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Prior Newsletters:
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In This Issue:

# Topics Overview

# Best of the Best Customer Marketing Links

# Question - New RFM: Web Analytics Vendors?

# Question - New RFM: Scoring e-Mail Campaigns?

# New RFM Metrics: Take 10 on Retention

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Topics Overview
=============

We have a couple of provocative customer marketing articles followed by two questions from fellow Drillers, one on comparing web site analytics vendors and the other on using scoring models for e-mail campaigns.

These folks may be a bit ahead of where you are in your quest to Turn Customer Data into Profits, but I absolutely believe understanding how **all** kinds of businesses look at these metrics will help you increase your ROI.  Every biz situation is different, and by exposing you to a lot of varied approaches, I'm hoping to trigger your personal "Ah-Ha!!" moment. 

Good To Go?  Let's do some Drillin'!

Best Customer Retention Articles
====================

This section usually flags "must read" articles about to move into the paid archives of major trade magazines before the next newsletter is delivered.  I highlight them here so you can catch them free before you would have to pay the fee.  This cycle there were no great articles from these particular magazines, but there were a couple of great articles from other magazines.  So check 'em out!

Note to web site visitors: These links may have expired by the time you read this.  You can get these "must read" links e-mailed to you every 2 weeks before they expire by subscribing to the newsletter.

CRM is a Four-Letter Word
October 1, 2003  Direct Magazine
Yes, here we are - again.  The author does not reveal which word he was thinking of, but hits on a problem we've discussed before with the way many CRM systems are designed - they depend on the customer to initiate contact.  That's insane; the real ROI is in proactive Customer Retention.  But many of these systems can't handle the batch operations so critical to High ROI Retention Marketing.  Oh...

Basic RFM Analysis Yields Binding Results
October 22, 2003   DM News
Some of the smartest web marketers out there are using offline postcards to retain or reactivate customers.  This article isn't specifically an online / offline story, but it does show you the power of RFM - a 600% jump in response rate for a $3,500 product is not something you can easily ignore.

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If you are a consultant, agency, or software developer with clients needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click here
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Questions from Fellow Drillers
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If you don't know what RFM is or how it can be used to drive customer profitability in just about any business, click here.

New RFM:  Traffic Analysis Technology

Q:  I own your Drilling Down book...

A:  Thanks for that!

Q:  ...and then recently (in your newsletter) you pointed me to look at your presentation at WebTrends...

A:  The one on customer retention, I take it...

Q:   I think WebTrends products are something we should use (over what we are using now) which is Gotoast.com

A:  Well, GoToast is a good product for what it does.  If all you really want to do is connect spending or source of customer to sales, seems to me it is pretty good at that, and makes it easy to report on these metrics.  For many people, that's all they ever want to know.  Truthfully, I wish more people used it, or used ClickTracks, which also does a fine job.  What is scary is the number of people who use no tracking at all!

Q:  I also was wondering if you have compared NetIQ's WebTrends against Omniture's service/product:

http://www.omniture.com/metrics.html

A:  I've never used Omniture, and I get nervous when a company doesn't provide sample reports or even pricing on their web site for such a web-centric tool.  I know the guys at WebTrends don't think a lot of the product, but they're biased.  It's hard for me to believe it is that much better / worse than most other tag-based reporting.

The truth is, all these products have upsides and downsides, and these depend largely on the kind of technology you are using on your web site, what it is you want to know, and what you will do once you know.

Q:  I actually think that WebTrends is superior, but am I missing something?

A:  Well, I'm sort of biased there too, I have to admit.  And the reasons I like WebTrends may not be important to you.  Here's my position, for what it's worth to you...

For me, it is important that I can analyze any kind of site, using any site technology, and get any type of information I need from the reporting.  Also, if a client changes their site technology, I don't want to have to ditch
all the history, I want to keep everything going along and not miss a beat.

WebTrends is the only product that allows you to do this, the only one that uses the same database for log-based analysis, tag-based analysis, and SDC, the SmartSource Data Collector, which is essentially a tag-based analysis that you host yourself - it's not an ASP.  The SDC approach is favored by banks and insurance companies who for legal and privacy  reasons can't have the data hosted outside their environment, but want to take advantage of the benefits of tag analysis.

It is important to me to be able to serve any client, regardless of their needs, and do that using the same interface no matter what the technology is.  So you really have to decide what it is you want, and then of course, there is price.  Here are some rules of thumb:

Publishers and people who rely on page views for revenue should always use a tag-based system.  It's the only way to actually see all your page views.  Many tag-based systems have poor path analysis, it tends to be From / To for each page as opposed to a linear "path".  So when page view counts are not really that important and pathing is (retail is a good example), then use log analysis or a tag-based product that offers linear pathing.

There are exceptions.  Companies with multiple servers in different time zones should probably be using tag analysis, as should sites with gigantic traffic or lots of traffic they don't care to analyze but a certain portion they do.   These are usually IT decisions, as in, it simply costs too much to aggregate / store all that info and a tag-based log file can be 1/10th or 1/20 the size of log data.

Also, a tag-based system is by definition forward looking - no data unless the tags are installed.  You can get history from the log files, and so many times logs are where an analysis project starts - for the history.

In general, since I'm a slicer-dicer, I have trouble getting to **exactly** what I want to see with most tag-based systems.  By definition, they are somewhat restricted in their ability to customize due to the "one size fits all" ASP model.  This is not true of the WebTrends Reporting Service ASP, though there are some "governors" on how many custom reports you can have, for example.  In the SDC model, you have total control and can track stuff most of these other providers have never heard of before.

