Marketing to Focus on Customer. Analytics?

February 14th, 2012

It’s been very popular among marketing types to talk about “the customer” but seek metrics for affirmation other than those based on or derived from the customer.  Web analysts have followed their lead, and provided Marketers plenty of awareness, engagement, and campaign metrics.  As I’ve said in the past, this is a huge disconnect.  Does it make sense (analytically) to have discussions about customer centricity,  customer experience, customer service, the social customer, etc.  and measure these effects at the impression or visit level?

Is someone who visits or purchases or comments one time really a customer, for the purposes of analyzing “centricity” ideas and concepts?  I think not.  Visit metrics simply don’t work for understanding these customer concepts, because by definition they unfold over time, not as single events.   Add in the fact most web activity is 1x in nature – even buyers – and you begin to realize that analyzing “traffic” yields very little in the way of “customer” insight.

From a Marketing perspective, hey, happy to have the 1x revenue, but these are interactions I’m not really excited about increasing spend on, knowing they will be a one-night stands.  This is especially true when you also know re-allocating some of the funds spent on the 90% 1x-ers to the other 10% could double company profits!

If you have followed my writings over the past 12 years, none of the above perspective is new.  What might be changing is this: more people in the online world are beginning to think the same way.

Now comes eConsultancy with a review of “major trends in marketing and technology” that lead to some key takeaways, which they outline here.  Marketers, they say, are moving towards customer-based metrics, and the organization has to be prepared for change.

I’ve been through this change several times before.   My experience is this: as an organization moves from campaign or funnel-based metrics to metrics based on customer value over time, old beliefs are shattered and new ones need to be accepted.

Take,  for example, the eConsultancy comment on attribution:

“Organizations have to be prepared for the changes that attribution should cause to media mix and budget.  If compensation packages are still tied to siloed spending, there will be resistance to adopting an attribution-based model.”

Wow, resistance?  Maybe full out internal warfare, if you ask me.  When people have paychecks tied to metrics, things can get ugly in a hurry if these same metrics turn out not to be in the best interest of the company (metrics are not authentic KPI’s).

Implications of this change

What could this change mean for web analysts?  In fact, much of your approach to the work, the thought process, the general concepts, do not change.  But as you might imagine, changing how success is defined from a visit-based value model to a customer-based value model can radically impact perception of “what works”.

Beneath what eConsultancy is saying, the driving principle for the tension, is that customer value metrics are the universal yardstick of success, because they can be used across any platform, media, and channel, allowing direct comparison of performance across programs in any silo.  Think about what that means.

Customer metrics are the end of silos hiding behind metrics customized to prove their own success and grab budget.  If the silo can’t move the needle at the customer metrics level, well, it doesn’t really matter what the metric championed by the silo says.  That silo-based, tortured to make us look good metric is now  obsolete.

As you might guess, this kind of thing creates considerable organizational tension.  When there is a move from “campaign” analytics to customer analytics, winning ideas can become losers, and quite often, losers become winners.

Take conversion, for example.  Did you know it’s common for campaigns with both high volume and high conversion rates to create the lowest value customers?  Are web analysts and surrounding silos ready for that, to accept the campaigns they are most proud of are in fact the poorest performing when the “value” goal posts are moved?

As the marketers move towards managing by customer metrics, conflicts like this will definitely surface.  Does management want short-term conversion / sales or long-term customer value / profit?  Who decides?  How will people be compensated?

This won’t happen overnight or next year or perhaps in 5 years time for many companies.  Leading companies with a proper analytical culture in place, especially those that face extreme competition, are already managing by customer value, because they must to survive.  Lots of others will follow slowly.  This will be especially true if Gartner is correct in the analysis mentioned here:

“In fact, industry watchers say today’s CMO must become the de-facto chief customer officer — or lose out.  CMOs have historically been the brand steward. This is an opportunity to be a customer steward”.  Love the subtitle on that article:  And If They Don’t, They’ll Be Relegated to Overseeing Promotions While Someone Else Takes Chief Customer Officer Role.

I’d guess “Relegated” is not a scenario most CMO’s are interested in…but is a correct assessment.  Why?

The nature of customer analysis is that it often ties very closely to company finances and quarterly reporting, meaning  natural alignment with strategic issues and on up to the C-Level folks.   A universal success metric like customer value that is valid and actionable across any program or platform creates the ability for the org to manage all aspects of the business using analytics, because apples can be  apples no matter where they are grown – Marketing, Customer Service, Merchandising, etc.  Also true:  it’s much easier to determine the source of bad apples, no matter which silo is generating them.

