provide a differentiated user experience. Basket analysis can be used to generate a channel affinity map that links channels most likely to be viewed together. Analyzing the data can result in appealing bundles based on viewing patterns. These analytics have even greater power when combined with voice and data usage.
Using IPTV Customer Usage Analytics, Accenture generated viewer segments based on usage data for a US communications operator. Segments were differentiated based on characteristics such as total viewing hours, premier movie viewership, regional and international viewership, channel loyalty, and time and day patterns. What resulted was a highly tailored channel-bundle recommendation for segments such as Premium Movie Watchers.
Identifying leaders, followers, and managing the reaction: The unprecedented adoption of social networking is spawning a breed of super-influencers who can make or break a product, service or brand in record time. More than two-thirds of consumers search and read about brands on social-media sites, according to Accenture’s 2011 global consumer survey.
Social network analytics help to identify customers’ social communities, such as family and friends. The Leadership Index helps identify leaders and followers. The actions of social influencers are obviously important to follow. For example, if a leader churns, followers are much more likely to take similar actions (contagious churn). The same phenomena can be observed in new product adoption and service take-up.
By adding social variables to a traditional model, mobile operators are able to predict churn earlier and more accurately. For example, Accenture found that a predictive churn model built for a North American wireless operator with input from social variables achieved 42 percent uplift over the traditional model.
Social network analytics also can be used to engage influencers with customized pricing, and to manage word-of-mouth, whether negative or positive.
Using location analytics to make smarter business decisions: Enhanced call detail records with location data are combined with usage patterns to separate travel and stationary patterns, and to identify network load and bottlenecks. Location analytics enable operators to manage the network and user experience, identify customers at relevant locations and drive location-based marketing in real time.
Tailoring recommendations in the moment: Accenture’s real-time decision analytics combine real-time contextual information, gathered while interacting with a customer, with pre-defined customer engagement rules and predictive models. This tool provides a powerful capability to maximize the impact of each opportunity in every customer interaction. Inputs include customer segmentation, predictive scores such as churn and propensity, marketing offers designed for the customer, previous contact history, campaign history and response. Real-time inputs include information such as reason for the interaction, current event and request.
For a UK telecoms operator, Accenture developed a Recommendation Advisor engine powered by real-time decisioning. The following scenario explains the process. The engine suggested a list of customers likely to buy a specific broadband package. A sales agent called one of the customers with the intention to cross-sell. However, the customer complained of recent poor service and also asked about his contract renewal date. The sales agent entered the complaint and query to the engine. The Recommendation Advisor identified high churn risk and recommended a retention offer instead of the cross-sell. The customer was pleased the agent had listened and decided to renew the contract. For this company, Recommendation Advisor has improved campaign effectiveness by 30 percent.
Building next generation customer profiles: To benefit from next-generation analytics, operators will need to expand their customer data models. Accenture has a patent-pending Customer Analytic Record (CAR 2.0) that helps to produce next-generation customer profiles. CAR 2.0 promotes a 360-degree view of customers by linking social identity to internal customer databases. The extended view encompasses unified service, behavior and usage data, and social analytics, along with analytics for TV, mobile and network usage.
Technology is only part of the solution
Analytics is likely to become a differentiator, and eventually a core capability, for telecommunications companies to achieve high performance. It will move away from siloed capabilities in engineering, customer service and IT to become an integrated and holistic capability.
Making this shift will require changes to organization design, and changes in roles and responsibilities, to nurture an analytics culture. To achieve next-generation results, companies will require an agile operating model, along with technology building blocks such as data management, data mining and real-time decision solutions.
Depending on internal capabilities, business leaders will make decisions on whether to develop next-generation capabilities in-house, whether to partner with other organizations, or whether to outsource the function for greater speed and results.
From strategy to execution, Accenture works with companies to develop analytics capabilities to outperform competitors. The Accenture global network of professionals has experience in the telecommunications industry, and knowledge of business strategy and advanced tools that can provide a competitive edge for mobile network operators.
Transforming data assets into high performance
Today’s fast-moving and dynamic environment presents mobile network operators with challenges and exciting opportunities. Insights that once took days or weeks to develop can now be produced in hours, minutes or even seconds. With a 360-degree view of customers, mobile carriers are able to better understand and more effectively engage with customers.
Operators have an increasing trove of data that draws on insight from multiple channels—including the call center, web, social media, and face-to-face interactions—as well as multiple platforms such as smartphones, tablets, personal computers, set-top boxes and networks. Add to the mix factors such as demographics, value, lifestyle, social influence and more for a veritable gold mine of data available to operators.
