The fat end of the long tail

In my observation about NetFlix as a purveyer of long tail media, I hinted at all of the other ways that online marketers are prospering with this new business opportunity, made possible by word-of-mouth, suggestive selling and virtual — instead of real — merchandise inventories.

I’ve since realized that so much of marketing technology can be heaped under this category that I need to add it as a tag, along with my intentionally general tags of direct response, database marketing, etc.

True, the term long tail has more than a whiff of a meme ready for replacement, much as how push technology in the early 1990’s crystallized into RSS, and how the overworked online communities, the other buzz of the 90’s, turned into what we’re now calling online social networks.

Regardless of what the phrase long tail becomes, it certainly has legs (why am I thinking of a lizard?). This Google Trends graph shows that in terms of searches and news coverage, it also has a fat end.

More evidence for the power of the long tail

A few days ago my wife and I were in a restaurant, commiserating with a friend about her difficulties as a film fan on a mission. She told us she has been taking in as many of the films of Woody Allen as she can rent. It hasn’t been easy. She has been forced to rely on the shrinking inventory of the neighborhood video store, Video Adventures. Not surprisingly, the store just announced it will be going out of business next month.

Alas, our friend has a lot of Woody Allen yet to cover. My wife and I almost simultaneously blurted out the obvious solution: Netflix.

Netflix has an extensive film selection, excellent search capabilities and the brilliant ability to build and share your film wish lists. It’s the perfect tool for a film completist such as our friend.

Allowing customers to rent videos from home, without the threat of late fees, is an obvious point in the favor of Netflix over the brick-and-mortar video store business model. But the other major reason Netflix has become such a marketing force, and a threat to the video store, is its ability to exploit the long tail phenomenon.

If the term long tail is new to you, I recommend you read Wired editor Chris Anderson’s article, if only to learn the origin of the name (here’s a hint: think of the slim, wedge-shaped outer region of a graphic showing gross sales numbers along the vertical axis and the amount of variety of titles along the horizontal).

The significance for marketers of the long tail is described well by Anderson below:

[The emergence of] unlimited selection is revealing truths about what consumers want and how they want to get it in service after service … People are going deep into the catalog, down the long, long list of available titles, far past what’s available at Blockbuster Video, Tower Records, and Barnes & Noble. And the more they find, the more they like. As they wander further from the beaten path, they discover their taste is not as mainstream as they thought (or as they had been led to believe by marketing, a lack of alternatives, and a hit-driven culture).

When I first read that article I was skeptical. After all, Hollywood has done a great job of dictating to the masses what they should be viewing. And true “video adventurers” like our friend are rare.

Are we truly willing to take a chance? When given the opportunity to scratch an itch for more movies like those that we’ve enjoyed, even if they are obscure and heretofore unknown, is the typical consumer really going to risk disappointment?

Now I have my answer.

In his latest column, New York Times technology writer David Leonhardt explains that Netflix stocks approximately “60,000 movies, television shows and how-to videos that are available on DVD (and that aren’t pornography).” He continues below:

Just as important … Netflix lets users rate movies on a one- to five-star scale and make online recommendations to their friends.

The company’s servers also sift through the one billion ratings in its system to tell you which movies that you might like, based on which ones you have already liked. [Something described in a blog entry last month.]

The result is a vast movie meritocracy that gives a film a second or third life simply because — get this — it’s good. [Here’s a]  brainteaser I have been giving my friends since I visited Netflix in Silicon Valley last month. Out of the 60,000 titles in Netflix’s inventory, I ask, how many do you think are rented at least once on a typical day?

The most common answers have been around 1,000, which sounds reasonable enough. Americans tend to flock to the same small group of movies, just as they flock to the same candy bars and cars, right?

Well, the actual answer is 35,000 to 40,000. That’s right: every day, almost two of every three movies ever put onto DVD are rented by a Netflix customer.

I’ve personally experienced the long tail in music. My musical diet is more varied today than it has ever been, all thanks to access to a nearly unlimited variety of musical genres and artists in digital format. It’s exciting to read that the same exploration is taking place by consumers in the film industry, with the same predictable disruptive effects.

Although I hate to see neighborhood businesses fold, with the resulting ripple effect on local economies, in this case that is outweighed by the fact that I’m a fan of many of those films found in the outer reaches of the long tail (and not found in any video store).

So I find this latest news comforting. Less so, the news that our friend actually enjoyed Woody Allen’s Celebrity.

When is an email click-through not a click-through? Think “unsubscribe”

When is an e-mail click-through not a click-through? When they’re telling you to kiss off!

It’s hard to believe it’s been nearly a year since I had lunch with my friend and long-time career doppleganger, Melinda Krueger, and she told me about her latest email metrics discovery. It was a way to take into account the click-throughs that people register from your emails when they are in fact clicking through to unsubscribe.

She described it, and it made perfect sense. Melinda’s formula in many cases would take meaningless data and actually tell us something. Specifically, it measures the power of a specific offer or message to cause a segment of your email audience to decide that enough is enough.

She was thinking of calling it the DI, the Disaffection Index. Personally, I thought something a little more dramatic was in order for a metric that could enter the email lexicon. I suggested, because it measured their very last click-through with you, the LCI — the Last Click Index.

She thought otherwise, and DI it remained. Do read this article, and the other articles and advice that Melinda provides as MediaPost’s “Email Diva.”

