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