Multichannel Marketing Metrics
with Akin

The previous post had concrete recommendations for proving the ROI of behavioral targeting. Several smart reader comments brought together a pretty clear picture.

However, when I was meeting with a number of experienced online bankers in Europe recently, the question that I received was more difficult to answer than just proving the ROI of targeting.

Namely, the question was whether one can expect greater ROI from testing or targeting? Whichever promises greater ROI, shouldn’t that be where you may want to start?

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Tags: Customer Centricity, Direct Marketing, Multichannel marketing metrics, Web Analytics, targeting by akin
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It is often said that building (or proving) the business case for (site-side) behavioral targeting has been a lot harder than justifying an investment in more straightforward site optimization techniques such as A/B testing.

As a result, you can read independent industry analyst reports observing that some applications that can do testing and targeting (hint, hint) are a lot more frequently used for just testing rather than targeting today.

You can even hear from some of the best known and experienced consultants in the online optimization industry that they don’t feel convinced by the business case for (site-side) behavioral targeting because they feel it is less clear cut vs. testing.

This doesn’t need to stay this way.

The problem is that we have been asking the wrong question.

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Tags: Direct Marketing, Web Analytics, targeting by akin
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You might be squandering a huge opportunity if you aren’t using web analytics as a rich source of behavioral insights on individual prospects and customers.

Read the full article published on the brilliant new online-behavior site. There you’ll also see uses of Venn diagrams for behavioral analytics that are more serious than the recent fun with the nerd vs. geeks Venn diagram post.

Kudos to Daniel Waisberg for launching online-behavior.com!

Tags: Customer Centricity, Direct Marketing, Multichannel marketing metrics, Web Analytics, multichannel marketing, targeting by akin
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The team at Online Marketing with RSS Ray kindly invited me to present on BrightTALK yesterday on five ways to increase returns from SEM.

This was a welcome opportunity for me to detail my recommendations for how to optimize search engine marketing from end-to-end rather than focusing only on search bid management and SEO.

If you are new to online or search marketing, you will hopefully find this a useful intro to help you plan your optimization efforts. If you are already experienced in online marketing, especially SEM, then the only useful piece for you in this presentation will be a reminder that SEM optimization requires you to take a complete view.

Otherwise, the weakest link in the chain will break your ROI.

Tags: Search marketing optimization, Web Analytics, multichannel marketing, targeting by akin
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Last Wednesday (March 31st) Eric Siegel presented on 5 Ways of leveraging predictive analysis using web analytics data.

Registrations and attendance were very strong which isn’t surprising because the WAA”s yearly survey had recently shown that predictive analysis is a top question on which web analysts seek to get more education.

You can access the recording of the webcast here.

Meanwhile, Eric was nice and speedy enough to answer all the questions that came in during the webcast. You can access the Q&A on the new Unica blog.

Check this Q&A blog post out even if you don’t have enough time to watch the webcast.

By the way, did you know Unica had a blog? It was recently restarted and is on fire with lots of contributors blogging across the company now.

Thanks much to Eric Siegel for a super insightful webcast and Q&A. If you had any doubts on whether predictive analysis makes sense on web analytics data, then be sure to watch this webcast to open your eyes.

Tags: Business Metrics, Customer Centricity, Direct Marketing, Multichannel Marketing Measurement, Multichannel marketing metrics, Web Analytics, multichannel marketing, targeting by akin
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In part 1 of this series I summarized the crossroads at which digital marketing has arrived in 2010. Then part 2 explored the surprising advances that turned database marketing into a digital marketing discipline.

Now it is time to look at online marketers.

Back in 2006, my colleagues and I at Unica were still joking about our web analytics competitors’ understanding of multichannel marketing. Back then it seemed much like a scene in the movie Blues Brothers where they would go into a bar to be told by the bar owner that he was interested in all kinds of music:

 ”Country and Western”

Similarly web marketing back then was multichannel only in a sense similar to:

“Google and Yahoo”.

The old web marketing

In the growth years of Internet usage, web marketers’ focus was centered on their own website and biased towards acquiring visits to the website through advertising.

Rocket science algorithms would optimize advertising spend automatically, e.g. with automated search bid management. Rocket science testing solutions would generate and evaluate thousands of multivariate versions of the same web page to test which one is best at persuading visitors.

But any thought of focusing on the customer was deprioritized.

For example, I recently called my iPhone carrier to say that I was thinking about cancelling the service since reception at my home was unusable. Yet, when I logged into my online account afterwards the website made no attempt to retain me or win me back.

Instead, it was still busy cross-selling me stuff.

