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The Simple Beauty of Social Media: Optimization Approach #4

Optimization, that fancy word for making a selling process more relevant and engaging for your customer, could be said to have four major approaches that have critical value for marketers: experimentation, targeting, behavioral and social.

In recent months I outlined the first three. Social media is the last of the major optimization approaches to explore (at least for now). 

What Is It?

Social media is a phrase most of us in marketing have come to recognize, though its meaning is not always clear. In its broadest sense, social media means the coming together of people within a community (whether that's an actual online community or simply a section on a website dedicated to allowing the consumer to have a voice) to actively participate in the creation of new and/or the management of existing content.

This could mean allowing users to post their own photos or videos, to rate and review products, to create tags for content, to write or respond to blogs, to change existing content (like wikis), and more.

Social media has allowed consumers to feel empowered and in charge of their web experiences, but it can be far more than that for marketers. When marketers harness social media elements, they can use the "wisdom of the crowds" to great advantage to increase sales and to generate good will among visitors.

Optimization using social media is a way to allow your visitors to influence what you show other visitors. When done correctly, it offloads the work of determining relevance, so that rather than having to guess or use something like a recommendation engine to offer relevant content to visitors, other visitors essentially take care of that for you.

 
Where Social Media Work

There are several simple ways to allow your visitors to engage in social media:

•  Reviews - In retail and travel, customer-written reviews have been a clear value to consumers. With their massive consumer base and long history of operation, those two verticals are still the kings. But companies like BazaarVoice can give this advantage to any site. The key with reviews is participation - the more coverage your products have, the more effective the reviews will be.

• Ratings - Ratings include stars, thumbs up and down, "paws," and myriad other forms.  Consumers register their level of approval, and the product's rating becomes based on some form of average of responses. This type of consumer feedback is incredibly useful for optimization because it is easily rankable. Have on-site search? Why not rank results by popularity. This is a fabulous way of providing relevance through ranking.

• Digg/Reddit - Social sites provide a clearinghouse for user responses.  The most well-known, Digg, allows sites to put up an icon on articles that lets the reader "Digg" the content.  More "Diggs" and the article shows up higher on Digg.com, a central site. Reddit is done in a similar fashion. This is cheap and relatively easy, but useful primarily for editorial content. It can increase reach by popularity ranking across a huge base, but it is easy to be irrelevant.

• Social Shopping - Like Digg, but for products.

The Good, the Bad and the Ugly

Optimization using social media is perfect when you have very broad product set - for example, a travel site that may represent literally millions of combinations of locations, properties, and services. 

It would be impossible for a single company to build up their own well-attributed database to help customers make decisions - in other words, to offer recommendations based on behavioral targeting - but by allowing consumers to do the work for you, others still get the benefit of recommendations.

Social media also works well where a deep level of interaction with the product or service significantly enhances the merchandising of that product. For example, high-end electronics like audio gear and printers are very difficult to merchandise based on manufacturer information. Consumers that use the products can often provide a layer of editorial content that can drive consumer preference and conversion.

On the other hand, highly branded environments are not as receptive to basic social optimization. In many cases, the selection of imagery and arrangement of products is a critical part of marketing. When you hand this over to customers, you run the risk of devaluing the product.

Social optimization only works when the consumer base is willing to participate. Promoting the fact that you have recommendations and ratings when nobody has actually recommended or rated anything is as compelling as entering an empty restaurant on a Saturday night. Having nothing may, in fact, be better. Even small populations of participants can be risky, as individual bad and good reviews can skew recommendations in a sub-optimal way.

Behavioral Targeting - Who owns the Customer?

Simple question - is Behavioral Targeting an advertising technology or a site marketing approach? 

Ask Tacoda or Revenue Science and the answer is a way to buy highly targeted traffic based on the behavior of consumers across a broad range of sites.  As an online media buyer, I can target consumers who are presumed to be considering an automobile purchase.

Ask TouchClarity or ChoiceStream and the answer is that behavioral targeting is an approach to capitalize on past behavior of consumers to target offers or products to visitors with higher precision during that consumer's visit.

Ask Offermatica, and we would say it is both.

What is the difference? Who is right? Which should you consider?

One major consideration is who owns the profile.

All targeting services require some form of profile, which is a record of information about a given visitor. It usually identifies a browser and has other information to create a richer picture of that consumer.  This information can be compiled from anywhere, and can contain prior browsing behavior, recency or frequency calculations, or really any form of segmentation information that can be gathered.

In the case of a behavioral targeting ad network (which also includes BlueLithium and Ad.com), the vendor/network pays publishers to provide information to build a single profile per consumer.  They own that data, and you pay to access the aggregated view. 

With on-site behavioral targeting, the profile tends to be owned by the advertiser or marketer. The vendor is paid to collect this information based on behavior on the advertiser's site and make it available to optimize the experience for the specific visitor.

In the first case, he benefit is that the ad network profile would likely have a broader reach than any single site would have access to.  Understanding the activities of a visitor on a wide range of site may have the potential to provide interesting insights into stage of consideration or other intentions.  In the on-site BT case, you will have the ability to structure your own rules and segments as well as have control of what to store and not store in the profile.

Which is better?  We believe that many advertisers and site marketers can benefit from both. Test the behavioral ad networks and if they give you a reasonable return, go with it. 

We will continue to offer an advertiser-centric profile so that our customers can provide a better experience for their customers both with their display advertising and on the site with offers.

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.

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.