Multichannel Marketing Metrics
with Akin

In a recent post readers and I mulled over the fact whether Amazon really is that cool with their customer analytics and interactive marketing as we keep saying in our industry. Or whether their real secret to success is that they simply offer the cheapest price.

To that effect, Jared Waxman was kind enough to leave another thorough comment as an ex-Amazon’ian. (or do they call themselves Amazones?) Thought, I’d point you to it so it doesn’t go under in its location within a past post.

Thanks much to Jared and Ned and others who commented.

Tags: Customer Centricity, Direct Marketing, multichannel marketing, targeting by Akin
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For all that buzz around Amazon’s sophisticated analytics and its targeted book recommendations, it is worth asking in Kevin Hillstrom’s priceless, heretic style: Is it just hype or does it really make the big difference for their business?

Do we really buy from Amazon because of the recommendations, personalized emails, the behavioral targeting through widgets?

Or do we buy from Amazon because they always have the cheapest price? (Not to the least because of the 3d party vendors and used books that are linked in.)

If they stopped being the least expensive would we still be buying from Amazon?

In other words, are they really competing on analytics? Or are they competing on price?

What is our willingness to pay extra for the kind of “marketing as a service” that Amazon has perfected?

Of course … this question isn’t really about Amazon, in the end. Much rather I am trying to double check what the true value of sophisticated analytics and targeted marketing are. All hype aside.

Separate things: What you will buy vs. where you will buy it

There is no doubt that Amazon is the go-to place for doing your research on books.

But being the greatest place for researching books doesn’t necessarily mean that people will buy the books there if they can get them cheaper elsewhere.

I imagine we all go to many web sites to research what car, electronics, gear, etc. we should buy. Where we will buy the item that we settle on tends to be a different question though. We might check on eBay or Craigslist, for example.

So are we giving too much credit to Amazon’s sophisticated analytics and marketing?

The answer …

As Anil Batra was joking yesterday when I saw him at the OMMA Metrics and Measurement in San Francisco, a typical consultant’s answer to such a question could be: “That depends on … what it depends on.”

It depends on …

Of course, I don’t have Amazon’s data. But I think that the answer will indeed differ by buyer segment:

1. High value prospects that buy books frequently and in higher quantities will likely appreciate the convenience and time savings. But given that these people buy so many books they would also be the segment that could get the biggest total savings by being disloyal to Amazon.
2. Infrequent buyers that are strapped for time will value the convenience over a few bucks of savings per book.
3. Infrequent buyers that are strapped for money but not for time would be more likely to put in the extra 5 minutes for buying the book at the cheapest vendor. Most books aren’t big ticket items. So this effect will likely be much less pronounced than, say, with electronics. But it would be there to a certain degree.

In the end, maybe the answer has not all that much to do with frequency but is a function of

• how much money vs. time buyers can save.
• how much value the individual puts on money vs. time

Time is money and money can buy time

The more time Amazon can save its book researchers, the more of them would buy their books on Amazon (assuming price is fixed). Still, those buyers who value money a lot more than convenience and time would be the hardest, if not impossible, to keep.

Anything else they could do?

Barnes and Noble has (or had) essentially a frequent buyer card. You could pay an annual fee and would get x % discounts on anything you buy.

But that really is just a return to the strategy of competing on price.

Could Amazon withhold access to book research features unless a buyer … purchased something in the last 12 months or joined some kind of for-pay club? That would be a return to subscription based content. Seems like it would backfire badly.

Bottom-line

It appears that the true value of all those analytics and targeted marketing for the retailer are in drawing the crowd into their (online) store. They get a shot at making sales (and cross-sales) that they wouldn’t otherwise have.

But converting researchers into buyers requires more than just targeted marketing. It also requires convenience, a “good enough” price, and of course customer satisfaction with previous transactions.

How would you go about measuring the value of targeted marketing effort XYZ?

If you have to measure ROI of targeted marketing effort XYZ you would probably do it through controlled testing.

That would be easy for Amazon’s targeted emails.

It would be harder for their book recommendations because you would wonder where they disappeared to if you fell into the control group.

Oh … but you could make them deliberately untargeted. Say you find that dinosaur book that the individual bought and shipped to somebody as a gift 3 years ago and recommend more dinosaur books.

Tags: Customer Centricity, Direct Marketing, Web Analytics, targeting by Akin
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(This post is part of a series on the state of multichannel metrics today, one year after the book came out.)

In a subscription based business model, e.g. mobile phone service, you have two ways to make a customer:

1. gain a new one
2. renew an existing one, i.e. retain them

When Unica’s clients from the mobile phone carrier industry report about their work they invariably start by describing how vital #2 is to their industry.

The market for mobile phone services is so saturated that the only way to gain a new client is to take them away from a competitor.

Sounds like a place for marketing innovation.

A few innovative Telcos have hit on ways to combine online and offline worlds in order to improve their success rate with retention marketing.

As I learned from one of my colleagues last year, here is how one company went about it.

