Tips and Ideas for Landing Pages and Sites

Omniture to Acquire Offermatica

Announced this morning. We are being acquired.

In the interest of keeping my new job, I will direct you to the press release on the deal.

We are about 3.5 years into building Offermatica, which started with a blank piece of paper and a simple goal give marketing back to the marketer. After about 5 years with Fort Point Partners, we had learned the simple truth that technology would not transform marketing until we embraced the marketer.

Our motto from the first brainstorms was "Easy to buy, easy to use, easy to own". We sought to save the marketer from IT and IT from a marketer.

We did not know it would end up here. We tried every idea, we figured out what worked, we threw out what didn't. If an approach didn't yield measurably better results, it was a no-go. If it couldn't get done by a reasonable person, it was a no-go.

One day I will try to list all of our experiments -- One that stands out is MorrisandGreen.com, an actual ecommerce site started and run by the company. We took orders and shipped products to test our own ideas with real consequences and real customers. We were humbled when our ideas were bad, and delighted when we found a gem.

What did I learn?

1. Great marketing comes from great marketers, machines help them aim better
2. Engaged marketers lead to engaged customers
3. Speed is everything.

I am excited, and I look forward to the future.

Leaning Back with LookBack from TimeWarner

The new Look Back offering from Time Warner (see: Look Back Service Forces Consumers to Watch Ads in the New York Times) is baffling.  My prediction: DOA.

And it didn't need to be that way. 

TiVo is a fabulous example of a lean-forward device that changes media consumption.  Instead of turning on the TV and disappearing into the Barcalounger, the TiVo consumer sits with remote in hand, engaged with the experience.  They are not a vessel, they are the captain. Time Warner failed to embrace this lean-forward consumer.

Reminds me of a CEO friend of mine, who regularly complains that business would be awesome if it weren't for all the pesky customers.

The new Look Back offering is a TiVo, only you can't fast-forward over commercials and you can't store shows for more than a day.  It takes a great concept, the DVR, and removes the very features that make it great. Here, kids, have a cheap ice cream cone! Oh, yeah, no cone, and no ice cream, but the napkin is in the shape of a cone!

But it is easy to take swipes at ideas like this.  Certainly we have to understand that Neilsen is changing the way they measure media, and an unwatched commercial is now a real loss to the media property.  We need to sympathize with their need to fund the content!

Yup, we do.

But pretending we can jam the customer back into the Barcalounger where they belong won't work.

Instead, why not embrace engagement?  Have them rate the commercials with thumbs up and down.  Let them mail the shows to each other with a pre-reel ad.  Even better, open up the cable delivery networks to real innovation and let the tens of thousands of tech and design engineers that fuel Web2.0 go crazy on the ad.

Odds are someone would devise an ad unit that worked.  Or come up with a way to introduce user-selectable commercial channels, or create something unimaginably unimaginable (think Twitter) that can change the way TV works.

Almost any industry on the planet would kill to have a customer lean forward and engage with their product.  The DVR has opened the door to this engagement.  Time Warner needs to embrace their consumer, not insult them.

The Magic of Behavioral Targeting - Optimization Approach #3

Back in January - eons ago! - I set out to clarify optimization, a term that is often bandied about and regularly misunderstood. After a crazy quarter (we grew our client base 30% in three months!), I am back to finish the job I started.

We first covered testing, the most frequently used method of improving consumer response. With the targeting article, we covered how systems based on rules can be used to create more relevant experiences with better outcomes.

In this third article, we cover the most seductive, and misunderstood form of optimization, Behavioral Targeting. (The fourth, social optimization, I will explain in the near future.)

What Is Behavioral Targeting?

The holy grail of direct marketing has been a system that detects consumer behavior and changes offers or strategic marketing plan.  The first incarnation of this approach was called Data Mining, and was focused on using data to drive strategic planning. There is an apocryphal story about Wal*mart:

"By scanning each sale into a data warehouse, grocery stores have determined that men in their 20s who purchase beer on Fridays after work   are also likely to buy a pack of diapers. Thus, a display of Pampers or another brand might be set up in the beer aisle, or merchants will put one (but not both) of the products on sale on Friday evenings."

In the online arena, it is actually possible not only to process historical data, but also to act on it instantly.  In an ideal case, the web marketer would just plug in new offers or products and the system would automatically put the perfect one in front of each visitor based on these patterns while that visitor was still on the site.

Behavioral Targeting is really a refinement of targeting, or "rules based" optimization.  The difference is that while targeting uses explicit segments, predictive models seek  to discover rules within the data that are counter-intuitive.  Instead of When the user has searched for 'TurboTax', show this offer at this price, the predictive model can look at prior searches and visits to pick the ideal offer,

In simple terms, it's like having the haystack find the needle for you.