Due to the ability to monetize information assets, more expansive benefits are on the horizon. Some infrastructure companies might choose to become information companies. Making this transformation will mean successfully managing the legal, privacy and policy implications of monetizing customer information. But these concerns, while real, are unlikely to stop visionaries from building next-generation infrastructures. Companies will look to industry bodies for best practices on how to comply with regulatory requirements and provide transparency to customers. (3)
Experience the power of next-generation customer analytics in the Agile Marketing War Room at Accenture Management Consulting Innovation Centers throughout the world.
(1) Accenture 2011 Global Consumer Research Study: The New Realities of “Dating” in the Digital Age, http://www.accenture.com/us-en/Pages/insight-acn-global-consumer-research-study.aspx
(2) The Mobile Movement: Understanding Smartphone Consumers. GoogleMobileAds. http://www.google.com/ads/mobile/insights/
(3) “How Big Data can fuel bigger growth,” Outlook 2011, Issue No. 3, http://www.accenture.com/us-en/outlook/Pages/outlook-journal-2011-how-big-data-fuels-bigger-growth.aspx
May 1, 2012
Download the PDF - Next Generation Customer Analytics Big Data:
http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Communications-Next-generation-Customer-Analytics-Big-Data.pdf#zoom=50
Today’s customers have a seemingly endless supply of information at their fingertips. Smartphones, for example, enable much faster access to brand, product and price-comparison information. As a result, companies in multiple industries are having difficulty attracting and retaining customers.
Accenture’s 2011 global consumer survey (1) shows that switching, or churn, is particularly high in the communications industry, which includes Internet, home phone, cable TV and mobile communications. Consumers in emerging markets are more likely to switch brands than those in more mature markets.
The big problem for mobile carriers is the vast amount of detail about new behaviors is largely opaque to them. With voice usage, they could track call length, who’s calling whom and more, but the data sphere remains an unknown terrain. Visibility into customer behavior is declining as the data sphere eclipses the voice sphere. More traditional mobile services, such as mobile voice and texting, are being joined by Skype, WhatsApp, ICQ, Facebook, Google+ and Twitter. Skype, for example, reported 207 billion minutes of voice and video conversations in 2010 between 170 million users.
Billions of apps are downloaded each year. Seventy-nine percent of consumers use smartphones to assist with shopping, 95 percent of mobile searches are used for finding local information and 88 percent use that information on the same day. (2) Due to the growing number of data applications, with details hidden to operators, increasingly large chunks of the picture are missing.
Fortunately, opportunities are appearing due to the plethora of Big Data and advanced analytics. New tools provide detailed clickstream usage in real time by location. It’s as if an immense sphere of data has suddenly been removed from a dark shadow and exposed in full detail to the light of day.
The Big Data gold rush
The proliferation of smart devices and popularity of social networking is generating unprecedented amounts of data, both structured and unstructured, whether it be text, audio or video. More data means more opportunities for operators to gain insight about customers, and hence new ways to serve customers, and to offer well-tailored products and services.
An operator can now know within 15 minutes that a subscriber has downloaded a specific application such as Skype, and that the subscriber’s usage pattern will likely change. Telecommunication companies possess information on close to 100 percent of mobile users in a region and can know which searches subscribers are making on Yahoo, Google, MSN and other services.
Operators hold a treasure trove of customer-behavior data, and there is gold in Big Data. Next-generation analytics can help mobile operators mine and refine the value of this new economic asset. The race is on to collect as many details as possible, and mobile network operators are in a prime position to know the most.
Mobile carriers have vast resources of data, essentially everything that passes through their pipes. The assets are considerably larger than what tablet manufacturers, retailers or content providers possess. New data-collection technologies are able to extract virtually 100 percent of mobile network data from a copy of traffic across an industry standard interface.
As Google and Facebook have proven, shareholders place a high value on information companies. Rather than remaining as infrastructure companies or utilities, a mobile operator’s strategic plan might include ways to monetize information assets. Leading operators will be thinking broadly about new products and services they might provide.
Operators have an opportunity to dominate the ecosystem, but they must act quickly or lose out to aggressive competitors, whether they are familiar industry players or innovative companies from other industries. Manufacturers, retailers, media and other companies will be battling with mobile network operators to win the hearts and minds of customers.
Next-generation insight
- Know immediately if a subscriber changes their SIM or swaps between multiple devices
- Know when a customer is tethering activities and whether content is being viewed on a smartphone or laptop
- Know where your customers call on Skype
- Know within minutes when your subscribers access a competitor’s website
- Know which apps your customers download, when they use them and how often
- Know the impact video and games have on your network
There is gold in Big Data. Next-generation analytics can help operators mine and refine the value of this new economic asset.
