Is it Engagement or Likelihood to Convert?
26/10/2012 | Written by | Categories: Analytics Philosophy

Engagement is a buzz word. It is a quest. It is an altar at which many worship.As is frustratingly typical, Avinash says it better than I can.  Engagement is one of those jargon words which are used very freely (including by myself) within Digital Analytics without really meaning much.  I did go so far as to include Engagement as one of the MeasureCamp swear words.

According to Eric Peterson, “engagement is an estimate of the degree and depth of visitor interaction on the site against a clearly defined set of goals”.  The common metrics used to measure this “degree and depth of visitor interaction” are frequency/recency of visit, the number of pages viewed and the time spent on the website.  Or you can create a calculated metric taking multiple factors into account as Eric did…

Unfortunately, engagement is actually fairly meaningless – you don’t get paid for engaged visitors.  These are nice metrics and it is great to show people care more about your website and you can prove this as they are visiting more frequently and spending more time on it.  But it detracts from your real purpose of having a website.  Multiple people have tried to tie engagement to ROI but it is still missing the point, your boss doesn’t care if visitors are engaged, s/he cares if they converted or not (however you define conversion).

Changing the Name = Changing the Focus

So what we really care about is whether a metric suggests the visitor is likely to convert in the future or not.  Therefore I say we should start calling these metrics Likelihood to Convert (LtC) metrics, not Engagement metrics.  Is this just semantics?  Well yes, but by changing the language, we change both of the purpose of our analysis and how we communicate with non analysts – i.e. the management team.

For a simple example, a basic engagement metric is Frequency of Visit.  So a visitor who is visiting on a regular basis is more “engaged”.  Brilliant.  Does it mean anything?  No.

But change the question to whether visitors who visit on a regular basis are more likely to convert.  Now it becomes interesting.  If this link exists, then frequency is a predictor of conversions and it should be described as such, i.e. as a LtC metric.  If you can’t demonstrate this link, then frequency may be a nice warm fuzzy metric but it doesn’t add value.

If you announce to the management team that Conversions remain steady but the key Engagement metric of Frequency has dropped 10%, they are likely to say so what, it was a good week.  If you announce that Conversions remain steady but the key LtC metric of Frequency has dropped 10% indicating Conversions will drop in the future, you will get a reaction (and ideally actions).

Really?  Is there really a difference?

I admit, I have had to think through this a few times myself to really understand what the difference is between Engagement and LtC.  The answer lies in which metrics qualify for each, what they represent and how they can be used.

Ask any analyst what Engagement metrics are and they will list off frequency, time on site, average page views per visit, etc.  But do these metrics indicate the visitor is more likely to convert – yes for some websites but not for other sites.  So Engagement metrics are not automatically LtC metrics.

Instead you need to cast the net wider for relevant metrics and include those normally described as micro conversion points.  Example here include view Product pages, create Baskets, interact with tool X, view Contact Us page and View Video > 90 seconds.  These wouldn’t be considered Engagement metrics but they can definitely indicate that the visitor is likely to convert at a later date.  Again not on all websites, so we can say that LtC metrics are business specific.

As to how they can be used, a true LtC metric trends with or in advance of conversions.  They can be used as predictors of future business performance and as warning signals that an issue is developing – so the issue can be reacted to before it impacts business performance.  They can also be used as an evaluation or success metric e.g. certain campaigns are expected to move a particular LtC metric, not directly deliver conversions.

Your Thoughts

So the key elements of LtC metrics are:

  • There is no typical list of these metrics, instead they are specific to a business
  • They must trend with the number of conversions, either current or in advance
  • They can be used as predictors of future performance

Do you agree?  Does changing this name change the way you or your management team will look at and use your Digital Analytics data?  Or is Likelihood to Convert just a new description of the old Engagement buzzword?

4 responses to “Is it Engagement or Likelihood to Convert?”

  1. Tim Wilson says:

    Nice post!

    I like the idea of using what could look like a semantic change to force a deeper conversation about what really matters for the site, and, once that is crystallized, what the right measures are. I kept reading “LtC” and thinking “purchase intent” — CPG brands regularly have to rely on voice of the customer data to *ask* visitors if they’re likely to convert in the future. From that perspective, what you’ve eloquently articulated here seems to be aligned with what VOC vendors regularly tout (and regularly get frustrated that they’re not being heard) — survey responses, integrated with behavioral data, can point towards relevant correlations between different “engagement” metrics and meaningful business results.

    Kudos for getting Eric’s formula into a post!

    • Peter O'Neill says:

      Thanks Tim for the positive feedback. It is weird, it is purely a semantic change but I think it really can help to focus on what is important. Purchase Intent sounds very similar except a point I forgot to include is you can easily incorporate different “likelihoods” with the LtC name (probabilities), again a semantic point but if you are already thinking in terms of likelihood. I hadn’t thought about the VoC aspect but does fit in nicely. Discussion to be continued…

  2. You and your readers are measurement experts. This means you have the tools, and the data, to tell the rest of us how we should view this topic.

    In other words, you have the skills/data/software necessary to change this from an opinion piece to a case study. Take the data driven approach so often touted on Twitter to solve this problem!! Be the person who solves it!

    • Peter O'Neill says:

      Excellent response to a theory Kevin – now go prove it. And you are right, I need to take this from an opinion to a case study where I can demonstrate how it adds value. I will say that not all data is available – without universal online/offline login using a single person ID – but it is the nature of the industry to use what data is available (and caveat appropriately). I don’t think this is an easy problem to solve but nudge me in six months if I haven’t published a follow up with practical real world examples.

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