Jonathan MacDonald is one of London’s best known social media / mobile media professionals. Jonathan has just written a book called Every single one of us

Would recommend it. Like many people I know, I follow Jonathan’s thinking in Social media and there is an online version and a print version.

A sad day for mumbai. Terrorist attacks kill at least 80 people and chief of Mumbai Anti terrorist Squad Hemant Karkare, also killed

Extending the ideas from my last blog ..

The idea of Digital footprints has increasingly been discussed in society – mainly from a privacy/data protection standpoint. However, we all agree that we are increasingly leaving a larger Digital footprint over time especially after the rise of social networks like Facebook, MySpace etc.

A Digital footprint is the persistence of data trails online by a user’s activity in a digital environment – which John Batalle eloquently calls Clickstream exhaust . According to the Pew Internet report , there are two main classifications for digital footprints: Passive digital footprints and Active digital footprints.

A passive digital footprint is created when data is collected about an action without any client activation, whereas active digital footprints are created when personal data is released deliberately by a user for the purpose of sharing information about oneself.

On the Web, many interactions leave a digital footprint such as creating a social networking profile, commenting on a picture in Flickr etc. In a mobile context, CDRs are the transactional data that constitute the user’s digital footprint. But, the mere availability of transactional data alone is not enough since privacy and data protection rules will apply to the usage of data(and rightly so!).

There is a paradox with privacy – on one hand everyone fears losing privacy. Scott Mc Nealy of Sun Microsystems famously said that: consumer privacy is a red herring – we have zero privacy – and we should all get over it. – A view that has gathered credence after 9/11. Ester Dyson argues that we need more granular control over our data . Esther Dyson says that the notion of privacy doesn’t fully capture the challenges of the current environment online and says that – “We need to stop talking about privacy and start talking about control over data,” she says, and argues that, in the future, users are going to want more granular control over their data–making detailed decisions about what gets shared with whom that more closely reflect the distinctions we make in offline life. “Users may be overwhelmed when first setting up an account, but when they get more comfortable with an application, they will exert more control.”

On the other hand, we all have an incentive to contribute data about ourselves so as to be more visible on the Web reflecting a manner which we want to be seen. For instance, even if you do nothing else except create a profile on linkedin , you are immediately visible on Google. Hence, you are contactable. While you have left a ‘digital cookie trace’ – you have created a positive reputation about yourselves which is controlled by you(besides being now findable on the Web). Using the same rationale, we all have a positive incentive to contribute to the Web since it enhances our own reputation and our searchability.

The question for providers is: How do we make the best use of digital footprints to serve the customer – keeping privacy issues into consideration? or how to get from passive digital footprints to active digital footprints?

As we have discussed before, Data patterns: The synergies between mass media, social media and mobile media, Social media marketing campaigns are the driver to increasingly understanding the participant’s digital footprint through a two stage process: Firstly to identify certain behavioural patterns in data and then secondly to verify the observations by specific social media campaigns which seek to also get permission from the customers if possible.

In this scenario, the provider is sending personalised messages to the receiver and over time, the visibility of the participant’s digital footprint grows and leads to better personalization which adds value for the receiver. The data patterns provide a clue but the verification is tied to the campaign and to interaction with social media. Hence, it is important to look at the interplay between media silos rather than focusing on the developments for a specific media type.

We are starting with passive digital footprints (based on behavioural data patterns) and transitioning to active digital footprints (based on trust)

We show this idea below. In the data pattern stage, many of the customer preferences are ‘potential truths’ but we can only verify them once we get validations from Social media campaigns. For example, we know that the participant could be potentially interested in Sopranos, Golf and Ferraris based on their behavioural analysis but we can only verify this when we run the Social media marketing campaigns based on the actual campaigns.

We thus transition from passive digital footprints to a more trusted, permission based model of active digital footprints through analysis of behavioural data patterns and social media marketing

We are seeing the emergence of three distinct media types: Mass media(TV), Social media(Social networks) and Mobile media.

Often these media types are discussed in isolation – but they make a lot more sense when viewed holistically in a converged ecosystem from their underlying data patterns

The idea is: Spot the patterns in data usage and validate the patterns using promotions. Do this over as wide a network as possible(either a converged network or in partnership with a broadcast media channel)

The goal of any media is to get the receiver of the message to take action – ideally a purchase decision(but not necessarily so)

Broadcast media/ Mass media(TV, newspapers etc) are great for brand building. They are best placed to get the message across to the widest section of the public. One could argue that mass media is ‘interruptive’ in nature – but without an interruptive message – I can only know what I already know – but I cannot know what I do not know ..(At the risk of sounding Rumsfeldesque .. ). On the other hand, a message that can be personalised to the recipient is truly useful to them. So, the goal is to work with these two end points

Also, irrespective of how the message is received, ideally the two ends of the media cycle need to co-relate i.e. for every recipient of the message – there should be a corresponding action. That action should be tangible and measurable in some way – i.e. you need to be able to measure how many people received your message and how many of these people took action based on that message.

