Archive for the ‘DataBase Marketing’ Category

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Customer Value in the Freemium Model

Friday, December 4th, 2009

The following is from the November 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: You kindly clarified a few issues when I was reading Drilling Down earlier this year – so I hope you don’t mind the direct email.

A: Yes, I remember!

I am working for www.XYZ.com, a social networking / virtual world site based abroad but visitors are 85% US.

Our growth up to now has been mainly viral and in the summer we hit 1.2M UVs operating on the Freemium model with only 5% of our registered users converting to paying customers and a significant portion of our revenue coming from ads.  On average our customers are active on the site for something like 4 months making their first purchase around day 28. 

But to take us to the next stage we are embarking on some marketing for the first time using AdWords and various revenue share campaigns, and of course to do this sensibly we need to arrive at a reasonable estimate of LTV.

A: Makes sense!

Q: To calculate an adjusted LTV I removed all customers with a lifetime of less than 4 months but this gives a low estimate as this calculation ignores the bumper summer months and the extra paid for features put in place earlier this year.  Calculating LTV using ARPU and monthly churn (not sure how to calculate this in our environment) gives another different estimate.  Is there any help or advice you could perhaps give us?  If not in the US then perhaps you could recommend somebody abroad – can’t find anything in the literature relevant for start-up like us.

A:  It sounds to me like you’re trying to make this too complicated, at least for the place you are at this time.  Monthly churn and the “28 day” threshold are nice to know on a tactical level, but LTV is more of a Strategic idea that does not necessarily benefit from analysis at that level.  And you may not really want LTV, but a derivative that might be more helpful.

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Posted in DataBase Marketing, Newsletters, Web Analytics | No Comments »

“X Month” Value

Friday, November 20th, 2009

The basic concept of LifeTime Value (LTV) was ably outlined by Seth Godin in a great post here.  If you know the average net value of a customer is $2500 over their “Life”, why would you not spend  $50 (or $200, really) to acquire each one?  As long as you stuck to the model, your company would be insanely profitable over time.

Their are 2 primary challenges to implementing this idea.

1.  “Over time” is a concept many management folks have a hard time embracing; what matters are the profits this year, or this quarter, or this month.  Unless the whole company embraces an “over time” measurement approach it is difficult for Marketers and Analysts to drive towards programs and practices supporting the LTV outcome.

2.  The $2500 is an average figure.  Most customers are worth less; 10% or 20% are worth much more.

Most people I talk to embrace the general idea of LTV models intuitively.  It’s really a cash flow concept, isn’t it?

So Financial people get it right away, and if Marketers could align with it, there would be no conflicts and the Marketing budget becomes virtually unlimited.

In fact, many folks in the PPC world follow just this model – they have unlimited budget as long as each conversion costs no more than “X”.  Because the company knows if it spends no more than X on a conversion, it always makes money.   Marketers and Analysts involved with these “Cost < X” PPC programs love them, because Management loves them. 

And Management loves them, why?  Because the CFO loves these programs  Why?  Because they are based on Cash Flow analysis, which CFO’s understand very, very well.

So then, what will it take to get more acquisition budgets like these Cost < X  PPC programs?  We have to address the two challenges above:

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Posted in DataBase Marketing, Web Analytics | 6 Comments »

Member Retention in Professional Orgs

Wednesday, November 4th, 2009

The following is from the October 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: I have recently purchased your book Drilling Down and going through the many interesting concepts.

A: Thanks for that!

Q:  I work for a membership Organization and we would like to conduct some analysis into who we may lose and approach them even before their membership lapses.  But the only problem here is that we carry data only on the purchases made (though many of our members do not purchase our products and stay a member) and web site visits.

A:  Are you *sure* that’s all the data you collect?  I once worked with a professional membership org that thought they only had one data source, but turns out they had 8 – from 8 different areas of the org – that nobody really knew about.

Q:  How do I know if a particular member is going to resign and lapse soon with this limited amount of behavioral data.  Recently it’s been a concern that we are losing members who have been with us for more than 10 years and who are in their mid career profession (aged between 30 to 45) and indicated no specific reason for resignation. 

This has been going on for the last few months and now we would like to strategically target these customers and approach them even before they react negative.  What concepts could help me to do this? Your guidance would be much appreciated.

A:  OK, my answer will be in two sections: if you (hopefully) find you have more data than you think, and if you really don’t have any other data to fall back on.

