Author: Annie Turner, Independent Analyst

Editor: Dawn Bushaus, Managing Editor

SMARTPAY

DIVIDENDSANALYTICS

TM FORUM QUICK INSIGHTS REPORT

CUSTOMER LI F

ECYCLEACROSS THE

CUSTOMERLIFECYCLE

We hope you enjoy the report and, most importantly, will find ways to use the ideas, concepts and recommendations detailed within. You can send your feedback to the editorial team at TM Forum at editor@tmforum.org

© 2018. The entire contents of this publication are protected by copyright. All rights reserved. The Forum would like to thank the sponsors and advertisers who have enabled the publication of this fully independently researched report. The views and opinions expressed by individual authors and contributors in this publication are provided in the writers’ personal capacities and are their sole responsibility. Their publication does not imply that they represent the views or opinions of TM Forum and must neither be regarded as constituting advice on any matter whatsoever, nor be interpreted as such. The reproduction of advertisements and sponsored features in this publication does not in any way imply endorsement by TM Forum of products or services referred to therein.

Report author:Annie Turner Independent Analystannie@anniet.co.uk

Chief Analyst:Mark Newmanmnewman@tmforum.org

Managing Editor:Dawn Bushausdbushaus@tmforum.org

Editor, Digital Content:Arti Mehtaamehta@tmforum.org

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Head of Research & Media Sales:Denise Tackdtack@tmforum.org

Director, Solutions Marketing: Charlotte Lewisclewis@tmforum.org

Advisor:Aaron Boasman-PatelVice President, AI & Customer Experienceaboasman@tmforum.org

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Published by:TM Forum4 Century Drive, Parsippany, NJ 07054USAwww.tmforum.orgPhone: +1 973-944-5100Fax: +1 973-944-5110

ISBN: 978-1-945220-40-1

Page 4 The big picture

Page 7 Section 1Why are CSPs turning to advanced analytics?

Page 10 Section 2Applying analytics early in the customer lifecycle

Page 14 Section 3Deepening the relationship with customers

Page 17 Section 4The vexed question of fraud

Page 22 Section 5Make it happen – Strategies for adopting analytics across the customer lifecycle

TM Forum’s Quick Insights research reports are free and can be downloaded by registering on our website

Smart analytics pay dividends across the customer lifecycle

4

The big picture

5

Communications service providers (CSPs) have long recognized the potential of data analytics. Yet their early efforts to pull actionable intelligence from the oceans of data they have access to were largely unsuccessful. Many tried a ‘big bang’ approach to building a central repository without knowing what they wanted to do with the data in it. The arrival of artificial intelligence (AI) – its machine learning subset in particular – has changed their thinking and approach.

Now operators are looking at advanced analytics (which we will use as shorthand in this report to refer to associated technologies such as AI and machine learning) in terms of key use cases. They are under pressure to become data-driven to improve their decision-making and profitability. Terrific customer experience is central to both. Being use case-driven makes sense, especially at this early stage, but it is essential that operators realize that the most powerful use of analytics is holistic, and right across the lifecycle. They need to work towards that, building on their successes.

As an executive responsible for analytics across a large European operator group says about his company:

“We are trying to put everything into one place. So far, it’s mostly been about efficiency and OpEx reduction, and more on the customer side [looking at the] business as a whole, particularly churn and subscriber satisfaction, and revenue assurance, data packages and bundles. But holistically if analytics are there, it is [going to be] the brain running the business.”

For this Quick Insights report, we surveyed 64 professionals from CSPs around the world who are applying, leveraging and/or planning to deploy advanced analytics in some capacity at various points across the customer lifecycle.

What is a customer lifecycle?It will be helpful for readers to understand what we mean by ‘customer lifecycle’ or ‘customer journey’. They are one and the same and can be broken down into three main stages: engaging, using and evaluating.

During the earliest phase, the customer becomes aware of the CSP’s products or services and interacts with the company to buy them. Then, as the customer uses the services they interact with the CSP about quality of service, billing and payments. In the final phase, customers either renew the services and recommend them to others, or they leave. The graphic on page 6, adapted from TM Forum’s Guidebook on 360-degree View of the Customer, shows what happens at each phase of the customer journey.

6

Read this report to understand:

n What CSPs see as the biggest drivers for deploying advanced analytics over the next two years

n How and where BT, Globe Telecom, Vodafone UK, Sky and Telstra are using analytics, from marketing and pre-sales,

Insights and knowledge

Experience events

Channels

Engaging

Be aware Consume ReviewInteract Manage ShareChoose Settle Decide

Using Evaluating

Website Efficiency User Needs Delivery and Information Satisfaction Behavior Sentiment Usage Statistics Demographics Preferences Interests Segmentation Device

View Ad Search Website Visit Points of Sale Information Received Service Failed Service Used Call Customer Care Bill Received Top-up New Offer Received Liked Cancel Contract

Web, Device, Email Call Center, Care Center Store, Points of Sale USSD, SMS, Messaging Social Media Field Service, Field Sales

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TM Forum, 2018

Phases of a customer’s journey

through origination, onboarding, engagement during the contract and renewals

n The opportunities and pitfalls around financing devices as opposed to or in addition to subsidizing them

n The scope for analytics to improve credit risk assessment and reduce fraud, and where operators are with this

7

Why are CSPs turning to advanced analytics?

Section 1

13%Fixed

operator

55%Converged operator (some

combination of mobile, fixed voice and data, and TV)

27%Mobile

operator6%

Other

38%Asia/Pacific

6%Global

8

With communications service providers (CSPs) under extreme pressure to increase profitability, they are turning to advanced analytics in all stages of the customer lifecycle. We surveyed them to understand why and how, and what the biggest challenges are.

