Hitting the target: Analytical imperatives for telecom marketers in emerging markets

Telecom companies seeking growth in emerging markets should abandon the outdated view of customers as large, indistinct, and coherent segments. They should tailor offerings to specific customer needs by employing analytical marketing, which mines the Big Data they already possess to respond continually to how individuals and market micro-segments behave.

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Hitting the target Analytical imperatives for telecom marketers in emerging markets

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Contacts

About the authors

Beirut Hicham Fadel Principal +961-1-985-655 hicham.fadel @strategyand.pwc.com Dubai Mahmoud Makki Principal +971-4-390-0260 mahmoud.makki @strategyand.pwc.com Riyadh Hilal Halaoui Partner +966-1-249-7781 hilal.halaoui @strategyand.pwc.com

Hilal Halaoui is a partner with Strategy& in Riyadh and a member of the firm’s communications and technology practice. He specializes in information and communications technologies and has extensive experience in marketing, business planning, technology planning, product development, sales strategies, and customer experience strategies. Hicham Fadel is a principal with Strategy& in Beirut and a member of the firm’s communications and technology practice. He specializes in customer analytics, commercial strategies, customer experience, and strategic transformation programs for telecom operators. Mahmoud Makki is a principal with Strategy& in Dubai and a member of the firm’s communications and technology practice. He specializes in integrated broadband strategy development, commercial turnaround, analytical marketing, and business performance management for telecom operators and over-the-top service providers. Mansour Mansour is a senior associate with Strategy& in Dubai and a member of the firm’s communications and technology practice. He specializes in market-facing programs, analytical transformation, and disruptive strategies for telecom operators.

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Executive summary

The telecom sector has reached saturation point in many emerging markets, with market penetration sometimes higher than 100 percent of the population. Mobile operators are finding it increasingly challenging to compete and grow in such markets by employing a traditional go-to-market stra­ tegy. This well-worn blueprint has rested on their understand­ ing of aggregate demand, and has been based on the under­ lying assumption that there are still new users to acquire and that these acquisitions would compensate for price reductions. Telecom operators must now rethink their entire approach to marketing, fundamentally reorganizing themselves to allow more effective growth campaigns. Telecom companies need to embrace a new level of targeting and reject the outdated view of customers as large, indistinct, and coherent segments. To tailor offerings to specific customer needs, companies should employ analytical marketing, mining the Big Data already at their disposal to respond continually to the behavior of individuals and market microsegments. Investment can therefore be channeled productively, rather than wasted through overly simplistic assumptions about the general market. For this transition to take effect, telecom operators must build five key capabilities within the marketing function: data management, microsegmentation, commercialization, product design, and adaptive learning. Deploying these capabilities will ensure that customers’ needs are more accurately identified and serviced. Customers will be more likely to respond positively to marketing approaches, with growth resulting from the combined profitability of each micro-segment.

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Reaching an impasse

The mobile telecommunications market in developing economies has become saturated, with subscriptions frequently outstripping the size of population. For example, market penetration (the number of subscrip­ tions divided by the number of people living in the relevant market) is estimated to have stood at 105 per­ cent in Arab states in 2013, while the corresponding figure in the Commonwealth of Independent States, comprising most former Soviet republics, was 170 percent.1 Most operators have been unable to sustain growth levels by relying on traditional go-to-market approaches. Any observed growth is for the most part produced by the prepaid SIM card market, with its low average revenue per user (ARPU). The traditional, aggregate method of marketing products — taking consumers as large averaged groups or as a few segments — is failing to yield results as it is now more difficult to use pricing as a means to stimulate top-line growth. Customers are becoming increasingly sophisticated, and have arguably become more advanced than telecom marketers themselves in their understanding of the mobile market. As a result, telecom companies are being forced to pay high subscriber acquisition costs, and have an elevated churn rate for subscribers and declining ARPU. Competition within the industry has also increased. Revenue from services has stagnated as operators seek to combine dwindling traditional offerings with new avenues of growth, such as broadband and content. Turning this tide poses a major challenge (see Exhibit 1, page 5). The following conditions now characterize the telecom sector in emerging markets: •  A decreasing number of new subscribers, who also represent a lower ARPU, not least because companies need to reduce their prices to attract them. • P  rice reductions that are cannibalizing existing revenues, with current customers moving to more attractive plans. •  Natural erosion of traditional telecom services (messaging and
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Most operators have been unable to sustain significant growth by relying on traditional go-to-market approaches.

voice) by over-the-top services, such as Google Voice, Skype, and WhatsApp, and increa­ sing competition within the industry itself. • O  perators are also confronting the need to rationalize their spending, with investment in marketing and sales falling victim to the ongoing search to improve profitability. Chief marketing officers therefore need to operate within constrained budgets, despite the fact that the ever-growing inflation of key marketing tools is raising the cost of subscriber acquisition and retention. Their current priorities are to minimize spending on customers who generate the least return, and maximize the value from existing customers by serving their untapped needs.

