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Recency: Web Retailing Example 
Drilling Down Newsletter # 33: May 2003


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

Get the Drilling Down Book!
http://www.booklocker.com/jimnovo

Also available online through Amazon and Barnes & Noble
but it's a lot more expensive there than at Booklocker!

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.  I wrote this manual with Bryan Eisenberg of Future Now, the visitor conversion specialists.  Download a free white paper on the topic:
Marketer's Common Sense Guide to E-Metrics

Prior Newsletters:
http://www.drilling-down.com/newsletters.htm
-------------------------------

In This Issue:

# Topics Overview

# Best of the Best Customer Marketing Links

# Tracking the Customer LifeCycle: Recency

# Questions: Operational versus Marketing Databases?

-------------------------------

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

Hi again folks, Jim Novo here.  

This month we've got the usual "best of" Customer Marketing article links section, but  only one link.  This is because we also have the final chapter on the Recency metric with IMissAsia.com, and it's a doozy.  Plus, a fellow Driller wants to know what is involved with using an operational database for real-time marketing (I can hear the IT folks on the list pounding the desk)...

My favorite topic lately is this one: why are companies so afraid to call an end to the customer LifeTime?  If you can't peg the LifeTime, you can't measure LifeTime Value, and if you can't measure LifeTime Value, you leave the majority of CRM ROI off the table.  Sound interesting?  If you want to find out how to attack this issue and bring home the ROI bacon using simple customer, models check out my searchCRM interview and webcast:

Interview        WebCast (on-demand, ignore "start time")

OK, let's do some Drillin'!

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

This section flags "must read" articles moving into the paid archives of trade magazines before the next newsletter is delivered.  If you don't read these articles by the date listed, you will have to pay the magazine to read them from the online archives.  

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.

Loyalty Breeds Success for Drug Mart, Vendors
Expires May 27, 2003  DM News
What is it with those Canadians anyway?  They sure love their loyalty programs - and so do the program partners when they can drive instant sales and share gains using the rich transactional database for targeting.  Like they say in the article, "past purchase behavior is the best indicator of future behavior".  Want to know more?  Download the loyalty case study and check out the book that teaches you how to use past behavior to increase your profits.


Tracking the Customer LifeCycle: Real World Examples
=====================
If you are new to our group and want to review the previous LifeCycle metric - Latency - that discussion is here, along with the Real World examples
Hair Salon and B2B Software.  The previous piece on Recency is here; this series on Recency starts here.

Recency: The Web Retailing Example

Recall last month, the owner of IMissAsia.com solved the question of why response to the newsletter keeps going down but sales remained flat: most of the buying activity was coming from people who Recently joined the newsletter.  Since the number of new people per month joining the newsletter was flat, sales are flat month to month.  

But as the newsletter list keeps growing each month, this "new blood" - new subscribers who are the most likely to respond and buy - becomes a smaller and smaller percentage of the total newsletter list.  So sales remain flat as response rate to the newsletter is falling. 

So now we have a chain of events, or LifeCycle, driven by Recency: the more Recent the subscriber, the more likely they are to purchase the first time; and the more Recent the last purchase, the more likely the buyer is to purchase again.  In fact, all subscribers and customers can be ranked for their likelihood to make a purchase at any given time.  The owner is quite pleased with discovering this idea, since now this ranking can be used to reduce the amount of the discounts offered, an area of great concern.

But on to the next challenge - how to increase newsletter subs?  The owner always tried to follow hot topics or trends talked about in the online community at the site, both in the newsletter and merchandising of the site.  The owner felt it was more logical to "put products in front of the traffic" than try to force or bend the traffic to come to the products.  Find a group of people and give them what they are already looking for - it's same reason why   search marketing works so well for the site.  The owner was not sure if this was the right thing to do on the content side, but there was really no way to prove anything in this area.

For example, some parts of the site get very high traffic, others lower traffic.  Would more newsletter subs be generated if the high traffic areas had more content?  If the low traffic areas were downplayed in the navigation?  Who knows, and implementing would be a lot of work, so just guessing was not a good idea.  

The owner had done some content analysis along these lines.  There were 10 major content areas on the site; all the pages in a single area are set up as “Content Groups” in the visitor analysis tool the owner is using.  This means the owner can track stats at a macro level for content areas as a whole very easily, instead of having to deal with tracking down individual page views and aggregating them into a report.