With behavioral targeting, you're saying that, if you have a big enough body of data around a consistent set of products and a consistent set of visitors, then it is quite likely that you will find correlations in how those visitors behave: different times of day, different types of product, different points of origin are some subsets that can be grouped together and perhaps predicted to behave in similar ways. 

Amazon.com

The classic example is the predictive models used by Amazon for product recommendation.  As near as I can tell, no other company has demonstrated a more lasting dedication to using predictive models:

To make behavioral targeting work, the system (or consultant) essentially works to build a model based on prior behavior that can be used to predict future customer preference. With this approach, a broad range of variables (time of day, source of traffic, prior purchase) are evaluated against performance, and a model is developed that is "fitted" to these conditions. The model is both explanatory, meaning that the past data supports its assertions, and predictive, meaning that it predicts how new customers will behave under similar conditions.

Where It Works

Ok, take a breath after that last paragraph.  You have my apologies for the excruciating terminology.  But there are simple places where you will run into predictive models every day.

Take search, for example.  When a consumer types in a search phrase, the search engines try to find the best list of sites.  To do this, they must build a model based on what they have found to be the most predictive characteristics that they can gather.  In Google's case, they weigh the content of the page, but also inbound links and a factor called "PageRank" (among other factors).  This model is employed every time a "SERP" or search result page is calculated.

Ok, so Google uses it, and Amazon uses it, so when should you use it?  The best times to use behavioral targeting are either:

1. In applications such as search and cross-selling where there are large sets of alternatives of a single type, and a large number of interactions.  This means books, music, web pages.
2. Direct marketing situations where you have a small number of offerings and a very large number of relatively well-profiled prospects or customers.  For example, credit card offers, loan rates, and other high-volume, high-value markets.

In both cases, behavioral targeting tends to work best when there is prior purchase behavior on the part of the consumer.  Such models are less effective in prospect situations as there tends to be less profile data on the visitor to correlate.

There are a number of vendors offering variations of on-site behavioral targeting and affinity marketing including Touch Clarity, Gilbert Systems, CleverSet and Loomia.  My company, Offermatica, offers Behavioral Targeting as well, based on our own profile.  Revenue Science and Tacoda (now part of AOL) are providers in the display ad market.  There are significant differences in the statistical approaches among the offerings. 

The Good, the Bad and the Ugly

On the surface, behavioral targeting seems like magic.  Why isn't all marketing done this way?

The biggest issue in targeting behavior is building the model of customer behavior.  While modern web analytics captures a staggering quantity of data, the models tend to be built to fit the available data, not the most predictive data. 

For example, the excellent music website Pandora would be impossible with most current data mining or analytics solutions. Pandora has distinguished itself for the quality of its recommendations, but its predictions rely as much on quality research and testing as on the math.

The secret behind Pandora's success is the Music Genome Project, a research effort that seeks to catalog and attribute songs based on major key tonality, dynamics of the lead singer's voice, the prominence of certain musical instruments, and the number of beats per minute within the song, and creates a channel that plays songs that have similar attributes.

An ecommerce website that sells music might use behavioral targeting in a different way. Rather than suggesting music based on attributes of the music itself as Pandora does, a site might look at the pages a visitor has viewed and make suggestions based on what was purchased by others who followed a similar clickpath.

Issues

But Behavioral Targeting has real drawbacks:

• For one thing, there is no such thing as a universal model. Models must be created and tuned for different applications - a process that can take months. 
• Behavioral targeting models and schemas require regular tune-ups to be certain that the groups or segments are still behaving in the way they were originally predicted to behave. They are opaque, meaning that a marketer has little opportunity to understand the reasons behind the correlations, and thus has little chance of learning from it.
• Behavioral models can also make some bizarre mistakes (offering a cross-sell that is completely irrelevant, for example).
• They tend to be "Black Box" and give much less information back to the marketer than testing or testing with targeting can provide
  
• And finally, they take the merchant or editor out of the equation, which can lead to serious "voice" issues. At its best, behavioral targeting is used to enhance the marketer, not to replace the marketer.

Behavioral Targeting is not likely to replace the marketer anytime in the near future.  As I have said before - Marketing is done by marketers. Machines just help us listen and aim better.

But it is an extremely important weapon in the marketer's arsenal, and savvy marketers should pay attention.

Other articles that you may enjoy on this topic (based on your behavior of reading this far!) :

Behavioral Targeting is Not Just Banners from Jon Mendez's optimizeandprophesize.com

The Promise & Challenge of Behavior Targeting (& Two Prerequisites) from Avinash's Blog.

