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    This is the personal blog of Simon Kendrick and covers my interests in media, technology and popular culture. All opinions expressed are my own and may not be representative of past or present employers
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Download the award-winning Brandheld presentation

As mentioned in my previous post, I had quite a successful experience at the Media Research Group conference in Malta.

My presentation on Brandheld: Unlocking the potential value* of the mobile internet, which won the IPA/Simon Broadbent award for Best Paper can now be viewed and downloaded on Slideshare. I’ve even included an amended version of my speaker notes, although due to the terms and conditions our research participants agreed to I am unable to show the three (frankly awesome) videos we produced.

The presentation is embedded below (RSS readers might need to click through to see it)

Additionally, most of the other presentations from the conference can be downloaded from the MRG website. It’s definitely worth checking out, though the presentations that were speech accompaniments rather than slides/handouts don’t make a lot of sense without the accompanying notes.

Any feedback or (constructive) criticism would be appreciated. My contact details are on the final slide, or on the “About the blog” page if you don’t want to do so publicly.


* The title occasionally switches between “potential value” and “value potential” – the former comes to me more naturally but the latter is probably better


The mobile phone is the drill to extract the data

Last week I wrote a blog post entitled “If data is the new oil, we need a bigger drill“, where I complained that we weren’t making enough use of the potential data available to us.

That post was in relation to online research. But on reflection, the opportunity is far greater elsewhere.

On the mobile phone.


And, as far as I am aware, it is an area even more underexploited than online data capture. Aside from the odd application (such as Everyday Lives – which looked very similar to Evernote last time I looked), mobile survey panels (such as One Point) or academic experiment (Contextphone in Helsinki), I’m not aware of any innovations in mobile.

Which is a shame, since it is arguably the most powerful media platform for data capture. The Wikipedia page on the 7th mass media lists the eight unique benefits mobile has. Of most relevance are that the mobile is

  • Always on
  • Always (well, usually) carried on the person
  • Available at the point of creative inspiration
  • Highly personal, and personalised

The unique benefits of mobile make it an ideal instrument for both active and passive data capture – for explicit answers and for implicit inferences.

Forms of data capture

I’ve drawn an arrow below of the five primary means a mobile can capture information. It is very much an early draft, so feedback or criticism is very much appreciated.

Ways in which data and information can be extracted from the mobile phone

Background capture

As mobile technology advances, devices incorporate more features that produce information on the location of the phone, and thus the user. These include:

  • Time – the time itself, and the time it takes to do something via a clock and stopwatch
  • Date – via a calendar
  • Space – via GPS
  • Proximity – to people, objects or events via GPS, bluetooth or RFID chips
  • Movement – in three dimensions via an accelerometer, or inferred through GPS and clock
  • Environmental factors – through thermometers, altitude readers and so forth

In addition to location, the following can also be determined through past or current behaviour:

  • Spend – via the in-built payment mechanism
  • Social graph – via the address book
  • History – via cookies or memory
  • Broad character traits – by how the phone has been customised or used

While in future, these will be augmented with innovations such as voice and face recognition (a Google Goggles type of service).

Either on their own or in combination, these features facilitate some extremely powerful data capture. They effectively allow us to understand the “where” and “when”, and potentially the “with”.

But it is only the first level of information capture.

Activity Capture / Activity Follow-up / Prompted Activity

I’ve grouped these three stages together, as they are essentially variations on a theme.

The mobile phone has a large number of features and services that can be used for data capture. These include

  • Voice calls
  • Text messaging
  • Voice recorder
  • Note taker
  • Calendar
  • Bluetooth
  • Games
  • Camera/scanner
  • Video camera/editor
  • Music player
  • Web browser
  • Email/social network use
  • Application downloads/use
  • Shopping/purchasing

The most passive form of data capture is in recording the functions that a person uses their phone for. Forms of analysis this facilitates include

  • Combining activities with the data dimensions outlined in the first section for understanding of individual uses
  • Utilising path analysis across all feature uses to understand how the phone is used as a single device, rather than as a collection of services
  • Converting phone calls to text, and then using sentiment analysis to infer meaning across all forms of communication.