Not surprisingly, respondents were spread across a range of functions. Interestingly, the ‘other’ option accounted for almost 30%. When we looked closely at the responses of those who had chosen that option, we found (as the infographic on page 9 shows) that they came from network, IT (including strategy and planning), business strategy and management, and innovation and transformation (we also reallocated some ‘other’ answers into original categories based on the respondent’s specification).

64 CSP professionals from 56 unique companies

36%Fewer than 5 million

27%5 million to 25 million

12%25 million to 50 million

14% 50 million to 100 million

11% More than 150 million

Who?

Where?

Type of CSP Size of CSP

3%North America

6%Latin America/Caribbean

30%Europe and/or Russia

17%Middle East and/or Africa

0% Cable operator

9

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Respondents’ job functions

Network is key to improving customer experienceIt’s encouraging to see respondents with network-related roles. For so long ‘network’ was a different field from customer experience, yet there is nothing more fundamental to a subscriber’s experience than the speed, reliability and coverage of the network. This was underlined powerfully at a Cambridge Wireless event in the UK at the end of September in a presentation by Dave Salam, Director Mobile Core and Data Analytics, BT (who is responsible, from design to operations, for

what was EE’s network – the mobile operator BT acquired in 2016):

“If there is one thing I’ve learned from working in ops, it’s that you need to know what’s going on in your network in advance of your customer,” Salam said. “When you’re hearing about if from your customers, it’s too late.”

Salam described the immense effort the company is putting into

TM Forum, 2018

3% 3%

8% 8% 8%

16%

5% 6% 6%4%

27%

2% 2% 2%

10

being able to personalize the network for every single user, in real time, for when 5G arrives. In the meantime, it is “applying intelligence” to gain a much greater understanding of how customers are experiencing services – not just connectivity, he stressed – which is often at odds from the indications given by network monitoring and key performance indicators. This leveraging of analytics is also having a profound impact on how BT will design and develop its network of networks known as 5G, with that unprecedented level of customer experience as the goal.

It is also encouraging to see that executives working in business strategy took part in the survey (including a CEO and a general manager of two smaller operators). We take this a sign that advanced analytics are moving from a day-to-day operations

Optimize the customer’s experience

Improve profitability

Reduce churn

Reduce fraud

Increase the number of subscribers

Other

Ranking the drivers for advanced analytics

TM Forum, 2018

tool to part of the strategy of transforming telcos into data-driven businesses.

What are the biggest drivers?We asked respondents what the biggest drivers of advanced analytics will be in the next two years, and to choose all that apply. A large majority chose optimizing customer experience, closely followed by the desire to improve profitability (see below).

In the No. 3 spot, with more than half choosing it, is reducing churn, which feeds into the top drivers. Satisfied customers tend to stay and spend more, and the operator avoids the hefty expense of recruiting new ones. It is a sign that markets around the world are maturing when reducing churn is almost 20% ahead of increasing the number of subscribers, and improved

11%

36%

47%

55%

70%

75%

11

profitability is a close second to better customer experience.

As Bharath Raghunathan, Business Strategy Architect Lead, Telstra, observes, “It’s three times the cost and effort to get a new customer to replace a lost one.”

In the ‘other’ category, which counted for almost 11% of answers, respondents mentioned optimizing customer service and/or increasing profitability. Only one respondent cited analytics and associated technologies as having an important part to play in business-to-business partnerships. However, we believe this will move up in importance in the next couple years as more services are delivered through ecosystems, including 5G.

Raghunathan sums up Telstra’s aims regarding analytics and associated technologies as “first, how to do what you do better,

for example to offer excellent customer service, and then how to externalize expertise and assets, and combine them with those from third parties to address new customers.”

Advanced analytics throughout the customer journeyWe also asked respondents about their current and planned use of advanced analytics, and they see uses throughout the customer journey. For example, a large majority are using or plan to use analytics in marketing and advertising campaigns and to try to reduce churn (see graphic below). But most operators see the greatest potential for using advanced analytics in the earliest phases of the customer journey.

While there are, indeed, many opportunities to apply advanced analytics to sell new services, CSPs may be missing out on an opportunity to use them to increase and improve engagement. We’ll look at this more in the next sections.

In marketing and advertising campaigns todrive acceptance/conversion rates

To proactively prevent churnTo understand as much as possible about the customer to tailor offers/

bundles by pulling data from many sources (origination)For fraud detection and alerts

To maintain customer engagement with the customer through the term of their contract such as with offers

To work out the next-best action

To automate onboarding processes

To assess credit risk of new customers

In collections and recovery

For renewals

How are CSPs using advanced analytics?

TM Forum, 2018

34%

36%

53%

53%

53%

56%

59%

69%

72%

81%

12

Applying analytics early in the customer lifecycle

Section 2

Operators see the most potential for advanced analytics in the early stages of the customer journey, when consumers are deciding whether to subscribe. In our survey, we asked CSPs if they use (or plan to use) advanced analytics for specific purposes when marketing new products and services, such as to prescribe offers on the fly or to personalize them. The most intriguing finding is how finely balanced all the results are, almost half and half for all options.

13

The largest number of positive answers came from respondents who are using the technologies to decide which contact channels to use, how and when. Yet it is less than 10% ahead of the option that received the fewest positive answers – using the technologies to model or project the expected acceptance rate for new products or services. So, although many operators have made progress in these key areas, there is plenty of scope for CSPs and suppliers alike to up their marketing game, particularly around personalization.

