Archive for the ‘Customer Experience’ Category

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Segmentation by LTD & LifeCycle

Monday, August 2nd, 2010

The following is from the July 2010 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: One of the first things I am doing in my new job is to identify the Customer Lifecycle pattern – how many periods (month, year) will it be before a customer is likely buy again.  In enterprise software industry, where software cost easily 6 figures, # of years is a reasonable time frame.

A: Yes, one would assume this.  But these notions would most likely be based on a feeling of the “average” behavior, and on average, it probably does take a long time.

What is not known is this:  if the “average” is composed of short-cycle and long-cycle buyers, who are the short cycle buyers, and what are they like?  What industry SIC code, for example?  And can we get more of them, or at least focus more resources on them, if they are the most profitable?  So the challenge is not only to look for the “average”, but then understand how this average is composed.  If you can break down the average by industry, or by salesperson, for example, this might be highly directional information.

Q: From my internal analysis, however, I discerned from the sales figures something quite counterintuitive – the period between first and next sale is much shorter than I would have thought for the SW industry in general.

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Posted in Customer Experience, DataBase Marketing, Measuring Engagement, Newsletters | 4 Comments »

LTV, RFM, LifeCycles – the Framework

Friday, June 18th, 2010

The following is from the May 2010 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 visited your website because I am trying to understand how to develop a customer LifeTime Value model for the company that I work at.  The reason is we are looking at LTV as a way to standardize the ROI measurement of different customer programs.

Not all of these programs are Marketing, some are Service, and some could be considered “Operations”.  But they all touch the customer, so we were thinking changes in customer value might be a common way to measure and compare the success of these programs.

A: Absolutely!  I just answered a question very much like this the other day, it’s great that people are becoming interested in customer value as the cross-enterprise common denominator for understanding success in any customer program!

If I am the CEO, I control dollars I can invest.  How do I decide where budget is best invested if every silo uses different metrics to prove success?  And even worse, different metrics for success within the same silo?

By establishing changes in customer value as the platform for all customer-related programs to be measured against, everyone is on an equal footing and can “fight” fairly for their share of the budget (or testing?) pie.  By using controlled testing, customers can be exposed to different treatments and lift in value can be compared on an apples to apples basis – even if you are comparing the effect of a Marketing Campaign to changes in the Service Center.

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

Acting on Buyer Engagement

Thursday, January 21st, 2010

Over the years I’ve argued that there is a single, easy to track metric for buyer engagement – Recency.  Though you can develop really complex models for purchase likelihood, just knowing “weeks since last purchase” gets you a long way to understanding how to optimize Marketing and Service programs for profit.

Which brings me to the latest Marketing Science article I have reviewed for the Web Analytics Association, Dynamic Customer Management and the Value of One-to-One Marketing, where the researchers find “customized promotions yield large increases in revenue and profits relative to uniform promotion policies”.  And what variable is most effective when customizing promotions?

The researchers took 56 weeks of purchase behavior from an online store, and used the first 50 weeks to construct a predictive model of purchase behavior.   Inputs to the model included Price, presence of Banner Ads, 3 types of promotions, order sizes, number of orders, merchandise category, demographics, and weeks since last purchase (Recency).

The last 6 weeks of data were used to test the predictive power of the model, and the answer to which variable is most predictive of purchase is displayed in the chart below, click to enlarge:

Weeks since last purchase dominated the predictive power of the model, controlling not only the Natural purchase rate (labeled Baseline in chart above, people who received no promotions) but the response to all three different types of promotion.

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Posted in Customer Experience, DataBase Marketing, Measuring Engagement, Web Analytics | 13 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 »

Net Meaningful Audience

Friday, September 18th, 2009

 

Not Meaningful

Not Meaningful

When you’re in the business of measuring the effects of Marketing programs, certain patterns begin expressing themselves over and over.  One of the oldest in the contribution to success of various parts of a Marketing effort, sometimes called the 60-30-10 rule:

60 percent of success is determined by the audience quality
30 percent of success is determined by the offer
10 percent of success is determined by the creative

Where do these stats come from?  Continuous improvement testing.  Over the years, if you run a lot of different tests, you just begin to see this pattern.  And the pattern holds across a very wide variety of business models – online and offline.

The key takeaway here: audience quality is the most important component of success in a results-oriented Marketing campaign.  This is why the CPM’s for niche Magazines, for example, are so high.  These Magazines are tremendously efficient marketing vehicles because they have high audience quality, which drives end behavior – results.

And the primary reason the audience quality is so high?

People pay for these Magazines.  When people pay for something, they value it with more Attention. Why? Simple.

In a magazine like Hot Rod or Concrete Decor or Vogue, the percentage of content that is interesting to the niche audience is very high. In fact, the Advertising is viewed as content.

Smaller audience, very high quality. Ads work like gangbusters.

Clearly, there are other ways to run a media model.  At the opposite end of the media spectrum, there is free.

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Posted in Brand Management, Customer Experience | 11 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 »

Post-Action Dissonance

Friday, July 10th, 2009

You may have heard of this concept as Post-Purchase Dissonance, an area where more research has been done, but the fact is that many actions other than purchase create dissonance.

