facing the bigmusic of · market research, crm, efm, etc. traditional market research collects data...

Facing theMusic of

BIG Data

Ipsos MORI - Big DataIpsos MORI - Big Data

FACING THE MUSIC OF BIG DATA

As the African proverb has it, when the music changes so does the dance. For those of us in the data analysis business, the evolution of Big Data has created a brand new beat – and if we fail to listen, we’ll soon be out of step.

Sometimes, Big Data is just noise – but when we synthesise it with other ‘noise’ and listen with a practised ear, meanings sound loud and clear. That’s the nature of Big Data.

Let us be clear from the outset: the market research industry has been evolving within the field of Big Data for over a decade, but this growth has been hidden by a lack of published case studies. There are many thought pieces but few tangible examples of how Big Data has been used by researchers to good effect. In this paper we aim to rectify this by demonstrating how we’ve been using Big Data to create smart insights for our clients, as well as presenting our point of view on this very big topic - big, not only in terms of the terabytes of storage space it can require, but in the number of people talking about it and the myriad of opportunities it presents. In doing so, we aim to answer the key questions that still abound...

• What exactly is Big Data, and what isn’t it?

• How will it evolve?

• How are we applying our skills as researchers to create value from Big Data?

BIG DATA MAY SEEM LIKE

THE NEXT BIG THING

BUT IT’S CERTAINLY NOT NEWFor over a decade, we’ve been working with many of our clients to mine their data sets and complement the findings with survey research. It’s the sudden ubiquity of Big Data that’s brought it to everyone’s attention.

VOLUME: Although it isn’t always big

Let’s dispel the myth: not all Big Data is big! While it’s true that many Big Data sources require terabytes of storage space, many do not. And big isn’t always better. As with traditional research, a smaller balanced sample will generally provide better insights than a larger skewed one.

VELOCITY: It often moves at warp speed

In a fast-moving world, many Big Data sources are characterised by the speed at which they move; a sales database for a global retail brand, say, would be an example of a fast-moving data set. In some cases, these can include information on every individual who purchases any product from that brand globally, in real-time.

VARIETY: It comes in all shapes & sizes

Variety is one of the biggest challenges of Big Data analysis. Because many sources are unstructured, the range of potential data formats is vast. In fact, one of the first steps to interrogating Big Data is identifying, classifying and understanding all of its variables – the equivalent of building a data map. This will typically be the largest component of analysis time, particularly when the data is in a raw or unstructured format.

VALUE: It may or may not help us

One thing is clear: not all Big Data has value. For this reason, one of the first skills any data analyst has to learn when interrogating Big Data is to quickly identify which data is important – and therefore has value – and which is not. Only then can we begin the all-important task of drawing out the key insights that lie within.

WHAT IS BIG DATA (AND WHAT ISN’T IT)?Four key characteristics of Big Data

By its nature, Big Data is difficult to define. It’s debatable whether market research data can be categorised as Big Data. Some of our programmes, such as the GP Patient Survey, involve several million interviews, but even this is not big or complex enough for many to consider it Big Data. This paper focuses more on the data trails, or impressions we generate as we all communicate, consume and conduct our day-to-day lives; the (often digital) breadcrumbs we leave behind after any interaction.

We’d suggest that Big Data tends to demonstrate the characteristics below but it’s not essential to demonstrate all four of them.

Ipsos MORI - Big Data


Big Data can be structured like market research, which is essentially a structured data industry, but the majority is chaotic, random, disparate, and without any obvious patterns. Often, the value lies in combining sources. By volume, Big Data is significantly wider, deeper, and richer than market research data; but unlike market research data, does not reveal its insights easily. To look at the three main sources in more depth...

STRUCTURED DATA:Market research, CRM, EFM, etc.

Traditional market research collects data in a more regimented and precise manner, applying thoughtful specifications and analysis throughout the process. Other structured data sets include CRM / EFM (Enterprise Feedback Management) databases and 3rd party market databases.

HUMAN-GENERATED UNSTRUCTURED DATA: Customer feedback, social media, etc.