How do CSPs use analytics to market new services?

TM Forum, 2018

Prescribe specific offers on-the-fly to improve acceptance/conversion levels

Model or project the expected acceptance rate

Predict an individual’s financial abilityto pay (credit risk)

Decide which contact channels to use, how and when

Set personalized prices

Dynamically define the characteristics and granularity of segments

n Yes n No

52% 48%

47% 53%

54% 46%

56% 44%

50% 50%

53% 47%

14

Predicting riskIt is surprising that more CSPs aren’t using analytics to predict a prospective customer’s credit risk, particularly as the information it garners about customers from many different sources can be put wider uses, such as customer care.

Katy Barnes, Head of Risk, Sky UK (and formerly at EE), has an interesting perspective on this. Sky Mobile is a mobile virtual network operator (MVNO), launched in January 2017, whose mission is to be the No. 1 choice for households that have other Sky products, such as broadband and pay-TV. It has a radically different approach to the market as a premium, rather than no-frills, MNVO and has scored several firsts in the market, for example, allowing customers to roll over unused data for up to three years. “When we entered the market we had a relatively straight-forward approach to lending, using many ‘out-of-the-box’ solutions; our speed to market was phenomenal,” Barnes says. “We’re now entering phase two, changing some of our technology solution and remodeling our strategy – almost starting again from scratch now that we have outcome data and a decent development sample to work with.” She adds that the credit-scoring systems commonly used by CSPs are often designed in a traditional manner, on incumbent technology platforms, by people who are not close to their diverse customer demographic. “We pride ourselves on our technology capabilities along with our customer closeness,” Barnes explains. “We have growth ambitions and to achieve them we need to move towards a

platform that can integrate easily with machine-learning tools and proper advanced analytics, to make the most of the insight we hold about our customers.”

The idea is for the paltform to inform decisions beyond just credit risk because it will include information about customers pulled from many data sources, although that too is challenging, of course.

Making offers on the flyThe ability to make offers on the fly to improve acceptance levels reveals a real chasm between the current capabilities of smaller CSPs and the mid-size to larger players. One hundred percent of respondents with 50 million to 100 million subscribers say they can do this, as do almost 86% of those with more than 150 million subscribers and 78 % with 50 million to 100 million. But only 26% of respondents with fewer than 5 million subscribers and 29% with 5 million to 25 million can make offers on the fly. This could be because the smaller operators feel it is a capability that is ‘nice to have’ rather than critical, and/or the gains in terms of customer experience and revenue do not justify the cost and effort of putting it in place.

TM Forum, 2018

Only 26%of small CSPs

can make offers on the fly

15

Origination and onboarding We also asked operators whether they are using advanced analytics in the origination and onboarding process – that is, selecting the right products or bundle of products for individual customers. Just over half of respondents said they are using analytics to:

n Lower operational costsn Automate the process across retail and digitaln Target and retain the most profitable customersn Mitigate credit or fraud risk

Again, while CSPs have made progress, clearly there is

considerable scope to leverage analytics in these crucial first stages of encouraging an individual to become a customer, then dealing with them smoothly and as an individual. This is underlined by the answers that achieved lower scores.

Personalization, such as pricing, is still a struggle, so many years after operators and suppliers started talking about it. Only 41% said they are using analytics for personalization, despite the super-high level of importance operators attach to it. Using analytics to sell more products and services to the customer once they are onboard is a neglected opportunity we will look at more in the next section, as is keeping in touch with customers after onboarding and before they are coming up for renewal.

How do CSPs use analytics in onboarding?

TM Forum, 2018

Lower operational costs

Automate the process across digital and retail

Target and retain the most profitable customers

Mitigate credit or fraud risk

Make it more convenient and personalized

Compliance purposes

Increase the number of products and services when the account is opened

56%

55%

53%

52%

41%

31%

27%

16

Most respondents said they are not using analytics for compliance, but since compliance with regulation and law is not optional, this suggests that traditional ways of doing this are still favored. This also may be a lost opportunity for operators to meet their obligations consistently and cost efficiently.

Subsidizing and financing devicesDuring the early stages of the customer journey, many mobile operators traditionally have offered a device as part of a service bundle. We asked respondents whether they subsidize handsets or offer financing for them, and whether they have plans to offer financing.

Subsidizing handsets has gone out of fashion in many markets, because operators are more interested in reducing churn than recruiting new customers. Also, CSPs are coming under scrutiny in various countries, most recently the UK, for maintaining tariffs that include an element of device payment long after the cost of the device was recouped, which does not make for a trusting relationship with customers.

Nor has financing become mainstream – almost a third of respondents said they do not offer subsidies or financing, and have no plans to do so. Operators need to think about these issues carefully as a means of differentiating themselves in the marketplace, as more customers keep the same phone for much longer periods and often opt for cheaper, SIM-only deals where the main distinction is price.

For operators that do offer financing for devices, there are pitfalls for the unwary. Several contracts in the US between finance companies and operators were terminated early because finance companies’ credit-checking parameters differed from those of operators, which led to some perfectly good customers being denied device financing at the point of sale.

Some CSPs opted to build in-house loan systems – an expensive waste of time, as financing changes little operationally. Lack of cooperation between operators’ marketing and finance departments sometimes mean that financing is not available for the devices that are part of a marketing push, or the terms are not appealing.