Koral, Salesforce, and a Video you should watch

I ran into an old friend and co-worker, Mark Suster, at a Valley event last week.  I haven't spoke to Mark in probably a decade, but he has been very successful and is one of the more thoughtful software/business guys I know.

I Googled him after the event and came across a video he did about his venture history and, more importantly, what he learned about through building and selling software to enterprises.  It is absolutely uncanny how similar our experiences were and the lessons we took away.

Watch his video on Scoble's blog here.

Seriously, watch the video.  He is a very thoughtful guy and much more pragmatic than most software execs you will run across.

What I liked:
1. Simple insights that are devastating to the model of his industry - A content management system without folders is almost heresy to anyone but the Web2.0 world, but Mark got it - folders are just places to lose content in at large corporations.
2. The real dynamics of software development - When you charge a lot of money to large enterprises for your software, it will never be usable.  This might actually be a law of physics.

There is more, so watch it.

Both Mark and I met while working on a very large custom software project for a huge utility in Southern California.  It was years long and impossibly expensive.  In the intervening years I started an ecommerce software integration firm (Fort Point Partners) and he started an Internet firm for builders.  We both raised a great deal of money, suffered through the downturn and created new products. His recent venture, Koral, is now part of Salesforce.com.

'Official' at Last: Faster, faster, faster Marketing

"Many people think the technology revolution in
marketing is about Web sites and interactive
advertising. It’s not. Speed and the customer’s
experience with the brand are the two most important
marketing strategies today."

This is the lead-in to a new study by management consultancy Sapient and the Kellogg School of Management at Northwestern University (blogged here). The report, titled The New Marketing-IT Power Partnership, finds that IT and marketing must work more closely together in order to speed innovation and boost the bottom line.

Speed, and customer experience. Hallelujah!

Whether you call it Agile marketing, or Velocity marketing, or just marketing that moves fast enough to notice, it is the future.  And IT's job is to do everything in its power to increase responsiveness and take itself out of the path of the marketing cycle.  Marketers must be able to change programs and campaigns based on the changing needs of our customers, our campaigns will never be as strong or successful as they could be.

Another great quote:

"How does Netflix stay ahead of the curve? They
constantly experiment with how technology can
enhance consumer and partner touch points. In fact,
Netflix makes significant changes to its Web site
every two weeks in order to improve their customers’
experience.

Lets put it more simply.  Your customer, if they have a noteworthy experience with your brand (positive or negative) can publish their opinion in about 5 minutes on a blog, forum, wiki, or review site.  With the current state of IT and Marketing at many large brands, you can publish new information to your own digital media in about 4 weeks to 3 months.

They are running circles around you.

As we're all learning in the age of the Web 2.0, it's all about the customer experience, and we must learn to maintain a site that is as changeable as our customers, and that is as varied as the needs of our various customers.

So I agree, wholeheartedly: Yes, marketing needs to work with IT. If we can understand each other's needs and fears, we will have come a long way. (Perhaps we can even convince them to do away with the holiday lockdown...)

On the other hand, we as marketers need to learn to stand on our own. If our every site need hinges on the ability for IT to work us into their schedules - no matter how closely we work together and how understanding IT has become of our needs - we're still hamstrung. Marketers need the ability to test and to optimize in real time.

This is why we created Offermatica. After building Jcrew, Nike.com, BestBuy.com and about 40 other ecommerce sites, it became clear that no amount of spending on IT was going to solve the problem.  That is why we set out to create a content delivery platform that could move at the speed of marketing and target customers anywhere they went. The holy grail was to eliminate IT from the path of a marketer who had a great idea and wanted to get it in front of a customer fast and learn.

On a final note, the report suggests that marketers be the ones to reach out. It's time to stop working in silos and work as a team.

MSFT to Acquire AQNT - Seattle Roars back

According to Marketwatch this morning, Microsoft is acquiring Aquantive.

Doubtless this deal is directly linked to the DoubleClick aquisition by Google, and it continues the battles between Mountain View and Redmond. With the announcement of 24/7 heading into the hands of WPP, this leaves only a few standing - Accipiter? Zedo?

There are two ways of looking at this frenzy - First, with search highly optimized and relatively stable around the majors, marketing money is searching for a home and display advertising is certainly the largest spend that is still open for competition. With a predicted continuing influx of brand dollars into interactive, owning a greater share of the spend is a good growth bet.

The other side, however, is even more interesting.

What this is also a likely sign of is the maturation of the online media market from a real estate perspective.  For over ten years, there has always been limitless "frontier" of available marketing real estate, and we have finally colonized the last bit of land on the Pacific Coast.

