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Drilling Down Newsletter # 21 - June 2002 - B2B Software Example, Free / Pay Web Site Content Model

Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, 
Loyalty, Defection

Get the Drilling Down Book!
Now also available online through
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Check out:

The Marketer's Common Sense Guide to E-Metrics - 22 benchmarks to understand the major trends, key opportunities, and hidden hazards your web logs uncover.  Download a free white paper on the topic:
Marketer's Common Sense Guide to E-Metrics

Prior Newsletters:

In This Issue:

# Topics Overview

# Best of the Best Customer Marketing Links

# Tracking the Customer LifeCycle: B2B Software #2

# Questions: Free / Pay Web Site Content Model

Topics Overview

Hi again folks, Jim Novo here.

This month we've got the usual "best of" Customer Marketing article links, the second installment of the B2B Software Real World Example, and a fellow Driller facing what many web sites are wrestling with - the combination ad-supported free / subscription pay model.

A special note: I'm on a panel at the upcoming DMDays conference in NYC:

Turning Data Into Gold - Increase Your ROI By Measuring What Matters
Monday, June 17, 10:15 AM - 11:10 AM
Bryan Eisenberg - Future Now, Inc.
Brent Hieggelke - WebTrends Division of NetIQ
Jim Novo - The Drilling Down Project
Brad Powers - eWOMP Technologies

If you're around the show, please try to make contact. Unfortunately, I have to leave later that day for other commitments and won't be 
around for the whole show, so try to catch me after the panel.  More info on the session can be found here.

Let's do some Drillin'!

Best Customer Retention Articles

This section flags "must read" articles moving into the paid DM News archives before the next newsletter is delivered. If you don't read these articles by the date listed, you will have to pay $25 to DM News to read them from the archives. The URL's are too long for the newsletter, so these links take you to a page with more info on what is in the article and a direct link to the article.

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.

Do You Make These 8 Testing Mistakes?
Expires June 14, 2002 DM News
Does Your Retail Site Implement All 10 Best Practices?
Expires June 15, 2002 DM News 
Both these articles contain best practices information on direct marketing techniques you should be using on the web.

Catalog Yields 'Uncommon' Benefit for E-Tailer
Expires June 20, 2002 DM News
Another story about online retailers making a boatload of money offline with a catalog.  Lots of stats on order size and so forth.

Tracking the Customer LifeCycle: Real World Examples
Note: If you are new to our group and want to know more about the following ongoing discussion, the background theory is here.  The prior example, Hair Salon, is here:

Latency: The B2B Software Example # 2

Recall where we left off.  The CFO has noticed a softening in sales of add-ons to the basic software application the company sells, and can get no "actionable information" on the situation from the standard sales reporting.  These reports are designed to report on products, not on customers.  

After pondering what the "real question" is that needs to be answered, the CFO requests a report from the CIO looking at the length of time between add-on sales by customer.  

If this is not enough information, you can read all of Part One right here.  

The CFO gets the custom report on the average number of weeks between customer purchases of add-ons.  It looks like this:

Average Weeks between Add-On Purchases 
Last year 8.6 weeks 
This year   8.9 weeks 

So it is taking longer for them to purchase, the CFO thinks, and darn it, now I have another question.  The IT people are going to have me for breakfast for not thinking this all the way through the first time!  I got the information I asked for, but this information is not actionable, I can't do anything with it.  There is not enough detail to act.

Fellow Drillers, when you are plumbing the depths of your data, try to think of what you will do with the information you are asking for. Imagine getting back your results, and taking an action based on those results.  If you can't imagine the action you would take knowing the information, you are not asking the right question yet.  The CFO thinks:

Our add-on modules have different prices and different levels of difficulty involved in their integration.  And they are usually installed in a particular sequence.  So what I really should have asked for is the average number of weeks between the purchase of add-ons by add-on - the time between base purchase and the first add-on, the time between the first add-on and the second, and so forth.

Maybe there are problems with installing one of the add-ons due to changes in the next generation of operating systems, for example, and this is slowing the installation of a particular add-on down.  If I can get the average number of weeks between add-on purchases by add-on, I can act on this, because I will know which add-on is causing the slowdown in sales.

