Making the Big Data Connection - Huawei

Making the Big Data Connection A Huawei White Paper by Peter Massam February, 2014

Business & Network Consulting

CONTENTS 1 Executive Summary......................................................................1 2 The Big Data Challenge...............................................................4 2.1 Preparing for the Challenge...................................................................................................................5

3 Headache Avoidance...................................................................7 4 Which Data is Significant? ..........................................................9 5 Actionable Data..........................................................................10 5.1 Customer Interaction Model.................................................................................................................11

6 Business Information Transformation........................................12 6.1 Scaling Out.........................................................................................................................................13 6.2 Dynamic Evolution..............................................................................................................................14

7 Organisational Impact................................................................15 7.1 Organisational Evolution.....................................................................................................................15

8 Case Study ...............................................................................17 8.1 Challenges..........................................................................................................................................17 8.2 Data Collection & Market Validation....................................................................................................18 8.3 Solution...............................................................................................................................................18

9 Conclusion ................................................................................19 10 References...............................................................................21 11 About the Author.......................................................................22

1 Executive Summary “Information is not knowledge. The only source of knowledge is experience.” — Albert Einstein

This white paper seeks to address the seemingly endless increase in data collection and the expectations on carrier businesses to manage it in a meaningful way, which threatens to increase IT budgets and staff requirements by an estimated 40% and potentially cause organisational disruption instead of graceful evolution. Telecoms service providers are not alone in their plight – other large industries in the retail, utilities, insurance and financial services are grappling with similar challenges of:



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what data to collect



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what data is significant to act on



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how to prevent big data becoming a big headache



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transforming data into meaningful business information



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what it means for the organisation and how it can be



best used

Data Collection The most important data you will collect will relate directly to your customers. Understanding how customers are using your services leads to an improved service, timely and more relevant offerings and better business decisions made on real business information. Understanding how a single customer is using your services leads to a personalised experience which allows you to grow with your customer during the lifecycle of their tenancy. 1

Significant Data Not all data is actionable. An example of this is the metric Net Promoter Score (NPS). While a good yardstick of willingness to be your advocate among friends, colleagues and family, it is entirely without a direct link to ways of improving products, services, care and brand. That must be done through other metrics which can be seen to have a tangible effect on NPS. For Service Providers (SP) there are three key data sources: Network, Survey and Customer Insight Data.

Headache, Panacea or Health Warning Many vendors and internal staff alike will find different ways of slicing and dicing data to populate dashboards, produce new customised reports in addition to the many canned ones available. However, the question to be asked is ‘How is this relevant to my customers? What are the implications for a customer?’ The particular response may not be a panacea for all current challenges but it may help keep the head clear when faced with a barrage of data. It is better to spot the symptoms early to avoid the pain.

Meaningful Business Information Transforming the collected data into information that helps you make informed business decisions is not a task best entrusted to a few ‘data scientists’ (as they have come to be known) working in the back office. Any solution based on that concept will quickly go the way of other historical stand-alone systems gathering dust in a forgotten corner of the organisation. Barriers to being able to derive good business information include:

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Gathering the wrong sort of data



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Gathering the right sort of data and doing nothing with it



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Gathering fragmented data which makes it impossible to make sense of the data

[1]

The key to the data transformation is to ensure the real-time or nearreal time collection gives a balanced view from carefully selected, representative areas of your network, be they high value customers, 2

corporate business parks, central business districts (CBDs) or consumer residential districts. Armed with massive samples gathered over a matter of days, you can build up a picture very quickly of how your network is being used, what applications are trending as they happen, which devices are ‘hot’ and which devices are holding customers back from the data experience they are just embarking on. This information is best shared across the whole organisation to drive the business with a common, customer-focussed purpose. It is then only a short few steps away to building business cases for new services, new combinations of services and even new business models in areas not considered traditionally to be the preserve of service providers. Some operators are already forging ahead down this path, breaking new ground and reaping the rewards of first mover advantage.

Organisational Implications “Analytics may be the most important thing in this data supply chain, but it doesn’t exist in a vacuum”

[2]

When introducing any new element into a network or bringing a new product to market, the effort doesn’t just stop at the technology, the tools or the people. Its successful introduction depends on adequate thought being given to the change in processes necessary to accommodate and make best use of the goldmine that has dropped onto your doormat. Yes, the right technology solution needs to be in place with minimal points of integration to be as swift and fruitful as possible, but you can now use customer behaviour and insights to drive your business. One such example from a North American retail group realised that not only did 30% of orders come from mobile phones, but that 65% of sales happened between 12-1:30pm every working day. The co-founder’s business strategy altered course with immediate effect. She states “It’s about tailored, one-to-one communication with the customer. Within a single minute at noon every day, there are over 3000 versions of our message that go out to customers, based on what they shop for, what [3]

they like, even what sizes they wear.”