Human-generated unstructured data is content provided by consumers across a variety of sources, and includes customer feedback, social media, photos, video, audio and more. Analysing these data sources requires new techniques, where we emulate a conductor who brings together strings, brass, woodwind and percussion to create a symphony.

MACHINE-GENERATED UNSTRUCTURED DATA: Transactional info, wifi, geolocation, etc.

Finally, there’s machine-generated unstructured data – which is mushrooming. And because it’s growing at a much faster rate than other data sources, it’s the most interesting to many companies. Examples include web logs, transactional information, call records, wifi, geolocation and other passive data.

WHERE DOES BIG DATA COME FROM?The three main sources


TREND ONE: Availability of increasingly sophisticated data aggregation software

We can now match data from a number of sources, enabling us to understand consumer behaviour in multiple dimensions – rather than the one or two dimensions we’re used to working with. Why is this important? If you can analyse data from multiple sources, the richness of insight multiplies alongside it. (A single beat is nowhere near as interesting to listen to as a rhythm).

Companies are already viewing their internal data as a very important asset; transactional data, CRM and EFM databases are being used to mine and extract insights that have a dramatic impact on customer experience, company strategy and, ultimately, profitability.

Now, we can mine structured data sources alongside unstructured data sources, merging data sets that were previously incompatible. This ability to integrate multiple data sources will be key if we are to achieve a single view of the customer or a complete picture of consumer behaviour.

TREND TWO: The rise of ‘filtering’ and ‘tagging’

With so much data being generated, especially unstructured machine-generated data, it’s becoming impossible (or certainly very expensive) to store everything that’s produced. Companies are having to become increasingly discerning about which data sources are valuable – and therefore need to be stored – and which can be discarded. This process is called ‘filtering’.

Once valuable data has been identified, the next challenge is to set up the means by which they can be used in analysis. This is called ‘tagging’. It is now possible to tag data in real time using software, so that analysis can be conducted as soon as data is read into the software. This is an important innovation, as it enables users to conduct analysis ‘on the fly’ as soon as data has been generated. TREND THREE: Monetising Big Data sources

Any company that owns a Big Data source which has value is now looking to monetise its assets. Primarily, companies are using Big Data to improve their business performance – and the focus in the Big Data space will very soon be on how analytics can be used to unlock the latent value held in these data sets.

The other way, of course, is to sell it in the marketplace. After all, the data collected in the course of a company’s everyday activities often has intrinsic value – for example, any comparison website has customer journey information that can be mined to shed light on how consumers make decisions. Most companies will have transactional information from their own customers, but this is only part of the picture; adding additional data collected by other companies helps to build ‘the story’ – or to use our analogy, create the melody.

WHERE IS BIG DATA HEADING?Three key trends

As the Big Data universe grows and evolves at warp speed, predicting the future is a challenge. However, the following key trends are, to a greater or lesser degree, shaping its development.

1.

2.

3.


Interpretation is kingIn many cases, customer data collected by organisations –

e.g. loyalty card data – can only tell us so much, i.e. the ‘what’. To truly understand the ‘why’, ‘who’ and ‘how’, we need to uncover the context and customers’ underlying motivations. Not everything can be explained and modelled by past behaviour and associations.

Most of the Ipsos Loyalty Council members we spoke to expressed a need to combine ‘Big Data’ with survey data and, crucially, interpretation!

Interpreting Big Data is as much an art as it is a science. As mentioned earlier, the focus of Big Data is now on how analytics can extract value from these data sets, but our experience suggests that narrative is also required to help draw out and effectively communicate the story in the data.

Are we intruding?In similar conversations, we also heard how some

communications have resulted in customer cynicism or mistrust – showing there’s a fine line between personalisation or useful targeting and what customers see as ‘Big Brother tactics’. Sometimes Big Data can have BIG unexpected consequences; recall the American father who discovered his teenage daughter’s pregnancy because she started receiving offers for baby clothes based on her purchasing behaviour at the local Target supermarket! Whether or not Big Data is viewed positively or negatively depends to a large extent on how and why it is used. If it is to improve the quality of our lives then few will object but, as the statistician Nate Silver says, Big Data can mean big errors. More often than not these are errors in interpretation rather than the data themselves. This is frightening if authorities wrongly predict a health scare and frustrating if advertisers try and sell you something you already have or simply aren’t interested in. This is where we see value in merging survey and server data to help separate the signal from the noise.

