Category Archives: Measuring Engagement

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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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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Defining Behavioral Segments

DataBase Marketing, Driller Q & A, Measuring Engagement, Newsletters

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Q: I purchased your book and have a few questions you can hopefully help me out with.

A: Thanks for that, and sure!

Q: We have 4 product lines and 2 of them are seasonal. i.e we have customers that year in year out purchase these items consistently but seasonally, for example, every spring and summer.   Then they are dormant for Fall and Winter.  Should I include these customers along with everyone else when doing an RFM segmentation?

A: Well, it kind of depends what you will using the RF(M) model for, what kinds of marketing programs will be activated by using the scores. If you know you have seasonal customers and their habit is to buy each year, AND you wish to aim retention or reactivation programs at them, I would be tempted to divide the customer base so that seasonal customers are their own segment.  Then run two RF(M)  models – one for the seasonals, and one for everyone else.

Q: If I include seasonal customers, and I run RFM say on a monthly basis, these seasonal customers will climb / fall drastically with time depending on the season, so it seems like it may complicate the scoring process.

A: Sure, and you could segment as I said above.  Or, you could run across a longer time frame, say across 2 – 3 years worth of data. This would “normalize” the two segments into one and take account of the seasonality in the scoring – perhaps be more representative of the business model.  However, the scores would become less sensitive due to the long time frame so the actions of customers less accurately predicted by the model.

Q: Can you provide me with some examples as to how segmentation is carried out?  Let’s say I being with RFM and all my customers are rated 5-5, 5-4, 4-5 etc.  What are the next steps, do we overlay with other characteristics like age, gender, etc?  Or are the 5-3 etc. our actual segments?

A: This goes back to what you want to use the RF(M) model for.  In the standard usage, each score will have roughly the same number of customers in it, those with higher scores will be more likely to respond to marketing and purchase, lower scores less likely.

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