Say you have a list of 500,000 e-mail addresses.  You send your standard campaign on a Monday. Later in the week, you tabulate your results:

• 500,000 recipients
• 20% open rate = 100,000
• Of the opens, 20% click through to the website = 20,000 visit website
• Of the clicks, 5% convert and buy something = 1,000 orders
• Average Order Value = $100
• Total Demand = 1,000 * $100 = $100,000
• Demand per Recipient = $100,000 / 500,000 = $0.20 [per customer]

He compares these finding with what you’d get if you did something called a mail/holdhout test. You compare a control group that does not get the email with a test group that does. For instance, he suggests this breakout:

• Mailed Group = 400,000 Recipients, $300,000 spent = $0.75 per customer
• Holdout Group = 100,000 Held Out, $45,000 spent = $0.45 per customer
• Incremental Lift = $0.75 – $0.45 = $0.30 per customer

Much more insightful!

This is why I’m not a fan of open/click/conversion. A mail/holdout test proves the actual value of an e-mail marketing campaign. In this case, we observe $0.30 lift, whereas open/click/conversion yields $0.20 lift. E-mail marketers, why would you not want to know that your campaigns are working 50% better than when measured via opens/click/conversion?

Kevin goes on to provide other interesting observations from his years of doing this sort of testing. You can’t go wrong by following his blog, and trying his approach to data-driven online marketing.

It’s a Catch 22 for online marketers. In order to boost response rates, we need to know more about the people viewing our ads. This work of behavioral targeting sounds like a win/win: “We’ll only provide you with the ads that you will likely care about.” But in practice, consumers get spooked.I’m reminded of direct response research done years ago. It was a survey to find out how people like to be hit up for contributions to non-profit causes.

Here’s how consumers responded:

1. Least favorite: Personal asks
2. 2nd Least: Telemarketing calls
3. 3rd Least: Direct mail

What researchers at the time found telling was the direct correlation that existed between disliking a method of asking for donations and its effectiveness in getting them. In other words, personal asks — your sister-in-law selling Girl Scout cookies for her daughter — are most effective in terms of closing rates. The closing rates of telemarketing (back when this was a more viable medium for fund raising) were nearly as good. And a distant third in terms of effectiveness was direct mail.

So what’s really going on here? The accepted theory is this: We all have limited money to contribute to causes, and we would prefer to put off making decisions about where that money should go. Therefore, the most effective ways of forcing a decision are the least preferred.

Similarly, we love DVRs, because they allow us to zoom past commercials. They give us a way to avoid participating in commerce. They can’t touch us because we’re averting our eyes.

Behavioral targeting, if done properly, presents ads that also touch us. Thus, we look for reasons to hate the practice. Privacy is as good a reason as any and better than most!

So what’s the solution?

I do think that attitudes are slowly changing, and this change will eliminate privacy concerns as a reason to hate behavioral targeting.

Here’s an example. Consumers using social media are getting more comfortable with the various personas that they present on networks. They’ll show their “all-business” persona on LinkedIn, and their more casual facade on Facebook. Both are true depictions of the user, but they’re single facets of a full personality. Context determines how consumers behave on these sites. They are becoming accustomed to being watched by friends and business associates.

Reading Online Body Language

These same consumers are seeing how they can watch their friends right back. They are learning to “read” a friend’s feelings and preferences, based on online behavior. Consumers are getting accustomed to the online equivalent of body language. Or maybe that’s too strong a word. We’re all quite conscious of what we’re conveying, and body language implies unconscious action. Maybe what we’re doing on social media is more akin to a Kabuki dance.

Reading these dances is what marketers behind behavioral targeting are learning at a mass level, and turning into surprisingly accurate algorithms. But when it’s done by machines, in the service of a sale, many consumers still insist it’s “so wrong.”

Perhaps not forever.

To predict how people will react in the future — especially in areas such as privacy — I try to look at real world analogies. It’s not hard to imagine a web site as similar to a retail store. A while ago I wrote about how stores are analyzing shoppers using arrays of cameras. One system, called PRISM, is finding some unexpected insights into shopper preferences and behaviors. Shoppers aren’t getting freaked out. The prime reason: Most are oblivious to the surveillance.