There’s no doubt that Big Data is an opportunity – but organisations need to be careful about how they use it to communicate with customers. The Ipsos Loyalty Council, a forum of senior leaders in customer experience and management from across multiple sectors, gave us their perspective.

THE LIMITATIONS OF BIG DATAEnsuring we don’t play out of key

NARRATIVE IS ALSO REQUIRED TO HELP DRAW OUT AND EFFECTIVELY COMMUNICATE THE STORY IN THE DATA


THE IMPLICATIONS FOR MARKET RESEARCHUnderstanding the rhythm and blues

Undoubtedly, Big Data offers the market research industry a significant opportunity. For a start, we already have the analytical capabilities that are essential in extracting value from data and the ability to communicate insights effectively...

There is, however, a reason far greater than our skillset for why we are in prime position to lead the charge. Collecting, analysing and reporting on Big Data (especially behavioural data) requires serious consideration of individuals’ privacy. The research industry is well placed to address these weighty issues because anonymity and respect for the individual are core considerations in all aspects of our work.

In The New Digital Age – a recently published book by Google’s Eric Schmidt and Jared Cohen – the authors discuss how the internet is a source for tremendous good but, in the wrong hands, potentially dreadful evil. The same can be said of Big Data, particularly when it contains personally identifiable information. Even anonymous data can sometimes be ‘pseudonymous’ and may reveal an individual’s identity when combined with other datasets. As we turn to new approaches in research (particularly those that involve passive measurement), we need to remember the core values of our industry. Eric Schmidt isn’t the only one concerned about the potential to do harm; so too are consumers, pressure groups and legislative bodies.

As an industry, market research is ideally positioned to overcome these challenges. It has a code of conduct at its core and many organisations, like ours, choose to go beyond these industry standards. We also have experience in dealing with large volumes of sensitive information; a significant part of Ipsos MORI’s heritage and current business involves handling data from, for example, financial institutions and government departments. Looking ahead, then, what should our watchwords be?

AcknowledgementBig Data is here to stay and market researchers are part of

the universe – a key part. The McKinsey Global Institute claims that by 2018 the United States alone could face a shortage of 140,000 - 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of Big Data to make effective decisions. Ipsos MORI already has the analytical, story-telling and data visualisation expertise to exploit this area of rich opportunity – not to mention the experience of dealing with sensitive information.

We have accepted that the data we analyse will not always be neat and structured and we have found a new breed of analyst or data scientist is emerging: one who is prepared to invest the time and effort in getting familiar with unstructured data sources.

Partnership The world is now ready to work in ‘n’ dimensions. We are

developing more and more relationships with organisations who collect Big Data or who simply have large market databases, whilst also forging relationships with software suppliers in the EFM and Big Data spaces. Now, we see ourselves as the ‘Third Party’ in Big Data value creation – finding and synthesising a myriad of Big Data sources to help solve our clients’ business issues. To return to our earlier analogy, we see the role of the market researcher as that of the orchestra’s conductor: to apply rigour, expertise and creativity to turn noise into music.

CautionWe are bound by the ethics of our industry – but as

collaborations become more commonplace, it will be critical for each party involved to put the right steps in place to respect the privacy of individuals and their rights to anonymity.


The Big Opportunity is not in Big Data per se, but in identifying data sources, understanding underlying data structures, creating hypotheses, applying appropriate analytical techniques to extract insights and then communicating these effectively. However, questions still remain. How, exactly, do today’s organisations find Big Data, make sense of it, and then maximise its value?

