Archive for the ‘*** Article Reviews’ Category

View-Throughs Again

Friday, September 14th, 2007

In my post ”Banners versus Search“, I talked about the problem with view-through measurements that so many folks use to justify banner buys.  Kevin Lee has written a short piece on ClickZ worth checking out for those interested in more on this issue and all the pitfalls of the view-through measurement.  He also includes a proposal for a new measurement called “view around”, which sounds to me like a classic controlled test / regression model scenario. 

This is a much better approach; after all, do you really care what the actual behavior behind the banner / search combo is (measuring accurately) or do you care about how to optimize the combo and make more money (measuring precisely)?

Since you can’t really control the way traffic interacts with a combined banner / search campaign anyway, attempting to measure something like “view-through” isn’t actionable; there are too many uncontrolled events.  Measuring the effectiveness of various combinations – regardless of which is viewed / clicked first, last, how many times, etc. is a much more actionable approach.

Check the article out here.

*** Customer-Centric IT Wins

Wednesday, August 29th, 2007

Yes, I know for many marketing folks this seems to be an oxymoron, but the fact is that Marketers – especially those with some understanding of business process and the IT world – can influence the direction of IT and generate genuine customer-centric wins.  This in turn makes all your marketing efforts more productive

Web analysts, this is the kind of work you will be supporting with analysis in 5 years…it’s just a much larger version of optimizing a web site, isn’t it?  And in many ways, a lot more fun…

Requires a different mindset?  Sure, it’s not buying media or developing creative or analyzing response.  But these are the kinds of projects Marketing folks (especially data-driven ones) should be championing by providing the customer models for IT to base a plan on and forge ahead.

Here are 3 great examples, case studies from CIO Magazine:

Washington Mutual  – a classic example of cross-functional teams looking at “how we sell” versus “how they buy” barriers; reminds me a lot of the Check Shredding Example.  I wonder how many online Marketing folks at banks have asked “why do we need signature cards?” in the past 5 years - what is the Root Cause?  Ron, make sure you check this one out, especially given your post - what do you think?

Best Buy - the offline retail version of “people who bought this also bought that”.  I’m sure this one will sound simple to many folks - all except those working in offline retail analysis and store logistics, that is.  A tough, messy business to optimize and even small wins are remarkable.

Hilton Hotels – another seeming no brainer, just let people order online.  But not just any people, we’re talking about event / conference planners ordering meeting rooms, food and beverage, A / V etc. not to mention guest rooms for thousands of people.  This is not a small deal on the infrastucture side, with plenty of politics to go around.

Check the cases out here, and let me know what you think.

Top 10 (IT) Projects in ‘07

Tuesday, August 28th, 2007

Interesting to see what our friends in IT are working on this year:

I would not have guessed Business Analytics / Intelligence was the Number 3 priority.  Good thing for analysts, someone is going to have to make sense of all that data and provide concrete direction…which seems to be the part hanging people up these days, not the analysis.  I imagine this BI activity has a significant role in driving success for #1 - BPM.

Full article with spending estimates and more details at Innovations Magazine here.

 

*** Call it E-RFM

Wednesday, August 15th, 2007

By way of Multichannel Merchant, we have this article: Call it E-RFM

by Ken Magill.  His idea will probably be a stretch for many in the e-mail space, but it’s a great example of what I was talking about in How to do Customer Marketing testing.

The gist of the article is you can reduce spam complaints and better manage reputation by anticipating which segments of subscribers are going to click the spam button.  Yes, anticipate.  You know, predict?

For some reason, online marketers seem like they are not really into the prediction thing – or at least are unwilling to fess up to it.  Test, measure, test, measure, web analytics is mostly about history, as opposed to predicting the future.  How about predict, measure, predict, measure?  Same thing, only much more powerful – if you can guess what customers will do before they do it, you have real marketing power.  Perhaps this is why folks don’t talk about it much…

The prediction model discussed, RFM, is one of the most durable and flexible models in the entire BI quiver.  As Arthur Middleton Hughes says in this article, “There isn’t a predictive model in the world that doesn’t have RFM inside of it”. 

And the RFM model is free.  You don’t even have to hire a statistician!

The RFM model sometimes gets a bad rap because people use it with very little imagination, simply reproducing the basic catalog model from the 1950’s, instead of understanding the guts of it and using it in new ways.  This Call it E-RFM article is a good example of how to use RFM in a new way; a broader explanation of using modified RFM for e-mail is here.

Those of you interested in how to really take advantage of the new Webtrends Score product should pay attention to this prediction area, because “Potential Value” – a prediction - is absolutely fundamental to optimizing a Score model.  You could use Score to predict which segments are most likely to click the spam button.  And then you could test, track, and fine-tune those predictions until you get them right.  Sounds like fun, huh?  Does to me, anyway…

But you don’t need something like Score to predict likelihood to click the spam button; sending an e-mail every week for 3 years to somebody who never clicks through should be a rough indication…

So, do you use predictive models in your work?  Why or why not? 

