Before every eMetrics (I’ll be in San Fran teaching Basecamp, at the Gala, etc.), I try to ask myself, what is the most critical issue facing the web analyst community right now?  Then, at the show, I ask everyone I run into what they think about this issue.

There’s lots of issues to choose from.  Career path I think is a big area of discussion, given the mergers in the space and trend towards outsourcing.  Then there’s the “we don’t get no respect” thing; senior management doesn’t seem to listen / understand / act on the information provided.  And one of my favorites from the past is still out there, data torture - people being pressured to manipulate data to reach a predetermined analytical outcome.

But seems to me, more important at this juncture is trying to resolve why there is so much written about the importance of “the customer” but very little measurement at the customer level.  Think about it.  Customer experience, customer centricity, the entire social thing, it’s all about customers.

But when folks wants to trot out “proof” that this or that approach is the road to the promised land, they analyze impressions, visits, clicks, etc.  Visitor-level stuff.  Does that seem like the correct approach to you?  Seems to me, if you want to provide knowledge about customers, you should measure customers.

One thing we know is customers do express behaviors through a web interface that are not relevant to the future behavior and value of the customer.  One of the earliest and most widely publicized incidents of this was with Amazon gift purchases.  People went on and on about buying a gift from Amazon unrelated to their interests yet having that category Marketed to them relentlessly over time, even though they never purchased from the category again.

This problem was eventually solved by Amazon using Recency, a classic customer behavior metric – only more Recent behavior was used to make suggestions.  Recency is predictive; and lack of behavior is often just as important, if not  more important, than expressed behavior when trying to understand customers.  Unfortunately, most web analysts are trained to look for expressed behavior, not the lack of behavior.

Further, just because an event of some kind happens in the stream of web activity does not mean the event had any affect on the behavior of the customer.  Display impressions, searches, social interactions, all of it – how can you tell whether the event had any effect on the customer at all?  The only way is to measure at the customer level, for example, comparing the behavior of customers who were exposed to the events with customers who were not exposed.  Or, modeling different mixes of events against customer behavior over time, a “marketing mix” model of sorts, to stretch the idea.

Now some people are going to say. “But Jim, we don’t have web tool access to this data!” or “We don’t pass web data to the back end” and all manner of other related excuses, to which I would say,

“Where is your curiosity?”

Clearly, a unified database is best.  But just because your company can’t afford an advanced WA tool doesn’t mean you can’t do this.

I mean seriously, get a dump from the order management system into a spreadsheet.  Run a query against the CRM database.  Look up individual cases in the customer service or lead management systems.  This the way analysts make breakthroughs, how  business cases are built.  If key web data (campaign codes, logins, etc.) doesn’t make it into the back end, why?  If form data crosses over, how hard could it be to send a campaign code, login, or other critical data?  With proof, then pitch the advanced WA tool, or systems, processes, people, whatever you need to make it easier to analyze customer level data.

OK, so let’s hear all the reasons why it’s fine to draw customer-level conclusions using visit-level data, or why you can’t do the above, which I’m sure will include some of the following:

1.  My boss doesn’t care about customer-level data, ignorance is bliss, pseudo-analysis is OK

2.  I’m too busy learning very little about a lot of things instead of going deep on the most important stuff

3.  Shiny objects rule, so see #2 above

4.  I’m a web analyst, back-end data is not my thing

Other reasons?  What do you think?

Do you see the hypocrisy in claiming to understand customer behavior based on visit behavior?

Let’s talk about this at eMetrics San Fran…and Toronto too.

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

All Talk, No #Measure

We looked for the most active member of the cluster (our “thought leader”) and asked them if they would help us with a “member get a member” program.  Would they be willing to distribute discount coupons to their friends, especially ones who were not already customers?  Time after time, the answer was:

“Honey, all my friends are already customers of yours”.