Crunching the numbers can expose myths

A recent article in the New York Times Magazine’s Freakonomics column, and one of my favorite books of the year, both remind me that a careful examination of data can dispel long-held myths. Neither is directly related to a particular marketing challenge. But they both inspire me to continue to goad my clients into thinking beyond the obvious. We can seize a strong competitive advantage by assuming nothing and testing our premises whenever possible.

The Freakonomics article is ostensibly about soccer and an odd correlation between player excellence and the month that player was born. In analyzing data about the birth months of some of Europe’s best soccer players, it was found that they were born far more than you would expect in the first three months of the year. When researchers looked deeper, they realized that a logical explanation is that children born in these months were exposed to more months of coaching in their schools — more repetition, more chances to excel.

It suggests that the power of the Two P’s of practice and passion — as opposed to simply having “raw talent” — is far more important in excellence than is commonly believed. Thus the title of the Freakonomics article: “A Star Is Made.”

If you know me, you know I care little about sports. But after reading Moneyball by Michael Lewis, I was so inspired I bought three copies*. One to keep, one to pass around to co-workers, and a third to give to my father as a gift. The message of the Freakonomics article was that stars are made, not born. Similarly, the message of this book, about the unlikely, data-driven success strategy of the Oakland Athletics baseball team, is: “A winning baseball team is made, not bought.”

Read the book, and marvel at how Billy Beane, the general manager, refused to believe the group think of baseball scouts and the status quo. He wouldn’t listen to them when they told him how to identify promising players for his under-financed, under-performing team.

It’s a great read, and another reminder that looking at the data instead of listening to the way things have always been done can pay huge dividends.

*Thank you Bret Stasiak, my boss from my BVK/respond360 days, for letting me know about this wonderful book!

Many customers who made the same types of phone calls as you also bombed The World Trade Center

I’m not ordinarily a defender of Bush Administration actions concerning its response to The World Trade Center attacks, but the database analysis proponent in me feels something should be clarified in the minds of most Americans. According to a recent NEWSWEEK poll, “53 percent of Americans think the NSA‘s surveillance program ‘goes too far in invading people’s privacy.'” This of course is the taking of cell phone and other telephone records and mining them for clues to possible terrorists.

The outcry, I think, is in part because when we think of phone surveillance we think of wire-tapping (or, in the case of cell phones, wireless-tapping). However, if I understand this situation correctly, the NSA used this vast database of phone call numbers (both of originators and recipients), along with call dates, times and lengths, to look for suspicious patterns that were similar to those found in known terrorists’ phone behaviors.

I know, I know. If you analyze for this type of activity, you can also find patterns in the activities of your political enemies. Imagine the blackmail potential! It could shut down Washington! (Hmmm … could the blackmail have already begun?)

But let’s assume for the moment that we could somehow shine some light on the activity, thus preventing such abuses. Is this data mining an invasion of privacy? I suspect it’s closer to the surveillance we’re all accustomed to — and appreciative of — in our quiet suburban neighborhoods.

Probable cause is a term used to justify a police officer pulling over a citizen for questioning. I would equate this database research to looking for probably cause. So how is the research done? It uses the same technique that marketers use to predict whether a consumer will like this product versus that one.

For instance, you buy a CD on Amazon, and the web site immediately says, “Other of our customers who bought that CD also purchased these.” Then it lists three or four other, often surprisingly unrelated, artists, along with their latest CDs. If you have a big enough music collection, and predictable enough tastes, you’re surprised that you already love the work of one or two of those other artists. Amazing!

Amazon, and other large marketers using this profiling, let you know in advance that they looked into their database and found those correlations (through the statement, “Other customers of ours …”). What they don’t tell you is that usually, those data relationships are — on their own — too obscure or unrelated to be recognized in any way other than by using a sophisticated statistical regression analysis.

The same for this NSA action. I think a lot of Americans are concerned because they imagine an all-seeing computer is examining every single phone call they make or receive. I also suspect they are angry because now they have yet another privacy vulnerability to worry about, along with identity theft, spyware, etc.

But I suspect the process of profiling that was done by the NSA is more along the lines of the Amazon example. The predictive model takes into consideration thousands of weak correlations — possible coincidences that are only significant because when added together they match the behavior of known terrorists, (I would say convicted, but good ole Mr. Moussaoui is about it, and that’s an awfully small sample to try to model against! Known domestic terrorists would include the guys who died in their planes on 9/11, and made plenty of phone calls before they did).

So, if that is the case, is this intrusive? That depends.

 Is a police officer driving down your quiet residential neighborhood invading your neighborhood’s privacy when looking for probably cause to investigate a possible crime? This officer may not stop if one suspicious fact is noted about someone in your neighborhood. Maybe even two or three aren’t sufficient for probably cause. Each on its own may be too subtle — too similar to the behavior of those not breaking the law. But if there are enough suspicious facts concentrated around the behavior of, let’s say, that guy parked outside your door, then the officer will conclude the correlation is too great. The behavior and evidence surrounding that guy show too many similarities to those of convicted criminals. This behavior taken as a whole is too close to that of a burglar, let’s say.

The brain of that cop isn’t going to retain much information the next day, or even the next hour, about the non-suspicious behaviors that were observed, and in a similar way, I don’t think the NSA’s computers will be able to do much else but identify the behavior patterns they are programmed to sniff out.

Which brings me back to my original observation. How in the world did I become a defender of Bush? The answer is the NSA, under his watch, found a non-intrusive way to comb this country for possible criminal activity. I only pray that there will now be enough judicial (and judicious) oversight to ensure that the profiling being done is for real enemies of the state, and not enemies of the administration and its incumbents.