Web marketing in 2010: Focus on individual level data for targeting and accurate ROI calculations

It wasn’t due to learning from more tenured marketing colleagues that web marketers changed. After all, in 2010 the web vs. other marketing teams still remain frustratingly silo’d.

But the addition of new online channels has thrust greatness on the online marketer:

Mobile

Mobile is an inherently personal device. So, web marketers aren’t just treating it as a second website but looking into opportunities for more personalized dialog.

For example, San Francisco based GoodGuide’s iPhone application allows users to scan barcodes in the store to get information on a product’s environmental and social acceptability, as well as healthiness. But users can also set lists of favored and “avoid these” products in their GoodGuide account on the fixed Internet website. When you login to your account from the iPhone your favoreds and avoids become available to you.

It is hard to think of a more crunchy-granola (i.e. socially responsible) business than GoodGuide’s. And yet they have integrated individual level data across channels!

Not as an evil scheme, but as a service to their customers! And with opt-in, of course.

That is very promising!

Behavioral Advertising and Email

While ads and email were mass marketing channels, they are now increasingly becoming an extension of a company’s website.

• The ads that you see when visiting e.g. a newspaper’s site can be targeted to you based on your prior behavior on the advertiser’s website. Many ad networks exist that, for example, help re-market to individuals based on products they abandoned or segments for which they were profiled.
• The emails that you receive can show personalized content and promotional offers (e.g. coupons) that were dynamically selected for you based on your click behavior on the website. For example, one Unica client in Europe is sending more than 1 million unique email variations per month.

Advertisers

It is most unexpected, but another push to go from the aggregate to the individual level comes from advertisers.

Why?

As more marketing funds are shifting online, accountability is king. Media buyers want to take credit for influencing individuals that were exposed to ads even if they didn’t click on them. That requires integrating web and ad serving analytics at the level of individual ad viewers and website visitors.

Several analytics vendors, including Unica, make that possible now.

Social Media

Finally, social media pushed web marketers over the edge in their appreciation for multichannel integration with an eye towards individual level interactions.

• Marketers are keen to learn which customers have interacted with their Facebook application even if there wasn’t a direct click-through to the website.
• The Facebook API provides information on an individual’s social graph, i.e. their connection to other Facebook users.
• Websites equipped with Facebook Connect can draw on Facebook authentication outside the Facebook.com domain. That means they can also draw on other Facebook API information in the visiting individual and include that in their analytics and behavioral targeting.
• Advertising networks have become available that target ads to individuals based on their social graph, i.e. assuming that you are more likely to care about XYZ if your direct friend connections purchased XYZ.
• Social CRM has become a buzzword and refers to various online interactions with individual customers. For example web marketers are keen to see that disgruntled Twitterers receive a direct response to turn them around. Meanwhile fans should get encouraged to keep spreading the word.

There is still a missing link for integrating CRM with Social CRM in terms of mapping individuals’ identities. However, vendors are already working on closing that gap.

• Social media monitoring tools such as Radian6 list together each individual’s blog vs. Twitter vs. Facebook identities if they can detect them.
• Vendors such as RapLeaf have begun offering social data append services for CRM databases.

Summary

1. Bottom-line, the web marketing world is in the midst of an onsite-offsite integration era.
2. That has required web marketers to move beyond aggregate level data and think about data at the level of individuals.
3. With that, they now share with direct marketers an appetite for individual level click data for the purposes of analyzing and behavioral targeting.
4. This happened at a time when technology has become increasingly integrated between analytics, email marketing, and behavioral targeting.
5. Online-offline integration is not main-stream yet. But never before have web and direct marketers been so parallel in their multichannel goals and thinking.

I am excited for 2010.

Tags: Customer Centricity, Multichannel Marketing Measurement, Multichannel marketing metrics, Social media metrics, Web Analytics, multichannel marketing, targeting by akin
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So much has been written about our little world of web analytics, that it is getting increasingly difficult to come up with useful new contributions.

But the Beyond Web Analytics trio, Adam Greco, James Dutton, and Rudi Shumpert, managed to do just that. Their series of podcasts featured past guests such as Gary Angel, Jim Sterne, John Lovett, Josh Manion, Greg Dowling, and others.

So, I was honored to be next as their guest. In the podcast that Rudi published today, we discussed many recipes for web analysis ranging from the web analytics wizards, to the desire for ever smarter web analytics tools, and more.

I must admit, I feel a bit tickled like Elmo to see podcasts on our nerdy subject show up within cool places such as iTunes.
Listen online here.

Tags: Web Analytics, targeting by akin
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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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