We launched the program anyway, because it was a pet project from upstairs  – I was a junior marketer at that point so I couldn’t kill it ;)  The program never, ever worked, no matter how hard we tried.  It generated very few new customers while giving lots of discounts to people who were already active buyers.  Basically,  the cost of those discounts overwhelmed the value of the new customers generated.

Apparently a similar thing happens online with Social marketing.

As part of a WAA program that reviews academic research for WAA members, I was able to take a look at a paper titled:  Firm-Created Word-of-Mouth Communication: Evidence from a Field Test by David Godes and Dina Mayzlin.

Here’s my Executive Summary:

The authors investigate the effectiveness of a firm proactively managing customer-to-customer communication.  In particular, they are interested in proving how, if at all, a firm should go about effecting a meaningful word-of-mouth (WOM) communications program.  This is done through two different data collection schemes: a large scale, 15 market test through BzzAgent with a client restaurant chain, and also through a controlled online experiment.  The results are somewhat counterintuitive and may change the way web analysts and Marketers should be thinking about WOM and social analysis, particularly if there is a hard monetary investment in the WOM program.

Specially, the researchers are trying to answer 2 questions:

1.  What kind of WOM maximizes incremental Sales?

The answer: WOM created by less loyal (not highly loyal) customers, and occurring between acquaintances (not friends).  Though perhaps surprising, this result is often found in Marketing program measurement; Sales would occur anyway without the program, especially among best customers.  These results demonstrate the pitfalls of not using control groups (people not exposed to the campaign) to measure Marketing effectiveness.

2.  Which people are most effective at creating the WOM above?

The answer: “Opinion Leaders” or “Fans” are not as effective in spreading WOM that drives incremental Sales because these efforts are “preaching to the choir”, per #1 above.  The networks that opinion leaders or fans have are likely to already know about the Product from pre-existing conversations, and spending money on creating a campaign to reach these people is ineffective because the social communication has already taken place.

In sum, if you want to invest in a WOM program that will drive Sales you would not have received anyway, you want the WOM conversations happening, as the authors say, “where none would have naturally occurred otherwise”.

Here’s a portion of my Review, where Awareness versus Persuasion is used as a framework to help explain the results:

The discussion of the difference between the need for a persuasive argument versus building awareness is something web analysts should keep in mind so they can make sure they understand the real needs of the Marketer or Product Manager.  For products with high awareness already, what is really needed to increase Sales is persuasion of the people already aware, not more awareness.  New products obviously need increased awareness.

Per this study, this persuasion versus awareness question affects the choice of who to recruit for WOM campaigns. Loyal customers are the best persuaders and are best used when the product already has high awareness.  If you want to drive sales through increased awareness – the goal of many WOM campaigns online – you should be recruiting less loyal customers and encouraging them to talk not to their friends, but to their acquaintances.  This approach appears to be contrary to the “opinion leader” or “fan” approach now thought of as best practices.

As is typical of academic research and testing, the paper contains an extensive review of the results of other WOM Marketing studies all the way back to the 1970’s upon which the hypothesis for this test was formulated.  If you are a WAA member, you can get a copy of the peer-reviewed paper from the journal Marketing Science (other versions are floating around) with all these footnotes listing previous test sources.   Instructions on how to do this are at the end of the Review.

————–

I’m not going to post the rest of my Review on the blog.  Rather, I’d like to ask some questions and get some discussion going on this topic, because I think this test and paper shines light on a fundamental flaw in the way people think about Marketing on the web.  So read the rest of the Review here if you’d like, then please come back and offer your opinions on these questions:

1.  Test results indicate if you’re going to maximize an investment in WOM, you should target less loyal customers talking to acqaintainces rather than loyal customers talking to their friends.  Do you understand this idea?  Believe in it?

2.  Do you think this is an important discovery?

3.  Do you think this general model – the idea that to maximize Sales, best customers should be treated differently than other customers - might apply to many different types of web / interactive marketing?