The owner used these Content Group reports to detect “hot spots” on the IMissAsia.com site.  By looking at the trend in total visits and page views over time, the owner got a good idea of where the interests of the audience were flowing, and used this information to “stand in front of” this topical traffic with articles and products.  But was following the traffic really the right idea?  Certainly not all traffic is created equal; quantity is not equal to quality.  But what else would you look at?

Wouldn’t you know it, the owner had just upgraded the visitor analysis tool, and found out that it now supports visitor Recency as a native reporting metric right out of the box.

Just in the nick of time for this Example, eh?

So, the owner excitedly turns Visitor History on in the analysis tool, and it starts building a record of last visit date for each visitor. Then, looking at the 10 different Content Groups, the owner ran a report on the average Recency (average days since last visit) of visitors to each of the Content Groups. What do you think the owner found?  Yup.  Dramatic differences in average Recency by Content Group.  In fact, generally the Content Groups with high overall traffic had the worst average Recency (longest time since last visit), and the low traffic groups had the best average Recency (shortest time since last visit):

Content Groups, by Traffic and Recency

Traffic Rank Average Recency
1 36 days
2 32 days
3 24 days
4 29 days
5 27 days
6 23 days
7 14 days
8 17 days
9 7 days
10 4 days

Well, there's a shocker, thinks the owner, who was getting used to this kind of slap upside the head from the Recency metric by now.  But it made sense.  The areas with specific, targeted content had the lowest traffic but this traffic was on average more Recent - visitors to these areas didn't just "repeat", they were Recent Repeaters.  The high traffic areas had relatively untargeted content so they drew a lot of activity but not a lot of loyalty; after a few visits that was it.

"How interesting" thinks the owner of IMissAsisa.com.  Perhaps the high traffic / low loyalty areas are frequented mostly by new visitors and potential customers, where the low traffic / high loyalty areas are frequented primarily by current customers.  Clearly there was an actionable idea in this chart, though it would take some more crunching with the visitor analysis tool to draw it out.

"Wait a minute", the owner thinks.  "I have been tracking newsletter subscriptions by Content Group.  You don't suppose..."

Well, fellow Driller, can you guess what the owner of IMissAsia.com is on to here?  What will the data say?  Here it is:

Content Groups, by Traffic, Recency, and Newsletter Conversion rate

Traffic Rank Average Recency Conversion:  Subscribers
to Visits
1 36 days .2%
2 32 days .6%
3 24 days 1.1%
4 29 days .9%
5 27 days 1.4%
6 23 days 1.7%
7 14 days 3.8%
8 17 days 2.1%
9 7 days 5.6%
10 4 days 7.2%

You guessed it.  The more Recent the Content Group, the higher the conversion of visits into newsletter subscriptions.  The owner, once again slack-jawed by the power of the Recency metric, sums it all up:

"On average, the more Recent the visitor is, the more likely they are to subscribe to the newsletter, relative to other visitors.

The more Recent the newsletter subscriber is, the more likely they are to make a purchase, relative to other newsletter subscribers.

The more Recent the last purchase is, the more likely the buyer is to make another purchase, relative to other buyers."

The owner continues on, a bit breathless with the rush of all this stuff coming together at the same time.

"What I am seeing is that becoming a "best customer" on IMissAsia.com can be seen as a process, one that starts with a visit, moves on to a subscription, then a purchase, and hopefully multiple purchases.  I always sort of knew that; now I see it in action.  But what's really powerful is I can rank each member of a group - visitors, subscribers, buyers - against all the other members of their group for likelihood to move forward in the visitor - subscriber - buyer - multi-buyer process.   

Knowing this provides me with three benefits:

1.  Having the source of the visitor, subscriber, or buyer, I can "track backwards" and find out what sources (media type / offers) generate visitors most likely to become subscribers, buyers, and multi-buyers - and using Recency, predict which of those are most likely to complete all the steps of the LifeCycle process.

2.  I can customize communications to the members of each group based on their likelihood to move forward in this LifeCycle using Recency.  By addressing specific people with the right message at the right time (like I did with the discount ladder), I will generally get higher response and conversion of the visitor to multi-buyer while lowering my marketing and discount costs.

3.  I can track retention and failure to progress in the LifeCycle with Recency, and be proactive about trying to "save" customers who are in the process of defecting.  At any point in the visitor - subscriber - buyer - multi-buyer LifeCycle, I can track decreasing likelihood to progress and take special action with those who have high potential value or current value based on their source or past buying behavior."