3

Leveraging the most important data (mobile + sales busy hours in this example) pays dividends. What is clear from the insights above is that relying solely on network data, marketing assumptions or on market research without validating against what is happening on your network from day-to-day is acting without having the full picture and the relevant facts. In our case, ‘the source of knowledge is customer experience’. Big Data opens the door on that opportunity and is yours for the taking.

2 The Big Data Challenge Big data is past its infancy now. Several industry sectors have grasped the nettle and are making tangible improvements to their bottom lines. Why consider other industries and not just service providers? With the advent of mobile connectivity to provide everything in the palm of your hand, the value chain is changing. Cross-fertilisation of business opportunities is forging strong partnerships between players and in some cases breaking down what are seen to be the traditional stamping grounds of adjacent industries. Only in May 2013 was Rogers Communications in Canada granted a banking license with plans to launch into the credit card sector to make the mobile phone the [4]

payment vehicle of choice . In November came the announcement of it having secured NHL media rights content for the next 12 years, following hard on the heels of a shorter but no less significant deal at BT for the rights to Premiership football (soccer). It was more than two years ago in August 2011 that Visa integrated analytics into its fraud detection processes, which by March 2013 had identified and blocked $2Bn in fraudulent transactions [5]. Such changes are taking place globally. Japan has seen NTT DoCoMo acquire a controlling stake in Radishboya, that country’s premium home-delivery service for vegetables and preservative-free foods. That initiative goes beyond just purchasing habits by facilitating ease of registration, ordering and payment, leveraging the in-built security and trusted partner status of service providers. 4

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Cross-fertilisation of business opportunities is forging strong partnerships between players and in some cases breaking down what are seen to be the traditional stamping grounds of adjacent industries.

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Insurance companies have been among the first to capitalise on big data sources, gathering driver behaviour data as an incentive to lower [5]

premiums , while perhaps for different reasons in Russia, dash-cams seem to be the required accessory to provide self-generated evidence to back up their claims and avoid becoming another statistic in the [6]

200,000 accidents that saw fatalities rise to 28,000 people in 2009 .

2.1 Preparing for the Challenge It is understandable that service providers, along with their parallel industry counterparts, are nervous and even sceptical about the scale of the challenge compared to the benefits. However there are steps which can be taken to alleviate the burden and evolve processes with this new information source. They are:

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Focus on the Customer and only select data sources which directly affect the customer experience



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Involve multiple departments in pilot implementations and secure buy-in from multiple stakeholders and business sponsors (e.g. CMO, CIO, CTO and COO), since there is no single beneficiary and therefore no single owner



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When selecting technology solutions, it is better to select only the necessary data feeds with minimal integration or API requirements to avoid spiralling costs



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Solutions should be provided in ‘Access for All’ mode, where shared dashboards are common talking points but more detailed views are customised for departmental use



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Ensure the talent resource pool has a cross-functional skill set that is both business and technically aware. It has been suggested by industry analysts that a potential 50-60% demand gap will exist in the U.S. by 2018 for deep analytical [7]

talent . Bringing third parties in to share their experience 5

of active projects and to grow your internal talent is one route to consider in the early years

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Process is not to be overlooked. Dropping even filtered quantities of new business information into an organisation requires alterations or adaptations to existing processes and procedures to make best use of that information. These do not have to be huge, but they do represent an opportunity to change an existing mindset or release a hitherto untapped energy to defend against a new entrant or provide a means of leapfrogging the competition to challenge the #1 position



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Data policies need to be set since data is not like other physical assets. Data has value beyond its source and the security, privacy, intellectual property rights and liability of that data needs to be established. The more comprehensive EU legislation relating to data privacy has been a determining factor in the lesser number of big data projects seen there, which highlights the need to examine the rights or licence to use such data

[8]

.

