Multichannel Marketing Metrics
with Akin

Exciting times! The consolidation in the marketing technology industry is producing a big league of solutions providers.

Dreaming ahead into the future, what can companies hope to achieve with this new breed of marketing software and services providers?

The end-to-end conversion optimization vision that still seemed far reaching to me back in February, looks much more limited now given the new outlook today.

Disclaimer: The following perspective reflects only my personal dreams and shouldn’t be taken to represent the positions, strategies or opinions of my employer.

Digital Enterprise Marketing++

It isn’t possible to do the coming future justice by calling it next generation analytics, campaign management, or marketing automation. The step up in caliber requires also a step up in language.

Might the following become every day terms in enterprise marketing technology in 2011 and beyond?

Marketing Mix Management (MMM)

Today’s discussion is at the level of point solutions such as search bid management tools (for PPC) and demand side platforms (for display advertising). In coming years, will we see all these combined into a single solution for MMM?

MMM would not only allocate advertising budgets towards marketing mix optimization. It would also automate the execution of these ad strategies, their testing, and the data collection into marketing performance management.

Marketing Performance Management (MPM)

Today’s discussion is at the level of web analytics, benchmarking, landing page and site optimization, social media monitoring, predictive analytics, BI, analytical CRM, social CRM, etc.

In coming years, will we see all these combined into a single MPM environment?

MPM would have to combine the aforementioned point solutions for analytics into one interoperable data environment. Users would want drag & drop flexibility to analyze across silos. Think Minority Report.

The data that flows into MPM would include in-house data marts with sensitive information. That means that we may see the pendulum swinging back to in-house software for things such as web analytics.

Interactive Marketing (IM)

IM is the successor to behavioral targeting and direct marketing. It is based on the accepted notion that marketing is more successful when it is timely and relevant. Therefore, interactions via websites, IVR, email, and any other addressable channel should take into account each user’s past and current behavior to personalize content and marketing offers.

Marketing – Fulfillment Synchronization (MFS)

The dream of automating marketing mix execution requires good synchronization with fulfillment. After all, you wouldn’t want to advertise on product XYZ if it is out of stock. And your ability to set a max PPC bid price for product ABC depends on its true margin, i.e. includes product costs and not just ROAS (ROAS=revenue/ad costs).

Companies such as Amazon have been working on creating ads programmatically for years based on inventory. MFS would take this capability to the enterprise software market.

Marketing Operations Management (MOM)

Already used by the largest marketing operations today, MOM is the successor to spreadsheets, notes on napkins, and the like. It is used e.g. at one famous furniture retailer to orchestrate the development of their catalogues. If any of the other ideas above are to become true, hordes of marketers in the organizations need to work together like a machine. Digital assets need to be created in support of personalized messaging. MOM provides project and workflow management on steroids to facilitate all that.

 …

One level down we may see practical applications that include software and services in support of specific steps in the customer lifecycle. These more confined solutions may help companies start small, prove value, and grow from there.

On-Boarding Concierge

Successful on-boarding is key for turning newly acquired customers into clients with a high lifetime value expectation (e.g. in banking). The automated concierge would connect to marketing performance management and interactive marketing in order to monitor and orchestrate what needs to be done.

Re-Marketing Optimizer

Re-marketing may be the oldest trick in the book. But it is still tricky to predict who needs just a reminder vs. who needs an incentive to come back. The re-marketing optimizer would provide that intelligence based on marketing performance management insights and connect to interactive marketing to get the message out.

Multichannel, Multi-touch Marketing Attribution

Point solutions for marketing attribution online vs. response attribution in direct marketing need to merge into one multichannel platform. Marketers should ask software vendors for more than just attribution reports. They should also ask for advice on which touch points deserve how much of the credit. Ideally, the marketing mix modeling function would also be covered by providing advertisers with a prediction as to what they can expect from placing their next ad dollar in each channel.

…

And the list could go on and on.

…

None of the above can replace good old fashioned, customer service with a smile.

But for companies that are already doing a good job at taking care of their customers and building products that delight, the next step can be to compete on Marketing.

Exciting times should be ahead for that.

For those about to rock & roll with marketing, I salute you.

Tags: Business Metrics, Customer Centricity, Direct Marketing, Multichannel Marketing Measurement, Search marketing optimization, Web Analytics, multichannel marketing by akin
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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?