Big Data is an evolving issue for our industry and it’s a journey we’re taking alongside our clients, our partners, and our fellow market researchers. However, as the accompanying case studies show, Ipsos MORI has already amassed a considerable amount of experience in this space. We have some principles by which we work with clients:

FRAMING THE BUSINESS ISSUEBefore embarking on any Big Data programme, as with

any research study, we first need to make sure we’re asking the right questions. Here we take your business issues and work with you to turn them into hypotheses.

BIG DATA AUDIT & ACCESS TO DATA SOURCESWe can help you to identify the relevant sources of Big

Data available – both internal and external – and ensure that information security standards are adhered to. Further, Big Data sets are rarely complete - and many will be missing crucial information such as demographics. We can help you understand the implications and potentially augment with other data sources.

PRIMARY DATAIf applicable, we can collect and integrate survey data

overlaying the attitudinal and behavioural to help you understand the ‘why’ as well as the ‘what’, and potentially extrapolate patterns to a wider data set via predictive models. Our experience suggests that, far from replacing survey research, Big Data analysis raises as many questions as it answers.

DATA REDUCTION & EXPERT ANALYSIS

Applying our expertise in managing complexity and analysing data, we would ensure that only the most accurate and valuable data is extracted. We build rules, apply taxonomies and create algorithms to draw out key insights.

QUALITY CONTROLAs with all of our research, we ensure that any data

outputs are quality checked and that commitments to data privacy are fulfilled.

COMMUNICATING INSIGHT & DATA VISUALISATION

Finally, we’ll help you make sense of it all, pulling the key insights into a meaningful story – or, rather, synthesising the different sources of noise to create music. Meanwhile, our multimedia and graphics team will use data visualisation to show that big really can be beautiful.

THE WAY FORWARDThe Ipsos MORI approach

Claire Emes

[email protected]

Charles Adriaenssens

[email protected]

James Randall

[email protected]

For more information please contact:

Ipsos MORI 79-81 Borough Road London SE1 1FYt: +44 (0)20 7347 3000

www.ipsos-mori.com www.twitter.com/IpsosMORI

CASE STUDY- POPULATION FLOWS

Claire [email protected]

Charles [email protected]

James [email protected]

79-81 Borough Road London SE1 1FY

t: +44 (0)20 7347 3000 www.ipsos-mori.com www.twitter.com/IpsosMORI


Facing theMusic of

BIG Data

BIG DATA CASE STUDY - PUBLIC SECTOR

Creating predictive models from a range of data sources to help HMRC understand attitudes, needs and behaviour among 40 million tax payers.

Small but beautiful insightsHMRC’s customer-centric business

strategy commits to tailoring services and processes to the needs, abilities and motivations of its customers, and moving away from a mass ‘one size fits all’ approach to customer communications. Following a large segmentation study, HMRC had collected attitudinal scores and segments relating to the needs and attitudes of 4,000 UK taxpayers towards personal tax issues.

HMRC wanted to embed the findings of its segmentation across more of its business processes. However, this required the identification of needs, attitudes and motivations at an individual taxpayer level, and it was clearly not possible to survey all of HMRC’s 40 million individual customers.

Creating the bigger pictureWe began by appending data from

HMRC’s own internal database and a variety of external databases to the 4,000 respondents in the original segmentation research; we then built statistical models to predict attitudinal scores for any individual respondent.

Having evaluated some 2,000 potential input variables, the team reduced these to an optimal set of around ten variables per model. The variables required from external suppliers could then be appended to the entire HMRC individual taxpayers’ database.

The upshot? Predictive models that could be applied to all 40 million of the UK’s individual taxpayers.

The value to HMRCThese models have significantly

increased the accuracy that could previously be achieved. Today, they provide HMRC with the tools to inform targeted communication strategies and

services to drive desired behaviours and improve customer communications.

HMRC now have a sound analytical basis for considering the use of attitudinal prediction models alongside other data to help target and tailor services to the needs and abilities of their customers.

