Better Behavioral Marketing

Behavioral targeting is emerging as a standard expectation for ad targeting and dynamic content serving on the Web. Behavioral targeting promises improved media efficiency and the ability to identify and zero-in on those with a higher propensity to buy.

Yet in this evolving field, there are no clear understandings about what inferences can properly or accurately be drawn from demonstrated behavior. Other than the individual who clicks through to a completed sale, most behavioral targeting at this point is guess work.

Consider several approaches for observing, evaluating or scoring behavior.

Repetitive Behavior

Logically someone who does the same thing again and again or visits the same place repeatedly is probably more interested than the average Joe. Assuming that most people only make one or two clicks in error, it is reasonable to guess that someone returning for a 3rd click is probably interested, if not a real buyer.

My wife is a prime example. She likes to visit future purchases frequently before buying. To her, multiple trips to the shoe store, the furniture store or the big box retailer to hover over her intended item are no big deal. In fact she enjoys the process of visiting and revisiting. With each incremental step she learns more, increases her desire and adds layers of nuance to her buying rationale.

Repetition confirms her interest or ratchets up her intent and her commitment to the purchase. The same holds true on the Web. She will click and click again on an item. Perhaps she’ll visit it at multiple sites in search of greater product detail, to compare prices or to discover a deal on shipping or a favorable return policy.

If we tracked her behavior with a cookie or some other technology, the vital questions would be -- how many visits signal her intent and on which visit should we prompt her to buy? Should we dynamically serve her content or intervene by popping up an offer on her third or fourth visit or on her sixth?? How do we know how much repetition is sufficient to encourage her to convert or at what point she might be freaked out by a big brother intervention and abandon interest?

Sequential Behavior

Perhaps if we watched where she went before and after visiting the product, we might get a better idea. If she visits the same product at a competitor site, does that signal intensity of interest or intent? If she looks at a similar product or a product that normally goes together and sells together with the first product can we infer a pending purchase?

If she puts the cherished item in the shopping cart and abandons it can we assume “No” is not really no? And it is a fair expectation that if you abandon a shopping cart, someone may follow-up with a question or an offer?
What about if she makes a beeline for something? Would that be enough evidence to treat her differently from the great mass of web surfers? Say she responded to an email and clicked on a designated landing page or navigated from the home page to a particular product page in the shortest possible sequence (3 clicks?), would that mark her as an A prospect and separate her from the herd?

Response Devices

How about if she fills in a form, signs up for an e-mail newsletter, downloads a whitepaper, prints out a PDF, uses a zoom feature, puts data into a calculator or clicks a “contact me” button? Assuming that only X percent respond frivolously, would using the provided response device qualify her as a hot prospect or merely mark her as either a
well trained consumer or as a tire kicker?

If she fills in only part of the form, what can we infer? Is a newsletter subscriber more interested than a downloader? Can we distinguish between serial subscribers and sequential downloaders, who could be anyone from your next best customer to a high school kid working on a project?

Direct marketers will tell you that even among those who answer the call to action and utilize the provided response mechanisms; most responders are generally interested but not ready-to-buy. So the act of responding, while rarely more than 2 percent of those exposed to an offer, still doesn’t turn you into a qualified, hot lead or give us any indication that for a little extra effort or TLC we can get you to buy.

So what’s a marketer to do?

Charged with generating demand, we want to do so in the fastest most cost effective manner. Accepting the notion that actions speak louder than words and that people are creatures of habit who often act in repeatable patterns, we buy into the notion of behavioral marketing. But how do we draw the right inferences from the behavior we observe?

The short answer is threefold. First we test and learn. It’s classic but it is limited because we cannot project our learning across product sets or categories. It’s even better if we can share data with each other, make generalizations and hypotheses based on products, categories, dayparts, gender and other psychodemographic factors and get collectively smarter at reading the digital tea leaves

Second, we watch behavior over time. Assume that somebody who does the same thing or related things over time is more interested that the person that does it once and moves on. As a corollary, assume that someone who accesses or responds in multiple ways or at multiple, different times is more interested and has a higher purchase intent that a person using only one media channel. If we collect data from multiple channels (web, e-mail, search words, trade shows, purchase history, coupon redemption, etc) we can begin to see patterns that will suggest how we can weight and model observed behavior.