These are enabling steps which can be seen as packing the essentials before an important journey. The good news is that you are not alone on this journey and the better news is that you can draw on the experience of others in a global context to assess the right path to take in order to achieve your business objectives. We will now consider which how the big data headache can be avoided and which data is significant to be acted upon. 6

3 Headache Avoidance

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By following a structured methodology that tracks use cases backed up with real data collection, their worth can be validated and verified against customer behaviour in sample sizes that dwarf any external surveys in a matter of days.

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When viewed as a whole, even in a mobile broadband ecosystem with people generating and receiving content, the landscape can appear to be a daunting proposition.

Figure 1: Analytics Landscape in a Mobile Broadband Ecosystem Owing to the very nature of the words ‘big data’, scalability and accessibility of that data leads some to act like squirrels, by attempting to store as much data as possible. With increasing numbers of channels of customer communication to monitor for constructive or adverse criticism, it is no surprise that IT professionals in 18 countries expect budgets to rise over the next three years due to big data strategies and that 81% believe this would require cloud computing capabilities

[10]

. More noteworthy perhaps was the

acknowledgement that other lines of business will engage by between 14-24% in these activities – namely Finance, R&D, Operations, Engineering, Marketing and Sales. With so many potential recipients of the business information, it is clear that the business case made for investment will have multiple beneficiaries, stakeholders and therefore perhaps multiple sponsors. Cost control is important in any project and a big data project is no exception. It is critical for the use cases to be considered before committing to the underlying technology in direct contrast to implementations of large-scale data warehouse technology

[11]

. By

following a structured methodology that tracks use cases backed up with real data collection, their worth can be validated and verified against customer behaviour in sample sizes that dwarf any external surveys in a matter of days, once the collection has started. 7

The beauty of the mobile world is that people move around, commuting from one area to another on a regular basis or travelling between locations for other reasons. So collecting data from one point can amass a considerable sample of unique ‘passers-by’, from which patterns and behaviours emerge. As with any large data collection, some inferences on reasons behind a particular behavioural pattern will be made, but for the large part it is a quantifiable and direct primary source which has all the benefits of being specific to yours and your customers’ needs as well as being current and quick to collect. It will not provide the more qualitative aspects which will still need to be sampled from time to time, either through an independent survey or through non-intrusive methods carried out as a post-experience activity e.g. on-device response surveys or web-based follow-up surveys. So what of the billions of Machine-to-Machine (M2M) devices set to hit the planet? With exabytes of data being created every day globally, it is unrealistic to think all such data is relevant to your business. Where no human intervention is present in a true M2M environment, then it is only the data gathered which impacts the human customer experience (one step removed from the machine) which is important e.g. monitoring a drinks machine’s stock availability is secondary to the over the air coverage availability which allows the transmission of such information, but both are necessary to fulfil a satisfactory customer experience. The grey areas are present increasingly in the automotive sector where mobility meets human interaction less frequently but where infotainment systems sit side-by-side with car maintenance and performance sensors. Whereas traditional network efficiencies and high bandwidth networks would normally dictate these are carried by the same technology, many M2M applications use either GPRS or GSM networks with little or no business case to migrate. Additionally, the data streams are likely to need to take different routes to different destinations, where third parties are interested in different data sets. It has been suggested that CIOs efforts focus on combining and analyzing information from things, people, places and systems

[12]

. However with the correct level

of filtering and with the customer at the heart of the data collection, this should not present a headache but a simplified approach, which can apply equally to both scenarios of human and non-human interaction. 8

The following section examines which data is significant in a data-rich environment to assess their relative priorities.

4 Which Data is Significant? For Service Providers (SP) there are three key data sources: Network, Survey and Customer Insight Data.

Network Data

Not just network availability, accessibility, ‘retainability’ and network performance (normally expressed as KPIs), but also service availability and service performance from point of despatch through to final delivery, expressed as KQIs. Like it or not, every key press, social network message, video played and web page rendered is the assumed responsibility of all service providers everywhere. Some data can be gleaned in real-time, but most will be considered as static or historical data, a snapshot in time.

Survey Data

Much maligned in some quarters, but a significant step up from research panels or focus groups, surveys of 1000+ people are an important validation point very much like a doctor or nurse taking a temperature at regular intervals or a yearly service check on a car. When constructed properly, these can be a rich, structured data source not only of current opinion, device and service preferences but also an indication critically of willingness to pay for new services. Again, this is a snapshot in time (of opinion this time) and therefore can be considered as static data. In today’s Big Data Age, the limitations when analysing consumer behaviour begin to become apparent when compared with the 800k unique samples gleaned over the course of just five days – see Case Study below.