4.  Will you do anything with this information?  What’s first step?

Update: Prompted by the questions in Comments, I offer these additional links to previous posts.  Here is a more detailed explanation of  “lift” and “incremental”.  Those who might want want to go “up” to a macro model for how to think about Marketing in a Social or Relationship  mode should see Framework for Engagement.  If you want to go “down” to analysis or execution and are interested in “How do I Measure and take Action on these Ideas”, see the Measuring Engagement Series.  If you want to understand how this model integrates with the traditional offline Push model, see the Marketing Bands Series.

More questions?

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Awareness versus Persuasion

We started talking audience segmentation and content at the eMetrics Summit, and specifically the “shootout” format from the old days.  You know, 10 vendors on the stage at the same time taking questions from the audience.  Those sessions were both educational and hilarious at the same time, as the vendors side-swiped each other on topics like accuracy, how visitors are counted, cookie structures, and so forth.

But that was back when the technology was in flux, and now that issue has settled down a lot.  Braden brought up the concept of returning the “shootout format”, but more on the business side.  You know, get some practitioners, vendors, and consultants up on stage and have them thrash out stuff like:

1.  Attribution – does it really make sense to even bother with attribution at the impression / click level when there is often not a strong correlation to profit?  I mean, just because someone sees or clicks on an ad does not mean the ad had a positive effect; in fact, it may have had a negative effect.  Why not go straight to action or profit attribution, instead of using creative accounting?

2.  Why is there so much focus on real time measurement and so little “taking action” in real time?  What’s the point of reporting in real time and then having monthly meetings to take action on performance?  Why not take a gut check on the numbers at noon each day and if you are behind target, do something now?  If your org will not “act now”, then why measure what happens “now”?

3.  Does tracking page views and visits really matter in all but a few models?  If your KPI’s are worth anything, why would you look at visits or page views except “forensically”, to troubleshoot a problem revealed by the KPI’s?

4.  Why do people spend so much time measuring really small, insignificant ideas while ignoring the “big levers” they could pull that would really make a difference?  Is it possible people don’t really understand the business model they are operating in and just measure a lot of meaningless stuff ”because they can”?

The panelists could define the problem (if any) and provide potential solutions and work-arounds for these challenges.  What do you think? 

Would you like to see “shootouts” between experts on these topics?  What other business / marketing topics would you like to see shootouts or group discussions on?

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

eMetrics “ShootOuts” We’d Like to See

For example, did you even know there is a peer-reviewed journal called Marketing Science, which calls itself “the premier journal focusing on empirical and theoretical quantitative research in marketing”?

Whoa, say what?

This journal is published by the Institute for Operations Research and the Management Sciences, and articles are the work of premiere researchers in visitor and customer behavior from the best known institutions around the world.  In case you didn’t know, “peer-reviewed” means a bunch of these researchers (not including the authors, of course) have to agree that what you say in your article is logical based on the data, and that any testing you carried out adhered to the most stringent protocols – sampling, stats, test construction, all of it.

And, most mind-blowing of all, they show you the actual math right in the article – the data, variables, formulas, graphs – that lead to the conclusions they formulate in the studies.  You know, like this:

So, the opinions coming from Marketing Science are probably a lot more reliable than say, the average blogger in the echo chamber.  Know what I’m saying?  And here’s a surprise, the findings in these  articles often contradict what is passed off in the blogosphere as “common knowledge” by the digerati.