The CFO reluctantly picks up the phone to call the CIO.  At least this time, the CFO thinks, I have thought the question out all the way through, and I know what action I can take with the information once I get it.  Shortly after a heated exchange involving resource allocation, budgets, and a hiring freeze in IT with the CIO, the CFO gets this report:

Average Weeks between
Add-On Purchases by Add-On 

Last Year This year
Base app to 1st add-on 12.3 weeks  12.1 weeks
1st add-on to 2nd add-on  10.5 weeks  10.2 weeks
2nd add-on to 3rd add-on  8.7 weeks  8.9 weeks
3rd add-on to 4th add-on 6.1 weeks  6.7 weeks
4th add-on to 5th add-on 5.2 weeks  6.5 weeks
Avg. time between add-ons  8.6 weeks  8.9 weeks

Fellow Drillers, it would be nice if the pattern was a bit more clear, yes?  It appears customers are ordering their first and second add-ons more rapidly than last year, but as they get to the third, fourth, and fifth add-ons, they are ordering more slowly than last year. What could this possibly mean?  The CFO thinks:

Well, I answered my question, but I've got another.  The reason why add-on sales appear soft is a longer purchase cycle for the average add-on, and the reason this is happening is the later add-ons are taking much longer to be purchased than they were last year, even though the first add-ons seem to be cycling much more quickly.  What does that mean?  I promised the CIO I would be able to act on this information, and I simply can not. 

Will the CFO beg the CIO for more reports?  Will the CIO extract a hiring deal out of the CFO in return for supplying the reports?  Just who the heck is going to do all this analysis work anyway?  And the deadly question - what is the ROI on all of this noodling?

We'll see how it all comes out next month... 

As a side note, for those of you interested in the difference between financial and customer accounting, and how reconciling these differences is essential to the successful implementation of CRM, you might want to read this case study I wrote, or the companion article on how this issue affects the design of employee incentive plans: Employee Incentives Lead to CRM Failure?

I can teach you and your staff the basics of high ROI customer marketing using your business model and customer data, and without using a lot of fancy software.  Not ready for the expense and resource drain of CRM?  Get CRM benefits using existing resources by scheduling a workshop

You can read the 3rd installment of the
B2B Software Example by clicking here.

Questions from Fellow Drillers

Jim's Note: The web site discussed here is one of the many now trying to find a balance between the ad supported model and the subscription model.  As we all know, this is a critical juncture.  

Since most of these sites never really tracked visitors / customers as much as they did page views / impressions, they are seriously lacking in the kind of intelligence they need to make this new model work.

Here's how to do it: understand where the visitors most likely to convert to paid come from, and understand what parts of the site are so valuable to them they will convert. 

If you don't know what Recency and Frequency are they are explained here, and RFM is covered here.  "Intensity" is Views per Session, in this case a "proxy" for visitor value.

Q: Hi Jim,

Should we use:

RFI - Recency, Frequency, Intensity
RFM - Recency, Frequency, and Monetary
RF - Recency, Frequency

to measure visitor value, and what should these terms ideally mean?  Total Sessions, Total page views, etc.  Also, when you measure Frequency, do you only include the Frequency during a specific period of time (i.e. one month, or one week), or do you include total lifetime activity per user?

A: On the advertising side of the business, I think the page views/session stat is probably the best to use.  The reality of the ad-based business is it doesn't matter if they come back, you are selling impressions, not people.  I don't think you have to overcomplicate it with formulas like RF or RFM, because you are primarily dealing with audiences, not individuals.  RF and RFM are about predicting if they will come back.

For the ad biz, you want to know which visitor sources generate the highest page views per session.  But for the subscription business you want to know what parts of the site create the most loyal visitors - those who will pay for content.  These are two different issues.  More on this below.  If you decide to track pure Frequency, yes, it is Lifetime activity, but I don't think you need that.

Q: It's interesting. There seems to be 2 major "customers" for your systems at my company: Marketing and Product groups. What are the most powerful deliverables, in your experience, that we can give to each?