But what if the people behind the cameras sprung up from their chairs and raced into the aisles, to say things like, “I saw you were looking at those lawn rakes. Can we interest you in some yard waste trash bags as well?” There would probably be lost sales, at the very least!

In the online world there are intercepts like this, but they are automated. This automated “intercept” is something people will become more accustomed to over time, as a generation weaned on social media, and used to their online movements being watched, comes into the majority. They will be able to understand that their behavior is being measured by machines as well as their online friends. They’ll realize there is no man behind the online ad “curtain” … just a predictive model.

Blurring The Lens To Reduce The Creep Factor

Another trend that will help the acceptance of behavioral targeting is a move toward more explicit boundaries. For instance, I expect an eventual backlash to camera surveillance such as PRISM. But before this reaction can take hold, the boundaries of store monitoring will likely improve. This improvement in technology will, for once, be toward discreetly blurring the “analytical lens” — instead of making it sharper.

Radio tomographic imaging is a new way to study store traffic. It uses arrays of extremely inexpensive radio transmitters and receivers, placed around buildings, to display people moving within. Below is a demonstration of the technology.

The benefits of this technology over cameras, at least to marketers, is cost — both in equipment and the labor of monitoring. Because people become moving “blobs” of color, it’s easier for computers to analyze traffic patterns and behaviors. Less can sometimes be more. Combine this information with RFID signals and you have a way to track a shopping basket all the way to check-out.

Imagine how this could be used:

Before he leaves that store, a consumer might someday pass an “intelligent end cap.” This smart store display knows — based on radio tomographic imagery, enhanced by RFID data sent to it — when to light up. The end cap would know the consumer has a yard rake, and offers him the trash bags he forgot to add to his list.

This hypothetical consumer will probably be grateful, since he really did need the bags for his yard project. And after all, he was just a blob of color moving through the store.

Ironically, this is the level of detail being used for most online behavioral targeting. This is the “privacy invasion” that is causing such a fuss on Facebook and elsewhere.

Over time, consumers will become more comfortable with behavioral targeting’s perceived betrayal of privacy.

That will leave only one valid objection the technique: They just don’t want to buy more stuff.

Rather than focus on attracting or pulling visitors to their website by publishing high quality content and researching popular language, organizations appear to be more interested with pushing out messages to “friends” through social media, even though, in many cases, those messages include hyperlinks back to their own websites. Until these organizations learn to develop a more sophisticated approach to building and managing landing pages and web content management on their websites, they will have difficulty evaluating their return on investment for these emerging channels.

(Emphasis mine.)

I found this report by reading a trendy headline somewhere. It proclaimed that marketing and especially PR executives are expected to possess skills that most are still scrambling to master. Here is a sample chart showing the data behind this assertion:

The employers surveyed should be commended for understanding the pressing demands of social media. However, they’re overlooking an equally important skill in their communications hiring checklist. They must hire people who understand the importance of good site content and how to measure its value. This is essential to making long-term gains from social media and search engine efforts.

It’s not enough to know how to attract eyeballs. The owners of those eyeballs had better find something on a site that’s worth experiencing and sharing.

1. Place your web address, with a compelling call-to-action, directly on the products being shipped. Make this call-to-action as time-sensitive as possible. Don’t be lame and do include a deadline. NO: “Fill out our warranty card online.” YES: “Set up an email reminder on our site so you’ll never forget to replenish. Do it by [date] and we’ll give you an automatic 10% off your next purchase, and free shipping!” Enclose a card reiterating the offer. This may be your last best shot at creating a repeat customer.
2. Follow up your shipment with a “We’d like to know if your products are fitting your needs” email or letter. Include a customer satisfaction survey that rewards them with something they can use with an immediate order. If you’re using snail mail, naturally you should enclose a printed catalog. Draw their attention to related items that can be found within it (or if it’s an email, found on the eCommerce site). If possible make the effort self-financing by generating an immediate re-purchase. Use the Net Promoter Score (NPS)* methodology in the satisfaction survey, to track current loyalty for this customer and as a way to track overall likelihood to repurchase as a trend over time.