Exhibit 1 Shrinking growth rates with competitiveness rising as market penetration increases
Market Competitiveness Index (HHI) & Penetration as Percentage of Population (Selected Middle East Markets, 2007–2012)
HHI 0.9 Average Evolution Market HHI 0.8 Market Penetration Market Revenues 2-Year CAGR1 0.7 0.6 0.5 0.4 0.3 Egypt 70% 80% Egypt Saudi Arabia Tunisia Morocco Bahrain Morocco Tunisia

2009 0.52 119% 13%

2012 0.43 147% 5% Bahrain

Qatar

Oman

UAE

Qatar

UAE

Oman

Saudi Arabia

0.2 60%

90% 100% 110% 120% 130% 140% 150% 160% 170% 180% 190% Penetration Size of bubble indicates 2-year market revenue CAGR (2007–2009 to 2010–2012)

Note: CAGR = compound annual growth rate, HHI (Herfindahl-Hirschman Index) is a common measure of market competitiveness, calculated by summing up the squares of operators’ market shares — lower index reflects higher market competitiveness. UAE = United Arab Emirates. 1 These figures are for (2007–2009) and (2009– 2012) respectively. Source: Strategy&

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Rethinking marketing: Finding the answer within
Following the lead of mature markets, operators should adopt an analytical marketing approach, identifying pockets of value in the market, and then generating maximum possible returns from these small areas. The rationale behind this strategy is twofold. First, while various markets taken as a whole may have approached saturation, there remains pent-up demand in a multitude of niche segments. Second, by mining such segments, and intimately understanding what prompts the relevant customers to make a purchase and why, operators can increase profitability simply by adding together the marginal returns from every micro-segment. Rejecting the one-size-fits-all mind-set of the past, some organizations have started to understand that they will need to develop a long-term relationship with each unique customer. More broadly, they are grappling with three sets of challenges and related questions. Boosting revenue: •  How can the customer response rates to promotions be increased? •  How can customer loyalty be strengthened? • How can unexploited pockets of revenue be spotted?

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Reducing customer acquisition and retention costs: •  How can the effort and investment made to retain customers yield better results? •  How can the best potential customers be identified and then acquired? Cutting the cost of service: •  How can campaign costs be reduced? •  How can communication costs be minimized without imposing a limit on the number of customers? Having collected large amounts of data relating to customers’ profiles and communication behavior, operators are in an ideal position to respond to these challenges. The answer already lies within their grasp, from what is commonly referred to as Big Data. We refer to the process of mining Big Data for such commercial purposes as analytical marketing. Such activities involve extracting commercially exploitable insights from every data set in order to improve understanding of individual customers and their behavior, as well as of the general dynamics of the market and its micro-segments. With the enhanced awareness that comes from analytical marketing, operators can develop completely tailored value propositions that specifically cater to individual customer needs, and reduce the overall cost to serve. To give two examples: •  A customer in the United Arab Emirates is frequently sending texts to his friend in Oman; the mobile operator detects his behavior and offers an additional service with discounted international rates to Oman. •  A customer is downloading her favorite show on her tablet; the mobile operator offers an additional service to improve the quality of service and the speed of download. Incorporating analytical marketing into the organization entails making data mining a priority, a major shift in operators’ marketing practices. Only when information is mined out of aggregated data does it become

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invaluable in helping operators to stamp out inaccurate assumptions about customer behavior and market trends. The transition from mass marketing to analytical marketing (see Exhibit 2) also implies among other changes: •  Target segment: Operators need to move from the aggregate to the unique customer level in their targeting efforts. •  Appeal: Operators must cater for latent needs that are specific to each individual customer, or a small group of customers, carefully tailoring their value proposition. •  Communication style: Communication needs to move from broadcast to narrowcast, with a focus on developing an individualized communication channel. •  Communication effectiveness: Operators should stop relying on retrospective, aggregated campaign assessments and replace them with near-to-real-time, highly specific evaluation of customer responses, thus honing the lessons learned about a campaign’s effectiveness and commercial success.