Evolution of customer analytics
Earlier analytics were essentially descriptive, relying on transactional data, weeks or months old, and involving relatively low amounts of volume. Current approaches include call detail records (CDRs) along with transactional data, and the volume of data has been growing year after year.
Next-generation analytics add clickstreams and locations to the mix, and the volume of data is growing exponentially. Nextgeneration systems provide the ability to capture new information from the web, geographic locations, social communities, smart devices and more. Because the wealth of data includes real-time input, the predictive analysis can also be in real time.
Increasing volumes of complex data, combined with the desire to make quicker decisions, have spurred the pace of evolution to next-generation customer analytics.
Mobile operators now have the opportunity to analyze, store and report combined records for data as well as for voice. Call detail records (CDRs), real-time clickstream data and much more can now be stored in the data warehouse.
The fast rise of smart devices—including smartphones, tablets and machine-to-machine communication—has resulted in data volumes doubling every 18 months. Fortunately, data storage costs are increasingly affordable.
The ability to systematically analyze data, both structured and unstructured, represents a new world of exploration open to mobile network operators. New tools provide great detail about mobile usage—whether voice or data-related—for each customer. Emerging insights will lead to next-generation customer service, as well as next-generation products and services, and entirely new business services.
Profiting from a 360-degree view of customers
New models and frameworks (Figure 4) are emerging to help operators surmount the limitations of current-generation analytics. Advanced tools enable operators to develop inventive strategies to win the intensifying battle for customers.
What follows are several examples of how next-generation analytics are leading to smarter business decisions being made in real time.
Improved agility to respond to competitive threats:
Accenture’s micro-segmentation and predictive analytics techniques allow operators to respond flexibly to market movements, changes in customer behaviors and actions that competitors take. The techniques draw data from real-time feeds and combine this information with historical data to generate real-time insights. As a complementary tool to strategic segmentation, micro-segmentation uses a smaller set of recent data to detect emerging "hot spots," such as current interests, new location patterns and reactions to competitor promotions. As a result, marketers are better able to detect emerging opportunities and present the right offer at the right time.
Leaders can realize significant benefits in terms of retention. A European mobile operator identified a micro segment of "promotional churners" by tracking a sudden surge of activity on a competitor’s website through mobile clickstream analysis. The operator was able to immediately launch a counter promotion to successfully prevent churn.
A Southeast Asian operator with a high pre-pay business was able to track consumers based on both SIM and device data. The analytics identified the customer segment engaging in "rate plan arbitrage" by identifying subscribers switching SIM cards and was able to offer a competitive plan to mitigate the behavior.
Richer insights from social media: Using web data extraction techniques and text mining, social media analytics can help detect sentiment and reputation, and identify key influencers and online sources from unstructured data. Collection techniques such as web-crawling macros and query scripts extract data from sites such as Facebook and Twitter.
Content categorization and text mining-based segmentation are used to generate insights such as likes and dislikes, top phrases, sentiment and social segments. By combining social characteristics with existing mobile behavioral knowledge, network operators are able to create extended and deeper insights to engage and retain customers via social and other channels.
Using analytics to differentiate the user experience: Data from Internet protocol television (IPTV) set-top boxes can help provide a differentiated user experience. Basket analysis can be used to generate a channel affinity map that links channels most likely to be viewed together. Analyzing the data can result in appealing bundles based on viewing patterns. These analytics have even greater power when combined with voice and data usage.
Using IPTV Customer Usage Analytics, Accenture generated viewer segments based on usage data for a US communications operator. Segments were differentiated based on characteristics such as total viewing hours, premier movie viewership, regional and international viewership, channel loyalty, and time and day patterns. What resulted was a highly tailored channel-bundle recommendation for segments such as Premium Movie Watchers.
Identifying leaders, followers, and managing the reaction: The unprecedented adoption of social networking is spawning a breed of super-influencers who can make or break a product, service or brand in record time. More than two-thirds of consumers search and read about brands on social-media sites, according to Accenture’s 2011 global consumer survey.
Social network analytics help to identify customers’ social communities, such as family and friends. The Leadership Index helps identify leaders and followers. The actions of social influencers are obviously important to follow. For example, if a leader churns, followers are much more likely to take similar actions (contagious churn). The same phenomena can be observed in new product adoption and service take-up.
By adding social variables to a traditional model, mobile operators are able to predict churn earlier and more accurately. For example, Accenture found that a predictive churn model built for a North American wireless operator with input from social variables achieved 42 percent uplift over the traditional model.