This enables the C-Level to finally start managing using analytics, rather than just nodding and saying the reports are interesting.  Since customer analysis often allows C-Level folks to act on problems before they become income and profit issues, customer analysis ideas and metrics are often quickly embraced once proven to be valid.

This is what many web analysts have wanted, right?  Respect and buy-in for analytics from the C-Level?

On a practical level, using the same yardstick to measure success across any silo means budgets can be reallocated not just within a silo, but across silos. For example, seeking the highest value, Marketing budget could be reallocated to Customer Service, if certain programs in Service are found to generate much higher customer value than some in Marketing.

What’s a (web) analyst to do?

The first question for web analysts is how they will handle the transition.  Here’s what you do not want to happen.  You are sitting in a conference with high level execs talking about how great a certain campaign is performing, and the customer analyst says,   “Actually, that campaign the web analytics folks are telling you is a success is one of the worst performing campaigns we have, in terms of customer value created”.

Ouch.  This  happened to me early in my marketing career, when I concentrated on the results of campaigns as opposed to impact on customer value.  I know what it feels like, and it’s not fun.
So, if you’re not doing the customer analysis yourself, make sure you are in the loop with those folks.  Help them figure out how to properly integrate web data into the customer analysis stream, and get yourself an inside look at how it all works.   Don’t make this transition a battle between the web analysts and the customer analysts.

Ouch.  This  happened to me early in my Marketing career, when I concentrated on the results of campaigns as opposed to impact on customer value, which translates to profits and stock prices.  I know what it feels like, and it’s not fun.

So, if you’re not doing the customer analysis yourself, make sure you are in the loop with those folks.  Help them figure out how to properly integrate web data into the customer analysis stream, and get yourself an inside look at how it all works.   Don’t make this transition a battle between the web analysts and the customer analysts.

The second question is this: as marketers move towards customer analysis, who will be doing this customer analysis?  Are current web analysts interested in doing it?  Or will someone else – maybe in Finance, maybe a BI unit, maybe an outside agency – be providing this service to the org?

I’ll go out on a limb here and say most web analysts with more than 5 years experience are not only capable of doing customer analysis, they’d be really good at it.  Segments, experience paths, value creation – you got a handle on that?  Same general idea.

The biggest difference is simply the time frame of the analysis –  analysis is not over when the initial goal is achieved; the org wants to know what value is created downstream by the customer – 3 months later, 6 months later, a year later.  Does the customer come back and take action again?  What is the downstream value created by this campaign versus that campaign, this content versus that content, this product versus that product?

Same general ideas a web analyst works with all the time, with a longer “tail” on the measurement of success or failure.

Sure, at the tool level there can be a difference between web analytics and customer analysis , depending on which WA tool you are using – more advanced tools often have native capabilities for customer analysis.  But the hardest part, the analytical mindset, most web analysts have got that covered.  And on the tools, if your WA tool lacks the chops, we’re talking about simple customer database queries and spreadsheets to start – is that so hard?

I doubt it, for the WA folks I know.  If the analyst doesn’t have database query skills (as I do not, ’cause I’m a Marketing guy), they know people who do, either within the company or as contractors.  Customer analysis is not nearly as difficult as many people would like you to think it is, there is a middle ground between doing nothing on customer analysis and working with regression models, neural networks, and other “Big Data” promised lands.

Then, once you define the impact of using customer value as a universal yardstick for success across the silos, you have made the investment case for moving up to specialized tools that allow automation and discovery, then on to modeling and prediction.

So, I’m just checking – isn’t this a path web analysts have been asking for?  C-Level attention and follow-through, an org that respects analytical work and will “do something” based on the analysis?

Are you up for measuring, understanding, and driving business action using models and concepts that can make an even greater impact on the business than what you do now with web analytics?