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Posted in DataBase Marketing, Marketing thru Operations, Newsletters | No Comments »

Relational vs. Transactional

Friday, October 2nd, 2009

The following is from the September 2009 Drilling Down Newsletter (original title:  Customer Retention for Restaurants).  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  I am hoping you can help answer a question for our team.  By way of introduction, I am the CEO of XXXX.  We are a specialty retailer / restaurant of gourmet pizza, salads and sandwiches.  We would like to know  restaurant industry averages (pizza industry if possible) for customer retention – What percentage of customers that have ordered once from a particular restaurant order from them a second time?  I am hoping with your years of expertise and harnessing data you may be able to assist us with this question.  Look forward to hearing from you.

A:  Unfortunately, in those said years of experience, I have found little hard information on customer retention rates in QSR and restaurants in general (if anyone has data, please leave in Comments).  It’s just the nature of the business that little hard data, if collected, is stored in such a way that one can aggregate at the customer level.  The high percentage of cash transactions doesn’t help matters much; there’s a lot of data missing.

Over the years, sometimes you see data leak out for tests of loyalty programs, and of course clients sometimes have anecdotal or survey data, but this is not much help in getting to a “true” retention rate.  More often than not you discover serious biases in the way the data was collected so at best, you have a biased view of a narrow segment.  Often what you get is a notion of retention among best customers, or customers willing to sign up for a loyalty card, but not all customers.  And the large “middle” group of customers is where all the Marketing leverage is.

What to do about this predicament?  

There are really two issues in your question; the idea of using industry benchmarks when analyzing customer performance, and the measurement of retention in restaurants.

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Posted in Analytical Culture, Customer Experience, DataBase Marketing, Marketing thru Operations, Measuring Engagement, Newsletters | 1 Comment »

Awareness versus Persuasion

Wednesday, September 23rd, 2009

In the early days of Home Shopping Network (live TV, not online), we were doing some ethnographic research and started to find “physical clusters” of customers – neighbors or people who worked together.  For example, one of these groups was nurses at hospitals,  especially nurses  who worked the night shift.

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.

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Posted in Analytics Education, DataBase Marketing, Web Analytics | 11 Comments »

RFM versus LifeCycle Grids

Friday, August 28th, 2009

The following is from the August 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  First of all, thank you for the excellent book!  I’m really excited about digging into our own customer data to see what we’ll learn.

A:  Thank you for the kind words!

Q:  However, when you’re creating the RF Scores, what is the standard timeframe you should use?  I have access to about 5 years worth of purchase data – should I create RF scores based on the last 5 years, 3 years, 2 years, 6 months?

Our sales are quite cyclical, so I think the baseline should probably be at least a year, and I’m considering doing two years.  It seems as though if I get too much larger than that, my results will be too watered down. 

I’m also planning on generating “historical” RF scores by filtering my data to reflect the purchases only up to a certain point.  So, to generate a Q1-09 score, I’d create it from sales data of Q1-07 through Q1-09.  The Q2-09 score would be from Q2-07 through Q2-09, etc.  Does this make sense?  It will allow us to see the changes that have been happening in our company even though we’re only just now looking at the data.  It will give me a picture of what it would have looked like, had I looked at it back then.

A:  I think you have accurately understood the situation and have the right approach!  This type of analysis is very sensitive to time frame.

There are really 2 broad types of customer analysis.  There is analysis for action in the present, a Tactical approach driving towards a “we should do this now” result, and the more Strategic analysis, which is informational and says “this is what we should have done then” and / or “this is why we should make these business changes”.  The shorter time frame is Tactical, the longer timeframe Strategic.

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Posted in DataBase Marketing, Measuring Engagement, Newsletters | 1 Comment »

Adoption and Abandonment

Friday, August 7th, 2009

Out of the Wharton School we have a nice piece of behavioral research on the effect speed of Adoption has on longer-term commitment.  The article, The Long-term Downside of Overnight Success, describes research finding “the adoption velocity has a negative effect on the cumulative number of adopters”. 

This research dovetails nicely with a lot of the topics discussed here on the blog lately, so I thought I’d use it (with a nod to Godin’s post on Strategy vs. Tactics today) to provide some fodder for thought.

First, the importance of Psychology in Marketing.  So many of the “discoveries” arrived at through  brute force testing of Online Advertising are already well known in the greater discipline of Marketing through Psychology.  For more on this read “The Other 3P’s” and if you’d like to do something about lack of knowledge in this area, make sure to read this comment on source books.