[Read more →]

Tags: Customer Centricity, Direct Marketing, Multichannel marketing metrics, 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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Unica announced the addition of innovative social media marketing capabilities this week. Among these capabilities are Social Media Analytics for Unica’s web analytics solution, NetInsight. Specifically, one of the components of the Solutions Pack released today encompasses analytics for Facebook applications. This enables marketers to gain insights on application usage and users including details from the Facebook API.

More specifically as a customer of Unica NetInsight, NetInsight OnDemand, and Interactive Marketing OnDemand you can:

• Instrument all aspects of your Facebook application for granular behavior analysis and optimization
• Rely on the highest degree of accuracy in their analytics by basing your sessionization and unique user insights on the Facebook ID and employing cache busting mechanisms to avoid the loss of click data due to caching (e.g. in the browser cache).

Report in Unica NetInsight on Facebook application usage trends by visit duration

You can also include any desired detail from the Facebook API along with the click-stream analysis as long as you comply with Facebook’s platform policies. The API data will help you understand usage trends, success, and user preferences based on available insights about users’

• Social graph, e.g. how do key influencers use the application vs. the average user?
• Demographics, e.g. how do people at various age ranges use the application?
• Geographic location, e.g. how to users from different parts of the country or world prefer to use the application?
• Relationships or affiliations, e.g. how to married folks vs. bachelors differ in their preferences for using the application?

Unica NetInsight on current locations of today's Facebook application users (based on API data on users)

Privacy and Facebook’s Platform Policies (Note: I updated this section on April 27th)

Key to including any insights from the Facebook API in analytics is not only marketers’ good stewardship of this data. This is also expressed in the Facebook platform’s developer principles and policies.

The policies previously used to limit the kind of API data that can be stored, including by web analytics solutions, for longer than 24 hours. However, with the launch of the Facebook open social graph on April 21st 2010 the policies were revised to remove that limit. Instead there is

1. A greater emphasis on the principles of using data towards a good experience for users which expressly excludes spam.
2. A greater emphasis on gaining user consent for access to API data beyond the basic elements which are user ID, name, email, gender, birthday, current city, profile picture URL, and the user IDs of the user’s friends who have also connected with your application
3. A greater emphasis on gaining user consent for using that data beyond the Facebook application.

I think that is a great move by Facebook but clearly means that marketers must act responsibly. It may only take a few violations to create a backlash by Facebook users. All marketers would suffer a set back as a result.

Unica NetInsight report on today's Facebook application users by gender and age range

Going Beyond Analytics to Interactive Marketing

As always with Unica NetInsight, the built in data warehouse stores the granular and complete interaction history of each individual Facebook application user keyed in their Facebook ID.

Unica NetInsight, granular data drill down to individual Facebook app users

Not only can the Facebook application remember its user’s preferences. But by going from analysis to action, Unica customers can also use the profiles of Facebook application users to personalize future emails or website sessions. This assumes, of course, that the Facebook user is identified with their email address or website cookie and that permission to market has been earned.

What data is available from the Facebook API?

As Facebook application developers can glean from the documentation of the Facebook API, rich access to details about app users is available through API functions such as Users.GetInfo.

It is however key to point out that not all data fields from API functions such as the one above are available for all users. Rather, only the fields for which the user’s privacy settings permit access are available to applications. Additionally, some particularly sensitive fields require explicit user permissions.

• For example the email address (even the proxy’d version) requires extended user permissions.
• For example, the gender info is only available if the user clicked the checkbox on their profile to include gender as part of their profile page

For more information

Unica customers can contact their account mangers for more details on the Solutions Pack for Social Media Analytics.

Tags: Customer Centricity, Multichannel Marketing Measurement, Multichannel marketing metrics, Social media metrics, Web Analytics by akin
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Watch this short video to learn about the unique highlights that web analysts and managers can expect at Unica’s customer conference, the Marketing Innovation Summit 2010. Don’t miss especially the Semphonic Think Tank workshops to be held on the Wednesday of the conference.

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Unica MIS 2010 – Highlights for Web Analytics Managers from Akin Arikan on Vimeo.

Go to the Unica MIS 2010 microsite for the full conference agenda and to register. You can also find the full course descriptions for the Semphonic Think Tank workshops there and register along.

Tags: Customer Centricity, Web Analytics 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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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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