Third, we aggregate the varying dimensions, try to weight them by watching what prospects do over time and then attempt to triangulate purchase intent and intensity. This applies particularly to high value, considered consumer items (cars, stocks, diamonds, real estate) and in B2B marketing where the shopping cycle is longer and where the decision set has a larger number of variables with complex relationships between them.

If behavioral targeting is going to become a standard, it has to have impact. Behavioral targeting must help us sell more things faster to those most likely to buy. If it doesn’t, it is just digital voyeurism.

For Technology-Enabled On-Demand Marketing -- Outsource

The CMO Council met recently in Monterey, California and focused on technology-enabled on-demand marketing. But for most attendees this notion is either an aspiration or an apparition.

That’s because relatively few marketing organizations have embraced a data-driven sensibility, implemented a functional CRM system or allocated resources to automate and measure marketing activities. In fact, in a survey conducted for the Council, CMOs confess that the lack of customer insight, business knowledge and marketing analytics and measurement are their three greatest weaknesses contributing to the devaluation of the marketing function and the marketing organization within Fortune 1000 companies.

The sands are shifting under their feet but CMOs can’t seem to react fast enough to these seismic events to keep their jobs longer than an average of 23 months.

Why?

Too many CMOs only know what they’ve experienced themselves. Most came up through the risk adverse ranks of corporations or were imported from the big agency world where branding was the only thing that mattered, where print and broadcast were the given pillars in any effort ad where customers were an abstraction and sales were an annoyance. Most have only a superficial understanding of the nuts and bolts of the businesses they are in, don’t get metrics or measurement and bristle at the idea that numbers rather than big ideas or dramatic images can change the game.

Too many CMOs don’t have enough people or the right people to embrace technology and on-demand marketing. Marketers have been systematically eliminated by cost cutting measures and many of the survivors don’t have enough grounding in technology and metrics to make a difference. And even when they adopt technology they rudely discover it takes or diverts people to learn and use the technology before you can realize the benefits In many cases, useful technology and CRM systems don’t get used because there is no time for training, experimentation or gathering content and data necessary to feed the beast..

Too many CMOs still are limited by corporate silos. They don’t control the full range of marketing assets and capabilities, have limited access to financial data and find themselves fighting other units to align with IT, web sites, sales, operations, telemarketing, PR and other functional units that should be deployed as a holistic marketing ecosystem.

So what should CMOs do?

Assuming the objective is to get more for each marketing dollar spent and find ways to sell more things faster, the short answer is …Outsource.

The effective CMO recognizes the limitations on resources and the lingering doubts about marketing in the C suite and will embrace a strategy to assemble the necessary components rather than build them from scratch or aggregate them through empire building. He or she understands that performance – measured by some kind of ROI calculation – becomes the barometer of job security rather than how much everyone loves the new campaign.

The trick then becomes finding the right external partners with the right attitude, the right cost structure and the right tool set. And then, CMOs must find the right lieutenant to efficiently assemble, direct and align the outside players with marketing and sales plans, timetables and existing internal organizations to deliver measurable results.

We are seeing the emergence of these trusted lieutenants, senior Marketing Operations executives, tasked with this job. And they are talking the talk of marketing resource management (MRM) and scouring the landscape for partners, usually not ad agencies that can jump into the fray, deploy expertise and technology quickly without prompting battles with IT or requiring too many additional people to provide liaison.

These emerging marketing companies are the ones to watch for clever innovations, breakthrough technologies and traction in utilizing CRM and other data sources to move forward in automating repetitive marketing functions.

Psyching Out the Voters

If demographics are based on the notion that “birds of a feather flock together” then psychographics works on the premise that on selected topics birds of multiple flocks care about the same thing in similar ways.