Customer Insight Data

While the other two sources have been around for many decades, this data source is the most recent addition and the most game-changing of them all. It cannot be taken in isolation from the other two data sources, but complements them with the luxury of providing real-time or nearreal-time data on customer behaviour. 9

Dependant on your industry (vertical or horizontal), the data collected will range from purchasing habits to remote meter readings to self-help health scores to foreign transactions abroad. It is significant as it helps you create relevant ways of interacting with your customers and refine business models and processes to align with this new found source of information.

Figure 2: Significant Data Source Equation Within those data pools, not all data is actionable. It is equally important to match the data, metrics or business information derived from it to the current process of interacting with your customers, so that improvements to that process can be actioned.

5 Actionable Data For this section we look at the internal uses of big data for operators, particularly the data collected by network deep packet inspection (DPI) probes as they are the toolset most often used to gather realtime customer information. The case for external use is covered in a later section. When considering data sources already in place related to the customer, Customer Relationship Management (CRM) systems immediately come to mind, which hold the most sensitive of customer information. As mentioned previously, extreme caution must be exercised in Europe when attempting to link data gathered from the network to any part of those systems. Data must be ‘anonymised’ or masked in a similar way to that of credit card numbers and broadly speaking, only used for Service or Network Management purposes. Legal advice should always be sought in matters dealing with data privacy in preparing a big data strategy. 10

While demographic sector can be useful for customer segmentation under traditional marketing techniques, it is not entirely necessary when the focus is on the application usage, especially in a world where mobile Smart phone and tablet usage seems to know no specific age boundaries. Indeed, many of the applications coming to the fore are lifestyle and health-based, which cut across all age groups.

5.1 Customer Interaction Model Consider this model of the Customer Interaction levels and the assessment of information which will be most useful. The example below looks at the significance for just two departments – Customer Care (as part of a wider Operational role) and Marketing.

Figure 3: Customer Interaction levels From Figure 3 it can be seen that the same information, set in a realtime, near-real time or historical context, can serve different but linked purposes. The Care function can now rely on application usage (delivered as realtime or historical information depending on the circumstance) to assist customers, determine root causes and achieve first-touch resolution. The Marketing function can provide new service offerings to groups of individuals linked by a common interest, usage or obsession (dependant on volume) or to individuals themselves in targeted campaigns. This does not spell the end of local or national campaigns but it does mean they are only used where it makes sense to make the big, above-the-line splash. Much of what can be achieved today with filtered business information is below-the-line out of sight of the competition and if managed carefully, 11

the source of recommendations in mouth-to-mouth conversations or on social media sites. While the objectives for Marketing following this model will be revenue generation and churn reduction or protection, their counterparts in Care will focus more on reducing average call handling times (ACHT) and making operational efficiencies through smarter customer interaction. Operations, among others, also benefit in a different way since the collected data provides important benchmarks of customer activity, which allows them to model data application usage more efficiently and include it in network planning tasks where capacity, coverage and capabilities all [9]

play equal roles e.g. in putting together a small cell strategy . Therefore, actionable data need not be tied to existing systems in the first instance. Much can be gained from relatively small scale deployments to start with, which will still deliver value at reasonable cost. The important factor is in the designing of use cases to match the customer insights gleaned.

6 Business Information Transformation If you look across any large organisation you will find a large number of disparate databases and systems supplying analytics from supply chain to operations to finance to enterprise performance. Some analysts believe the only approach is to combine them together into one amorphous big data cake, with the icing being provided by customer intelligence, predictive analytics and sentiment analytics from social media sources. In a survey conducted in the Asia-Pacific area among 134 CIOs revealed that 66% believed they would have [13

customer intelligence analytics by 2014 ]. The good news is that the tools available today make the parsing of such structured and unstructured data much more palatable. The less good news is that targeting too many components at once is likely to incur significant delays to the desired outcomes due to the not inconsiderable integration work involved. 12

Many insights can be gleaned from customer behaviour analysis alone. It can be used to spot emerging trends in application usage and volumes, which in turn can be used to develop new services and/ or associated data plans aligned with that usage. It can also be used to target ‘lifestyle groups’ where combinations of applications indicate a common interest or to promote personalised packages based on individual usage – the appropriately named ‘segment of one’.