Those are my recommendations. What are yours? Comments are especially welcome, for me and my friend.

*I’m including this because, although NPS has fallen out of favor as a predictor of company growth, and in other ways is definitely not perfect, I agree with Dale Wolf in that I like its simplicity. You need to use something as a predictive baseline that can (hopefully) be compared with real loyalty measurements. The NPS methodology, associated with Satmetrix Systems, Inc. and Fred Reichheld, is good enough to do the job.

Teams around the world got to work. It took them three years to reach the 10% milestone. And 30 days after one team did, the best results over that threshold took the prize. Here’s the leaderboad.

We can learn from these teams’ struggles. The leaders who were interviewed all agree they couldn’t have done it without an interdisciplinary approach, tight collaboration and a willingness to be wildly creative. According to a piece in the New York Times, “the formula for success was to bring together people with complementary skills and combine different methods of problem-solving.”

In the physical world, we know that the more hands you have to lift something, the heavier an object you can lift. But most of us in our digital, information age careers, have a difficult time imagining that this synergy is possible when the heavy lifting is computational. We need to think again.

Quoted in the Times piece, David Weiss, a member of one of the teams competing, said, “The surprise was that the collaborative approach works so well, that trying all the algorithms, coding them up and putting them together far exceeded our expectations.”

We’ve seen it work with open source software and multi-player online games. Now we have a very public example that in the digital world as well, many hands make light work.

Related posts:

• The Metaphysics of Netflix
• Netflix understands the strong ROI of customer satisfaction

And who might these winners be? A little over a week ago, the team called “BellKor’s Pragmatic Chaos” delivered a 10.05% improvement. The Netflix Prize competition has now declared “last call.” The other teams have thirty days to improve on the winning algorithm.

Two things strike me about this competition. The first is how difficult it is to predict our tastes in films. I’m frankly amazed that anyone is taking the prize. (Remember, teams have been trying for three solid years!)

The second and more important take-away is this: You can never be content with your present efforts to satisfy customers. They can always be improved — and they should be improved. Even when the cost is surprisingly steep.

Marshall makes some excellent points (he’s not @WebMetricsGuru for nothing!), including this one: “Social Media isn’t really designed, at this time, to analyze Acquisition or Retention - but Web Analytics, is — and I maintain this is one of the strongest arguments to merge the two, in a formal way, rather than in an informal way.”

Datamining and CRM

How do you begin merging these data in a “formal” way? Tools are emerging to allow for the mining of conversations, and linking them where possible to a CRM database. Here’s Marshall’s take on this process:

David Berkowitz talks about Target Audiences, but you’d first have to figure out what your Target Audience is for your Brand or for a particular product or promotion of your Brand – then do CRM datamining using house database lists, or the Social Media CRM outreach to collect names and classify them according to Target Audience Segmentation — best done with data analytics.   Let’s say, that for the purposes of this post, my article on Entrepreneur.com on Learn to Measure Your Web Presence using Unbound Technology or Rapleaf, is the way to go.

If you’re a mom-and-pop shop, you’d do nothing as elaborate, more just Twitter research, much as I’ve shown above, but if you’re Zappos, or Dell, well … that’s another story — the story I tell in Learn to Measure Your Web Presence and others, like it.

Of course, a big brand can make a lot of money whereas the mom and pop shop, probably won’t — so a big brand can afford to spend a lot of money on data mining — and it’s well worth doing because of the potential money and value that can come from it.

Scarcity of Resources

The biggest constraint in doing this sort of work isn’t technology. It’s time. Even properly guided, the process takes many people-hours, and that is a resource in short supply for most businesses today. I see a major challenge in the linkage between prospects / customers and Twitter profiles. (Ack!, I can hear you yell. Yet another datapoint to capture in our CRM databases: The client’s Twitter handle!)

But it is becoming clear that this is an area where a business should focus some of its energies — assuming the business passes David Berkowitz’s Ten Ways test.