Online-offline integrated analytics for detecting attrition signals

At a large US mobile phone carrier, traditional retention marketers were working on predicting which customers were about to leave for the competition. These clients would be included in retention marketing efforts.

Originally, the statisticians had been going after this job the old fashioned way, i.e. trying everything from a customer’s contract details to transactions (i.e. usage) and demographics details to find something that would predict attrition.

But the only variables that showed any influence were the age of the subscriber’s phone device and the amount that they paid on the last bill.

Not exactly enough to catch someone in the hot act when they are about to walk out through the door.

Yet, it was going OK.

To put it in numbers, the marketers were able to reach 70% of customers at risk of leaving by contacting 40% of the possible audience. So their predictive models were giving them some amount of lift.

But wait a minute … If someone is thinking about switching would they not likely be coming to the web site and doing something on there that deviates from their usual click behavior?

Might they not be checking available promotions or upgrades or ways to strike a deal?

The idea seemed so promising that the statisticians gave it a go.

They took a chunk of historical web data for registered clients. They paired that up with the same customers’ historical churn data in order to train a predictive model (along w/ the offline data).

And what they found was impressive

Indeed there were predictive click behaviors on their web site but it wasn’t intuitive.

• Clients on a low subscription contract would have one kind of online signal that revealed their intention, e.g. address change.
• Clients on a higher rate plan however turned out to send a different signal with their clicks.

The numbers rewarded them.

Now, when contacting 40% of the potential list they were able to reach an extra 15% responses for a total of 85% of potential responses.

That doesn’t just means lots of stamps and mailers saved.

It means foremost saving the cost of special discounts that they would have extended unnecessarily to clients who weren’t thinking about leaving anyway.

Highly worthwhile.

Real time?

Most of the online-offline integration case studies that you may have read about in this series were of interactive nature, i.e. online click behavior would prompt action within a short period of time.

Here we have an example of how a company first took a historical chunk of data to train their model. No real time needed here.

But now that the model has been trained, fresh web analytics data would be fed to it regularly in order to keep predicting current customers at risk.

The morale

This is yet another strong business case for integrating online and offline analytics.

No wonder the case is strong. After all

• The case for competing on analytics is strong.
• The case for using behavioral data is strong
• Click data is a rich lather of behavioral data

It is time for the lollygaggers to stop acting surprised and jump on board!

Tags: Customer Centricity, Direct Marketing, Multichannel Marketing Measurement, Multichannel marketing metrics, Web Analytics, targeting by Akin
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Other than “How about cookie deletion?” the second biggest question that I have received in the past year when discussing the topic of online-offline integration is the question about privacy.

• Will it be OK with privacy regulations if I integrate click data from web analytics with customer data in order to improve the relevance of my marketing communications?
• More importantly, will it be OK with web site visitors’ expectations?

The regulations side is usually a short answer for me. Mind you, the regulations seem rather cumbersome to read. But the bottom-line boils down to:

• Have a clear privacy policy on the site
• Make it as easy to opt-out as possible, ideally a single click
• Extra credit, if in addition to opt-out you allow the individual to set their own preferences of how they’d like to be contacted and on what topics
• In countries where it is required, work with opt-in

To me, the bigger question seems about site visitors’ expectations.

It may seem we are wearier of being tracked than ever. There is always a big outcry when Facebook et al announce a move towards ad targeting.

But in reality, we are much more public with our lives than ever. especially in our social networks. See this article for instance.

So what is it about this privacy thing that we really want?

The following examples help me.

Privacy in a store

We hate walking into a small store if the sales person is too much in our face and doesn’t let us browse the items on our own. Maybe we fear getting pressured into buying something before we are ready. Heck, we may well be browsing for entertainment and not thinking of buying anything at the moment. And the shop keeper that is in our face makes us feel bad about ourselves.

But we also hate being in a big box retail store and not being able to find someone to answer our questions when we are ready to ask them.

Really, we want the person to be right there — magically — just when we need their help but not before. And we love it if they understand us so well that they can recommend just what we will benefit from buying.

Privacy in a restaurant

We hate when the waiter is too much in our face, especially after we are done with the meal. Maybe we fear pressured in vacating the table for the next guests.

Just as much we hate it when the waiter is nowhere to be found when we need the check or want to order something (else).

The waiter should just — magically — refill the glasses as soon as they are empty. They need to be right there with the desert menu and our check just when we want it.

Magic???

How does it work in those stores and restaurants that do this well? Is it magic?

No magic.

The perfect shop keeper and waiter are super observant. They put a web analytics tool to shame when it comes to tracking our behavior.

But they aren’t in our face about it.

And they don’t pressure us into buying something or ordering an appetizer along with the expensive main course.

They are at our service.

And yet they still do bring us the best cross-sales offer at the best time.

Marketing so relevant that it feels like a service

I still cringe when I hear that marketing should be so relevant that it feels like a service. At first glance it seems a cheesy thing to say. It seems a utopian dream of techies like me.

