IgnitionOne won a Econsultancy Innovation Award earlier this year for their work in web analytics and optimisation.
James Yancey is VP, Global Strategy at IgnitionOne, and we asked him about engagement optimisation…
When you won one of our Innovation Awards for 2012, the judges said that you proved “engagement optimisation is a science, rather than a dark art”. This might be because some people just don’t know what it is, can you strip it down and explain it simply?
There’s a massive issue in digital marketing at the moment, which is an over emphasis on the importance of the last click when attributing value to digital media.
For too long, the last medium a consumer clicked on before converting got all the credit. That’s like giving all the glory to the goal scorer in a football match. A customer’s journey is similar – there are many touch points that aid them on their way to a conversion, so marketers need a way to value this media.
Engagement optimisation helps advance attribution and gives marketers a better way to value their spend. Imagine understanding each player’s contribution in the build up to a goal – measuring engagement helps marketers do this for digital.
Our real time, automated engagement collects data from non-converters as well as converters. It credits each action along the customer journey so we can, for example, work out a strategy to target someone who is highly engaged, but doesn’t convert.
We are scientifically calculating every customer’s value, not holding a finger in the air, as is often the case with attribution solutions.
Can you provide us with some insight into the science bit?
The algorithm is self-learning, comparing the behaviour of the visitor on the site to all of the visitors before them and gauging the likelihood that they will become a converter (propensity scoring).
There are 120 variables that are looked across recency, frequency and monetary value. The key thing is we are predicting future behaviour by considering that likelihood as compared to other converters and non-converters. This method is in contrast to business rules that are based on assumptions (often unjustified or out-dated).
Is this only relevant to enterprise businesses with high traffic?
While any model becomes more accurate with increasing amounts of data. The ideal threshold for best performance is a minimum of 50,000 unique visitors per month.
Who should be responsible for this internally? Is it the job of the performance team? Or should it be someone else?
To realise the benefits of engagement-informed attribution analysis it should be the job of a head of digital or someone who can see across digital channels.
To realise the benefit of optimising media in a specific channel, it is possible for a head of search or Facebook etc. to get immediate value and uncover hidden opportunities.
Is there an opportunity for agencies to get involved in this, to help justify their work for a client?
Absolutely. When you operate only in a one-dimensional environment of conversion, the value of activity leading to that end is erased.
Engagement optimisation offers a spectrum of value and return beyond the old dichotomy of converters and non-converters being translated as good and bad.
Brand marketers suddenly have a much more clear way to gauge value on this spectrum. 97% of visitors aren’t simply non-converters, they are spectrum of aptitude to take action. Not viewing them in such a way is ignoring the vast majority of your visitors for the sake of 3%.
There’s a renewed focus on connecting business objectives to social metrics, but shouldn’t that be applied across the board to any marketing spend?
I think in this case, social metrics (liking posting commenting and sharing) are actions that are proxy values for ROI.
If you speak to most marketers honestly about how they are deciding these proxies, it’s mostly shots in the dark.
Applying proxy values to actions related to any marketing spend is more of the same flaws as sequential attribution. Unless they have way to view the propensity of the actions tied to a visitors leading to an action, it’s just a newer version of the “dark art” that you referenced before.
Why are people struggling to join the dots in terms of multiple channels? It is different technologies, or different ideas of success? Are the needs of different teams internally the real problem?
There are two main problems:
- Most often companies lack one clean unified set of data across all channels measured with the same counting method.
- If they do have this, then it’s related to the first answer in viewing value over-simplistically – arbitrarily giving ascending amounts of credit across first to last exposures.
The bottom line is that you need one data source, and the value of action and engagement across all channels to make it valuable.