The value of a good display ad network is not the technology (a team of quality engineers could relatively quickly).  It is the network.  Aquantive and Doubleclick have enormous numbers of impressions per month (DCLK is 6B/month).  The tags that drive this traffic are the value.  They are the real estate.  Just getting the tags on the sites took almost ten years.

As recently as a few years ago, it was not unreasonable to start up a new ad serving technology.  Falk AG, Zedo, and others were successful with pricing and technology strategies that drove demand. But this was because there was still new real estate that had not been exploited.

These days, the combination of ad networks like Blue Lithium and Federated Media (and many others) plus the publisher networks have opened up some new real estate in the long tail of small web sites and blogs, but the mainstream of traffic is already represented, and new, large sites like YouTube are rare.

I am glad to see Redmond in the game.  It is a hard business, and the question of what to do with Avenue A/Razorfish dwarfs the prior speculation about what Google should do with the Performics division of DoubleClick.  But strong product development will likely give a real boost to Aquantive's platform and that should benefit advertisers.

Google's Move to TV - Black Box or no?

Google has entered into television advertising with a new partnership with Echostar. The partnership includes the creation of an automated platform for ads running on EchoStar DISH Networks' 125 networks. Google will gain access to DISH ad inventory from across all channels and dayparts; Google's platform will then allow it to sell that inventory and provide measurement on those buys.

The buying of DISH Network ads will work much like AdWords and use a web-based auction system. The real-time reporting allows advertisers to see how their ads performed on a second-by-second basis and adjust creative or daypart scheduling accordingly. Advertisers can target by age or demographic data. Reporting data will be pulled anonymously from the four million DISH boxes currently in use. The program will, according to Google's Eric Schmidt, allow for the ads delivered to be more relevant and therefore more valuable to the viewer and advertisers, because they can be targeted more closely.

On the surface, this sounds like good news for advertisers. After all, commercial data on a second-by-second basis is something advertisers have long been searching for, yet even Nielsen's new commercial minutes ratings do not provide such detailed measurement.

But relevance and the ability to measure viewership alone are not enough. Advertisers will not be acting in their own best interests when they purchase television time via the AdWords ad management platform. Online, advertisers can look at the quality score assigned by Google, along with keyword bid, ad copy and landing page, to try to determine how the minimum price bid was determined and how they can improve how often their ad is served.

But on television, a "black box" process could hamstring advertisers, because the same tools for improvement do not exist.

There's also the problem of testing. Online, Google can be used to good advantage because advertisers can test different combinations of keyword, ad copy and landing page to see which drives the best ROI.

With television, testing is obviously far more cumbersome and costly. So while Google may be able to offer better measurement than the broadcast and cable networks themselves, what exactly are they offering to measure? Without a control ad against which to run a test, the measurement is far less useful. An advertiser may find that 120,000 people watched an ad. But, did they take an action as a result of the ad? Would a different ad from the advertiser have worked better?

As consumers lean more and more toward creating and/or choosing their own media for consumption when and where they want it (via linear television, time-shifted television with Tivo, via online sources like YouTube or broadcast network sites, via mobile devices, etc.) ads that aren't relevant haven't got a chance.

But I would love to see Google TV move beyond the black-box automation that it uses online.  If they do so, I believe that advertisers will be well-served.

What is Marketing Optimization? Testing, Targeting, and Behavior

It is the Year of Optimization.  The recent acquisition of TouchClarity by Omniture is yet another confirmation of an intense surge in interest in technologies that make computers sell better to people. 

But what in the world *is* optimization?

As a matter of disclosure I am not a PhD.  My ADD is a strong inoculation against advanced scholarly pursuits.

However, I have the unique viewpoint of experience.  I co-ran a company, Fort Point Partners, that was responsible for deploying a dazzling range of technology for companies like Nike, Best Buy, and about 50 other firms.  We launched rules-based systems (ATG Scenario Server, e.Piphany), search systems (EasyAsk and Endeca) and more advanced segmentation and modeling software (LikeMinds, Personify, netPerceptions to name a few).  We also ran a lot of tests.

Our goal was simple - make the computer capable as a salesperson. For us, optimization is a fancy word for making a selling process more relevant and engaging for your customer so that they make you more money. And the best optimization tool was one where a marketer could adapt and learn, but the machine did the work.