But wait.

How about all those educational webinars on the web that I love to attend and learn from?

Guess what! The people doing them (e.g. me, myself) aren’t altruistic at all. Their purpose is purely marketing. They cost a ton of money, by the way. Yet, it is a service and doesn’t feel like marketing at all unless the speaker is too salesy.

There are other examples too:

• How about book recommendations on Amazon
• Movie recommendations on Netflix?

They tend to be quite relevant and not at all in your face. Ignore them easily if you want.

In fact, haven’t you come to expect and demand that any product page on a retailer’s web site will contain information on accessories that go with the item?

So, it can be done

These examples prove that marketing, in the ideal cases, can:

1. feel like a service
2. be not in your face and not pushy

My take away is that the combination of click and customer data, if used the right way, can absolutely enable service oriented marketing. But if you abuse it for span, you will cause all of us marketers to look bad and to lose out.
——–
(This post is part of a series on the state of multichannel metrics today, one year after the book came out.)

Tags: Business Metrics, Customer Centricity, Multichannel Marketing Measurement, Uncategorized, Web Analytics, multichannel marketing, targeting by Akin
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(This post is part of a series on the state of multichannel metrics today, one year after the book came out.)

The previous post focused on a travel company that listens to their customers’ online behavior so to get their cross-sales - or last minute - offers to those who are most likely to care.

Now how about the retail banking industry?

Retail banks and credit card companies have been great innovators in the use of analytics for direct and relationship marketing. For example, the case of CapitalOne is well described in the book Competing on Analytics.

For good reasons:

1. Banking is a relationship business.
2. Banks know embarrassingly much about their customers based on all those transactions and other data
3. And what is the profit from a new bank customer at the moment when they first open their account? Negative, probably, until over the life time of the relationship the bank makes back their initial costs (and then some)
4. Banks have learned that they are most successful with cross-selling products to customers within the first 30 to 90 days of the relationship. After that, customers seem to go on auto pilot with their relationship.
5. Banks also have learned that new customers are most at risk in the beginning of their relationship and may spring back to their previous bank if something goes wrong.

So banks plan out all their customer communications very carefully by predicting which offer or message sent to which customer at what time is most likely to amount to maximized net present value.

(You might joke that we wouldn’t have the stupid mortgage crisis today if the direct marketing departments had been in charge of their banks)

So why is it then that many banks are such laggards when it comes to online offline integration?

Few industries have it easier.

• You visit your bank’s web site frequently
• You are almost always logged into your account when you go to your bank’s web site.
• There is a big incentive for not deleting your cookie so that you won’t have to go through all those annoying security questions to “register” your computer.
• Quite likely you even clicked the “remember my user ID” option so that even the bank’s home page greets you with “Hi Joe, welcome back”

All your bank’s products are outlined online so you have it easy to answer your own questions.

Looking for a car loan or savings account?

Likely you check rates online.

So … hellooo … is anyone listening?

Customer 1:  European bank cross-sells savings accounts

It seems a no-brainer.

The bank, with the help of a nifty online marketing consultant, mined their web analytics and customer data for clients who

• have a checking account
• but no savings account
• YET who have recently been on the web site looking at savings rates.

All it took was a feed from web analytics looking at web pages relating to savings accounts and keyed by login-name of customers.

For extra credit, they could have extended the campaign to those who weren’t logged in but whose cookie was previously registered during an authenticated session.

According to the consultancy, the campaign was easy to do and very lucrative. So they are expanding the program.

Customer 2: Stock brokerage looks at web data to cross-sell options-trading

Another no-brainer.

Options-trades are a great way for an online stock brokerage to increase transaction volume and to lock in clients into a tighter relationship.

But options are risky and somewhat more complicated than trading stocks. So they aren’t everyone’s cup of tea.

Who is ready to receive the cross-sales offer?

Could it be those clients who are not yet authorized for options trading but who have recently been on the web page studying the process of how to sign up for options trading?

You betcha!

So what are the couch potatoes waiting for?

Legitimately, banks want to make sure that they stay within the accepted norms for privacy. The online-offline integration feels very new to them. So bankers are quick to throw up their arms and say that their hands are tied by the privacy rules of their organizations.

Are banks really that concerned about our privacy though?

Every night banks crunch through our daily account transactions to watch for unusually large deposits so that they can quickly bring us a cross-sales offer before we move the money elsewhere.

How is that for privacy!

Personally, I think that some of the arguments that we hear today are similar to what happened when the first train came out. Namely, it was said that traveling at the never-before-heard speed of 20 MPH was not healthy for human beings!

No doubt, we will see more banks review the opportunities very carefully. Innovators are already tapping into the integrated data. Others will follow.

In some future edition of Competing on Analytics we may well hear about some bank that rode the opportunity out to their advantage.

Tags: Customer Centricity, Multichannel Marketing Measurement, Multichannel marketing metrics, Web Analytics, multichannel marketing, targeting by Akin
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