Exhibit 2 Increased customer-centricity is forcing the transition to analytical marketing
Dimension
Target Segment Appeal Value Proposition Data Mining Communication Style Communication Effectiveness

Mass marketing

Any customer is a good customer

Evident needs

Broad value proposition — “build it & they will come”

We aggregate the data we have

Broadcasting to the market

Identifying the communications budget

Analytical marketing

Each customer cluster has a different adressable value

Evident & latent needs

Value proposition tailored to each target customer’s needs

We mine the data we have at the deepest level possible

Building relationships

Measuring return on investment

Source: Strategy&

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Getting the most out of analytical marketing
Analytical marketing methods have demonstrated proven results in markets that are becoming saturated. The techniques of analytical marketing run through the entire marketing cycle (see Exhibit 3).

Exhibit 3 Customer analytics can be applied across the entire marketing value chain
Analytical Marketing Offerings Along the Marketing Value Chain
Upstream marketing Insights Portfolio risk analysis Proposition Product development Midstream marketing Channel strategy Channel optimization Supply chain management Web commerce analysis Pricing Profitability analysis Promotions optimization Posted pricing (I.E., B2C pricing) Negotiated pricing (I.E., B2B pricing) Commercial channel optimization Media 2.0 strategy Cross-/ up-selling opportunities Sales footprint optimization Vendor performance analysis Commercial strategy Sales Downstream marketing Product experience Post sales

Brand, portfolio & product innovation Time-tomarket improvement

Trade & marketing effectiveness

Loyalty program design/optimization

Device Customer segmentation subsidy optimization Customer value analysis Geomarketing integration

Range & assortment optimization Customer experience optimization Product life-cycle management Revenue/ yield management Granular performance monitoring Predictive churn management & win-back campaigns

Product synergy development Risk management

BTL promotions program & revenue uplift initiatives Distribution optimization

Granular demand forecast/ Gamification smart broadband deployment strategy

Note: B2C = business to consumer, B2B = business to business, BTL = belowthe-line. Source: Strategy&

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The three main phases of marketing are: •  Upstream (researching market potential): Applying analytical marketing techniques can help to identify and analyze existing needs, prompting new marketing concepts. •  Midstream (preparing to go to market): Analytical marketing can also help to accelerate the marketing cycle time by identifying the optimal offering and channel for a particular customer. •  Downstream (going to market): Using analytical marketing methods can markedly improve customer experience at those key moments that influence acquisition and retention. Two examples of analytical marketing in action are from Turkcell and Verizon. Turkcell — Below-the-line promotions and revenue uplift programs: Turkcell is a long-time player in the market, but by 2011 had been facing increasing challenges in customer retention, coupled with flattening subscription growth. In response, using the strength and creativity of its analytical teams, Turkcell introduced an initiative that provided the company with a new real-time view of the data from all its 34 million customers. Turkcell started to offer its customers the right product at the right time. This solution resulted in the marketing cycle time being reduced from a matter of weeks to a matter of days, and an increase of approximately US$15 million in gross revenue in 2011.2 Verizon — Sales footprint optimization: In 2007, Verizon identified a growth opportunity in the midsized company market. This market had become highly competitive, and was also very fragmented, leading to an inefficient sales outreach process. To differentiate itself, Verizon decided to incorporate analytics into its operations, and identified micro-segments with different service needs and preferences. The operator tailored its offerings and selected the appropriate sales channel for all customers. Two years later, Verizon’s targeted campaigns generated 25 percent more revenue than generic campaigns. The number of sales went up by 250 percent year on year.3

Operators must build their data management capabilities — data collec­tion and cleansing, storage and ease of access.