3%

16%

31%14%

36%

Mobile operators’ approach to device financing

TM Forum, 2018

n We currently provide financingn We currently subsidize devicesn We currently subsidize devices and

provide device financing

n We currently subsidize and have plans to offer financing

n None of the above

17

Also, CSPs typically do not use securitization to provide financing, unlike many other verticals. Instead they use long-standing low-cost debt arrangements. Securitization means an issuer (for example, a CSP) designs financial instruments by merging various financial assets and marketing them to investors. This process promotes liquidity (activity) in the marketplace and takes debt off

the operator’s balance sheet. Experience in other sectors show the first to market normally reaps substantial competitive benefits.

In the next section, we’ll look at the ‘using’ phases of the customer journey and how advanced analytics can be used to improve engagement.

Almost a third of respondents said they do not offer subsidies or financing, and have no plans to do so.

“

”

18

Deepening the relationship with customers

Section 3

Once customers have engaged with a communications service provider (CSP) and purchased services, they move onto using and paying for products and services. It is during this phase that the CSP has an opportunity to improve customer engagement and centricity by applying advanced analytics.

Touchpoints and optimizing experienceWe asked operators whether they collect and use data from every customer touchpoint to optimize customers’ experience and drive next-best actions. Given the volume, velocity and variety of data CSPs generate, we were pleasantly surprised that a fifth of respondents said they make full use of data from every touchpoint in near-real time.

19

Nearly 9% of respondents said they collect and analyze data from every touchpoint, which is also encouraging. However, most respondents said that they either collect data from only some touchpoints or that they made use of it in a limited capacity. This casts light on why personalization is still very much work in progress and how far operators are from being truly data-driven (see page 13).

Recovering late paymentsWe asked respondents how their companies interact with customers when there are issues concerning payments, and the results are somewhat surprising. Only about two-thirds report that collections processes use electronic messages rather than phone calls.

A gentle, discreet electronic reminder is likely to be far more welcome than the embarrassment of phone call with a human, particularly if the agent catches someone while they’re working in an open-plan office, for instance. Also, engaging with customers via call centers is by far the most expensive option as well as the one that typically has the highest level of customer dissatisfaction.

This out-of-date approach needs to be remedied, fast, if telcos really want to act and be perceived as digital companies – that is, by providing great customer service at low cost. However, we do have reason to believe that we might see some big changes on the way (see page 28), as it seems fraud departments are as concerned about not upsetting late-paying customers by the way they go about collecting those payments as about recovering the money. This seems to reflect just how far operators have come in recognizing the importance of customer experience right across the lifecycle and within every department, even if it is still work in progress.

2%

27%

17%20%

9%

25%

How do CSPs collect and use data?

TM Forum 2018

n We collect, analyze data streams in near real-time, and make full operational use of data across every customer touchpoint.

n We collect and analyze from every customer touchpoint

n We collect data from some touchpoints

n We collect data in a limited capacityn We have plans to introduce thisn We have no plans to do this

20

Penny Clarke, Head of Credit Risk, Vodafone, says that late payments need to be managed carefully, as many are accidental. It is important to look at a customer’s full relationship and in the first instance never take an aggressive tone.

“Getting it right in care estate can make or break it,” she says. “Customers are able to leave, and we have to get it right in all the touchpoints to ensure we maintain that positive relationship. We need information for decision-making, and we want customers to want to stay – churn is a big priority. Our focus is on how we make every element more straightforward, or customers just get frustrated.”

Disappointingly, only 20% of respondents engage with late payers through their preferred channels – perhaps because

operators don’t know what they are or cannot easily implement them. Again, there is a big opportunity for differentiation here. As Bharath Raghunathan, Business Strategy Architect Lead, Telstra, points out, “Telcos are in a time when it is very important to differentiate, but the points of differentiation have narrowed.”

Finally, 48% of respondents said their company uses its own agents to call late payers, and an identical percentage outsource the calls and collections to a third party. In-house or outsourced is not so much the issue as getting the approach right. Whoever makes the calls, are outbound calls really the best option? (See panel on page 21.)

Communications channels with customersWe also wanted to know which communications channels CSP use most with their customers. Among survey respondents, text

How do CSPs communicate about late payments?

TM Forum 2018

We send customers automated (electronic) messages

We outsource calls/collection and recovery to a third party

We use our own contact center staff to make outbound communications

We deliver customer engagements that are tailored to their specific preferences and profiles of our customers – for example,

most convenient time of day or predicted financial ability to repay.

Other

67%

48%

48%

20%

9%

21

Globe Telecom in the Philippines has 50 million mobile subscribers, 3.5 million broadband customers and 860,000 landline customers. As it grew, so did the problems of churn and late payments. Attempts to address these issues by employing more call center agents were costly and ineffective: Customer complaints and churn rose; customer satisfaction continued to fall; and the debt problem continued. One of the issues with agents was lack of consistency in advice and approach, and often resources were allocated in the least profitable places.

“We were looking for non-traditional ways of reaching out to our huge customer base without compromising cost and customer experience,” says Mon Pernia, Head of Customer Collections, Globe Telecom. The company chose FICO Customer Communication Services to intelligently automate their efforts without increasing the number of staff. Results are shown in the graphic opposite.

The system can orchestrate any number of contacts and run 24/7. It automatically selects the most suitable type of message, the right channel (IVR, SMS or email) and the time of day most likely to result in a positive outcome for each customer. Globe Telecom’s policies are embedded in business rules, and data about the account (level of risk, previous behavior, customer’s

Globe Telecom addresses late payments and churn with automation

TM Forum, 2018 (source for data: Globe Telecom)

40%Delinquency rates down

Collection costs down

15%

3 daysTime to collect payment reduced

L west

Fewest

levels of churn ever

customer complaints ever

contact preferences etc.) and compliance with regulation.