In case you are thinking, “Well, these lab coats can’t possibly be exploring anything that would be interesting to me“, take a look at some article titles in the Mar - Apr 2009 edition of Marketing Science:

* Website Morphing

* Real-Time Evaluation of E-mail Campaign Performance

* Optimal Bundling Strategies in Multiobject Auctions of Complements or Substitutes

* Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

* Click Fraud

 Or article titles for the Jan – Feb 2009 edition of Marketing Science:

* Quantifying the Long-Term Impact of Negative Word of Mouth on Cash Flows and Stock Price

* Going Where the Ad Leads You: On High Advertised Prices and Searching Where to Buy

* Content vs. Advertising: The Impact of Competition on Media Firm Strategy

* My Mobile Music: An Adaptive Personalization System for Digital Audio Players

Tell me you want to put more faith in your RSS feed than in what these folks have to say in Marketing Science.  What blogger shows you the math?  What Research for Press Release piece passed around the echo chamber shows you the survey questions, the sample distributions, the confidence intervals?

But wait, there’s more.

The Research Committee at the WAA has been working hard on a program to open up some of these influential resources to WAA members.  You can see the beginning of those efforts here.  Expect more in the future as the effort ramps up.  The knowledge sources currently in the hopper are these:

By the way, if you are a WAA Member and would like to participate in reviewing these articles for the WAA Membership, volunteer for the Research Committee.  Contact Christopher:

christopherb “at” criticalmass.com

Now, you don’t have to agree with what folks who publish in Journals like these have to say, but as an analyst, I sure want to know what they think, see how they arrived at those thoughts.  And gut-check my own reality against them. 

Because the analyst’s mission is truth seeking, finding better truths.

Can you afford to ignore some of the most respected Marketing researchers in the world when formulating your hypothesis?

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Marketing Science (Journal)

Jim Humphrys has the research on salaries and demand in the sector, Shaina Boone of Critical Mass is the practitioner who has both taken the Certificate classes and is applying this knowledge in the real world, and Bernie Jeltema is a UCI Instructor for the Certificate classes.

Here’s the official description:

UCI Webinar: Certificate in Web Intelligence
Wednesday 19-Nov-08 2:30 PM to 3:30 PM EDT

Web Intelligence is a combination of web analytics and business intelligence. As companies expand their reach into the global marketplace, the need to analyze how customers use their web sites to learn about products and make buying decisions is becoming increasingly critical for survival and success.  Wondering how to position yourself for these career opportunities and how specific coursework can be valuable?  This planning session will provide pre-registration educational and career advancement advisement. Also learn more about the web intelligence certificate program, courses being offered in upcoming quarters, and career planning resources available through the UC Irvine Extension and the University of British Columbia, Continuing Studies

To register visit: http://unex.uci.edu/certificates/it/web_intel/

On this page, you can either sign up to “Stay Informed” about the program (green bar) or Register for the webcast in the box below this bar, which is called Web Intelligence Education Planning Session.

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Webcast on Web Intelligence 11/19

I’ll be speaking about LifeCycle analysis and providing “how to act on the analysis” for the Marketing side.  If you are being asked to cut back Marketing budgets, LifeCycle analysis is a great way to understand the Financial ramifications of Marketing budget cuts and start getting Predictive.

Coming in on Tuesday so will miss the WAA event on Sunday for the first time.  On the flip side, I will be there through Friday afternoon ’cause I am presenting at the WAA Board meeting. 

So, for the first time since probably 2004, I will actually be there when the shindig closes.

Who’s doing what Thursday night?

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

See Ya @ eMetrics

We met in person for the first time doing a presentation together at the DMA show in Chicago this fall, and because he used to work at Crutchfield – a truly customer-driven remote retailer – we share some experiences and beliefs.

For those of you who might be wondering where a lot of the Marketing Productivity ideas I post here come from, this interview-podcast is probably a pretty good backgrounder.  We talk about a lot of stuff, including:

Monetizing customer experience

Importance of Control Groups / Source Attribution

Multichannel Marketing Strategy

LifeCycle Contact Strategy versus Calendar-based

Retail Business Models / Lab Store

Search box or not? / Serendipity

How to tell if online customers are really engaged – without web analytics

Here’s another link to the Interview-Podcast.  Enjoy! 

That was lots of fun, thanks Allen!