A: I am guessing Marketing has to do with getting people to come to the site and Product is the people developing the site content.

For Marketing, the most important thing you can do is to track source, because none of these other metrics will do you any good if you can't actually do something with them.  For any given metric, ask yourself this: If I knew this metric, what would I change to improve the business?  If you don't know "source" of the customer (which ads, search engines, etc. are the visitors coming from?) then you can't change your marketing strategy to attract the visitors with highest page views/session. 

You want to know "original source", as in when you set the first cookie, what was the referring URL or Ad?

Visitor source is very powerful, because it is largely responsible for the long-term quality of the visitor, and it is something you can often control or influence.

You don't have to report on source at the individual level, report on it at a meaningful level that you can use to make changes - again, if you can't use the info to do something then it is not worth tracking.  So for ads where you are paying money for visitors, you would track by ad or by network or whatever control point you have.  For search, you track at the engine level - Google, MSN, AOL, etc.  When you find the average visitor from Google does 8 pages/session, and the average visitor form MSN does 2 pages/session, now you have information that is actionable that can be used to optimize campaigns and search.

For Product, I would also track Recency in some aggregate form, say "30 day Recency" - what percent of visitors have visited at least once in the past 30 days?  You will find that your different "products" have different levels of 30 day Recency, and the ones with the highest percent Recency are the ones where your customers are most loyal - this is the true definition of "stickiness", if you think about it.  This information will help guide product design - which products are the most satisfying to customers?  The above approach is what I call Hurdle Rates - it's in the book or you can read some about it online here.

And, by measuring it this way, you really have a yes/no type tracking - either they came back within 30 days or didn't, you can use a simple "switch" that will be easy to implement on the IT side, rather than tracking the actual Recency by number of days.  You have to decide how you handle visitors using multiple products - they could be 30 day Recent on weather but not on news, for example.

You can also use source for Product.  Even though the average 30 day Recency for a Product may be 30%, you find that for Visitors from Google have a 60% 30 day Recency and visitors from MSN have a 10% 30 day Recency - for the same particular product. Knowing what you know about these different audiences, which one is more desirable, and what does this mean about the product?

This intersection of "visitor quality" with "product quality" creates a grid you should be able to use to tweak both ad and site effectiveness.  It will show you where the very highest and very lowest quality combinations are.  The highest quality visitors to the highest Recency area of the site are the most likely to pay for the content.  You should be able to optimize both ad revenue and subscriptions using this matrix.

Q: Also, given that we will most likely use a "phased" implementation, what do you think are the logical steps to take in phase I, II, III, etc?

1. Get your source tracking down.

2. Figure out how you will aggregate users - what will be the most meaningful way to understand the source data, how will you use source, where are the control points?  Don't bother aggregating unless you can do something by knowing the result.

3. Track page views/session, and aggregate by source for reports.

4. Track 30 day Recency as a measure of the visitor loyalty to any particular "product" on the web site.

With just these 3 metrics - source, views/session, and 30 day Recency across each product, you should have enough intelligence to keep you busy for several years figuring out how to optimize the business. 

The next step would be to figure out who converts to paid subscribers - where do they come from, and what products do they use and stick with?  Then you can refine your products and advertising to attract visitors most likely to convert to paid.  I just did a study on this for another site, and found of the 38 ads they were running, a lot of them got high click-through to the site, but only 2 ads resulted in people becoming paid subscribers - and those two ads had the lowest initial click-through rates, something very common in direct marketing.


If you are a consultant, agency, or software developer with clients needing action-oriented customer modeling or High ROI Customer Marketing program designs, click here.  If you are in SEO and the client isn't converting the additional visitors you generate, click here.

That's it for this month's edition of the Drilling Down Newsletter. If you like the newsletter, please forward it to a friend - why don't you do this now while you are thinking of it? Subscription instructions are at the top and bottom of the newsletter for their convenience when subscribing.

Any comments on the newsletter (it's too long, too short, topic suggestions, etc.) please send them right along to me, along with any other questions on customer Valuation, Retention, Loyalty, and Defection right here.

'Til next time, keep Drilling Down!

- Jim Novo

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