As brands are increasingly focusing on “publishing” content on the web, and building communities of people who engage with and share that content, the question remains, exactly what impact does that earned engagement have on brand equity and product purchase. This whitepaper will put forward a method not only to track engagement but to understand how that engagement actually impacts perceptions and behavior.

1. Return
on
Engagement
-­‐
White
Paper
Tracking
The
Impact
of
Content
Lead
Marketing
Philip
McNaughton
Published
20.04.12
w w w . f a c e g r o u p . c o m

2. INTRODUCTION
As brands are increasingly focusing on ‘publishing’ content on the web, and building
communities of people who engage with and share that content, the question remains,
exactly what impact does that earned engagement have on brand equity and product
purchase.
How can we track engagement and help to diagnose what types of content and levels
of engagement actually shift the needle in the way we want it to. What are brands
really looking to achieve when they act this way?

3. RETURN ON ENGAGEMENT
The idea is simple: connecting real time ‘implicit’ behavioural data around our content
and campaigns online with ‘explicit’ survey data captured around a specific group of
people who are engaging with our content.
By tracking a group of real users over time in terms of equity scores and claimed
consumption we can see how engagement with different types of content does or
doesn’t impact on the things we really want to measure – consumption.
To gain even more perspective we can add a 3rd layer of data which is that same
groups’ general social media behavior: brands they like, content they view and share,
to give us a more complete picture of the competitive set for our own brand and how
that impacts on their likelihood to consider and buy us more often.
In short, it’s a synthesis of 3 different data sources, aimed at understanding how
engagement with content actually impacts on perceptions and behavior:
• Database of actual behavior around our content
• Survey data amongst a specific cohort
• Social media data around all online content experienced

4. HOW IT WORKS
To do this a brand has to be able to obtain specific data in terms of a group of users
who are engaging with its content online (usually through a registration process which
leads to a database which can be linked to analytics – actual behavior.)
We can then initiate a multi-stage, multi-level tracking process
1. Pull a specific sample of engaged people or ‘users’ into a short survey capturing
brand equity scores and claimed consumption.
2. Conduct same survey with a control group of ‘non-users’
3. Tap into social media data attached to ‘users’
4. Survey is repeated at intervals (e.g. biannually) with the same people to track
shifts in equity and consumption.
5. Survey and social media data are linked to database analytics to fully diagnose
how behavior and attitudes are affected.
Because we are tracking a very specific group of ‘users’ who we know are engaged
ongoing with our content we can start to see:
• Any impact of engagement on equity and consumption at a macro level
• Specific diagnostics of what types of engagement are correlated with the
biggest changes in equity / consumption
• How different segments of an audience are likely to be affected differently (e.g.
are we just doing better with those who already love us)

5. APPLICATIONS
What can you do with the outputs?
• Fine tune content strategies to isolate those parts that have the most impact
• Understand who and how content strategies are really influencing
• Identify the behaviours we are really looking to influence in the digital space
• Assess the impact of our content in the context of what competitors are doing
• Better understand the audiences and communities we are building online
Going forward, we see the return on engagement model becoming a key metric to
understanding how using content as an engagement strategy can really pay for itself
and how to maximise its impact in the areas that matter most – equity and sales.

6. FOR MORE INSPIRATION
Please contact Francesco D’Orazio at Francesco@facegroup.com or at @abc3d on
Twitter for a chat and take a look at some of our other presentations
The Brand Graph – Dynamic audience mapping through social media data
http://www.slideshare.net/Facegroup/mapping-the-brand-graph-a-study-of-the-o2-audience-
on-twitter
From Data to Insights – Building accurate consumer insights from online
conversations
http://www.slideshare.net/Facegroup/from-data-to-insights-building-accurate-
customer-insights-from-online-conversations