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Interview-Podcast w/ Jim Novo

But C-folks don’t really care much about what has already happened, because they can’t do anything about it.  What they really want to know is what you think will happen.  For example, ideas like “sales pipeline” – a forecast.  If you start forecasting – and you are right - you will get attention from the C-folks pronto.  The web is a great forecasting tool because it’s so frictionless; it tends to provide tangible signals before many other parts of the business.

So: Do you have any KFI’s – Key Forecast Indicators?

I have one for the Lab Store, and it tripped about 2 months ago.  It’s the Unwanted Exotic Index (UEI).

As part of the Lab Store, we run a moderated board where people who want to give up exotic pets can post the availability, and people looking for exotic pets can post requests.  Typically, the ratio of people giving them up to wanting them is about .25 - for every post looking to give an exotic up, there are 4 posts looking to adopt.

A couple of months ago, this ratio starts popping higher.  A couple of weeks ago it hit 1.25 – for every 5 posts looking to give up an exotic there were 4 posts looking to adopt.  The last time something like this happened was prior to the mini-recession of 2004, when the Unwanted Exotic Index tagged 1.0 for a short time.  After this happened, our sales got soft about 2 – 3 months later.

Why is the UEI predictive?  Let’s go through the logic – my logic, anyway!

Keeping certain types of exotic animals can be a strain on a family, both from a time and money perspective.  They can be high maintenance.  On the margin, as the economy gets tougher and people look to manage household budgets, these pets can get some scrutiny – particularly if kids have lost interest or gone off to college.  So more go up for adoption.  At the same time, requests to adopt fall, as families who might have considered an exotic pet put the “owning decision” on hold.  Taken together, these decisions cause the UEI to spike higher.  Both giving up and deciding not to own exotic pets affects Lab Store revenues “expected” in the future.  So the UEI ends up being predictive of future demand.

Makes sense to me.

Now, I’m a pretty good student of macroeconomics and pay attention to many economic indicators, especially predictive ones like the ECRI’s US Weekly Leading Index.  If you’re an analyst, you should too; economic indicators provide context for any analysis you might have to do, and clients often want to understand the impact of these external issues on their business.

As far as the Lab Store specifically, I don’t usually pay much attention to the macroeconomic cycles.  The pet business tends to be insensitive to the economic cycle; people don’t stop caring for pets as the economy wobbles up and down.  That’s why it’s such a good business – if you can find a niche.  So I don’t get too concerned when I see these predictive macroeconomic indexes forecasting a slowing economy.

However, what we have here with our Unwanted Exotic Index is a confirmation of the broader economic forecasting tools that is specific to our exotic pet business.  That makes me sit up and take notice!  Looks like our business is setting up for a repeat of the 2004 slowdown - the last time the UEI spiked like this.  Why is this important?  Because I can do something with this knowledge.  I can re-allocate and re-prioritize based on this knowledge.  For example, I can move from a “grow bigger” to a “grow smarter” mode.

And please note: this KFI has nothing to do with traffic or sales on the web site; traffic and sales are “rear view”.  By the time you see the sales slow down it will be too late to do anything about it.  And that’s why the C-folks don’t care much about web analytics reports.  

You could track an index like the UEI with a web analytics tool, but you’d have to come up with the idea first.  My point is you will probably have to look outside the usual “rear view” metrics to find one with forecasting ability.  I caution you not to substitute a “survey” for a predictive model; people’s opinions are a notoriously lagging indicator.  You’ll be up to your ears in the slowdown before people start turning bearish.

So: Do you have any KFI’s – Key Forecast Indicators?  Tell us about them. 

If you don’t have any KFI’s, now is the time to start looking for them.  What can you see now that predicts what will happen in the future?  Think about the business, think about the data sources, and put together a bunch of different ideas.  Track them back a couple of years and post them monthly going forward.  You’re bound to find something predictive.  Perhaps something about posting, like the UEI.  Recommendations / comments as a percent of visitors or something like that.