Traditional media focussed on these two ends of the equation.

Into this mix comes social media and mobility – both of which have a unique and a disruptive effect. What I am trying to show below is that they have a synergistic effect when viewed together:

The effect of social media is as follows:

The defining characteristic of social media is it’s two way nature i.e. social media participants are not passive consumers of the message but also potentially creators of the message and indeed have the ability to mutate that message. So, social media could be the reinforcer of the message or it could be the destructor of the message. Hence, on one hand we get the viral effect (reinforcer) and on the other hand we get the death spiral for the message (destructor)

The effect of mobility is also very disruptive ..

The mobile operator /provider has the opposite problem from the broadcaster. They know the customer individually but do not know much about that customer. Specifically they do not know the user’s preferences which can be used to create a more personalised message (and by extension a message that is potentially useful to the receiver)

So, the approach could be: Spot the patterns in data usage and validate the patterns using promotions. Do this over as wide a network as possible(either a converged network or in partnership with a broadcast media channel)

Mobile media complements mass media (like television) by providing the individual personalization which mass media lacks. This can happen in potentially three ways – all these ways are interconnected

a) Patterns: Spotting behavioural patterns

b) Promotions: Specific promotions to validate observations from the patterns

c) Partnerships: Creating partnerships between traditional media and mobile media to get as wide a network as possible(or extend your own network in a converged ecosystem)

Patterns: Patterns in usage can be gleaned by merging data streams from outside the telecoms network along with Telecoms data (CDRs).

Consider program viewing schedules. Suppose an Operator analyses their data based on program viewing schedules and discovers that at a certain time every week, a group of people on their network communicate via text message.

If that time co-incides with the transmission time of a specific program(say a TV serial) – then potentially these customers are fans of that serial and are communicating with each other as they watch the program.

This information can be validated via promotions i.e. specific promotions designed to test the hypothesis with an opt in clause for further promotions.

Finally, a telecoms provider can get insights if it partners with a TV station. They can also extend the scope of the network itself beyond mobile. This can be done by offering fixed network services, IPTV services etc – either on their own or in partnership.

The key is to get a single view of the customer from their data patterns and / or to get an incrementally detailed view of their customer through their social interactions(who they interact with) and behavioural interactions(what type of behaviour do they exhibit – which may need external data sources) – and then refine that analysis by specific promotions to validate the observations.

The wider the network they can do this over(either on their own or in partnership) – the more effective the result

Many thanks to Tomi Ahonen for his feedback on the mobile related ideas in this blog

At least two women I spoke to last week said that they love the iPhone and wanted to upgrade to one ..

In contrast, I am not a fan of the iPhone .. and actually not very a fan of visual/ touch interfaces etc per-se

I suspect I am not the typical user of devices and appliances.

For instance, I started developing on Unix and never even got comfortable with Windows .. i,e. Graphical user interfaces ..

I kept going back to command line prompts since it was faster to work that way and in the times I worked as an Oracle DBA, this was cool i.e. I never really needed to work with the visual elements

So, what I am asking is:

a) Is there a male/female usage difference for the iPhone?

b) Perhaps there is a place for the anti-iPhone i.e. a complex phone driven by simple textual commands(think ‘vi’, ‘shell scripting’ etc here) – and I hope that there may be at least some people will relate to this

The two topics are not related .. i.e. women may well be users of the proposed anti-iphone .. but these are more random thoughts in my own mind

I have been following the Web 3.0/Internet of things debate at the European Union(and indeed am a part of it based on my various activities) – and hence this is a fascinating post from Tim o Reilly which talks of sensor based interaction and Cloud integration

>>>>>

And that of course is the future of mobile as well. A mobile phone is inherently a connected device with local memory and processing. But it’s time we realized that the local compute power is a fraction of what’s available in the cloud. Web applications take this for granted — for example, when we request a map tile for our phone — but it’s surprising how many native applications settle themselves comfortably in their silos. (Consider my long-ago complaint that the phone address book cries out to be a connected application powered by my phone company’s call-history database, annotated by data harvested from my online social networking applications as well as other online sources.)

Put these two trends together(sensor based interaction and cloud integration) , and we can imagine the future of mobile: a sensor-rich device with applications that use those sensors both to feed and interact with cloud services. The location sensor knows you’re here so you don’t need to tell the map server where to start; the microphone knows the sound of your voice, so it unlocks your private data in the cloud; the camera images an object or a person, sends it to a remote application that recognizes it, and retrieves relevant data. All of these things already exist in scattered applications, but eventually, they will be the new normal. This is an incredibly exciting time in mobile application design. There are breakthroughs waiting to happen. Voice and gesture recognition in the Google Mobile App is just the beginning.

Hello

Is there a Carnival(like carnival of the mobilists) for trusted information sharing? covering privacy, trust, reputation, P2P, Government, social factors, regulation, best practise, technologies etc? This is a subset of Enterprise 2.0 but with a different emphasis and within a community setting

Apologies for the delay in posting .. I have been a bit tied up with some business and personal issues. Getting back on track this week.