6.1 Scaling Out Another spin-off from this analysis is the opening up of new opportunities or ‘scaling out’ with third parties. This extends beyond ties with existing partners e.g. discounted offers for loyalty customers in parallel industries, to something more dynamic and game-changing. Your customers are an open invitation to strike up new relationships with OTT players as their application trends in your area or country. These are not a threat but an opportunity to develop new business models based on real customer behaviour and operators are perfectly positioned to monetize the opportunity for the benefit of both parties.

Figure 4: BI Transformation Concept In their turn OTT players, retail chains, financial institutions, insurance companies may provide insights of their own in a 2-sided business model , which would not mean sharing (or worse, selling) their data to the other party, but to help build up a customer lifecycle profile to extend the lifetime value of the customer. The possibilities are many as they are varied and what is more, the customer insight can be expected to change more dynamically. So the response needs to be as dynamic and flexible, which will pose some challenges in the understandings reached and expectations of all parties concerned, particularly where ‘data sets are seen as a competitive advantage’ not to be traded [7]

. However, with both parties working towards the best customer

experience for their clients, those challenges will be easily overcome. 13

6.2 Dynamic Evolution The dynamic nature of customer behaviour is also its strength. It can be seen as a process of continuous offer improvement and customer experience evolution. It cannot be compared readily with the normal data-centric examples given from Google, Amazon and eBay as their mobile offerings are evolutions of web-based analytics, whereas the mobile broadband ecosystem boasts many hundreds of thousands of applications of which their application is but one. There are definitely lessons to be learned from them in terms of how to cope with occasional users, which don’t fit the data analytics mould because those users do not generate enough data to make useful recommendations. The results can be catastrophic with at best the same products being touted at you numerous times (how many toasters do I really need?) or at worst, firing random product selections at you. Similarly, a recent campaign presents ‘local’ offers to you as an evolutionary advance. However, the grasp of relevant distances in each country has yet to be accounted for as offers for a 3-course meal 50 miles (80kms) away is short on appeal and long on fuel consumption. Some thought has to be given to how this dynamic information is to be used. It would be perhaps a mistake to think that the data collection could serve each quarter to benchmark customer behaviour, so that the five ‘next-best-offers’ can be distributed to the Care representatives and retail channel to fit in with an established promotional cycle. Not knowing who is going to call or call into the shop puts pay to any idea of personalisation if the static rule or decision tree approach is followed. With a little more investment in integration and synchronisation, personalised offers across all channels are still the ultimate goals to make the customer experience the best it can be. To make this happen though, it is not just a stream-lined communications mechanism which is required. The corporate culture needs to adapt to these new information sources in a way which values its technology as the delivery vehicle of new services, but places the customer experience at the top of its Key Quality Indicator (KQI) metrics. Operators have always wanted to achieve this but only now do they have the tools to do so. Not only do they need to be ‘informed by customer demand’, but 14

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The dynamic nature of customer behaviour is also its strength. It can be seen as a process of continuous offer improvement and customer experience evolution.

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they also need to adapt to more ‘agile business processes to ensure innovation and growth’

[13]

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It is to this aspect of the big data impact we now turn our attention: the organisation and its processes.

7 Organisational Impact Those familiar with product management agile methodologies will recognise the need to be flexible and to review progress on a daily basis, if necessary, to match the service or product being built against customer requirements to ensure customer satisfaction. The ‘agile purists’ will also insist that progress meetings are held with all participants standing to promote the spirit of dynamism and to cultivate an atmosphere of creativity. The reason for this is that there is recognition in an agile methodology that customers can and do change their minds and that nothing stands still. In the same way, customer behaviour and habits change over time and services must be able to adapt to those changes as part of a concerted effort to extend the lifecycle of each customer. Reduced churn, increased revenues and loyalty are the tangible benefits of such a strategy which begins and ends with improving the customer

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experience (CE). So how this would work in practice?

Customer behaviour and habits change over time and services must be able to adapt to those changes as part of a concerted effort to extend the lifecycle of each customer.

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7.1 Organisational Evolution This is not a revolution but merely a re-focussing or re-stating of intent. We are looking at the means to make minimal adjustments to processes to achieve the required outcomes. Attitudes to process and process change can vary wildly. In some quarters they are viewed as a necessary evil to ensure the smooth running of a company’s deliverables but in others they have underpinned the very fabric of a company from its inception. The mistake those that fall into the latter category sometimes make is to believe that it is a one-off exercise which need never be revisited. 15

There are three required outcomes:

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Customer Insight and visibility of that information should be at the heart of the operator’s business



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Based on those insights, simplification of the processes and identification of areas for change by focussing on the service layer



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Improving customer experience is the goal of all departments

The adjustments to change and the order in which any such transformation should take place will be determined by asking several fundamental questions of your organisation in response to survey or anecdotal feedback. The following figures are examples of common complaints together with some of the questions asked internally, which can identify possible barriers to customer experience improvement.