If you’re stuck, start with a simple “engagement” idea – percent visitors / members / customers who visited / logged in / bought in the past 90 days.  If this percentage is falling, so will your business in the next 3 – 6 months.  If your business has a lot of seasonality in it, look to year-over-year comps of the same metric.

If you’ve never played this game before, you won’t have proof your KFI’s work until after the business is in the soup, but you’ll be ready with accurate and actionable KFI’s the next time around!

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

KFI’s: Key Forecast Indicators

Want to see the answers to previous questions?  The pre-blog newsletter archives are here, “Best Article” reviews here.

Q:  I ordered your book and have been looking at it as I have a client who wants me to do some RFM reporting for them.

A:  Well, thanks for that!

Q:  They are an online shoe shop who sends out cataloges via the mail as well at present.  They have order history going back to 2005 for clients and believe that by doing a RFM analysis they can work out which customers are dead and Should be dropped etc.  I understand Recency and have done this.

A:  OK, that’s a great start…

Q:  But on frequency there appears to be lots of conflicting information – one book I read says you should do it over a time period as an average and others do it over the entire lifecycle of a client.

A:  You can do it either way, the ultimate answer is of course to test both ways and see which works better for this client.

Q:  Based on the client base and that the catalogues are seasonal my client reckons a client may decide to make a purchase decision every 6 months.  My client is concerned that if I go by total purchases , some one who was  really buying lots say two years ago but now  buys nothing could appear high up the frequency compared to a newer buyer who has bought a few pairs, who would actually be a better client as they’re more Recent?  Do I make sense or am I totally wrong?

A:  Absolutely make sense.  If you are scoring with RFM though, since the “R” is first, that means in the case above, the “newer buyer who has bought a few pairs” customer will get a higher score than the “buying lots say two years ago but now buys nothing” customer.

So in terms of score, RFM self-adjusts for this case. The “Recent average” modification you are talking about just makes this adjustment more severe.  Other than testing whether the  “Recent average” or “Lifetime” Frequency method is better for this client, let’s think about it for a minute and see what we get.

The Recent average Frequency approach basically enhances the Recency component of the RFM model by downgrading Frequency behavior out further in the past.  Given the model already has a strong Recency component, this “flattens” the model and makes it more of a “sure thing” – the more Recent folks get yet even higher scores.  

What you trade off for this emphasis on more recent customers is the chance to reactivate lapsed Best customers who could purchase if approached.  In other words, the “LifeTime Frequency” version is a bit riskier, but it also has more long-term financial reward.  Follow?

So then we think about the customer.  It sounds like the “make a purchase decision every 6 months” idea is a guess as opposed to analysis.  You could go to the database and get an answer to this question – what is the average time between purchases (Latency), say for heavy, medium, and light buyers?  That would give you some idea of a Recency  threshold for each group, where to mail customers lapsed longer than this threshold gets increasingly risky, and you could use this threshold to choose parameters for your period of time for Frequency analysis.

Also, we have the fact these buyers are (I’m guessing) primarily online generated.  This means they probably have shorter LifeCycles than catalog-generated buyers, which would argue for downplaying Frequency that occurred before the average threshold found above and elevating Recency.

So here is what I would do.  Given the client is already pre-disposed to the “Recent Frequency” filter on the RFM model, that this filter will generally lower financial risk, and that these buyers were online generated, go with  the filter for your scoring.

Then, after the scoring, if you find you will in fact exclude High Frequency / non-Recent buyers, take the best of that excluded group – Highest Frequency / Most Recent – and drop them a test mailing to make sure fiddling with  the RFM model / filtering this way isn’t leaving money on the table.

If possible, you might check this lapsed Frequent group before mailing for reasons why they stopped buying – is there a common category or manufacturer purchased, did they have service problems, etc. – to further refine list and creative.  Keep the segment small but load it up if you can, throw “the book” at them – Free shipping, etc.  