The operator has found that an electronic reminder is generally much preferred than a phone call, and an email reminder can include a link to the portal to pay. Globe also plans to include an option to pay by phone for customers who are selected to be called by agents. Every contact and its outcome are used as part of a feedback loop to constantly analyze and improve the collection and recovery processes.

22

messaging and call centers came out on top at 94% and 92%, respectively.

Texting seems curiously old-fashioned given that WhatsApp and other messaging apps outstrip customers’ use of SMS and MMS. The heavy reliance on call centers seems out of joint too. As Vodafone’s e notes, there will always be people who prefer to communicate with call centers, and some complex issues need to be addressed by a human. But many issues could be dealt with better by other means, at less cost.

It’s also interesting that email surpasses operators’ level of communication with customers via social media like WhatsApp and Facebook, which at 63% is even lower than engagements via physical stores. Given that anecdotally younger people just don’t use email (never mind make phone calls), this seems high and out of step with people’s everyday preferences. In

contrast, at the time of writing this report, Dubai’s biggest bank announced a new service entirely based on WhatsApp.

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Targeting millennials:CSPs use social media and sub-brands to improve customer centricity

For more about using social media to target customers, see our Quick Insights report Targeting millennials: CSPs use social media and sub-brands to improve customer centricity

Which channels do CSPs use to communicate with customers?

TM Forum 2018

Text messaging (SMS, MMS)

Call center

Email

Mobile app

Retail outlets/dealerships

Social messaging (Facebook, WhatsApp, etc.)

Web chat

Chatbot

92%

94%

84%

72%

64%

63%

52%

27%

23

Customers like digital channelsResponses to our survey show that web chat is increasing in popularity, with just over half of respondents citing it as an often-used channel. Vodafone announced recently that it will be the first global telecoms company to launch an “intelligent digital human” to put a human face on an analytics and AI engine to ‘talk’ to customers online.

There is reason to believe this initiative and others like it will be successful. A survey carried out in 2016 by Capgemini, Unlocking Customer Satisfaction: Why Digital Holds the Key for Telcos, had almost 6,000 respondents across Europe and found that most people, across all age groups, prefer the digital (online) option. Indeed, 58% said they would switch to a digital-only ‘telco’ if one existed. It might be a tough step, but phone companies need to learn to (largely) cut the cord too, using automation and advanced analytics.

Use of chatbots is growing fast too: Their relatively low score doesn’t reflect their speedy growth over a relatively short time, as it’s just a snapshot. Vodafone’s Clarke says her company was the first in the telecoms industry to have a chatbot, named Tobi, which has proved extremely popular with customers both in terms of usage and satisfaction. Now the company is looking at how to use it in different areas.

“We can do so much more, and it’s much easier for customers

For more about how CSPs are using chatbots, see our Quick Insights report AI & customer experience: Emerging best practices

than traditional credit risk and barring – customers can self manage,” she says.

Overall, however, our survey about channels shows that CSPs are failing to keep up with what their customers do in their everyday lives, a view that was supported by answers to the survey question that followed: “If a customer opts out or expresses a desire not to be communicated with on a given channel any longer, can you easily act on this across all channels and touchpoints?” Almost half said no.

Why do operators contact customers?In addition to understanding how CSPs communicate with customers, we also wanted to find out why they communicate

According to Capgemini, 58% of consumers would switch to a digital-only ‘telco’ if one existed.

“”

24

with them across the customer lifecycle.

It is perhaps surprising, but heartening, that cross-selling came out ahead of billing and payments, but we did wonder about the fact that more respondents chose cross-selling over suggesting packages better suited to customers’ usage patterns and preferences. This smacks of “We have this to sell” rather

than, “This would give you better service/be better value/save you money,” potentially illustrating, again, that personalization has some way to go.

In the next section we’ll look at how CSPs are addressing the issue of fraud throughout the customer lifecycle and how advanced analytics can help.

CSPs are failing to keep up with what their customers do in their everyday lives.

“”

Why do CSPs contact customers?

TM Forum 2018

Offers of new services (cross-sell)

Monthly bill/payment reminder

To take a customer satisfaction survey

To suggest packages better suited to their usage pattern and preferences? (upsell or downsell ie reduce their costs)

Late/unpaid monthly bills

To advise about price changes

Other

73%

66%

56%

55%

55%

39%

9%

25

The vexed question of fraud

Section 4

Fraud remains a tedious but serious problem for communications service providers (CSPs), and it happens during all phases of the customer journey. According to industry estimates, the annual cost of telco subscription fraud could exceed $12 billion in 2018.

“You need granular understanding and continuous learning to address [fraud],” says Penny Clarke, Head of Credit Risk, Vodafone. “It’s sophisticated – it requires continuous investigation of complex and expansive data. You need a great level of detail. Also, fraud changes. [Fraudsters] are relentless, testing opportunities to work their way round systems and safeguards, and then new patterns emerge.”

She explains that many of the organized, criminal attackers have the means to continuously challenge fraud defenses: “They have bots hammering away at processes looking for chinks in our defenses all the time.”

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Early days for fraud analyticsWe asked survey respondents how their companies detect fraud at the point of sale and when onboarding customers. While 70% said they use traditional, reactive alarms and rules-based systems, just over a quarter are using adaptive models and machine learning (see graphic below). However, the percentage fell to just under 9% among respondents from companies with fewer than 5 million subscribers.

About 20% are using a managed service or software as a service (SaaS), but again, this drops to 13% among respondents from operators with fewer than 5 million subscribers, even though it could be cost-effective and avoid up-front expenditure – with the right supplier whose parameters are aligned with those of the CSP.