Years ago, Don E. Schultz co-wrote Measuring Brand Communication ROI. In this marketing chestnut, he and his co-authors built a surprisingly relevant model for tracking spending and estimated returns for each brand communication (How old is this book? The included Excel file was loaded on a 5.25″ magnetic diskette). A huge category — and ROI black hole — was customer service.

Twitter is a communication channel more than a marketing tactic, and this channel has more to do with customer satisfaction and brand education than driving sales. It’s another touchpoint and nothing more.

But like email and other important touchpoints, it should be measured. Conversations like the one taking place today will help determine how this measurement takes place and to what end.

Kevin Hillstrom, author of Multichannel Forensics, suggests a reasonable way to approach web campaign valuation. It works well for e-commerce site visits, and even sheds light on valuing other types of visitors.

Placing value on non-buying visitors

Start the valuation process with the obvious: Visitors who immediately convert to a sale. But then keep going. Apply common sense numbers to those visitors who do not immediately buy. And why not? Visitors may come back two, three or more times before making a purchase. Like a fish that nibbles before biting, these “lost” visits aren’t so lost after all. They should be fairly depreciated, not totally ignored.

Here’s what Kevin says about ignoring all but the “one-visit” purchasers:

We try our hardest to allocate orders to the advertising vehicle that caused the order, seldom considering a series of events.

Take paid search as an example. Assume that a paid search campaign results in a 3% conversion rate and a $100 AOV [Average Order Value]. We run a profit and loss statement on the 0.03 * 100 = $3.00 demand generated by the campaign, factoring in the cost of the campaign.

What about the 97% of visitors who did not purchase?

Hillstrom asks, “What if you had this data?”:

• Of those who are left [i.e., 97% of the base], 50% will visit the website again within one week, with 3% converting, spending $100 each.
• Of those who are left, 50% will not visit again. Those who are left will visit again within three weeks, with 3% converting, spending $100 each.
• Of those who are left, 50% will not visit again. Those who are left will visit again within one month, with 3% converting, spending $100 each.
• Of those who are left, 50% will not visit again. Those who are left will visit again within four months, with 3% converting, spending $100 each.
• Of those who are left, 50% will not visit again. Those who are left will visit again within six months, with 3% converting, spending $100 each.

He goes on to explain:

There is value in each case, value that most of us choose not to measure.
When I iterate through the five cases above, I calculate an additional $2.75 of future visitor value. [I get $2.68 in my number-crunching, as the number in the lower right of the graphic shows. Here's my math in a Google Spreadsheet.]

In other words, we measure the $3.00 generated by short-term conversion. We don’t always measure the $2.75 of future conversions.

Now there may be additional expenses associated with the $2.75 number — that customer might require additional paid search expense or might use a shopping comparison site, whatever. So we need to run a true profit and loss statement on the additional $2.75 generated by future visits.

If each first-time visitor (one that doesn’t convert immediately) is worth $0.30 profit over the next twelve months, you think differently about attracting visitors, don’t you?

I agree. The BrandWeek article I linked to in the first paragraph said that the CMOs surveyed, “Expressed an awareness of digital’s potential, along with a recognition that they weren’t close to tapping it.”

Building sales models that take into account the messy realities of online behavior is one way we can start.

(Although we never met, I am to this day indebted to her for inspiring me to move my career from a direct response to a digital marketing focus. She also, during her talk, introduced me to the jargon-y word paradigm. For that I’m not so grateful, and do my best to use this MBA-scented doozy of a word only with heavy irony.)

The book, and the new “paradigm,” quickly generated its detractors. But much of the book has withstood the test of time.

Remember, 1994 was practically the digital stone ages. The year the book hit the marketing mainstream, was a time when frequent flier programs were still in their infancy, and the concept of “free, advertising-supported email” was one of the few internet developments worthy of mention in the first edition of the book (Hotmail was founded three years later — and went viral shortly thereafter — gobbling up market share until purchased by Microsoft).