Figure 5: Customer Sentiment Analysis There are likely to be several areas which can be benefit from this new business information, so it is prudent to focus initially on the area or areas where Quality Improvement will have the biggest impact on Customer Experience but these areas should not be considered in isolation where dependencies exist between processes. The figure below shows how visibility of customer insight information can impact on different areas of an operator’s organisation. It focuses on different aspects of the service layer, its management, performance and processes, aligning these to four major areas:

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Marketing



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Operations



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Product & Account Management



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Business Development

Each area can be considered as a project in its own right moving towards that common goal of improving Customer Experience.

Figure 6: Process Flow for Organisational Evolution– Customer Insight Visibility For Marketing, this means improving the Experience – from UX design to On-device portals (ODP), from Lifecycle Management to dynamically bringing personalised services to market. For Product and Account teams, this means improving the product experience for their accounts to meet their usage needs and directions. For Business Development, this means improving the Partner mix which may change over time to meet new customer group segments, rather than the more traditional customer segments.

8 Case Study It is very unlikely and inadvisable for all transformations featured above to happen at the same time. Small steps are good and it doesn’t need to take years to see a return on your investment and derive positive benefits for your customers. One Tier-1 market-leading operator in the European region is already seeing the fruits of big data labours in a joint business innovation project.

8.1 Challenges Their challenges are familiar to many of our customers – low data usage, low Smartphone penetration (or ‘Smartphone-as-an-Accessory’ 17

under-utilisation) and protecting their quality offering without decreasing profitability. New ideas were submitted to meet those challenges and passed through a high level filter to establish the quick wins and mid to longer term options.

8.2 Data Collection & Market Validation DPI data was collected from a small area of the network (000s of cells) on application, usage and device type to validate the proposed high level initiatives for data monetization. Within a few days, samples approaching 1M had been amassed. Due to the high mobility of the commuting population, these were all unique subscribers who provided excellent indications on device, application usage and volumes on a per application per user basis. Sound business cases could then be built to validate each of the use cases with real customer behaviour data. Instances where applications were popular but unsuccessfully run were quickly apparent as were new opportunities to partner with a 3rd party to promote and monetize their service. This approach opens up a world of opportunities to personalise service offerings but, equally importantly, provides stepping stones to move towards that goal in manageable bite-size chunks which can deliver real value in the interim. The potential revenue, averaged across all use cases, adds an impressive $160M p.a. to the data revenue stream.

8.3 Solution The technical assessment was deliberately ‘light-touch’ in its objectives. Fewest points of interconnection and fewest network elements with zero API work. The solution includes just three elements:

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Netprobe – Data Collector and DPI for data streams



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PCRF – existing network element (NE)



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PolicyView – software to collate, filter and view results

With these elements in place, you can get a very good idea of what customers are using your network for, which are the most popular applications and which third parties you should perhaps be partnering with as a result of the gathered usage information as a starting point and revenue stimulus. 18

This is a good example of the approach outlined in Fig.2, where some local network was made available and some historical survey data was known about but not used, since the solution data provided all the necessary market validation and more.

9 Conclusion Collecting big data is easy and there is no shortage of systems, historically or currently, that can provide it in reams. However, selecting and filtering the right data is essential so as not to be overwhelmed with the sheer volume and complexity of the data sets available to you. The number one filter, through which all big data must pass, is the ‘customer experience’ test in order for it to be admitted as a possible candidate for inclusion in the customer insight strategy. Once selected, turning that big data into meaningful business information is the key to improving the quality of the same customer experience. Once captured, this new data and derived business information can present new opportunities for:

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growth into new service areas



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expansion of existing offerings with relevant partnerships



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improvements in operational processes and procedures to enhance service performance



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development of current products to meet a target segment’s needs better

All on the basis of customer behaviour data collected from inside your own network. Which area you concentrate on first depends on your own priorities under your particular market conditions and will vary from one operator to another. Given the right methodology for implementation and business-focussed analysis of the sample data collected, an operator can be up and running with tangible results within months in the short to mid-term. To reap longer term benefits, this needs to be treated as an iterative process which feeds into the customer lifecycle management of all customers. The more forward-looking of our own customers have a 19

firm vision to place customer insight at the heart of their organisation and are looking for assistance to make that transformation through customer insight visibility and customer experience evolution. With the correct CxO level of sponsorship required to deliver such projects, this process can be managed carefully to evolve the current business models and not just embrace new trends but pre-empt them. What is business-changing for you can turn out to be life-changing for your customers in their perception of you and your company as a provider of services. Customer Behaviour will drive your business and Customer Insight can transform your business.

