Category Archives: Customer Experience

Marketing Responsible for Customer Experience?

 The Data

According to this survey, Marketers are not now really “responsible”  for the customer experience (whatever responsible means in this context) but will be over the next 3 years.  If it was just the vendor (Marketo) trumpeting this idea, I’d be more skeptical.  But this vendor hired the Intelligence Unit from The Economist organization to do this work and the report includes the actual questions, meaning you can check for bias.  Population is 478 CMO’s and senior marketing executives worldwide, seems decent / not cherry-picked.

So I will cut the vendor some slack.   Questions though, right?  Just what is customer experience, in particular for the purposes of success measurement?  How does it fit with related ideas like Customer Journey / LifeCycle and Engagement?  Certainly if the above is a significant macro trend we ought to sort this all out first?  And of course, putting some analytical rigor (structure, process, and definitions?) in place to support the effort ;)

The Story

I know a lot of marketing people who have either had this authority for years (multi-channel database marketing) or are moving in this direction, so the results make sense to me.  To be clear(er), “experience” for these people reaches all the way back from UX into fulfillment and service.  So when they talk about experience, they are talking visitor and customer; not just navigation and landing pages, but also shipping times and return rates.

Perhaps increased access to customer data is revealing the significant impact customer experience in this larger sense has on long-term customer value?  This idea, coupled with increased focus on accountability (also covered in the survey) could be driving this trend.

Worth the read, only 20 pages long with a lot of charts.  Here’s 4 snippets to hook you:

Continue reading Marketing Responsible for Customer Experience?

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Do NPS / CES Feedback Metrics Predict Retention? Depends…

Survey Says?

Several questions came in on the ability of surveys to predict actual behavior, covered in the post Measuring the $$ Value of Customer Experience (see 2. Data with Surveys). My advice is this: if you are interested in taking action on survey results, make sure to survey specific visitors / people with known behavior if possible, then track subjects over time to see if there is a linkage between survey response and actual behavior.  You should do this at least the first time out for any new type of survey you launch.

Why?  Many times, you will find segments don’t behave as they say they will.  In fact, I have seen quite a few cases where people do the opposite of what was implied from the survey.  This happens particularly frequently with best customers – the specific people you most want to please with modifications to product or process.   So this is important stuff.

You’ve Got Data!

Turns out there’s a new academic (meaning no ax to grind) research study out addressing this area, and it’s especially interesting because the topic of study is ability of customer feedback metrics to predict customer retention.  You know, Net Promoter Score, Customer Effort Score and so forth, as well as standard customer satisfaction efforts like top-2-box.

The authors find the ability of any of one of these metrics to predict customer retention varies dramatically by industry.  In other words, you might want to verify the approach / metric you are using by tying survey response to actual retention behavior over time.

Continue reading Do NPS / CES Feedback Metrics Predict Retention? Depends…

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Measuring the $$ Value of Customer Experience

 Marketing IS (Can Be?) an Experience

Early on I discovered something from the work of leaders in data-based marketing business models: they were always very concerned with post-campaign execution – not only from marketing, but also through product, distribution, and service.  I thought this strange, until I realized they knew something I did not: when you have customer data, you can actually identify and fix negative customer value impacts caused by poor experience.

This means you can directly quantify the value of customer experience, budget for fixing it, and create a financial model that proves out the bottom line hard money profits (or losses) from paying attention to the business value as a result of customer experience.

And critically, this idea becomes much more important as you move from surface success metrics like conversion and sales down into deep success metrics like company profits. Frequently you see the profit / loss from “marketing” often has less to do with campaigns and more to do with the positive or negative experiences caused by campaigns.


You might think taking the time to provide special treatment to brand new customers would always encourage engagement and repeat purchase.  You’d be wrong.  Sometimes this works, sometimes this does not work, depending on the context of the customer.  Does it surprise you to find out customers often do not want to be “delighted”?

Continue reading Measuring the $$ Value of Customer Experience

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Segmentation by LTD & LifeCycle

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.

Continue reading Segmentation by LTD & LifeCycle

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LTV, RFM, LifeCycles – the Framework

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.

Continue reading LTV, RFM, LifeCycles – the Framework

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Acting on Buyer Engagement

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.

Continue reading Acting on Buyer Engagement

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Relational vs. Transactional

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.

Continue reading Relational vs. Transactional

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Net Meaningful Audience


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.

Continue reading Net Meaningful Audience

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The Other 3 P’s

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 (channel), and Promotion – Promotion (Advertising) is the weakest element 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.

Continue reading The Other 3 P’s

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Post-Action Dissonance

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.

Continue reading Post-Action Dissonance

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