This area of  Psychology is more generally referred to as Cognitive Dissonance.  Along with Norms of Reciprocity, Dissonance is one of the most important pieces of Psychology for today’s Marketing folks to understand.   This is doubly true if you are serious about using a two-way Social model in Marketing.

Here’s why:  The Social sword has two edges.  If you are going to use a two-way Relationship Marketing approach, you will create higher expectations with those who Engage.  If you fail to perform, or just act like an Advertiser would, then you will end up creating more damage than if you had simply ignored the two-way idea.

For Marketing, the important idea to understand is the human brain always questions actions taken, however briefly, and tries to resolve conflict.  Any unresolved conflicts tend to taint the action, they create Friction, and drive down the Potential Value of the experience.

The important action item for Marketers is to know this will happen beforehand, and take steps to counteract the Dissonance.  The result will be customers who have generally better experiences, and you know what that means, right?

In other words, by planning for Post-Action Dissonance you are using a Prediction that increases Profits or cuts Costs down the road.

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

Norms of Reciprocity

Friday, June 26th, 2009

Social Marketing Doesn’t Rely on Social Media

Do you believe human beings share certain fundamental traits that define “being human”?

If so, do you believe that human beings tend to behave in certain ways under certain circumstances?

If so, do you then believe since human behavior has these tendencies, it can often be predicted?

If so, then do you think perhaps the study of Psychology and Sociology might provide you some clues to creating successful businesses, campaigns, products, and services?  While your friends and competitors are all iterating their way into oblivion?

On the web, time and time again, we see the same themes repeating.  Yet with each introduction of a new technology, these themes tend to be treated like a new discovery, even though the theme has been well established in the past.

Norms of Reciprocity is a constant human theme.  You may know the expression of these norms as ”Sharing”.  Web old timers will probably recognize this idea as “Give, then Take” from the I-Sales discussion list as early as 1995.  In various forms, this theme goes back to the beginning of human history, all the way back to the handshake and other greeting gestures.  This same theme is embedded in countless Religions all over the world: “Do onto others as you would wish them do onto you”.  At least a couple centuries old, this idea.

Norms of Reciprocity simply means this: When you do something nice for a human being, help them in some way, this human tends to feel Gratitude towards ”the doer” and tends to do something nice back.  Gratitude drives the desire to Reciprocate, because it’s just what humans do, it’s normal, a “norm”.

Norms of Reciprocity.

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Posted in Analytical Culture, Customer Experience, DataBase Marketing, Measuring Engagement, Web Analytics | 9 Comments »

Hacking the RFM Model

Friday, May 29th, 2009

The following is from the May 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 your help.  I have some questions I would be pleased if you answer them for me.

A:  No problem!

Q:  1. RFM analysis – is it possible to use some other ranking technique rather than quintiles? Using quintiles for bigger databases will cause many tied values, isn’t it a problem?

A:  Sure, you can use it any way it works best for you.  There is no “magic” behind quintiles, you can use deciles or whatever works best. It’s the idea of ranking by Recency, Frequency, and Value that is the key concept in the model.

I’ve seen dozens and perhaps hundreds of variations on the core RFM model, depending on how you classify a “variation”.  One change that’s common is changing the scaling, as you mention above, to accommodate the size of the database.  Smaller databases use quartiles or even tertiles.  Larger databases, choose the ordered distribution that meets the need.

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Posted in Customer Experience, DataBase Marketing, Measuring Engagement, Newsletters, Web Analytics | 2 Comments »

Got Discount Proneness?

Friday, May 15th, 2009

Discount Proneness is what happens when you “teach” customers to expect discounts.  Over time, they won’t buy unless you send them a discount.  They wait for it, expect it.  Unraveling this behavior is a very painful process you do not want to experience.

The latest shiny object where Coupon Proneness comes into play is the “shopping cart recapture” program.  Mark my words, if it is not happening already, these programs are teaching customers to “Add to Cart” and then abandon it, waiting for an e-mail with a discount to “recapture” this sale – a sale that for many receiving the e-mail, would have taken place anyway. 

The best way to measure this effect is to use a Control Group.

When I hear people talking about programs like this (for example, in the Yahoo analytics group) what I hear is “the faster you send the e-mail, the higher the response rate you get”.

That, my friends, is pretty much a guarantee that a majority of the people receiving that e-mail would have bought anyway.  Hold out a random sample of the population and prove it to yourself.  There is a best, most profitable time to send such an e-mail, and that time will be revealed to you using a controlled test.  The correct timing is almost certainly not within 24 or even 48 hours.

That is, if you care about Profits over Sales, and trust me, somebody at your company does.  They just have not told you yet!

When you give away margin you do not have to give away on a sale, that is a cost.  Unless you are including that cost in your campaign analysis, you are not reflecting the true financial nature of the campaigns you are doing.  If you are an analyst, that’s a problem.

If you are using cart recapture campaigns, please do a controlled test sooner rather than later.  Because once your customers have Discount Proneness, it will be very painful to fix.

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Posted in Analytical Culture, Customer Experience, DataBase Marketing, Measuring Engagement, Web Analytics | 8 Comments »

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