I hope so.  We’re going to need you…

“Missing” Social Media Value

October 12th, 2011
I have no doubt there is some value in social beyond what can be measured, as this has been the case for all marketing since it began ;)  The problem is this value is often situational, not too mention not properly measured using an incremental basis (as you point out).
For example,  to small local businesses who do no other form of advertising, there is a huge amount of relative value to using social media, versus no advertising at all.  Some advertising is much better than none, and since it’s free, the incremental value created by (properly) using social is huge.
On the other hand, I wonder why social analysis seems to forget that people have to be aware of you to “Like” you in the first place.  Further, it seems unlikely a person would “Like” a brand or product if they have not already experienced it, and are already a fan.  If this is not true, if people “Like” a company even thought they do not (paid to Like?), then the problems with social go way beyond analysis…
But if true, , the number of “Likes” doesn’t have as much to do with awareness as it does with size of customer base, and is much more aligned with tracking customer issues (retention, loyalty) than anything to do with awareness / acquisition.
Add the fact many companies are running lots of advertising designed to create awareness, and the incremental value of social as a “media” may be close to zero, or at least less than the cost to analyze the true value of it.
And this last, really, is the core of the issue.  It’s simply not possible to measure “all” the value created by any kind of marketing, and there are hugely diminishing returns as you try to capture the last bits.  I think it’s quite possible the optimism for “value beyond what can be measured” is less than the cost of measuring it *if* people keep looking in the awareness / acquisition field.
Folks who want to find this “missing” social value should start doing customer analysis, and look in the “retention / loyalty” area, where the whole idea of social is a natural, rather than a forced, fit.

Has to be There

I find it really interesting that whenever there is a discussion of measuring the value of social media, there’s such a bias towards believing there is value in social beyond what can be properly measured.  See the comments following this post by Avinash for a good example.  Speculation is fine, but the confidence being expressed that a new tool or method will uncover a treasure trove of social media value seems un-scientific (as in scientific method) at best.

I don’t doubt there is some value in social media beyond what can be measured, as this has been the case for all marketing since marketing measurement began.  These measurement problems are not new to social either:  Marketing value created is often situational, it depends on the business model and environment.  What works in one situation may not work in another.

For example:

To small local businesses who do no other form of advertising, there is a huge amount of relative value to using social media, versus no advertising at all.  Social advertising is much better than none, and since it’s free, the incremental value created by (properly) using social is huge.  It’s also really easy to measure the impact and true value, since the baseline control is “no advertising”.  Lift, or actual net marketing performance, can be pretty obvious in his case.

On the other hand, many companies are running lots of advertising designed to create awareness, and the incremental value of social as a “media” may be close to zero for these companies, or at least less than the cost to analyze the true value of it.  Possible explanation:  Social events such as “Likes” or comments are simply representations or affirmations of awareness already created by other media, so by themselves, create little value.  In other words, events such as Likes might track the value of other media spending, but may not create much additional marketing value.

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Defining Behavioral Segments

May 5th, 2011

The following is from the April 2011 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 purchased your book and have a few questions you can hopefully help me out with.

A: Thanks for that, and sure!

Q: We have 4 product lines and 2 of them are seasonal. i.e we have customers that year in year out purchase these items consistently but seasonally, for example, every spring and summer.  Then they are dormant for Fall and Winter.  Should I include these customers along with everyone else when doing an RFM segmentation?

A: Well, it kind of depends what you will using the RF(M) model for, what kinds of marketing programs will be activated by using the scores. If you know you have seasonal customers and their habit is to buy each year, AND you wish to aim retention or reactivation programs at them, I would be tempted to divide the customer base so that seasonal customers are their own segment.  Then run two RF(M)  models – one for the seasonals, and one for everyone else.

Q: If I include seasonal customers, and I run RFM say on a monthly basis, these seasonal customers will climb / fall drastically with time depending on the season, so it seems like it may complicate the scoring process.

A: Sure, and you could segment as I said above.  Or, you could run across a longer time frame, say across 2 – 3 years worth of data. This would “normalize” the two segments into one and take account of the seasonality in the scoring – perhaps be more representative of the business model.  However, the scores would become less sensitive due to the long time frame so the actions of customers less accurately predicted by the model.

Q: Can you provide me with some examples as to how segmentation is carried out?  Let’s say I being with RFM and all my customers are rated 5-5, 5-4, 4-5 etc.  What are the next steps, do we overlay with other characteristics like age, gender, etc?  Or are the 5-3 etc. our actual segments?

A: This goes back to what you want to use the RF(M) model for.  In the standard usage, each score will have roughly the same number of customers in it, those with higher scores will be more likely to respond to marketing and purchase, lower scores less likely.

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Increase Profit Using Customer State

April 5th, 2011

The following is from the March 2011 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: We’ve been playing around with Recency / Frequency scoring in our customer email campaigns as described in your book.  To start, we’re targeting best customers who have stopped interacting with us.  I have just completed a piece of analysis that shows after one of these targeted emails:

1. Purchasers increased 22.9%
2. Transactions increased 69%
3. Revenue increased 71%

A: There you go!

Q: My concern is that what I am seeing is merely a seasonal effect – our revenue peaks in July and August.  So what I should have done is use a control group as you described in the book – which is what I am doing for the October Email.