Pioneers are often criticized for events beyond their control. I suspect that Peppers and Rogers felt more than a little queasy as they watched one corporation after another buy into large, expensive, and mostly doomed customer relationship management (CRM) initiatives. Most of the initiatives were predicated on grossly unrealistic expectations.

I predict that current and future investments in this type of marketing intelligence will deliver on the vision of this seminal book.

The truth will bear out, and we’ll see that the problem with the “one-to-one future” wasn’t the vision itself, but a dearth of analytical vigor coupled with poor training for the people actually expected to use the systems.

What will be different now? I see three things:

1. Post-boom sobriety — If Pepper and Rogers had been doing business in a less explosive business environment (read: abundant venture capital mixed with what Greenspan aptly called “irrational exuberance”), their message would not have been so quickly and rudely out of fashion.
2. The growth of small business — Good CRM must be wired into every part of an organization. That is nearly impossible in large, hide-bound companies. Expecially if they have two dozen databases that need to talk to each other. (Yes, two dozen. I wish I was kidding.) Thanks to the phenomena of SaaS (Software as a Service), cloud computing and good ‘ole Moore’s law, powerful business intelligence is now within reach of small and mid-market businesses. But that just the technology. Here’s the biggest difference:
3. A truly plugged-in workforce — By far the largest barrier to successful implementation of automated systems is getting the workforce to recognize them as their friends. This is happening more today, as a new generation is becoming more familiar with the vast world within their web browser. It’s an entirely new way that small organizations are growing into bigger ones that I wrote about in my post on networked leadership.

For these reasons I’m optimistic about a future that fulfills Peppers and Rogers’ promise.

No, it won’t exactly be “one-to-one.” Even Peppers and Rogers conceeded that economies of scale call for more of a mass-customization of contacts and segmentation than the portfolio-based management they first discussed. But this “future” will bring us to the same destination.

It will be a scalable way to extend a business into niches as narrow the miriad reasons we choose one brand over another. Fifteen years ago I thought this book might be on to something. In spite of the economic jolt we’re experiencing (or maybe even because of it! ), the last year has assured me I wasn’t wrong after all.

Wikipedia describes the account of the Oakland A’s unprecedented success on a shoestring budget this way:

Statistics such as stolen bases, runs batted in, and batting average, typically used to gauge players, are relics of a 19th-century view of the game and the statistics that were available at the time.

The book argues that the Oakland A’s front office took advantage of more empirical gauges of player performance to field a team that could compete successfully against richer competitors in Major League Baseball.

Rigorous statistical analysis had demonstrated that on base percentage and slugging percentage are better indicators of offensive success, and the A’s became convinced that these qualities were cheaper to obtain on the open market than more historically valued qualities such as speed and contact. These observations often flew in the face of conventional baseball wisdom and the beliefs of many baseball scouts and executives.

By re-evaluating the strategies that produce wins on the field, the 2002 Athletics, with approximately $41 million in salary, are competitive with larger market teams … who spend over $100 million in payroll. Because of the team’s smaller revenues, Oakland is forced to find players undervalued by the market, and their system for finding value in undervalued players has proven itself thus far.

I found the book inspiring then, and I find it even more exciting today. In spite of my general lack of enthusiasm for the sport of baseball, I found it the best business book of 2003.

Reading Digital Tea Leaves

The author argues that most teams still rely on the black art of talent scouting. Wizened, tobacco-spewing scouts  watch a young athletes play and proclaim him either unfit or “slugger material.” Does this sound like the seat-of-the-pants metrics that marketing has traditionally used to evaluate markets, products and campaigns?

Back when I read it, I was thrilled to imagine new ways to read a different sort of tea leaves — namely, the ones found in market and customer databases. What I’ve learned since is that once you start looking at data in a new way, you can find breakthrough insights.

You really can.

Although I don’t think he’s a marketer, here’s what “Ryan,” in his GoodReads review of the book, wrote:

Awesome – story of how the Oakland A’s built a great baseball team on one of the league’s smallest budgets by using innovative statistical analysis to identify undervalued players. I think that’s pretty neat.

I couldn’t have put it better. Pretty neat indeed.