Four thousand becomes 40 million



Trevor [email protected]




BIG DATA CASE STUDY - POPULATION FLOWS

A tale of three cities

One billion rows of data per hour

Using mobile network data, which generates one billion rows of data per hour, we studied population movement to three cities on one day. This enabled us to see who (age, gender, etc.) travelled to the city and where they came from, and allowed us to create ‘population flows’ and ‘heat maps’. As we were able to analyse groups of customers’ mobile online and app

behaviour, we were able to understand not only where they were going, but – at a macro level – what they were doing before, during and after they visited these various locations.

In every instance, mobile operator subscriber data was anonymised and aggregated, and only subgroups above 50 were used.

A myriad of applications

The applications of this approach are many and varied: helping advertisers to understand who has visited a particular event, enabling site planners to discover who frequents particular locations, and informing local government about population movement patterns.

Harnessing the power of geolocation insights from mobile operator data to understand population movements

Ipsos MORI has been leveraging some of the latest techniques in geolocation data analytics to understand population flows, to link events, to map flows between places, and to compare the profile and volume of a location over time.

A tourist hot spot: this analysis shows the draw of Oxford Circus, attracting visitors from across the UK

Scotland

Northern Ire

landN

orth

Wes

t

Wes

t Mid

land

s

Wales

South West South East

Greater London

East

East Midland

s

North East

Oxford Circus

Edinburgh

Manchester

30-34 %

35-39 %

40-44 %

45-50 %

50+ %

0-4 %

5-9 %

10-14 %

15-19 %

20-24 %

25-29 %

The width of each line represents the proportion travelling to each city from a given region of the UK.

Scotland

Northern Ire

land

Nor

th W

est

Wes

t Mid

land

s

Wales

South West South East

Greater London

East

East Midland

s

North East

Oxford Circus

Edinburgh

Manchester

30-34 %

35-39 %

40-44 %

45-50 %

50+ %

0-4 %

5-9 %

10-14 %

15-19 %

20-24 %

25-29 %

The width of each line represents the proportion travelling to each city from a given region of the UK.


Steven [email protected]





BIG DATA CASE STUDY - MEDIA

Three screen behaviour

Integrating active and passive research to reveal how three screen ownership affects online & offline behaviour

This visualisation shows which devices were used, when, and for how long, to access the internet over the course of a weekday 24 hour period. Mobile sees constant ‘snacking’, whilst tablet consumption is more prevalent in the evening and PC shows longer, focused online use.

Three questions about three-screeners

Ipsos MORI, on behalf of Google, sought to answer three key questions about ‘three screen users’ (owners of a PC, mobile and tablet):

1. How, if at all, does three screen ownership affect online and offline behaviour?

2. Does tablet ownership create new online behaviour, or cannibalise the existing use of PC and mobile?

3. Does three screen ownership lead to a change in use of offline media such as TV and print?

WEEKDAY - WEDNESDAY

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Adam [email protected]

Ben [email protected]




Active research meets passive measurement

Aware that delayed recall of online use is often inaccurate, the team met this challenge through active and passive measurement. For the former, a sample of our three-screeners had one device removed and were asked to create blogs, whilst another group was tasked with completing a one-day mobile diary and follow-up survey. As for the latter, the team conducted 30 days of passive device measurement using meters to gather every single online action on each device.

The total data haul?75 hours of user-generated content,

1,782 diary entries covering 1,546 hours of media activity, and 30 days of online use totalling 30,814 online sessions.

The insights• Respondent recall is not as

accurate as passive measurement at recording more frequent online mobile use

• Three screen users are constantly online

• Tablets create more online use than they cannibalise

• PCs dominate in-home online use

• People use smartphones and tablets while watching TV

• PCs dominate secure activity such as commerce

And the key truths?Users cannot accurately estimate the

time they spend online – their devices do a much better job – but, if we rely on passive measurement alone, we need to be sure that we’re monitoring all devices they use.

The users can tell us what drives their behaviour – we just need to help them by reminding them of the things they did.

Only users can help tell a research story – observation from device measurement and diaries alone doesn’t provide the human context to understand it.

BIG DATA CASE STUDY - SOCIAL MEDIA

Navigating the social media morass

Using text analytics to reveal the customer loyalty landscape

An ugly, swirling mass of data...