These aspects augment the “where”, “when” and “with” with the “what” and “how” – at least in terms of mobile phone behaviour.

A slightly more active form of data capture would move closer to capturing the “why”.

For instance, a push notification could be triggered when a certain activity is undertaken. This could request a simple answer to a question.

For instance, if I were to use my camera to take a picture, I would know

  • Where it was taken
  • When it was taken
  • What it was taken with
  • How it was taken (landscape or portrait, flash or natural, first attempt or fifth)
  • Potentially who/what it was taken of
  • Potentially who the person was with at the time

But it wouldn’t be known why the photo was taken, or whether the person was happy with the photo taken. A simple question or two would solve that.

An even more active version of data capture would be to explicitly ask the person to use their phone for a particular person. For instance, they could be asked to use the camera to scan each item they buy on the high street or to use the voice recorder or note taker each time they spot a certain advertising campaign. These methods are used by a couple of organisations – MESH spring to mind – but have little noticeable traction to date.

This manual mechanism may eventually be superseded, as technology allows us to automate more of the data capture. Its only real relevance would be in forcing someone to participate in a behaviour where they naturally wouldn’t.

Direct questioning

As should be obvious, the more explicit forms of data capture are those that are most prevalent – primarily because their implementation is independent of technological advancement. For instance, we’ve always been able to interview people over the phone. As technology improves, the interfaces underpinning this method will also improve – we will move from SMS surveys to java to html to html5 or native applications, with touch screen drag and drop functionality.


As I mentioned in my previous post, we aren’t close to reaching the level of data capture that is possible. We need to augment explicit questioning with the context that can be inferred from the situational data collected. The mobile phone, moving across space and time and with its unique benefits, offers even more scope for collecting meaningful data.

Potential uses for the data capture include

  • Calculating sleep quality/efficiency (an iPhone app already does this, to a degree)
  • Monitoring movement, speed and proximity of people across an environment could be used for town planning
  • Alternatively, it could be used to plot the efficiency of layouts in supermarkets. If the phone could calculate eye line (it would probably need to be attached to a necklace), it could even inform how the shelves are stacked
  • Providing an understanding of people’s lives – when using and not using their phones.
  • Exploring how things spread across mobile phones. For instance, one person could undertake a type of behaviour, come into contact with someone else, and then the second person undertakes the behaviour. Network effects could be used to identify the mythical influencers
  • Tracking spend can be used for financial management
  • A networked calendar/diary could become predictive e.g. rescheduling a meeting to take place 15 minutes later due to traffic
  • Tracking movement can improve the measurement of exposure to outdoor advertising
  • Sound recognition could be used for radio or TV exposure, and improve out-of-home consumption measurements
  • Inefficiencies of usage could be explored e.g. the time it takes to connect a phone call can be compared across devices and networks

Practical obstacles

Evidently, the previous section was quite speculative and fantastical, but I hope it underlines the potential. Nevertheless, several obstacles need to be overcome before this point is reached

  • What is the best way to collect such information? Within the operating system? The network/SIM? Via the web or an application? The O/S with control of the API would appear to be best placed, but do they have the inclination?
  • Although phones are always on and regularly used, they are also regularly upgraded. Information collected would need to be portable for long-term tracking
  • Similarly, a phone is more susceptible to breakage, theft or loss
  • Background data capture would be a tremendous drain on the battery
  • Effective data capture would require an entire network of people using it – this is highly unlikely, not least because there will always be a significant proportion of people for whom a mobile phone will just be a device to make and receive emergency calls
  • More behaviour will be transferred to the mobile, but it will only ever capture a small proportion of our lives
  • Coverage and connectivity isn’t good enough (in the UK) for full capture – unless information can be stored natively before it is uploaded to a central server
  • Massive issues of data protection and privacy. Some people (such as Nicholas Felton) would enjoy tracking their movements, but I suspect – outside of paid-for testing – few would appreciate it. Particularly since the mobile is the most personal of devices Imagine if large corporations were able to track the movements and social graph of its employees through mobile phone usage?