I see four major approaches to optimization that each have critical value for the marketer (I will use this space over the next week or so to go into more detail on each approach):

1. Experimentation - testing approaches including A/B, multivariate, Taguchi, optimal design and others. Showing different experiences to different control groups to determine a "winner" or "best recipe" based on conversion rate, revenue, or other outcome. Read more here.
2. Targeting - also referred to as "rules-based optimization".  Defining explicit segments and rules for delivering content experience. These can be simple definitions like "show the iPod when our customer searches for "iPod" on Yahoo or very sophisticated behavioral segments.
3. Behavioral - applying AI or linear regression to prior data to determine predictive factors from data to drive the display of content.
4. Social - offloading the work of relevance to the community through ratings, reviews, tagging, or other forms of participation.

Take Google, for example.  They are algorithm guys, right? They use a predictive model that is finely tuned to determine the elusive grail of "relevance" and their results are unbelievable. Yet they also use targeting and testing. True, they outsource the work of specifying the rules to us through keyword selection, bidding, and match type, but this is targeting at its finest.  And they test regularly - evaluating different treatments of the SERPs.

So what is the best optimization approach? Optimization is just marketing with math. If your user base ratings improve the relevance of your search results, then do it!  If testing helps to eliminate your CEO's bias towards acres of copy, do it! The marketing "mix" for optimization is going to take time to get right, but will yield tasty morsels of revenue improvement every step of the way.

We started Offermatica not because we discovered the magic algorithm that turned a computer into a selling machine, but because we found out that the keys to selling online were speed and control. Speed - because marketers had no time, so the machine was going to have to do the work.  And Control, because the marketer still needed to be "in the loop", either driving new ideas or removing crazy outcomes.

And remember this: Marketing is done by marketers. Machines just help us listen and aim better.

Convergence - No More Ducks!

I just read David Berkowitz's post, A Reluctant Case for Convergence, in the MediaPost insider. As usual, I appreciate David's thoughtfulness and perspective. But I feel compelled to warn about ducks.

Think about a duck.  Mediocre swimmer.  Adequate flyer.  A duck is a convergence of flying and swimming technologies. But you would never train ducks for flying contests, and marlin, wahoo and barracuda are probably better emblems for swim teams (Oddly enough, there are Duck football and hockey teams, but that is a different story).

Combining useful technologies into a single packaging because you can is not useful.  Combining them because it enables a fundamental shift in usefulness for a third purpose is the goal.  How do we tell the difference?  The new function must create sufficient value to a specific type of buyer to compensate for rendering the component technologies inconsequential.

Put another way, when being a great duck is valuable enough that it can be a lousy swimmer or flyer.

To extend the metaphor: Light bulbs are a great technology in their own right. As is internal combustion.  And while it might not be the latest in engineering marvels, but a comfortable leather chair is still a worthy achievement.

But it would strain credulity to imagine the invention of the automobile as the "convergence" of headlights, engines and seats.  It is an automobile.  It is an elegant adaptation of technologies, and it is eminently useful for a commuter and others. 

A car is a car is a car.

Put a different way, the car is not a convergence of lighting and lifting technologies, it is a car. It is an adaptation of technological development to a totally new and useful application for a consumer with a specific desire.

The distinction between convergence and useful adaptation is not ambiguous. 

The iPod is the convergence of laptop computing technologies (hard drive, USB), video displays (mini-LCD), signal processing and compression.  It is transformational.  But it is a lousy way to watch a tv show and has not been embraced as a back up file server.  It is a good music listening thing for people who like music, and that is enough.

I am not naive enough to think that the urge to create Swiss Army knives full of technology isn't a major driver of consumer electronics.  Hope will spring eternal in the form of refrigerator-based web browsers and cell-phone-based puppy finders.  But I do know that if the duck can't thrive on his duck-ness alone, then no amount of clever marketing about the merging of flying and swimming is going to get him through the winter. 

Chrysler, Time, and the Unpredictable Outcome

Just love this -picked up at Jaffe Juice (a marketing blog).  Time magazine names "You" as the person of the year.

On the time site, Chrysler runs an ad:

I know, someone is going to make the case that the ironic juxtaposition will increase awareness, and therefore it will be a positive campaign.

In reality, it is the poster child for why fire and pray marketing is too dangerous. Yes, it is what we are used to.  It takes time and money to produce a spot or build a site, so you have to take your best guess and go with it.

But if, after seeing this, you are still not sold that marketers need to have more ability to respond quickly, and that they need to get instant feedback on customer respose, and that they need to be able to make changes on the fly, then we shall have to part amicably.

No marketer on the planet can predict the future, and the future is guided by an ironic hand. No quantity or research or predictive modeling would have avoided the Time/Chrysler situation.  The ability to respond quickly, however, could have enabled Chrysler to capitalize on the situation.

What if they immediately redeployed a set of new ads, ads that might even make fun of their own?