Clearly, it is very early days indeed for analytics in this sphere, but they potentially have a useful, economically viable role to play.

How do CSPs detect fraud at point of sale and onboarding?

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Traditional reactive alarm and rules-based systems

Application scoring

Reference and fuzzy match against known fraud details & profiles

Adaptive models and continuous machine learning

Managed service or SaaS

Other

70%

45%

44%

27%

19%

9%

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Detecting fraud later in the lifecycleWe also wanted to know what mechanisms operators use to detect fraud once a subscription has been activated. Although almost 83% use the traditional but typically inflexible reactive alarms and rules-based systems, it is encouraging to see that about a third are employing self-learning customer profiles that highlight unusual behavior in near real time – real time must be the ultimate goal for all.

Further analysis reveals that 85% of respondents from operators with more than 150 million subscribers are using self-learning customer profiles. Respondents from among the group of operators with 50 million to 100 million subscribers was distant second with 33% saying they use this technology.

Again, it is perhaps surprising that managed services and SaaS scored so low, as they can bring the advantages of scale and deep expertise. And at 42%, proportionately more respondents from within the operator group of more than 150 million subscribers chose this option.

Overall, however, this feels a million miles from operators’ stated intention of having a real understanding of each of their customers individually, which would have a terrific impact on detecting fraud – including protecting customers from it – and is in stark contrast to the relatively advanced state of using analytics in marketing (see page 13).

How do CSPs detect fraud after activation?

TM Forum 2018

Traditional reactive alarm and rules-based systems

Self-learning customer profiles that highlight unusual behavior in near real time

Managed service or SaaS

Other

83%

31%

17%

8%

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Fraud and customer experienceWhen it comes to addressing the challenges surrounding fraud, respondents are just as concerned about ensuring that fraud prevention doesn’t have a negative impact on customer experience as they are with reducing losses.

This is encouraging and leads us to believe that the answer to the approaches CSPs take to recovering late payments might be

different in the not too distant future (see page 20).

Although worry about cutting operational costs in the fraud department was a distant third with only 13% of the total, the great thing about analytics is that they can cuts costs while improving results – human capabilities at the speed of computers (see page 21).

What are CSPs’ biggest challenges around fraud?

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9%

39%

13%

39%

n Reducing losses to fraudn Making sure fraud checks don’t

negatively impact customer satisfaction

n Cutting operational costs for the fraud department

n Hiring and retaining the right fraud expertise

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Make it happen – Strategies for adopting analytics across the customer lifecycle

Section 5

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As our survey shows, communications service providers’ (CSPs’) use of analytics and associated technologies is still in its infancy, and they are not nearly as widely used across the customer lifecycle as they could be. The excellent news is that there is ample opportunity to improve customer centricity by applying intelligence.

Deploy automation intelligently The key is knowing what you want to achieve and finding a supplier with track record to deliver. Every contact and its outcome can be used as part of a feedback loop to constantly analyze and improve processes such as collection and recovery processes. Operators should consider an approach similar to Globe Telecom’s (see page 21), which has resulted in a 40% reduction in delinquency rates.

Start as you mean to go on with every customer Ensuring that every customer’s offer is aligned with what they need and what they can afford should be inherent in the origination and onboarding processes. If customers are happy and confident they have achieved the best value, they should have a profitable relationship with the operator. Automation helps keep the cost of acquisition down, which is a major contributor to profitability. That starts with the credit analytics team, but it needs to be fast and it needs to be accurate.

The plan here is to treat everyone as the individual they are, but at scale, which seems like a contradiction in terms. The only option is automate, analyze, learn, improve and repeat.

Deepen engagement and build trust Although getting the relationship off to a good start is essential, CSPs could do much more to build a trusting relationship with customers in the middle stages of the lifecycle, rather than tuning out after the customer has been onboarded and maybe taking an interest again when renewal looms (the figures regarding using analytics for this were low). Renewals shouldn’t be viewed as the end of the customer lifecycle so much as the beginning of the next.

This focus on the early stages at the expense of later ones feels like a hangover from the days when recruiting customers was more important than their satisfaction and keeping them. It also is at odds with the responses to the survey which consistently said improving customers’ experiences and reducing churn are priorities.

Use the right channelsUnderstanding which channels customers prefer for what purposes is a really important part of customer experience, as well as for compliance with privacy regulations. It is arguably more fundamental to understanding customers and dealing with them in the way they want to engage than enabling agents to predict the next-best action, for instance.

Looking at the most common ways operators communicate with their customers suggests they are out of step with the way people communicate with each other and companies in their everyday lives. In particular, the continuing high usage of call centers is

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something of a mystery, as it is hugely expensive for CSPs well as a common source of severe customer dissatisfaction. There is no question that there will always be customers who want to communicate with their service provider by phone, and there will always be issues that need a human to deal with them. However, much call center traffic could be eliminated by the intelligent automation of self-service and self-care. The astounding success of digital-native companies such as Amazon and Alibaba is the most compelling proof.

Address fraud and collections They might not be as glamorous as marketing, but fraud and collections are direct contributors to the bottom line and perfect candidates for intelligent automation and can have profound impacts on other parts of the business. Dealing efficiently with fraud is about reputation, trust and customer service because defending customers from fraudsters is a fundamental responsibility of the CSP. Ultimately customers bear the cost of fraud too. Furthermore, preventing fraud is better than recouping losses, although it will never be possible to prevent all fraud.