This post seems to have been sidetracked into future gazing, but my underlying point remains. The technology is available for us to capture far more information – and thus understanding – then we currently do. Organisations should look to harness and utilise this data, to provide contextual meaning to what people are doing.

Thoughts on how we could do this – or on how people are already doing this – would be much appreciated


Image credits: http://www.flickr.com/photos/kioan/3011984637/ and http://www.flickr.com/photos/_parrish_/2575256484/

The perception of disruption

Network effects hasten the rate of innovation. Therefore, the rate of technological change is faster now than it has ever been (at least if my memory of Solow-style exogenous growth models is correct.

This tends to be iterative. Small, continual improvements that improve the efficiency of processes and provide new opportunities for people to achieve their desires.

But, over time, this can be problematic.

Particularly with user perceptions.

The core proposition (and branding) of a product or service will try to remain fairly constant. But feature creep will bloat and complicate.

It is even possible that some innovations will supersede the original benefit in terms of usefulness and relevance, but it gets lost in the perceptions of users since it is only additive to the core proposition.

In order to focus upon the most useful innovations, a disruption is necessary. A break with the past.

Mobile is a good example of this.

Mobiles have evolved at a rapid rate. They got smaller as technological processes improved but then bigger as new features emerged. Cameras, music players and internet connectivity all augmented the core proposition – a device to make calls on, wherever you are.

But the internet has superseded the phone network. Email and social networks (and Skype) sit alongside voice and text, along with the numerous other benefits the mobile internet offers.

And a disruption was needed to make these innovations apparent. Because ownership doesn’t equate to usage.

This disruption was led by the iPhone.

Nokia has tended to lead technological innovations, but Apple repackaged the device. It brought back usability and simplicity, with the mobile internet at the core of the offering.

Nokia, Sony Ericsson and Samsung (NSS) may offer “smartphones” or internet enabled phones. But they are perceived fundamentally differently to the disruptors – Apple, BlackBerry and HTC/Google (ABH).

NSS represents an easy choice – a safe upgrade on something familiar with. The bells and whistles may be a bit shinier, but the phone is basically the same. And behaviour remains similar.

ABH are disruptive. They represent a new type of phone. People will think more carefully about switching. The benefits are framed in what is different or better to their current phone. Once they have invested, this behaviour needs to be justified and so they utilise the functionality. Behaviour changes.

The data from Essential Research’s Brandheld study illustrates this.

Looking purely at those claiming to own a smartphone (we gave them a consumer friendly definition outlining benefits; many wouldn’t know whether their phone allowed third party apps to be developed), there was no real difference in claimed internet use via a computer. ABH owners spend 25 hours a week online; NSS owners spend 24 hours.

But when the data for mobile internet usage is explored, a different story emerges.

  • 65% of ABH smartphone owners access the mobile internet every day; 29% of NSS smartphone owners do so
  • 78% of ABH smartphone owners access the mobile internet at all; 63% of NSS smartphone owners do so

The ABH figures are actually skewed by BlackBerry. 87% of iPhone owners say they use the internet on their phone on a daily basis. They are also far likelier to use services such as games, maps and commerce based services.

Is there hope for the incumbent? I’m not so sure. Clay Shirky noted, with regard to media companies, that there is no incentive to disrupt the core business model. Executives are used to things working successfully in one way, that they will seek to protect this for as long as possible rather than embrace the risk of the new.

Can this be combated? Maybe, but maybe not. It seems to be cyclical. Eventually the disruptor becomes the incumbent, and the process repeats itself.

On a sidenote, as previously mentioned I don’t think the iPad will disrupt the computing space. It is disrupting a market that is nascent; the mobile market was well established before it was disrupted. If anything, I think the iPad will just precipitate touchscreen laptops.

The data I used above was from Brandheld. More information about the project can be found here, and I’ve included a Slideshare presentation below that indicates some of the key findings (Although I worked heavily on the project and analysis, I didn’t write this document. As you can tell. I don’t possess Keynote and I would never include the word “insight” in a presentation)


Image credit: http://www.flickr.com/photos/jesse_sneed/2383953694/