Category Archives: Analytical Culture

Measuring dis-Engagement

Analytical Culture, DataBase Marketing, Marketing thru Operations, Measuring Engagement

Engagement Matters – Until it Ends.  Right?

Here’s something that continues to puzzle me about all the efforts around measuring Engagement and using these results as a business metric or model of online behavior.

If Engagement is so important to evaluate – and it can be, depending on how you define it – then doesn’t the termination of Engagement also have to be important?  If you desire to create Engagement, shouldn’t you also care about why / how it fails or ends? And if the end is important, what about how long Engagement lasts as a “quality” metric?

Seems logical the end of Engagement might matter.  Let’s call it dis-Engagement.  Simple concept really: of the visitors / customers that are Engaged today (however you define Engagement), what percent of them are still Engaged a week later?  3 months or 1 year later?

Whatever dis-Engagement metric you decide to use, a standard measurement would create an even playing field for evaluating the quality of Engagement you create.  From there, a business could invest in approaches producing the most durable outcome.

Since Engagement is almost always defined as an interaction of some kind, tracking dis-Engagement could be standardized using metrics rooted in human behavior.  Recency is one of the best metrics for an idea like this because it’s universal, easy to understand, and can be mapped across sources like products and campaigns.  Recency is also predictive; it provides comparative likelihoods, e.g. this segment is likely more engaged than that one.

Plus, using Recency would align online customer measurement with offline tools and practices.  This could have implications for ideas like defining “current channel”, e.g. customer is now engaged with this channel, has dis-engaged from that channel.

Taking this path brings up a couple of other related ideas, in line with the discussion around customer journey and entwined with the whole customer experience movement.

Peak Engagement

Let’s say there is Engagement, and because we’re now measuring dis-Engagement, we see Engagement end.  So, is Engagement a one-shot state of being, meaning the value should be measured as such?  Or, does longer lasting Engagement have value, and if so, what about when it ends? Shouldn’t we want to find the cause of dis-Engagement?

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Marketing Responsible for Customer Experience?

Analytical Culture, Customer Experience, DataBase Marketing, Digital Analytics, Marketing thru Operations

 The Data

According to this survey, Marketers are not now really “responsible”  for the customer experience (whatever responsible means in this context) but will be over the next 3 years.  If it was just the vendor (Marketo) trumpeting this idea, I’d be more skeptical.  But this vendor hired the Intelligence Unit from The Economist organization to do this work and the report includes the actual questions, meaning you can check for bias.  Population is 478 CMO’s and senior marketing executives worldwide, seems decent / not cherry-picked.

So I will cut the vendor some slack.   Questions though, right?  Just what is customer experience, in particular for the purposes of success measurement?  How does it fit with related ideas like Customer Journey / LifeCycle and Engagement?  Certainly if the above is a significant macro trend we ought to sort this all out first?  And of course, putting some analytical rigor (structure, process, and definitions?) in place to support the effort ;)

The Story

I know a lot of marketing people who have either had this authority for years (multi-channel database marketing) or are moving in this direction, so the results make sense to me.  To be clear(er), “experience” for these people reaches all the way back from UX into fulfillment and service.  So when they talk about experience, they are talking visitor and customer; not just navigation and landing pages, but also shipping times and return rates.

Perhaps increased access to customer data is revealing the significant impact customer experience in this larger sense has on long-term customer value?  This idea, coupled with increased focus on accountability (also covered in the survey) could be driving this trend.

Worth the read, only 20 pages long with a lot of charts.  Here’s 4 snippets to hook you:

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Is Your Digital Budget Big Enough?

Analytical Culture, Digital Analytics, Measuring Engagement

At a high level, 2014 has been a year of questioning the productivity of digital marketing and related measurement of success.  For example, the most frequent C-level complaint about digital is not having a clear understanding of bottom-line digital impact. For background on this topic, see articles here, here, and here.

I’d guess this general view probably has not been helped by the trade reporting on widespread problems in digital ad delivery and accountability systems, where (depending on who you ask) up to 60% of delivered “impressions” were likely fraudulent in one way or another.  People have commented on this problem for years; why it took so long for the industry as a whole to fess up and start taking action on this is an interesting question!

If the trends above continue to play out, over the next 5 years or so we may expect increasing management focus on more accurately defining the contribution of digital – as long as management thinks digital is important to the future of the business.

If the people running companies are having a hard time determining the value of digital to their business, the next logical thought is marketers / analysts probably need to do a better job demonstrating these linkages, yes?  Along those lines, I think it would be helpful for both digital marketers and marketing analytics folks to spend some time this year thinking about and working through two of the primary issues driving this situation:

1.  Got Causation?  How success is measured

In the early days of digital, many people loved quoting the number of “hits” as a success measure.  It took a surprisingly long time to convince these same people the number of files downloaded during a page view did not predict business success ;)

Today, we’re pretty good at finding actions that correlate with specific business metrics like visits or sales, but as the old saying goes, correlation does not imply causation.

If we move to a more causal and demonstrable success measurement system, one of the first ideas you will encounter, particularly if there are some serious data scientists around, in the idea of incremental impact or lift.  This model is the gold standard for determining cause in much of the scientific community.  Personally, I don’t see why with all the data we have access to now, this type of testing is not more widely embraced in digital.

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