Image of a honey badger - social data is the 'honey badger' of personalisation

It’s digital marketers’ ultimate prize: consumers fill up the internet with their ideas, preferences and interests. (This is the big, big data, people!)

Turn that into real-time offers on your website, and jackpot! Angels sing. All the web’s kittens dance. And you’re badder than honey badger overnight.  

 A whole host of technologies have emerged to make this happen. Marketers have no problem finding solutions to personalise their websites.

The core challenge remains: what’s the best way to do personalisation?

Go to the source data

In June last year Econsultancy and Adobe published ‘Personalisation, Trust and Return on Investment’, which concluded:

  • Over 52% of respondents agreed that ‘the ability to personalise content is fundamental to their online strategy’.
  • 41% are ‘committed to providing a personalised web experience’.
  • Only a small proportion of companies are actually able to provide personalised content in real-time. 

The survey explored the sources of data brands use for personalization. Of these, only 6% of brands use social graph data. Significantly more users of social data felt the impact on ROI was high than the equivalent proportion of users of any other source of data (such as purchase history or web behaviour).

 Graph

Impact on engagement told the same story. 88% said that impact was high when using social data whilst 74% said the same of purchase history and 72% of behaviour on your web properties. 

Econsultancy & Adobe Personalisation Report - Figure 6 

The social data uplift

What’s the difference with social data?

First of all, it’s the best proxy for real life we have, and therefore both a broader and a more accurate expression of intent. Social networks are full of user-generated content – content that people give (for the most part) without gain.

Once you’ve filtered out “likes” for discounts and offers, you have the most holistic source of intent around. It reaches across relationships, interests and roles, and spans trends. 

Purchase data says “I purchased something before” and behavioural data says “I can find a pattern by looking at where you click”. This tells us what specific purpose a customer had in coming to the site once – seldom a great indicator of who a person is, what she loves or what she wants to do next time.

It’s one moment in time. For loyal customers, you can build up a persona in time, but not in real-time (and I’d argue, not to the same quality or accuracy).

If you combine behavioural data with purchase data, you get an improved picture; stores like www.bestbuy.com in the US can do this very well. Some do it too well

If you are really trying to get at a customer’s intent or real interests, you want to identify something important to them, but not transitory. Ideally, it provokes pleasant surprise, serendipity. To give this point some context, an anecdote: Having grown up in Kenya, I have a great affinity with Africa in general and Kenya specifically.

It’s not something most people who aren’t close to me would realise. But if you presented interesting articles about Kenya to me on a news site, for example, or art on a retail site, I would be delighted and most definitely delve deeper. 

When looking at personalisation, one of the most important filters is recency. Ad re-targeting is based on the concept of recency and uses reminders to take you back to a site that you have recently left.

Done badly, this can be distracting and annoying. Done well, it can be effective. Using social data is a much more subtle way of doing this, offering someone products or services that are relevant to a user “right now”, after they have recently talked about it on Facebook or shared it on Twitter. 

Marketers are always looking to a find way to target customers at the right time – something that can’t be solved with behavioural or purchase data alone. It can be done with social data, though. 

The social data discipline

Social data does come with some caveats. For example, we tend to operate in spaces – e.g. my friends’ space (Facebook), my work space (Linkedin) and my family space (a private network).

For a great read on this topic, I’d suggest a book called ‘Grouped’ by Paul Adams or some of Danah Boyd’s earlier work on Facebook. Social networks tend not to overlap much between these spaces (we seldom use Facebook for work) so – in deciding which social data to extract and use for your site personalisation – decide what sort of intent is right for your business. 

Using social data to personalise the web experience is a new discipline – it’s an emerging science at the cross roads of behavioural psychology and data.

Because it’s new, it’s experimental but extremely exciting, and as the Econsultancy & Adobe paper and subsequent infographic reveals, the propensity to affect a good result is by far the greatest from social data compared to other forms of web data available today. 

I’d love to hear your comments and feedback on this topic.