Second, this research is a great example of isolating the true drivers of behavior.  The idea of looking at baby names to isolate the real behavior from “technology and other commercial effects” while including “symbolic meaning about identity” results in a broad, Strategic-level answer to the question, not a Tactical one. 

Why is this important?  It means the results can be applied across a host of different Marketing situations, rather than only a specific one. 

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Posted in DataBase Marketing, Marketing Research, Measuring Engagement, Web Analytics | 10 Comments »

Loyalty Program Structure & Tracking

Friday, July 31st, 2009

The following is from the July 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  I’m involved in a loyalty program analytics project.  This client is a local pharmacy.  All sales are done directly in store, the web site is just for communication purposes.  The general problem we are trying to solve is the manager doesn’t have any detailed ideas about shoppers behavior apart from human observation. 

The idea is to launch a card-based loyalty program which will track sales activity and give insight into customer behavior.  The program will be points-based calculated on amount spent.  Points can be redeemed as rebates, coupons, gift certificates, or use points to buy items in loyalty program catalog.

The task is to segment customers according to their recent purchase behavior and determine the customer lifecycle.  I’ve been able to do some basic analysis using the R package and MySQL database, but am unable to detect customer lifecycle. 

Can you please give me guidance on this?

A:  What is the Objective of detecting the LifeCycle, to create a more “active” customer retention program?  Loyalty programs can be quite “passive” and often benefit from a more active overlay.  But there can be many reasons to want to understand the LifeCycle…

Q:  My 2nd task is to use the behavioral data with demographics to  build a direct marketing strategy and provide management with insight into the customer base, for example: percent new customers, % of Gold customers who passed to Silver in last quarter.

A:  Again, it would be helpful to understand how management would take action on this data.  But I suppose you are in the common position of not knowing the tactical approach, and nobody will lay it out for you (a.k.a. they are clueless)…and you don’t know the right questions to ask or how to ask them.

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Posted in DataBase Marketing, Newsletters | No Comments »

The Other 3 P’s

Friday, July 24th, 2009

It’s interesting most folks that consider themselves Marketers, especially of the online variety, seem to only discuss and have ideas about Advertising.  But of the 4 P’s that make up Marketing - Product (which includes People), Price, Place, and Promotion – Promotion (Advertising) is the weakest of the four.

I say weakest because Advertising cannot fix a poorly thought out Product, Pricing Strategy, or Distribution system.  It just can’t.  Yet huge amounts of money are wasted trying to do exactly that.

Perhaps this why someone feels they need to publish a book that tells people Product is important in Marketing.  To me, that’s the most circular or redundant idea for a Marketing book I’ve ever heard.

Marketing starts with Product, which should include all the audience or market segmentation studies (People) that drive the creation of the Product - defining the need.  If you do this first and develop a Product which truly fills the need, AND you get the Pricing and Distribution right, the Product will literally sell itself to the core audience.

If you can make it that far, THEN the Product can perhaps be sold to the next segment out from the core through Advertising.  All “Marketers” should know this.

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Posted in Customer Experience, DataBase Marketing, Marketing thru Operations | 7 Comments »

Lead Scoring and Nurturing

Friday, July 3rd, 2009

The following Q & A is from the June 2009 Drilling Down Newsletter.

Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, Feel free to leave a comment.  Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: I received this article (Norms of Reciprocity, measuring value of Social Marketing) via a friend’s Twitter account.  Very interesting.

A:  Glad you enjoyed it!

Q:  It has made open up my ACT! database, and my Outlook databases and add the metric of Growing / Strong / Weakening / Failed to my normal Sales and Business progress metrics.  If I group those categories and correlate to traditional metrics, it’s impressive how they reflect each other.

A:  Yes, most people are surprised.  It’s a very, very simple idea that seems to work across just about any human activity including crime, attendance, and so forth.  

The more Recently someone has done something, the more likely they are to do it again.  Conversely, the longer since an activity last took place, the less likely the person will do it again.  Often called Recency in Psychology and studied quite a bit.

Q:  Now I have to think about how I really use and apply this. : )

A:  Well, if I can guess you are in Sales from your title, typically one of the best applications is in what Strategic Marketing folks might call “allocation of resources”, which probably translates into “lead nurturing” for you.

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Posted in DataBase Marketing, Measuring Engagement, Newsletters | 2 Comments »

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