No one would dispute that social media is a source of valuable insights on a large scale – but unless we focus social media research on specific strategic objectives, it’s just an ugly, swirling morass of data. Even when data has been matched to a suitable objective, analysis can be a daunting task...

Applying a multi-stage Big Data process

With millions of social media

messages relating to mobile network providers each year, Ipsos MORI focused its attention on one very specific objective: to understand how people talk in social media about switching from one mobile network provider to another. Specifically, we used a multi-stage process: we began by leveraging text analytics to map the key discussion themes by provider; we followed this with qualitative analysis to get under the skin of the issues; and then we complemented the findings with data integration from external sources. These final two steps are key; text analytics provide the foundations, but we get a richer picture when we follow individual customer journeys via longitudinal

analysis of Twitter conversations and overlay other data sources.

Text analytics & social media

We found, over an 18-month period, 50,000+ tweets related to switching mobile network providers. Text analytics allowed us to take advantage of the sheer volume of data by uncovering switching patterns and triggers for each provider – in a way that would be impractical to do manually. The text analytics also allowed for a consistent approach across the market and an exploration of changes over time.


Eoghan O’Neill eoghan.o’[email protected]

Jean-Francois [email protected]




Coverage

3 EE O2 Orange T-Mobile Vodafone

Customer service

Handset

Internet

4G

Upgrade

3G

Deals

Rewards

PULL FACTORS PUSH FACTORS

The key findings?Broadly speaking, we found a

correlation between share of voice on Twitter and market share. There is a time lag between the two but that’s exactly what we’d expect to see for a considered purchase. Network coverage and customer service were the issues that appeared in conversation most often. By exploring the sentiment related to each issue, we were able to build up a picture of push and pull factors from one brand to another.

Overall 4G is coming out as a pull factor for EE but a push factor for O2. Rewards are still a positive differentiator for O2

3G

Shops

Upgrade

SMS

4G

SIM

Pac code

Deals

Plan

Internet

Handset/iphone

Customer service

Coverage 10%

8%

7%

6%

4%

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4%

3%

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% Mentions

BIG DATA CASE STUDY - RETAIL

Combining survey and transactional data to understand the motivations of Homebase’s four million customers.

Applying the core principle: attitude drives behaviour

When it comes to applying attitudinal research to database marketing, Ipsos MORI starts with the same principle: each customer is motivated to behave in a particular way as a result of their attitudes and circumstances.

Homebase sought to build its brand through a CRM programme based on investment prioritisation using the predicted value of each customer – and then targeting and tailoring communications according to the needs and motivations for each customer.

Investment priority segments for four million customers

We began by undertaking a survey of 1,500 customers from the loyalty card database, capturing all potential motivational drivers. Respondents were matched with their corresponding database records to construct an analytical dataset for all subsequent modelling. This dataset was then used to explore relationships between attitudes, circumstances and behaviours. For each customer we predicted their likely spend on DIY (potential value) and the likely share of this spend that Homebase could expect over the next 12 months. The matched analytical dataset was used to develop predictive models and validate their accuracy. All four million active customers on the database were then scored according to these predictions and allocated to an appropriate investment priority segment.

And that’s not allThe matched analytical dataset

was also used to develop and validate a model for estimating the most likely motivational segment for each customer, which was used to flag the segment for each customer on the database and score them with the likelihood of being in that segment. The database scores and flags can be updated readily using the models and algorithms we have developed.

The benefits to Homebase?As a result of this study

Homebase achieved a much greater understanding of customer value and customer motivations. The net benefit was a CRM programme that delivered the right messages to the right customers at the right times to make the most efficient and effective use of the one-to-one marketing budget. Consequently there were significant improvements in the returns achieved from the circa 20 million pieces mailed each year.

Modelling motivations

RIGHT MESSAGE, RIGHT CUSTOMER,

RIGHT TIME



Trevor [email protected]




BIG DATA CASE STUDY - POPULATION FLOWS

Getting to work in Lewisham


Steven [email protected]





Identifying the infrastructure needs of Lewisham residents byanalysing population movements.