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Preparing the organization for analytical marketing
The analytics-based approach requires a far-reaching transformation within various aspects of the marketing function, in particular three areas: data gathering, insight generation, and customer interaction. Data gathering: Capabilities for data capture and storage have to be second to none, enabling quality data mining to register a positive impact on IT and network operations. Insight generation: Dedicated marketing teams should be trained in the skill of hypothesis formulation and confirmation. There is currently a great shortage of people in the marketplace who can translate raw data into meaningful conclusions that generate revenue opportunities. Customer interaction: Decision-making processes should be made more efficient, allowing for a multitude of market simulation initiatives to be run concurrently, and adjusted on an ongoing basis according to user response. Operators should embark on a major change program for marketing, revolving around five key capabilities: First, operators must build their data management capabilities — data collection and cleansing, storage and ease of access. Doing so provides comprehensive, reliable data assets that are amenable to analysis. This project demands close collaboration between commercial and IT functions so that the right systems and software for business needs can be acquired. Second, operators should strengthen the analytical prowess of the organization through improving micro-segmentation and profiling capabilities. Expertise in dissecting data is necessary, but not sufficient. Relevant business acumen, built around an intimate understanding of telecom customers, is also essential. Indeed, the marketing function as a whole must undergo a process of commercialization, becoming more nimble and responsive to customer
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needs. New ideas must be encouraged and then disseminated quickly, resulting in a more efficient decision-making process where the commercial concepts with the highest potential are seized upon without delay. Fourth, better product design capabilities are necessary to accelerate the marketing cycle. Marketers should be trained in all areas of design — innovation sourcing, pricing, economic analysis, product prototyping, testing — and encouraged to introduce innovations throughout the product/service delivery chain. Such innovation can be made possible only by flexible network and IT platforms that allow complex products to be implemented quickly.4 Lastly, operators should foster adaptive learning capabilities, with more accurate measurement of the effectiveness of marketing activities by means of performance tracking and efficient feedback, and through strengthening the ROI (return on investment) mind-set within the marketing function. This transformation program may at first seem complex. However, operators often fail to realize that they already possess most of these capabilities — they simply need to be refined and unleashed. Success will eventually hinge on the interaction between three core enablers — people, systems, and processes — across all capabilities (see Exhibit 4, page 13).

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Exhibit 4 Building differentiated analytical marketing capabilities
Capabilities

1
Data Management

2
MicroSegmentation Qualified “hypothesisdriven” marketers, with capabilities spanning business & technical areas

3
Commercialization

4
Product Design

5
Adaptive Learning Cultural mind-set based on performance monitoring (ROI per initiative), adaptive learning & know-how sharing

People

IT team with consolidated view on all data sources & clear understanding of business needs

“Empowered” marketers — encouraged to innovate & synchronize with marketing strategy

Qualified product managers, trained on design best practices (pricing, etc.)

Systems

Reliable system infrastructure, allowing real-time data update & management

Advanced yet user-friendly & reliable analysis tools

Knowledgesharing platform, promoting innovation & increasing collaboration

Flexible & adaptive technical platforms, able to deliver against product manager needs Streamlined service development life cycle — ensuring coordination between commercial & technical teams

Enablers

Real-time reporting & performance tracking systems

Processes

Coordinated, strict & frequently tested data gathering, cleansing & storage mechanisms

Dynamic segmentation framework, frequently updated & monitored over time

Streamlined ideation process, allowing for faster decision making

Top-down support for program institutionalization & continuous development

Desired end states

- Clean, comprehensive & realtime data environment - Analyticsfriendly data assets with reliable/timely access channels

- Precise, comprehensive & continuously dynamic customer profiling capable of delivering measurable & actionable insights

- Higher throughput of new commercial concepts, backed by quantitative data & insights into customer behavior

- Faster time-tomarket, bringing innovative products/ services with superior customer experience

- Transparent operating model conducive to ideation & continuous learning (based on ROI measurement, trial & error, etc.)

Note: ROI = Return on Investment. Source: Strategy&

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Conclusion

The deluge of customer information is just beginning. Every digital packet transmitted through the operators’ network is a potential source of information. When this is combined with customer information from external sources (such as from social networks, or from online shopping and behavior), companies will attain a more holistic view of customers’ lifestyle and preferences. Telecom companies will have a natural advantage in the new era of Big Data, as they possess more customer knowledge than their counterparts in a range of other industries. As a result, they have a growing number of monetization opportunities.5 However, many operators still have a great deal of catching up to do. The race is on.

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Endnotes

International Communications Union, “The World in 2013: ICT Facts and Figures,” February 2013 (http://www.itu.int/en/ITU-D/Statistics/Documents/ facts/ICTFactsFigures2013-e.pdf).
1

An Ultimus Customer Success Story, “Turkcell Improves Competitive Advantage and Achieves Annual ROI of 1 Million Euros with Ultimus” (http:// www.ultimus.com/results-case-studies/turkcell/).
2

SAS, “Verizon: Creating Their Own Upturn” (http://www.sas.com/success/ verizon.html).
3

Hilal Halaoui, Dany Sammour, and Hani Zein, “IT and network integration in telecom companies: Creating efficiency and customer satisfaction,” Strategy&, 2013.
4

For an example of such opportunities, see Telefónica’s latest Telefónica Dynamic Insights venture, which proposes to sell anonymized subscriber insights to third-party entities (http://tinyurl.com/c2mufby).
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