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10 References [1]

CMS Wire Webinar Using Analytics and Customer Data to Build Great Customer Experience, 2 May 2012 and Forrester Consulting on behalf of Extractable Data-Driven Design: Digital Experience Teams are focussed on Web Metrics that don’t demonstrate Business Value, April 2012

[2]

Mayank Bawa (co-president /co-founder of Teradata Aster) and Sanjeev Sardana, Big Data: It's Not A Buzzword, It's A Movement, Forbes.com 20 Nov 2013. Retrieved on 21 Nov 2013 http://www.forbes.com/sites/sanjeevsardana/2013/11/20/bigdata/

[3]

Alexis Maybank (co-founder Gilt Groupe) and McKinsey&Co: Chief Marketing & Sales Officer Forum Nov 2012

[4]

Robin Arnfield When a telco turns to banking, Mobile Payments Today 19 Nov 2013. Retrieved on 22 Nov 2013 http://www.mobilepaymentstoday.com/article/223345/When-a-telco-turns-to-banking

[5]

John Brock, Ralf Dreischmeier, James Platt, Robert Souza Opportunities Unlocked - Big data’s five routes to value, The Boston Consulting Group Sept 2013

[6]

Alex Davies Why Russian drivers have Dash Cam, Business Insider Dec 2012. Retrieved 22 Nov 2013 http://www.businessinsider.com/why-russian-drivers-have-dash-cams-2012-12

[7]

Big Data: The next frontier for innovation, competition and productivity, McKinsey Global Institute May 2011, using sources from US Bureau of Labour Statistics, US Census, Dun & Bradstreet and company interviews

[8]

Andrew Brydon (Pinsent Masons) Every company needs a ‘big data’ strategy to avoid falling behind rivals, Out-Law.com 24 Sept 2013. Retrieved 9 Oct 2013 http://www.out-law.com/en/articles/2013/ september/every-company-needs-a-big-data-strategy-to-avoid-falling-behind-rivals-expert-says/

[9]

Dr. Yan Q Bian, Deepak Rao Small Cells Big Opportunities, Huawei white paper Nov 2013

[10] Big Data and Beyond: Data in Motion, Cisco Connected World Technology Report 27 Mar 2013 [11] Justin van der Lande Big data deployment projects: top tips for operators, Analysys Mason 4 Feb 2013 [12] Hung LeHong The Information of Things: Why Big Data Will Drive the Value in the Internet of Things, Gartner 17 Apr 2013 [13] Clare McCarthy, Shagun Bali Big Data Analytics and the Telco: How telcos can monetize customer data, Ovum 20 May 2013

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11 About the Author Principal Business Consultant, Huawei Technologies Peter was an early champion of Customer Experience Management when monitoring from the customer viewpoint was deemed superfluous. With over 20 years experience spanning mobile, carrier, enterprise, retail, government and education markets, he has comprehensive operational end-to-end knowledge of mobile, wireless and fixed networks, customer access methods, devices and customer touch points as well as back end IT, service management and OSS/BSS platforms.

Peter Massam

Creating business intelligence from customer analytics data, he has led unique customer-focussed strategies at Nortel Networks, 3UK and Mformation Technologies, taking a mobile CEM solution from concept to production and implementation at several tier 1 operators globally. Peter has authored a book entitled Managing Service Level Quality across Wireless and Fixed Networks, John Wiley & Sons Ltd, 2003 ISBN 0-470-84848-0 and has also published a paper entitled ‘A Decent Service Level’, IEE Communications Engineer, June 2003 that illustrated an innovative method of maintaining customer service quality across multiple operator domains using today’s technologies. He also holds a patent in the US and Europe entitled “System and Method for Service Quality Management for Wireless Devices”. United States WO/2007/016337 July 27, 2006 A system and method for detecting and recording events related to the quality of service experienced by a wireless device in a wireless network.

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