And see what happens.  If you get minimal  response, then you know they’re dead.

The bottom line is this: all models are general statements about behavior that benefit from being tweaked based on knowledge of the target groups.  That’s why there are so many “versions” of RFM out there – people twist and  adopt the basic model to fit known traits in the target populations, or to better fit their business model.

Since it’s early in the game for you folks and due to the online nature of the customer generation, it’s worth being cautious.  At the same time, you want to make sure you don’t leave any knowledge (or money!) on the table.  So you drop a little test to the “Distant Frequents” that is “loaded” up / precisely targeted and if you get nothing, then you have your answer as to which version of the model is likely to work better.

Short story: I could not convince management at Home Shopping Network that a certain customer segment they were wasting a lot of resources on – namely brand name buyers of small electronics like radar detectors – was really worth very little to the company.  So I came up with an (unapproved) test that would cost very little money but prove the point. 

I took a small random sample of these folks and sent them a $100 coupon – no restrictions, good on anything. I kept the quantity down so if redemption was huge, I would not cause major financial damage.

With this coupon, the population could buy any of about 50% of the items we showed on the network completely free, except for shipping and handling.

Not one response.

End of management discussion on value of this segment.

If you can, drop a small test out to those Distant Frequents and see what you get.  They might surprise you…

Good luck!

Jim

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

What’s the Frequency?

Anyway, the weeks that were.  First was a WAA Webcast on Money, Jobs and Education: How to Advance Your Career and Find Business Opportunities (site registration required, but you don’t have to be a WAA member) to get ready for and execute.

And there was the ongoing wrestling match to establish a framework for higher educational institutions to create course offerings in Web Analytics, leveraging the course content the Web Analytics Association has developed.  Very tricky stuff dealing with these Higher Ed folks, but we think we have it figured out.  The WAA’s first partner in this area will be the University of California at Irvine – not a bad start, methinks.

Then of course, it’s Conference season.  I’m going to be on a “Measuring Engagement” panel at WebTrends Engage October 8 -10.  The following week is of course the eMetrics Marketing Optimization Summit where I will be doing a conference presentation in the Behavioral Targeting Track and then sitting on a no holds barred “Guru Panel” with Avinash Kaushik and Bryan Eisenberg immediately after. 

Part of getting ready for the Summit this year was a review of the WAA BaseCamp teaching materials, a pretty substantial piece of work all by itself.  We’ve done some tweaking based on comments from students in previous classes.

Unfortunately, I have to split the Summit right after the Guru panel for the Direct Marketing Association Conference in Chicago, so if you’re going to eMetrics and you are looking to chat with me, make sure you hit me up before my presentation Tues at 1:30 PM (I will be there Sunday 10/14 @ 4 PM for the WAA meeting). 

At the DMA, I’ll be doing a presentation with fellow web analytics blogger Alan Rimm-Kaufman in the Retention & Loyalty Marketing Track called Smart Marketing: Advanced Multichannel Acquisition and Retention Economics.  Control groups, predictive models, oh boy.

The next day, I’ll still be in Chicago doing a real “stretch event” at the invitation of Professor Philippe Ravanas of Columbia College Chicago for The Chicago Community Trust.  Nine (9!) non-profit arts groups are battling for grant money to help execute their marketing plans, and yours truly is going to vet those plans and teach donor / membership marketing in a live format – with all nine institutions exposing their guts to me and each other -  in real time!  Budgets, response rates, web sites, direct mail, newspaper, radio, database marketing, it’s all on the table.

Should be a real kick – if I survive the format, that is.  As a musician, I have always had a great interest in arts / donor marketing and this will be a great opportunity to interact directly with the folks in the trenches.

So, I apologize for the lack of posts the past couple of weeks as we now join our regularly scheduled life (in progress).

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Speaking Schedule, WAA Projects, etc.