A longitudinal approachUsing mobile network data, Ipsos

MORI conducted a longitudinal study to identify the infrastructure requirements of Lewisham residents. Using a sample of more than 26,000 mobile operator customers in Lewisham, we reviewed the flow of groups of residents within the borough over a one-month period. We then analysed the flow of over 7,000 residents who leave the borough during the day – and compared the requirements of these two groups.

We found that around three quarters of residents tend to stay in

the borough during the day, suggesting that infrastructure needs within the borough are just as important as those linking Lewisham to other parts of London.

A key element of this project was correctly identifying residents; requiring a series of rules to be developed and applied. These removed business users and night workers and ensured that groups of customers defined as residents had phones registered and used regularly in Lewisham. This step of adding rigour to the data was crucial to extracting valuable insights. The mobile subscriber data shared with Ipsos MORI was anonymised and aggregated, and all subgroups were greater than 50.

The power of ‘do’ versus ‘say’

This approach offered us powerful insights into REAL flow – which often differs from what people say they have done. Ultimately, the Lewisham example shows that data has the power to enhance our whole way of life, bringing economic growth, wide-ranging social benefits and improvements in how government works.

Looking to the futureThe potential here is truly exciting.

Future applications for social research include: monitoring attendance flow and profile at local events (supporting improvements to events and services); understanding and improving community cohesion and integration; regeneration/urban development (understanding infrastructure needs and evaluating investments); and providing insights around behaviour change (helping design and evaluate interventions).

BIG DATA CASE STUDY - MEDIA/MOBILE ADVERTISING

8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00

Smartphone visitors to bbc.co.uk at Olympic Park during 4th August 2012

Men’s Fours

Robson & Murraythrough to mixeddoubles semi-final

Usain Boltwins 100m heat

Women’s TeamPursuit

Mo Farrah10,000m

Greg Rutherford Men’s LongJump

Jessica EnnisWomen’s Heptathlon

Winning the ad race

Exploring mobile web data to understand the mobile advertising opportunity at live events.

Does location matter? With smartphone penetration now

at over half the UK population, the London 2012 Olympics represented a watershed in terms of how people follow, view and share experiences of live sporting events. By the closing ceremony, it was estimated that 150 million tweets had been shared during the Games, while a notable 29% of those who followed the Olympics on a smartphone did so on the move.

On behalf of Google, Ipsos MORI set out to understand whether location matters to mobile online use and, if so, what specific activities offer the optimal benefits.

A sample of 789,000Over a 17-day period during

the Games, Ipsos MORI analysed aggregated and anonymised online mobile population data from a mobile operator’s customer base.

To begin with, we identified a group of Olympic attendees by logging all mobile activity within range of pre-listed cell towers (repeat daily visitors in each location were removed to exclude those who work / live near the Olympic Park). We then analysed the sample’s online activity over 3G between their entering and leaving the location – as well as 3 hours either side to understand any before and after effects. We compared against control locations and time periods to create benchmarks.

The total sample included 637,000 Olympic attendees and 152,000 non-Olympic attendees.

Windows of opportunityUndoubtedly, attendance at live

sporting events is linked to higher mobile online use than average, with 29% of Olympic attendees using their phone to access the web whilst at the Olympic park. This is compared to just 11% on the previous Saturday before the Games began. Olympic attendees were also 13% more likely to use their mobile to go online whilst at the park than those at the control locations.

This certainly suggests notable sponsorship and advertising opportunities. However, those not attending also displayed changes in online mobile behaviour in reaction to high profile events, which indicates a wider opportunity.

Although not instantaneous, online reactions to key events – as seen in the rise of online use of social networking, news and search – are within a relatively fast 20 minutes of the event occurring. This points to a window of opportunity for advertisers to place messaging in real time, to benefit from these usage peaks.


Adam [email protected]

Ben [email protected]




facing the bigmusic of · market research, crm, efm, etc. traditional market research collects data...

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