If you don’t use prediction, is it because coming up with a great campaign for a prediction is the problem?  Or because nobody really cares about customer marketing, it’s all about customer acquisition?

An overview of the Potential Value idea is here, or for a more comprehensive version including marketing direction on what to do with the results, get the PDF.

*** How to do Customer Marketing testing

Wednesday, August 15th, 2007

“We don’t need testing. We know what works.”

“If you do no testing at all, no one will complain.”

OK, the title of this article by way of DM News is actually How to do direct marketing testing, but I figured some folks who should read it might not with “direct marketing” in the title.

Arthur Middleton Hughes is one of the great educators in database marketing, and this article hits on several issues that are very well known in the offline customer marketing business, but few folks in online practice.  Control groups, half-life effects, best customer segmentation, effects of promotion beyond the campaign.

He also briefly addresses a problem I run into all the time.  Things are “going great”, so we don’t need to test.  Underlying this statement is frequently a very weird emotion peculiar to many online operations, especially when I talk about control groups.  It’s the ”what if we find out our results are not as good as management thinks” problem.

In other words, the “not broke, why fix it” issue.

Not sure why this occurs so much with online when compared with offline, though it probably is simply an issue of undeveloped analytical culture.  Why else would people be afraid of failure, if failure is truly embraced as a learning experience?

Perhaps a culture problem:  Testing is OK as long as it doesn’t rock the boat too much, doesn’t push the edge of knowledge out too far, is safe and sterile and won’t result in any quantum leaps in knowledge.  ”Safe testing” only.

Perhaps an idea problem:  The testing culture is fine, but has become too robotic, no really new ideas, people don’t know of any high-impact, meaningful tests to conduct?

What’s going on where you work?

***** What Data Mining Can and Can’t Do

Monday, July 2nd, 2007

Timing, Counting, & Choice.  “Most real-world business problems are just some combination of those building blocks jammed together” – Peter Fader

Over at CIO Insight we have this very practical article on Data Mining by Fader.  What it’s good for, what it’s not good for.  If you have wondered how you might use this tool, especially if you are a Marketer, you should read this article. 

I say the article is practical because even though there are many ways to create mathematical models of customer data, if the end result is not something a Marketer can use to actually increase Marketing Productivity, then you really cannot do much with the output.  The models have to create leverage of some kind that can be used to take real world action.  In other words, a model can be “technically correct” but completely useless to a Marketer.

For example, just because you can identify a segment doesn’t mean it is practical or viable to address that segment with a unique marketing treatment.  And just because the segment has unique characteristics doesn’t mean those characteristics create any real marketing opportunity.

Key takeaways for Marketers from this article should be:

1.  Too much data tends to mess up a model.  This is especially true if you try jamming all kinds of demographic crap into a model that is trying to predict behavior.  If you want behavior as an output, use behavioral variables in your models.

2.  Data mining is a great classification tool; it is good at telling you why segments are different.  But in order for this to be useful, you need actionable segments to begin with.  For example, data mining can tell you the demographic differences between people likely to respond versus people not likely to respond – if there is a demographic difference.  But you have to know this “likely to respond” element first.  While we’re on this topic, the same idea holds true for surveys.  If you want the survey output to be actionable, get to known behavioral segments first, then do your surveys of each segment.

Often, people use technical tools for the wrong Marketing reasons.  I see this problem coming down the tracks in web analytics, people are getting so wrapped up in the minutia and the automation of testing they are missing out on the basic stuff.  Just like the data mining wave got people off track and into the bushes with “collecting all the data so we can mine it”.  But it doesn’t matter how much data you have, the tool does what it does and doesn’t do what it doesn’t do.

Check out the article What Data Mining Can and Can’t Do here.

Any thoughts from the Data Miners out there on this?

**** Where You Should Stick Your Ad and Why

Friday, June 29th, 2007

A list of the posts in this series on Brand Management is here

By way of iMedia Connection is this article by Joseph Carrabis of NextStage.  The piece is about ad placement on a web page – and how most of the current Display Media placement ideas are just plain terrible or wrong.

Given my recent posts on display advertising, and in particular Online, the Web Site is the Ad, I thought this article would be a great read.  I met Joseph at the eMetrics Summit and attended his presentation, which was a fascinating brew of cultural anthropology, brain-works stuff, and social modeling.

As this article bounced around the Brand-ing world, the reaction was pretty much this: he’s probably right, but there’s nothing much we can do about it.  If you ask me, they’re right.  But the thing that shocks me is this: not only do you have these folks completely ignoring what happens after the click, but also you have them essentially saying “we know it sucks but hey, what can we do?” about the screwed up nature of the display impression itself.

I’m not sure how the Brand-ing folks can sell display as a “mass media” with a straight face.  These are the fundamental reasons why Johnny Can’t Brand.  No “weight”.  Strapping together completely fragmented impressions does not create a mass media.  They are probably going to need a Google AdSense kind of mechanism to create practical value for this display space.

By the way, kudos to iMedia for having the guts to publish an article like this.  If you are interested in the measurement of media, you really should check this article out.  What do you think?