Dealing with late payments and collections in a tactful way and making it easier for customers to pay their bills also builds trust with customers, as the Globe Telecom case study shows (again, see page 21). Good or bad, every contact, transaction and incident can be part of the feedback mechanism and contribute to the constant refinement and improvement to processes and experience right across the customers’ lifecycle.

Monitor outcomes Careful scrutiny of outcomes is required. It’s not just how many customers are signed up or how quickly late payments are raked in, but how many profitable customers are alienated by being sold the wrong package or by an ill-timed collections phone call and churn as a result. Understanding consequences across the lifecycle is certainly a challenge, but as analytics are applied to specific use cases, careful thought should be given to how the thread of each customer can be followed across their entire journey by advanced analytics to map complex interactions and outcomes. As the European group analytics expert quoted in The big picture noted (see page 5): “Holistically if analytics are there, it is [going to be] the brain running the business.”

Artificial intelligence and machine learning at FICO

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With the arrival of the fourth industrial revolution, the World Economic Forum commented that “the possibilities of billions of people connected by mobile devices, with unprecedented processing power, storage capacity, and access to knowledge, are unlimited”. This puts artificial intelligence (AI) and machine learning (ML) firmly into the spotlight within most communications service provider (CSP) boardrooms as executives look to the technology to improve customer experience and reduce operating expenses.

In this new reality, there are two key questions that CSPs must answer:

1) What can we do to retain customers?2) How can we drive revenue growth and create customer

value?

Ultimately, the key to answering these questions will lie in the CSP’s ability to get the right insight, to the right people, at the right time, and take action on these learnings. While ML will help accelerate this and AI may, in future, be allowed to make certain decisions that aren’t escalated to a human, it is likely that many CSPs are some distance away from being able to use these technologies to personalize experiences for customers and optimize resources at scale.

Using data to create a competitive advantage requires vision and leadership. If decisions are to be made from the insight that data creates, then this is as much about the buy-in of management as it is about investing in the right technology.

There is no doubt that CSPs are uniquely positioned to make use of a wealth of customer relationship data, however, how many CSPs would profess to know what to do with it all? It can even be argued that an abundance of data makes decision-making harder given that a large amount of collected data is effectively useless and would waste precious resources to invest in going deeper into analysis.

Furthermore, being able to access and integrate all of the data can be a challenge, particularly in CSPs that have grown quickly (often through merger and acquisition) leaving a tangle of legacy data siloes to unravel. One must address the essential foundation of data availability and integration of data across the organization before embarking on ambitions to utilize that data to drive better customer decisions.

Creating a culture that aligns analytical talent with customer experience strategy will enable you to identify the right technology and tools, realize business value, and allow advanced analytical innovation and predictive modeling to flourish.

CSPs aspire to reach a level of sophistication where they can anticipate a customer need and fulfil this need before the customer even knows they have it. Proactive customer engagement is easier said than done, especially in large CSP organizations which can often be slow-moving, risk-averse and organizationally siloed. To achieve change, the C-Level needs to set a clear direction for what goals data and analytics are expected to achieve, and for the business to invest in the right people and technology to bring this

to life. A combination of managerial curiosity coupled with analytical experience as well as industry and problem domain expertize will allow you to establish the right hypotheses and rapidly test these against the right data, with the right context, and therefore lead to meaningful insights that fuel customer experience decisions.

The ability, not only to garner insight, but to operationalize the insight into action, allows leaders to win through improved loyalty and satisfaction by executing complex customer decisioning systems. This business agility and culture to continuously compare and evolve different strategies will result in more meaningful interactions with customers as CSPs understand more than ever before about customer needs and behaviour.

Explainable AIAI comes with many challenges – one of which is the need to decipher what these AI models have learned, and thus understand what criteria has led to a given decision. One of the major areas of exploration, and latest scientific research, is the concept of explainable AI (xAI), which attempts to crack open the black box and explain how and why a model reaches an outcome. xAI is required for AI to be used in regulated environments, and is also essential to build trust amongst customers and business leaders. This need is especially important when CSPs need to enable machines to make autonomous decisions around mission critical infrastructure such as network operations and security.

At the recent FICO World 2018 conference in April, chess grandmaster Garry Kasparov — the man famously beaten

So, where are the smart bets being placed for AI and ML to create business value?

Customer experienceCSPs have long struggled as an industry when measured by NPS (Net Promoter Score), and digital first companies have now created a ‘new normal’ for customer experiences around the world. It is no surprise that now many CSPs are exploring and investing in AI and ML to improve customer interactions in areas such as marketing and sales, retention, and customer support.

There is no doubt that ML can help CSPs handle a wide and diverse range of data inputs from the complex and continuous flow of information available from both network events and customer interactions. This data can be streamed in real-time from multiple sources, capturing dynamic events from all customer channels, CRM information and the network itself in order to learn and find hidden combinations in this massive river of structured and unstructured data.

These insights are then used to drive contextual action, including decisions that are shown by the data to impact measures such as churn propensity and customer lifetime value. For example, using AI and ML enables CSPs to generate sophisticated, highly granular segmentation models, and to personalize offers in realtime. These intelligent offers fall into many categories, such as usage stimulation, loyalty programmes, device upgrades, household engagement and customer education.

Similarly, customers expect to interact with a CSP across

a variety of channels, whether it is directly through agent conversation, self-service or user communities. Digital customer experience is currently the most mature use case for AI, where virtual agents, chatbots and voice assistants are deployed to help automate answers to customer queries, or even collaborate with human agents by helping them with predictive cross-sell and upsell or making it easier to locate the required answer.