Mike Bloomberg used psychographic models and segmentation to get beyond “Soccer Moms” in his re-election campaign for Mayor of New York. Some observers are saying this changes the political calculus, though most of us think a mastery of psychographics is the secret genius Karl Rove has traded on for years.

Demographics are generally good predictors of the gross segments. Age, education and income pretty much dictate your taste in consumer goods, real estate and politics. When you overlay purchase data, be it anything from cars, clothes to magazine subscriptions the picture gets more nuanced, though the conclusions are clearly inferences not facts. For many marketing purposes this level of specfication is good enough.

Psychographics take this data and mix it with attitudinal data to produce a richer profile. It’s a simple formula. The more data you mix the more nuanced the profile you get. The more nuanced the profile, the higher the cost. Most marketers can’t or won’t justify the incremental cost of psychographic profiling because the value of a sale is too low or the need for such precision targeting isn’t as great. Generally only the highest value items with long or complex selling cycles are willing to invest in a psychographic approach.

For years big research firms have been creating segmented profiles which combine demographic data with purchase history or purchase intention to yield discrete groups which are likely to be targeted by marketers. That’s how Volvo and Bill Clinton discovered “Soccer Moms” as a distinct subset of the population worthy of specific messages and a bit of romancing in the first place.

Psychographics approach the compilation of a group by zeroing on a common need, attitude or behavior. For Bloomberg, “Fearful or Anxious New Yorkers” (FANS) are lower income people heavily dependent on the City and its social services both to provide income, income support and basic social services. The group cuts across zip codes, age and ethnicity lines.

His message to them reinforced the idea of security. The City will thrive. They will keep their jobs. The City will keep services open and flowing. The proof points were his record on fighting crime and terrorism and his track record on job creation and heath care.

Compare this appeal with that to “Cultural Liberals” those higher income New Yorkers concerned that the arts, music and culture scene be maintained both for their own enjoyment, the status of the City and as a lure for tourists. The pitch here was his background as a businessman and his strong fiscal management which allows the City to afford these things and “do more with less.”

Why did Bloomberg go to this extent when he was paired against a has-been politician from the Bronx? I’m guessing because he can. Will this create a new paradigm for political campaigns, as suggested by Jim Ruttenberg in the New York Times?

Don’t bet on it. Bloomberg spent 10 million dollars on what his campagned called "list development". That's a number that would scare even a national campaign.

But look for a clever contender, with a good database marketer advising her, to take existing psychographic data sets from commercial vendors and enhance them with voting records, data on political or charitable donations and real-time polling data to yield the same insight for targeting at a shade of the price.

Gauging Google's Gameplan

Google is on the march. The announcement of Google Analytics extends the search firm’s easy, intuitive interface and its tracking mechanism to a broad range of Internet advertising and in no time it can be extended to other forms of inbound and outbound communication.

Not only does the move to give away these services threaten an array of web tracking vendors, it points to a Google strategy to concentrate, aggregate and dominate how messages are distributed, measured and valued in cyberspace and beyond. The new service will allow any marketer to track clicks on banner or text ads, clicks on rival search engines and clicks through to websites from e-mail campaigns.

In tracking this activity, it will be easy to track content sequences, frequency of visits and make some generalizations about the effectiveness of selected vehicles and selected messages. If the tools for accessing this data will be as easy-to-use as Google Adwords, the new service will potentially give marketers a faster insight into the efficiency and effectiveness of their messages and media choices. It could also cue publishers about the true use and value of their inventory which might lead to different pricing and bundling models.

Based on technology developed by Urchin, a firm Google acquired earlier this year, the free provision of analytics could spark an embrace of metric tools; a category still in its infancy both it terms of acceptance and use by marketers and technical sophistication.

This development provokes two thoughts …

I. Knowing is just the first baby step.

If less than 20% of million dollar companies with web sites have adopted web metrics, the Google move could instantly expand access to data about web surfing. And while FREE is a powerful incentive, you still have to have a sensibility for measurement and people who know something about metrics to meaningfully grasp the Google largess.

