WEB ANALYTICS ROLE IN MEASURING ONLINE ENGAGEMENT
Before we look at what aspects of a customer's online engagement Web Analytics can capture we need to clarify the meaning of the concept of customer engagement.
Definition of Customer Engagement (Engagement Index)
'Engagement' is a word with many meanings (vow, betrothal, involvement etc). For marketing they can be boiled down to a single concept: one-way relation. If x is engaged with y, x is related .
The concept of customer engagement only deals with a particular kind of one-way relationship:
Although 'visitor engagement' is better in that it takes into account non-customer visitors to your website/store, its focus on measuring people's engagement with your brand on your own premises is too restrictive.
It is important to measure the engagement of customers, prospective customers and detractors with our brand, in every they engage with it in.
The subject's relationship with a brand / company / product / consumption topic.
Now that we have defined what kind of relationships customer engagement deals with let's look at the criteria with which we can refine and classify the ways in which customers engage:
A more in-depth examination of kind would reveal its content, usually a mixture of emotional states and rational beliefs, such as, in the case of positive engagement, sympathy, trust, pride, etc
Degree: The degree of positive or negative engagement lies on a continuum that ranges from low involvement, namely, the psychological state of apathy, to high.
An engaged person is someone with an above average involvement with his or her object of relatedness.
With the context setting out of the way. . . . .
What aspect of customer engagement can web analytics capture?
Having defined customer engagement we are better able to delimit what web analytics can and cannot tell us about the engagement of our website's visitors.
Let's look at some of the widely used web analytics metrics and understand what aspect of engagement they capture.
Unique Visits: Shows how many people decided to engage with you for the first time by visiting your website.
of Visit: Frequency must be contextualised within a specific time frame.
A customer who has engaged 10 times with the company in the past 10 years has a lower degree of engagement for example in relation to a customer who has also engaged 10 times in the company in the last 2 months.
Contextualised 'frequency' help us to identify the relative degree of our customers' engagement.
Recency of Visit: This metric speaks of the recency of our customers' last engagement.
Jim Novo has proven that it correlates well with of engagement. A customer whose last engagement with a brand is more recent than that of another is also likely to be more engaged.
Like frames our customers' degree of engagement only relatively.
of Visit: This tells us how many pages long our visitors' journeys through the site were.
Although a deep journey signifies a high degree of engagement this metric again does not distinguish between the kind of engagement.
Do your visitors passionately disagree with what you are writing about? Are they simply unable to find what they are looking for?
In both of these a high degree of engagement may be of a negative kind.
Time Spent on Site: Same story as with depth. Time spent correlates with of engagement but as it does not discriminate between kind it may simply be negatively spent desperately trying to find the content your visitor is after.
Similarly, most online metrics are only able to capture degree not kind of engagement:
1. Subscribing (feed, email, newsletter)
3. Feedback (comments, complaints, inquiries etc)
4. Rating\tagging\filtering\bookmarking its content
5. User submissions (UGC)
6. Printing or downloading a piece of content
7. Brand index
Degree of Engagement
What comes out of the above discussion is that
it is impossible to derive the kind (positive/negative) of your visitor's engagement using web analytics alone, and, therefore, that
when we are talking about customer engagement in the context of web analytics, we are in fact talking about of engagement.
This is not to say that we cannot make inferences and state hypotheses about the kind/content of engagement, based on what we can measure (degree of engagement), nor that these hypotheses are unlikely to be correct.
It is only to say that using web analytics it is impossible to make or support such inferences.
Such inferences, about the kind of engagement, must necessarily be informed by considerations that lie entirely outside the field of web analytics.
Before we begin making inferences on the basis of of let's discuss this metric a bit more.
Following a number of leading Web I also believe that a customer's degree of engagement is better calculated as a synthetic metric composed of several basic metrics, rather than as a solution e.g. measuring customer engagement by means of 'duration of visit' only. This requires an argument unto itself that will not be pursued here.
The score each of these component metrics takes however only makes sense if contextualised. Example: a frequent and recent visitor is 'more engaged than' someone who is not, but is he engaged? If yes how engaged is he? There is little we can do with relative statements such as this.
In order to make such statements meaningful and we need to contextualize the component metrics that constitute a customer's degree of engagement on a high/low continuum, beginning with apathy and proceeding with progressively higher degrees of engagement.
This means that both the lowest (apathy) and the highest degree of engagement need to be defined. The easiest way to do this is to define the of engagement (the average score for several metrics of your choice across your site or based on a competitor-specific or industry-wide benchmark), considering everything that falls short of it as (increasing degrees of) apathy and everything beyond it as (increasing degrees of) engagement. In this a customer's degree of engagement assumes a non-relative meaning (it remains of course relative to your website's, competitor's or industry's historical performance).
By inserting relative statements such as 'x is more engaged than y if and only if x does z and y does >z' into a continuum that is based on website \ competitor \ industry benchmarks, it is possible to provide a reference point which although relative in itself (historical performance) is sufficiently stable and pertinent to business performance, to provide with useful insights into visitor behaviour and business\campaign success. (Substitute z with any or an aggregate of the visit metrics score(s)).
No web metric, or of metrics, can discriminate between kind of engagement i.e. positive engagement. This requires primary research.
All web metrics can do is discriminate between relative degrees of engagement.
Basic metrics can only discriminate between low degrees of engagement.
A customer with a high score in his visit metrics may nevertheless feel apathetic towards the brand.
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