Network operationsNetwork operations automation is another area where AI and ML will undoubtedly have a high impact for CSPs. The burgeoning internet of everything (IoE) introduces unprecedented scale and velocity into how the network processes events at a level that becomes unfeasible to manage with manual processes. As software defined networks (SDN) and network functions virtualisation (NFV) become the norm, the complexity of these networks will require ML to learn how best to automate and manage the orchestration of network resource and capacity, among other functions, to ensure uninterrupted service availability.

CSPs such as Telefonica have already transitioned from a network operations centre to a service operations centre. The goal? To “maximize capacity and solve any problems before end users even notice anything”. Telefonica aims to use data from the network to move from a scheduled maintenance model to predictive and proactive maintenance.

AT&T continues the adoption of their ‘Domain 2.0’ initiative to transition from hardware-centric to software-

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by IBM’s Deep Blue at chess, and who has become an expert on human-machine collaboration — reinforced the importance of understanding how and why algorithms make their decisions and how humans and AI can better collaborate to achieve value.

FICO’s research into this area has produced a number of ways to crack open the black box with new and innovative solutions for xAI . These include:

n Scoring algorithms that inject noise and score additional data points around a specific data record, to observe what features are driving the score in that part of decision phase space.

n Models that are built to express interpretability on top of inputs for the AI model.

n Models that change the entire form of the AI to make the latent (hidden) features visible. With this approach, we can rethink how to design an AI model from the ground up, with the view that we will need to explain all features that drive outcomes.

As CSPs become more familiar with artificial intelligence, machine learning and the benefits they bring to streamlining operations, this adoption of AI will lead to freeing up staff to focus on more value-add tasks and doing things that humans do best – connecting to others on an emotional level, understanding new and not yet defined problems, and being creative. Together, people and advanced analytics can improve service, reduce churn, effectively manage credit risk and keep businesses and consumers protected from criminal activity.

Artificial intelligence and machine learning at FICO

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Artificial intelligence and machine learning at FICO

centric as they realize the dynamic approach delivered by SDN and NFV enables greater flexibility at a lower cost. Automating network functions provides a range of benefits to the business and facilitates an improved ability to give customers what they want. “It’s like moving from devices to apps…we recently brought back unlimited data, one of the reasons we were comfortable doing that is we know this software centric network can adapt to meet the demand.”

Having the ability to analyze temporal network data (i.e data over time) allows AI and ML to predict likely failures and the confidence level that failure will occur, thereby allowing for corrective action to be determined and executed. The end goal here is to combine advanced analytics with AI and allow networks to self-heal and operate autonomously.

Business assuranceFraud negatively imapcts everyone. Losses increase operating costs for CSPs and impact the bottom line. Criminals continue to become smarter and use ever more sophisticated means of committing fraud, whilst carriers need to balance fraud prevention measures against the impact to genuine customers. Fraudsters often systematically test counter-fraud measures until they are able to exploit a weakness or gap, and are ready with a barrage of identities, whether true, sythnetic, manipulated or stolen.

Given that connectivity is now essential to our personal and professional lives, criminals are using this connectivity as a platform and gateway to commit multiple other types of fraud across other industries.

As CSPs adapt to new business and operating models that allow them to grasp new opportunities the digital, connected world offers, the fraud risks of the future are somewhat unknown. Thus, emerging risks will certainly continue to surface.

Fraud teams have tended to rely on rules-based monitoring of call detail records (CDRs) to identify suspicious behaviour and trigger alerts and/or service suspension. However, CSPs are quickly evolving, offering an expanding array of digital services, exploring NFV, developing 5G as well as evaluating new business models. Thus, fraud management needs to also evolve to support the move toward digitization, but without the luxury of additional headcount. Part of this evolution is a move toward adaptive controls and ML algorithms that can understand the subtle changes in behaviour that increase the likelihood of fraud.

So, whether combatting subscription fraud at point of sale, or through ongoing behavioural monitoring and mitigating risks such as ‘international revenue share fraud’, CSPs are developing and deploying ever more sophisticated approaches that incorporate AI and ML to enable fraud management professionals to focus on the right cases, reduce false positives and mitigate negative impacts to the customer experience.

AI and ML fraud also impact the credit management lifecycle, for example within the customer onboarding process. Credit decisions act as the gateway to customers coming onto the financial books and thus will control the quality and profitability of the portfolio. Credit teams have

evolved over recent years to factor multiple dimensions into the credit acceptance decision, as well as to look at alternative credit offers where the customer circumstances dictate high risk of early default.

It is clearly preferable to detect subtle changes in customer behaviour that may indicate a likelihood to fall into delinquency, hence why leading CSPs are using the rich data at their fingertips to execute ML scores that can identify pre-delinquency risk and determine the appropriate mitigation before a customer falls past due and enters the collections process.

Similarly, once in collections, prescriptive analytics help CSPs refine treatment paths to reduce the cost to collect, improve operational efficiency and increase automation. This, coupled with an analytically-driven contact strategy through all available channels, reduces losses and improves customer satisfaction.

Robots and the futurePopular culture often paints a dystopian picture of technology with machines ‘taking over’ and marginalizing humankind. There is no doubt that AI will transform industries, but how worried should we really be? The likelihood is that we should be talking about and exploring ‘augmented Intelligence’ and why the reality of humans and machines working together is a more realistic outcome. Through the various phases of the industrial revolution, humans have harnessed machines to make us stronger; this fourth phase will allow us to use machines make us smarter and to focus on what we do best.

For more about TM Forum’s work on customer centricity, please contact Aaron Boasman-Patel, Vice President, AI & Customer

Experience, via aboasman@tmforum.org