The other good thing about WT Reporting Service is you can start with "basic" and work that, then if you want to get into some more advanced stuff, they just flip a switch and you have it (at a higher price, of course) all the way up to Enterprise, which includes visitor history tracking.  This capability is what is needed to produce the key metrics from the retention presentation - Recency, Frequency, Latency, and LifeTime Value - at the individual visitor level.  It also captures initial (first visit) and most recent referrer, search engine, search phrase, campaign, and more.  Hopefully, in the future these visitor history tables will be exportable and also capable of ODBC connections to the outside world - Excel, Access, analytical databases, CRM, etc.

And that will be when things will really take off in web site analysis.  It's the missing link to really driving profitability on the web.  GoToast / ClickTracks / Omniture / HitBox can all tell you if your clicks are paying out today.  But that's not the real question, it's did a click 6 months ago pay out today, and then the ability to predict **in advance**  which ones will pay out in 6 months.  The only way to do that is with individual visitor history, and the only way you used to be able to get that is with a very high end package or a custom-built solution.

Hope that helps.  If you have any questions, feel free to ask - you're a customer now!

Jim

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If you are in SEO and the client isn't converting the additional visitors you generate, you can help them make it happen - click here.
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New RFM:  Scoring E-Mail Segments

Q:  I recently purchase your book "Drilling Down". Really enjoying reading it!

A:  Well, thanks for the kind words.

Q:  I had a question about the implementation of the RFM model against email campaigns. Say we have a client that has done this:

- Has sent out 2 emails to entire database (in June and July)
- Has sent out 3 targeted emails to a specific segment of the database (June, July and Aug)

From my CTR and Open Rates I know that the targeted segment performance is better.  For my scoring I am using the following:

- Recency, last email responded to, and
- Frequency, number of emails where an action (a click-thru) was taken

So the question is when trying to apply an Recency / Frequency (RF) score to the entire database, do you / can you use all 5 email programs?  Would Recency include the email to the specific segment in August?  Would frequency include the segment that received the email in August?

A:  The fact you are asking this question tells me you understand the methods better than you think you do.  The correct answer is yes, and no, depending on the objective of the scoring.  As long as you **understand** that there is the potential for the marketing to the target segment to skew the scoring of the overall group, then you are thinking about the problem correctly.  Whether you decide to do the scoring as "everybody" or you score the targeted segment and "everybody else" separately really depends on what you are trying to accomplish or the objective.

Q:  Wouldn't some folks in the targeted segment have a higher RF score?

A:  Absolutely.  But this is not bad, I mean, a bunch of them responded, so they "deserve" a higher RF score, yes?  Isn't response good, and so they should have higher scores?

If you look at the case for scoring the entire database, it generally tells you who is most likely to respond to **any** campaign.  If you know a lot about your customers, you probably will not send them all the same campaign, but create different campaigns for different customer segments and hope to generate sales (or sign-ups, or downloads, or whatever).  What you are really getting from scoring everybody together is identifying specific individuals or groups who:

1.  are most likely to respond, and 

2.  appear to be defecting so you can be proactive and go after them with a specialized campaign addressing the potential defection.

You can either decide to attack certain groups or not spend the money because they are "already gone" and there will be no ROI.  This doesn't have anything really to do with any specific campaign, it is the more about the aggregate, overall decision to spend on any specific customer or group of customers.  The fact you did a campaign to a certain segment has no bearing on this, because if you are successful with your campaign, those targets will have higher RF scores - and quite frankly, that is what you want, right?  The higher the RF score, the more likely they are to respond and the less likely they are to defect.

Now, that said, the business of database marketing is about creating test programs, looking at the results, and realizing that certain segments respond better than others to certain campaigns.  A classic example is the "discount ladder", where you set the discount by RF score in order to maximize response and ROI.  It is certainly OK and desirable to break the customer base into sub-segments and score these segments against themselves as well as the overall population.

For example, score everybody who bought a lamp as their first purchase by themselves and everybody who bought a chair as their first purchase by themselves, or everybody who came from Google as a group and everybody who came from MSN as a group.

What you get from this approach is new insight and uncovery of new, profitable segments.  So, for example, you find a customer segment with an average RF score of 35 (average) in the overall scoring of the entire customer base, but it has a score of 55 (highest) when just scoring people who came from MSN.  Though these people are not a "best customer" segment overall, they are "best" within the MSN segment and through testing you find they are generally responsive.  They are best customer segments in terms of all segments from MSN, and as such, are probably worth targeting.

Another common use of multiple scoring groups comes into play for 1x buyers (see page 98 in the book).  One time buyers by definition all have a Frequency of "1", and online the percentage of 1x buyers in the database tends to be huge.  So if you score the whole database together you get a very warped view of the world - if 80% of the database is 1x buyers, some of these poor quality customers will get fairly high scores.

A better approach is to split the database in one-time and multi-buyers. and score each group individually.  This way, you have created segments which have very similar members, and a relative ranking score like RF becomes much more meaningful.  When scoring the one-time buyers, you dump the frequency variable since it is the same for all, and use just Recency or perhaps Recency and Monetary.  Then you test and see which approach is the best predictor of response and defection.

Your ability to formulate this question means you are on the right track.  It's not an either / or situation, it's more like a "both" depending on what you are trying to do.

Jim

New RFM Metrics: Take 10 on Retention
====================

If you would like to know more about how to use the new RFM metrics to improve your profitability on the web, check out the free "Take 10 on Retention" package I wrote.  It includes a 10 minute presentation on the strategy and reporting behind increasing web customer ROI using simple predictive models.

Here's the idea in a nutshell: when you make investments, you expect the value of them to rise in the future.  You have web investment choices to make - ad design, media, building out content, etc.  Retention metrics tell you which of these investments are the most likely to generate increased profits in the future.

Click here for the Take 10 on Retention

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