Social network analytics also can be used to engage influencers with customized pricing, and to manage word-of-mouth, whether negative or positive.
Using location analytics to make smarter business decisions: Enhanced call detail records with location data are combined with usage patterns to separate travel and stationary patterns, and to identify network load and bottlenecks. Location analytics enable operators to manage the network and user experience, identify customers at relevant locations and drive location-based marketing in real time.
Tailoring recommendations in the moment: Accenture’s real-time decision analytics combine real-time contextual information, gathered while interacting with a customer, with pre-defined customer engagement rules and predictive models. This tool provides a powerful capability to maximize the impact of each opportunity in every customer interaction. Inputs include customer segmentation, predictive scores such as churn and propensity, marketing offers designed for the customer, previous contact history, campaign history and response. Real-time inputs include information such as reason for the interaction, current event and request.
For a UK telecoms operator, Accenture developed a Recommendation Advisor engine powered by real-time decisioning. The following scenario explains the process. The engine suggested a list of customers likely to buy a specific broadband package. A sales agent called one of the customers with the intention to cross-sell. However, the customer complained of recent poor service and also asked about his contract renewal date. The sales agent entered the complaint and query to the engine. The Recommendation Advisor identified high churn risk and recommended a retention offer instead of the cross-sell. The customer was pleased the agent had listened and decided to renew the contract. For this company, Recommendation Advisor has improved campaign effectiveness by 30 percent.
Building next generation customer profiles: To benefit from next-generation analytics, operators will need to expand their customer data models. Accenture has a patent-pending Customer Analytic Record (CAR 2.0) that helps to produce next-generation customer profiles. CAR 2.0 promotes a 360-degree view of customers by linking social identity to internal customer databases. The extended view encompasses unified service, behavior and usage data, and social analytics, along with analytics for TV, mobile and network usage.
Technology is only part of the solution
Analytics is likely to become a differentiator, and eventually a core capability, for telecommunications companies to achieve high performance. It will move away from siloed capabilities in engineering, customer service and IT to become an integrated and holistic capability.
Making this shift will require changes to organization design, and changes in roles and responsibilities, to nurture an analytics culture. To achieve next-generation results, companies will require an agile operating model, along with technology building blocks such as data management, data mining and real-time decision solutions.
Depending on internal capabilities, business leaders will make decisions on whether to develop next-generation capabilities in-house, whether to partner with other organizations, or whether to outsource the function for greater speed and results.
From strategy to execution, Accenture works with companies to develop analytics capabilities to outperform competitors. The Accenture global network of professionals has experience in the telecommunications industry, and knowledge of business strategy and advanced tools that can provide a competitive edge for mobile network operators.
Transforming data assets into high performance
Today’s fast-moving and dynamic environment presents mobile network operators with challenges and exciting opportunities. Insights that once took days or weeks to develop can now be produced in hours, minutes or even seconds. With a 360-degree view of customers, mobile carriers are able to better understand and more effectively engage with customers.
Operators have an increasing trove of data that draws on insight from multiple channels—including the call center, web, social media, and face-to-face interactions—as well as multiple platforms such as smartphones, tablets, personal computers, set-top boxes and networks. Add to the mix factors such as demographics, value, lifestyle, social influence and more for a veritable gold mine of data available to operators.
Due to the ability to monetize information assets, more expansive benefits are on the horizon. Some infrastructure companies might choose to become information companies. Making this transformation will mean successfully managing the legal, privacy and policy implications of monetizing customer information. But these concerns, while real, are unlikely to stop visionaries from building next-generation infrastructures. Companies will look to industry bodies for best practices on how to comply with regulatory requirements and provide transparency to customers. (3)
Experience the power of next-generation customer analytics in the Agile Marketing War Room at Accenture Management Consulting Innovation Centers throughout the world.
(1) Accenture 2011 Global Consumer Research Study: The New Realities of “Dating” in the Digital Age, http://www.accenture.com/us-en/Pages/insight-acn-global-consumer-research-study.aspx
(2) The Mobile Movement: Understanding Smartphone Consumers. GoogleMobileAds. http://www.google.com/ads/mobile/insights/
(3) “How Big Data can fuel bigger growth,” Outlook 2011, Issue No. 3, http://www.accenture.com/us-en/outlook/Pages/outlook-journal-2011-how-big-data-fuels-bigger-growth.aspx
May 1, 2012
Download the PDF - Next Generation Customer Analytics Big Data:
http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Communications-Next-generation-Customer-Analytics-Big-Data.pdf#zoom=50