A: Yep, that’s exactly what control groups are for – to strain out the noise of seasonality, other promotions, etc.  But don’t beat yourself up over it, nothing wrong with poking around and trying to figure out where the levers are first.

Q: Two questions:

1.  What statistical test do I use to demonstrate that the observed changes are not down to chance

2.  How big should my control group be – typically our cohort is 500-800 individuals

A: Good questions…

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All Talk, No #Measure

March 11th, 2011

Hypocrisy in Web Analytics?

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.

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But What is an Impression Worth?

March 8th, 2011

Seems like coming up with a value for social media has become a cottage industry, for example, $3.60 Facebook Fan Valuation Is Just the Tip of the Iceberg.  These values are often derived from what is paid for online media.  So you have to ask, if someone is basing the value of a Facebook fan on the value of impressions generated, what is the real value of those impressions?  Because unless this is known, the whole framework is faulty.

Just because you pay $5 / CPM for impressions, does not mean they are worth $5 / CPM, does it?  Do people really still have that kind of mentality?  Is the price of the media equivalent to its value?

For example, I’m sure you have heard of multi-million dollar campaigns that generate very little lift in sales.  Happens frequently in fast food, for example.  What is the value of that media?  Is it the millions paid?

What really blows my mind about this approach is it’s so offline, so old school PR. Do the folks who put forth these kinds of models believe nothing has changed in 50 years?  What happened to the whole rap of online being “different”, that you can’t measure it like offline, blah blah.

Except when it’s convenient to do so?

If you want to know the value of a Facebook fan, why not measure the value of a Facebook fan?  Because it’s hard, and would require organizational discipline?  Too bad.   Substituting the kind of models used in the example above for actually measuring the value of a Facebook fan is misleading at the very best.

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Optimizing for Customer Value

February 28th, 2011

The following is from the February 2011 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: Thank you for creating this useful website!

A: You’re welcome!

Q: When figuring out retention rate for an annual or a 8 months life time cycle period, how do I pick the starting period?  Do I look at their first orders on a date?  Or I pick a time frame such as one month?

A: It depends on:

1. What kind of “retention” you are talking about, the definition, which is probably impacted by the audience for the data

2.  What you will do with the retention data, what kind of decisions will be made and actions be taken because of the data

You should always ask these questions above  when someone requests “retention data” – or any other kind of analysis, for that matter!  For example, there probably is a huge difference in what you would provide to the Board of Directors for an annual benchmark and what you would provide to Marketing people for executing campaigns.

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When Does a Visitor Need a Coupon?

December 17th, 2010

The following is from the November 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: First off, I very much appreciate you sharing all this wonderful content on your blog and conferences such as eMetrics.

A: Thanks for that!

Q: My question is a simple one, but I think the answer may be hard: When does a visitor “need” a coupon?  *Need* defined as: visitor would not have placed an order unless presented with the coupon.

A: Hmmm…methinks we’re going to have to define a few concepts and be clear on the goals to make sure we are nailing this down… visitor versus customer, sales versus profit, etc.  In other words, answer is not hard, but could be complex without defining context.

Q: It’s still a mystery to me why so many retailers seem more than willing to hand over all their margins to Groupon or give coupons to basically all visitors.  I am curious whether you would approach this question using  observational data (eg web analytics) or experiments (eg AB testing), or both.

A: Right – is a mystery to me too!

There are certain situations where this approach might be appropriate, but the problem with much web “marketing” (which often is really just advertising without much thought about marketing) is often there is success in a narrow or special situation.  Then the pundits jump on and say “if you’re not doing this you are stupid”, regardless of the business situation and / or without recognizing the special circumstances that are driving success.  This is all the real Marketing stuff people leave out; understanding why it works, under what circumstances, for which segments, involving which products.

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Freemium Customer Conversion

November 9th, 2010

The following is from the October 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 was wondering if you’ve done any work with, or given thought to, companies who have a cloud based Freemium business model?

Should they be tracking usage (or anything) at the free level?  Should they be tracking usage at the paid level?  I’m sure defection rates are a big problem, but I’m wondering how many focus on engagement thru mass marketing versus trying to keep what they’ve got, or influence the free users to make the leap to paid.  Any thoughts on this?  Maybe you could do a blog post on it.  It seems like a good fit with your brand of analysis but I’m just starting to think it through…

A: I just finished an analysis that’s a good example of this problem.  Behavior during the Freemium period can predict who is highly likely to become a paying customer, who will need marketing efforts like additional sampling / package discounts, and who will not become a customer no matter what you do.

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Segmentation by LTD & LifeCycle

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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