***** Bring Out Your Dead

Thursday, June 14th, 2007

Yes, a rare 5 star article, my “must read”.  Why?  For one thing, it is based on analysis and real-life data modeling by Professor Peter Fader and friends at the Wharton School – it’s not a trade mag article by some Consultainer.  Fader has a major track record in customer analysis and modeling.  The article is Beware the Walking Dead.

What we’re talking about here is the effectiveness of cross-sell and retention efforts based on the Customer LifeCycle, and how if you execute too late in the Cycle, you could end up “waking the dead” and driving defection rather than encouraging retention.  The specific example given is cable television, though this article provides support for a lot of ground covered on this blog and others, including my piece on engagement, and Ron’s numerous pieces on customer experience.  I love it when academia synchs up with real world experience - academics are so much more rigorous, you know…

The article is a summary of the results from the academic paper, and includes a link to the actual paper for those of you who are into the math and modeling.  If you’re unfamiliar with academic papers, they typically provide the actual formulas for the proof of the assertion.  Reading the actual paper is not for the faint of heart, but I know some of you folks dig the high level modeling math.  Not into the math?  Read the article itself, an excellent summary.

Bottom line: another reason to downplay the LifeTime Value approach in favor of Customer LifeCycles.

*** Company-Wide BI

Monday, May 28th, 2007

…Inches Closer to Reality, according to this article in Optimize Magazine.  See also how Microsoft wants to make BI “Ubiquitous” in this article from Information Week.

 

(graph above from Optimize Magazine article)

Now, I generally think giving people access to data is a good thing, but I’ve seen it go horribly wrong in many cases.  You can’t just open up data access and let people construct analysis without ground rules.  You’ll have complete chaos, with people torturing the data in whichever direction best makes their case.

Rather, follow a couple of simple rules to prevent analytical chaos:

1.  Please set up a “best practices” team or “center of excellence” concept so people who want to do honest analysis on their own can get help with the simple stuff.  If you have experienced endless cycles of re-analyzing the same problem over and over with the business side, you know what I am talking about here.  People need help really tightly defining the questions they are trying to answer and guidance on what the best (most accurate?) way to generate answers is. 

Do these new analyzers know how seasonality affects the business?  Do they understand how time-frames can distort an analysis?  How cycle billing or lag times in fulfillment can affect sales recognition?  Are they aware of any data quality issues? 

2.  If you can’t set up a best practices team, at least try to create a best practices manual / library of some kind.

3.  Please, please define all the data people will be using.  Some of this definition problem you can control with the drop-downs you provide, but make sure everybody knows specifically what the drop-down items mean.  For example, if someone is doing a “customer count” of some kind, what is a customer, who is included in that count?  If someone ordered and then cancelled - no net positive financial transaction ever took place - will that person be included in a “customer count”?  If not, what is that person called, what is their “status”?  Is that status available as a drop-down selection?

If they select “Sales” as a variable, are we talking about Gross Demand?  Net of cancels / returns?  Net of discounts?  Net of bundling, packaging, volume pricing?

4.  If an analysis will be used for strategic decision making, it must be “vetted” first by the best practices group.  If people want to make “local” or silo decisions based on their own analysis, well, they are making their own bed.  But when it comes down to major shifts in business practices, you simply cannot rely on a local analysis, there is too much risk involved.

You really have to think some of this stuff through first so you don’t end up in a meeting with the CEO where 3 different people present data that should be consistent but end up divergent.  That’s the fastest way to induce a full-on beating CEO forehead vein I know of. 

I am all for people being “exploratory” and going through their own “what if?” kinds of exercises as they try to discover more about their products, services, or customers.  This is a good thing.  But confusion and chaos due to lack of standards and a central authority on the validity of an analysis is a great way to doom this kind of effort.

Anybody else been through one of these “everybody in the data pool” rollouts?  Do you have any other “rules” you would like to add to help people get through one of these implementations successfully?

**** Great Brands Never Rest

Friday, May 25th, 2007

From Target Marketing Magazine, this article embodies what I (and many customer-centric Marketers, I believe) think about when the subject of Brand and authentic Brand Advertising comes up.  Here’s the six disciplines of a Great Brand, according to the article:

#1: Know Your Position
#2: Know Your Customers’ Position
#3: Share Your Position
#4: Stay Focused on Your Position
#5: Leverage Your Position
#6: Delight and Reward Those Customers Who Support Your Position

Hmmm.  Sounds like Marketing to me.  This as opposed to the “awareness, recall, intent” mantra continually put forth by the Brand-ing folks.  You know who they are,  They’re the ones that believe when people “interact with your Brand” though a game on your web site that something of value occurs.  Perhaps so, but it’s not Brand, at least not unless you are an online game developer.  Awareness is not Brand, folks.  Recall is not Brand.  Intent is not Brand.

Trial is Brand.

I’ll have more on the history of this problem and how it applies to many of the current issues in Marketing shortly (I hope, promised a while ago).  Meanwhile, read the above short article and let me know what you think, here’s another link to it.