It was late, and the owner was exhausted.  No point in trying to map out all the implications of these discoveries now; this Recency thing was obviously quite powerful and it would take some time and testing to fully develop.  For example, rather than determining "what's hot" just by visit volume, if I look at the Recency of visitors I can make a better guess on whether the issue is important to core customers or casual visitors, and adjust the message and offers appropriately.  To think all of this came out of trying to answer one simple question on newsletter response.

Having now discovered the secret of the Recency Chain, the owner was confident IMissAsia.com could be taken to the next level of profitability.  Yes, things are sweet, the owner thinks, as she turns off the light and "heads home" - down the stairs to her living room.

End of Recency: the Web Retailing Example

Jim's Note: The capability to track Recency (or Latency) has up until now been addressed with either a home-built system (all you need is a way to recognize users and store last visit date) or a data warehouse solution like WebTrends Intelligence Suite, CoreMetrics, Quadstone, several others.

What is new is the availability of Visitor History tracking without building it yourself or getting into a solution that has too much horsepower in other areas for what you need.  In other words, it just got a lot cheaper and easier to track visitor history - and also to have these critical metrics integrated into the rest of your web site reporting.

WebTrends Reporting Center provides Visitor History capabilities in the Enterprise Edition, storing Recency and Latency data, plus many more "source" variables such as 1st campaign, 1st referrer, etc.  Intellitracker out of the UK also provides reporting on Recency / Latency.

Bottom line: You've got no excuse now...

-----------------------------------------
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.
-----------------------------------------


Questions from Fellow Drillers
=====================
Jim's note: If you still don't know what RFM is and how it can be used to drive increased profitability in almost any business, read this.

Q:  As of today, I am in the early planning stages of an Operational database.  This Operational database communicates with merchant terminals.  Loyalty cards will be distributed  to merchants' customers.  The  loyalty and gift card functions are not problematic, but the expense to track migration of customers at the Operational database level is questionable.  With a marketing database created using the software from your book...

A:  Ummm...  I don't know how many customers you are talking about, or what kind of operational systems you have, but the scoring application that comes with the book is just a Microsoft Access application.  My application doesn't "create a marketing database"; it runs on a database you have already created.  You probably know that, but just to make sure...

Q:   Can various reports be made on the Marketing database that track: who (of a particular segment) had a particular offer (specific to their buying behavior) and report the redemption or non-redeemed offers in a specific duration of time?  Can I avoid this expense on an Operational database with a Marketing database?

Q:  Well, you can report on anything that makes it into the database.  As long as you can get "who the offer was made to" and "who responded" data into the Marketing database, yes, you can report on it, and use it to create offers.

A:  The programmers of the Operational Database encourage using a Marketing Database for various reasons. First, the specific offers made to particular customers at a particular merchant will be communicated to the merchants' terminal using the Operations database.  This protects merchants from customer fraud or abuse of repeat redemption.  Second, they want to make more money.  What do you recommend?

Q:  I'm not sure I am following this completely, but I think the situation is this: your ops people don't want you running marketing stuff on their database, because they are afraid it will slow down performance, and that is bad for them.  So they want all the coding, scoring, tracking, and development of offers to take place "offline".  This is quite typical and standard procedure.  

Usually the marketing  database doesn't have to be "real time" where the operational database does.  So each night, the operational database updates the marketing database with transactions and you do whatever you want on the Marketing database - analysis, scoring, creation of promo codes, and so forth - without slowing down operations.  You get
the update, then do all your analysis work outside the operational system.

Now, if you want the ops database to be able to respond in real time based on customer scores and provide promotional codes or other data, what you do is send "customer codes" back to the ops database from the marketing database after you run your scoring.  

For example, you get data from ops at 1 am, run scoring until 2 am, then send promotional codes back to the ops database at 3 am.  So the next day, the operational database can respond to customers in real time based on scores and codes but it doesn't have to do any calculations because it has the scores and codes already.  This dramatically reduces the "load" on the operational system, while still allowing it to be "smart" in real time.

Scoring of transactional data for real-time use in operations is a classic benefit of RFM, because the scores are "standardized" and are the same format each time. Since the scores rank likelihood to purchase and customer value, it is fairly easy to set up a rules-based system to make offers according to customer value and predicted reponse.

Am I understanding the question correctly?  If not, feel free to ask again!

Jim

-----------------------------------------
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.  
-----------------------------------------

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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