Yet this just starts the process. Once things get counted, someone has to apply some intelligence and experience to “read the tea leaves” and to understand what the patterns mean or to hypothesize and test assumptions that can be drawn from data. And assuming you have an interest and a capability to do so, then you need to figure out what to do about the data to achieve your own stated goals. The beauty of the Internet is the ability to track and count all kinds of things. The challenge is to figure out what all this data means and how to use it to get what you want.

Google Analytics will collect and display numbers, lots of ‘em. Google Analytics won’t do it for you, nor will it tell you what to do with the conclusions and inferences you draw from the data sets.

Google Analytics is a shot across the bow. It will make web tracking and metrics firms paranoid and possibly prompt a scramble for cheaper, more powerful tools to overcome the Google offering. But for the mass of marketers, its not much more than a clarion call to embrace tools that can make your money go farther and your messages work harder.

II. Google has a Grand Vision

They didn’t hire a zillion computer science geeks just fine their search engine. Google is thinking big and constructing a broad universe of offerings that they expect to change the game. Expect them to leverage the strengths of the net and the strengths of the technology they are building and acquiring like mad.

They are thinking big. They have a vision of mass, value and dominance just like Gates and Ballmer. Their moves are provoking worries about scale and changing business models. For little guys the Google vision could mean extinction. But the real impetus is the coming battle of giants that will pit Google against Microsoft and Yahoo.

For some this clash of titans ironically subverts the promise of the digital age where technology was supposed empower everyone to take a shot at the American dream. But like most maturing markets, roll-ups and dominant players look for opportunities to corner the market and use either innovation or distribution (think MS Explorer) to create captive or long term customer relationships.

And while many will lament the emergence of conglomerates in the digital space, we have always countenanced the price, speed and innovation benefits that big, well organized companies with a vision have brought into the market and into our lives in other contexts. Google will be no bigger or no badder a “big brother” than GM, GE or IBM.

The real challenge is to guess at Google’s end game and align your own plans to take advantage of their offerings or position your products and services in ways to leverage the disruptions that are coming.

Why We Still Hate Phone Companies

Everyone still hates the phone company. My recent interaction with T-Mobile, validates a common shared ambivalence. Why are telcos so easy to hate? Their arrogance and complete disregard for customers is expressed by their customer service actions.

I signed up for T-Mobile “pay as you go” service after overcoming the shock that Wi-Fi wasn’t free at Starbucks. Beginning the moment after my credit card was processed, the T-Mobile Hotspot network at the Starbucks on East 77th and Lexington got funky. Every 90 seconds it dropped my connection and forced me to log-in again. This went on for the better part of a vente café au lait or until my murmured curses prompted the woman at the next table to acknowledge how spotty the network connection was and how frustrated it made her too.

I e-mailed T-Mobile customer care to complain and demanded a refund for the session. I immediately got an acknowledgement e-mail assigning me a case number (773354) promising real communication within 48 hours.

Two days later I got an e-mail from “Vasha” thanking me for contacting them and apologizing for the inconvenience. So far so good, though what kind of name is Vasha?

By the second sentence, they are laying out reasons why the cnnection might have been so unreliable. But each reason offered implies that the problem was my fault because I might be running a firewall or anti-virus program or because my wireless card driver might not be installed correctly. Rather than just admit that everything isn’t perfect on their network all the time, T-Mobile puts the perfoirmance burden on me.

The implication is that users screw up network performance which probably reflects the
company notion that customers are a necessary evil. What about all the possible reasons a network might not work properly that are network-oriented faults? Would Catherine Zeta-Jones be so quick to blame me for spotty performance? And how come there wasn’t a peep about my refund request?

In a 1-to-1 world, why did I get a one-size-fits-all response designed to get rid of me with faux politeness?

T Mobile, like other telcos, pretends to be infallible, mildly insults the customer who takes the time to interact with them and then wonders why we churn so quickly and why its so hard to create brand advocates.

Whatever happened to the idea that the customer is always right?