Will Work for Data

But will do a sub-optimal job…

Trying to catch up on what is going on in the analytics blogosphere, and it seems like I’m seeing a common thread – we’re getting much better at analyzing customer data, but whoever is in charge of Turning Customer Data into Profits is not quite with the program yet. 

Based on my experience, and assuming the people responsible are Marketing folks, the challenge to solving this problem often lies in understanding the difference between executing against behavioral data and executing against data about “characteristics” like demographics.

Marketing is not always about buying mass media, yet most Marketing people have never had to create and execute a campaign using behavioral data against a behavioral Objective.  So they do what they have always done – they create campaigns based on characteristics – and then execute against behavioral objectives using behavioral data.

This is a recipe for sub-optimal performance.  It’s like buying a car with a high performance engine then putting the cheapest gas in it you can find and never getting a tune up.  Sure, the car will run, but it’s not going to run very well, and you sure are not going to win any races with the competition.  Provided, of course, they don’t treat their car the same way.

For example, Ron is commenting on weak segmentation practices and lack of understanding the new customer experience in banking.  He is absolutely right.  Segmenting by “number of products” is often a static characteristic; segmenting by “change in number of products” is behavioral and many times more profitable.  As for new customer experience, the initial experience defines a customer’s “view” of the company and I don’t think I have to explain the importance of that.

Kevin is bemoaning the lack of temporal segmentation and use of appropriate creative for this segmentation by many e-mail folks.  He is absolutely right.  You want to speak to the customer based on their level of engagement with the company, not in terms of static perceptions.

Avinash perceives a problem coming down the road with behavioral targeting, that is, while the machine is smart, the results are only as good as the content you feed the engine.  Absolutely right.  If you run campaigns designed around static demographics on a behavioral platform you have created a way to “efficiently target crap to your customers”.

Is anybody listening?  If the message is not clear, try this:

Most Marketers are looking to drive “behavior” of some kind – even the Brand folks, who simply have a longer time horizon.  If behavior is the outcome you want, the campaigns must be created around “when”, “what”, and “why”, not “who”.  “When”, “what”, and “why” are behavioral ideas, “who” is a static characteristic (like a demographic) that probably has nothing to do with past or future behavior.

I know, you have probably been told segmenting by demographics is the way to go, or read so somewhere.  Was the source talking about buying media or data-driven marketing?

Sure, if you don’t have any behavior – when buying TV for example – then you go with what you can get.  Some segmentation is always better than none at all.  But if you have behavior, then using demographics to drive campaign segmentation is going to be sub-optimal.

Static characteristics like age and income do not predict behavior.  Behavior is in motion; it changes over time.  You can’t take a static characteristic and expect it to do a very good job predicting behavior because behavior changes over time.  Behavior predicts behavior.

The fact I am a 48 year old male predicts nothing about my behavior.  These characteristics are simply a proxy for buying media against me more efficiently; they really mean nothing when you cross the line into using data sets with actual behavior in them.  The fact I stopped visiting / posting / purchasing or that I am in the top 10% for writing reviews is much more powerful.

When addressing behavioral segments, first ask When?  When did I stop visiting / posting / purchasing?  Over what time period am I in the top 10%?  Am I still in the top 10%?

Then ask, What?  What events led up to my behavior?  What campaign did I come in from, salesperson did I talk to, products did I buy, areas of the site did I visit?  What has happened to me?

Then, understanding my experience, ask Why am I behaving like I am?  Then knowing Why (or more likely, making an educated guess), can you think of a message that is going to change my behavior?

Now you are ready to design and execute a campaign that will blow the socks off of anything you can do by knowing I am a 49 year old male, because you can directly address me with a message that is more relevant to me.

Marketers, please take the time to think about “when”, “what”, and “why” in campaign design and execution if using behavioral data, and forget about “who”.  You will be glad you did

Analysts, have you ever run into this problem?  Rich evidence of a behavioral “edge” you might have that is ignored in the creation and execution of the campaign?

P.S.  The glad you did link above shows what you can learn by looking at behavioral segments as opposed to demographics.  All the folks in this test are in the same demographic segment, with a 10% overall response rate to a 20% discount offer – better response than any other demographic segment.  But they sure had different levels of profitability, based on behavior.  The more engaged they were – as measured by time since last purchase – the less profitable they were for this campaign.  And you can predict this result, because it will happen every time you use the same behavioral segmentation and offer, with slight variations possible across demographic segments.

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3 thoughts on “Will Work for Data

  1. WOW!!
    I can simply put this word for this interesting and informative post. I have always favoured the use of all bits and pieces of information be it demographic or behavioral. All add value and the way you have put the difference in their use is simply awesome.

    The only point where I dont think I agree with you in the implementation part. I would prefer to use all attributes and their information value first irrespective of their type and source. Then when they show some significance statistically, then find a good way to use it. The target answers can be “what”, “when”, “who” or anything. I am not much worried on one particular answer but more lean to the actionable insight that drives profit.


  2. Adelino and Bhupendra,

    I understand the need for an analyst to “prove” to themselves that certain structures and methods are in place. For example, the idea of “then when they show some significance statistically” above. This would be the proper, purely scientific way to approach a problem. Like Ron’s comment “but that’s a decision for the statisticians to make”.

    As a database marketing person, I have to tell you that when you see an outcome repeated over and over and over in many different tests across many different industries, you tend to believe it is true without the need for elaborate science in place first to prove it is true.

    This is the case with using behavioral data versus demographic data to drive profitability in marketing campaigns. If you build a predictive model using both elements – and the objective is behavior – you will find the behavioral variables always move to the top, get the largest weights.

    Now, as I said in the post, having demographic data is better than having no data at all. Many times, especially in acquisition marketing, this is the case. So you use what you can get.

    What drives me crazy (about marketers, not analysts) is they tend to ignore the behavioral and focus on the demographics, despite evidence the behavioral variables are controlling outcome.

    Before this comment turns into a post, I think I will do a follow-up

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