advertising in 3 E-Z slides

Has the Internet ushered in a revolution in advertising, or is web advertising destined to fail?

I couldn’t begin to have an opinion without some basic context about advertising, so I gave myself a crash course.  Here’s the 3 most important things I learned:

1.  Advertising has multidimensional sectors.

Two of the fundamental axes in advertising are the lines between brand and direct response marketing, and between online and offline ads.

ad status

I can’t do the differences justice here, but essentially brand marketing is intended to make you feel a certain way about a product, while direct response is intended to make you take an immediate action regarding a product.

The concepts seem simple, but whenever new media arises, it can be quite tricky to determine what kind of advertising is suited to the media.  When the Web first burst into mass acceptance, some advertisers treated this new medium as a branding opportunity, plastering their logos and flashy campaigns wherever they could.  Google was among the first to realize that direct response principles fit the Web much better than branding – deliver ads against search results and you have a natural audience to act upon that hyperlink.

But the Web continues to evolve, giving continued opportunities to make the wrong choices about ads.  When social networks like Facebook reached mass popularity, many advertisers tried to deliver targeted direct response advertising to demographics discovered through the social graph.  But “banner blindness” and the very social intent of these sites combined to make pure direct response ads ineffective.  The better strategy for advertisers in social networks is to build a community and create engaging viral media to enhance the brand.

So the lesson here is that advertisers have to make very savvy choices between brand and direct response advertising as the evolution from offline to online continues.

2.  Online and offline ad spending patterns are currently inverted.

In the excitement about the growth of online advertising, it’s easy to forget that offline is still much bigger, with online making up roughly $23 billion of a $137 billion U.S. ad market.  These numbers are even more interesting when examined along the divide between brand and direct response.

 

According to one estimate, around 75% of offline ad dollars are spent in brand marketing, while 80% of online ad dollars are spent in direct response.  Because offline is so much bigger than online, that means that direct response offline (a.k.a. “junk mail”), makes up around $28 billion.  Yep, junk mail is bigger than the entire Internet ad industry.

Now here’s a point that’s a little more abstruse, but I hope it’s worth the time to understand it:  the advertiser’s spending pattern is inverted in online vs offline.

Offline brand advertising is expensive to create, but reaches a mass audience, so the spend per viewer is low.  Take a Super Bowl ad:  a 30-second commercial can cost $4 million (for air time and a lavish production cost), but with 95 million viewers, that’s only 4 cents per viewer.  Let’s call this low cost per viewer a mass spending pattern.

Offline direct response advertising total cost is lower, but higher per person reached.  For example, it can cost $50K to produce and mail a catalog to 10K recipients.  At $5 per person, that’s 125 times more expensive per person than a Super Bowl ad!  But it works because of the targeting – those 10K people have been identified by the advertiser as being likely to be interested in the product.  This low threshold, high cost per viewer is a targeted spending pattern.

The patterns are rewired online.  Search advertising and email campaigns are direct response in that there is a clear desired action (usually a click).  Though the cost of the keyword or email campaign can be relatively low, the distribution is very broad, so the cost per viewer is extremely low –  this is a mass spending pattern.

Conversely, doing effective brand advertising on a social network requires really identifying the target demographic and crafting a creative campaign to get that ballyhooed viral explosion.  That means relatively high creation cost and a specific audience, resulting in a high cost per viewer – this is targeted spending.

So offline, brand advertising is mass spending while direct response is targeted spending.  And online, brand advertising is targeted spending while direct response is mass spending. Or at least, that’s the way it is today . . .

3.  Successful advertising tactics will seek equilibrium.

Pundits are always rushing to declare failure, or any new method the death of all old ones.  But offline advertising feeds online, and online direct response may morph into “brand response.”  Advertising, like nature, restlessly searches for equilibrium.  The story above is heading towards a more stable balance so the value of the spending better matches the returns.

ad future

It’s not controversial to suggest that offline ad dollars will move online – that’s more an observation than a suggestion at this point.  And it’s also been an observable trend that offline direct response marketing is declining at an even faster rate than offline brand marketing, because Internet direct response has rapidly become effective for larger audiences.  But I’m adding two conjectures that aren’t easily observable today.

First, online brand marketing will grow at a faster rate than online direct response.  This means that social media like Facebook and Twitter (like them, not necessarily those two) will grow revenues faster than Google.

Second, online brand spending will revert back to the offline spending pattern of mass rather than targeted, and online direct response will similarly go to targeted spending rather than mass.  I believe that dominant social media sites and practices will arise that allow brand advertisers to reach a large audience at a low cost per viewer.  At the same time, increasingly effective data collection on Internet consumers will allow data holders to sell highly targeted direct response ads at premium prices per consumer.

What does it take to get from here to equilibrium?  In monetary terms, holding the total ad industry constant at $140 billion (not a safe assumption):

  • $50 billion will move from offline to online
  • $15 billion will move from offline direct response to online direct response
  • Online direct response will grow by $20 billion, while the revenue per viewer seeks a relatively high number
  • Online brand marketing will grow by $30 billion, while the revenue per viewer seeks a relatively low number

That is a lot of money sloshing around, in a lot of different directions.  I think it’ll happen within 5 years.

love the machine

The Wall Street Journal reported that Google is working on an algorithm to predict which of its employees is likely to leave. Putting aside for the moment whether there is some creepy aspect to the machine anticipating personal career choices, there are two aspects to this that I find interesting.

First, exactly how and why did this make it into the news? I’m not questioning whether it’s newsworthy (it is), I’m wondering about the exact path that this took from fact to front page. Was this program broadly announced at Google, and a random employee alerted the media? That would be one typical path. Was it an unannounced program, and someone in the HR department leaked it to the press? Or perhaps the HR folks proactively fed this to the press. If one of the latter two, what’s the motivation? Does it make Google look smart as a company? Does it comfort employees to know that Big Brother is watching and cares whether or not you leave? I don’t think any answer to any of these questions is necessarily a bad thing, I’m just wondering what the answers are.

Second – and more answerish this time than questioney – it’s really interesting to consider the reported inputs to the algorithm. According to the WSJ, “data from employee reviews and promotion and pay histories” comprise the formula. At first glance, this might seem logical: you would hope that reviews and compensation have some correlation to job performance and satisfaction.

Ah but then – you’ve been hoping that all your life, haven’t you? And haven’t you found that the content of employee reviews is often radically disconnected from actual performance, that the “wrong” person often gets promoted, that compensation has at best a tenuous connection to performance and satisfaction?

So the data set might not be as logical as it first seems; fortunately the magic of the sample size means that this isn’t a logic problem, but simply a correlation exercise. There is certainly some correlation between the content of that data and the incidences of employee departure, or Google wouldn’t be trying this.

But I’d bet that the correlation is much weaker than you’d think, and much weaker than Google would like. Any manager knows that reviews and compensation are blunt tools, and these are likely to be trailing indicators rather than the leading indicators of dissatisfaction that you want for real predictive power.

I worked at a company that somewhat famously had a “Love Machine” – a simple tool for employees to express appreciation for each other. Basically, as an employee, if a colleague did something that you appreciated, you could send them a point of Love, along with a one-line comment as reason for the gift. It was just a neat little way to say Thank You, and people really appreciated both giving and getting Love. And as very minor benefit, periodically the accrued Love points would be paid out to employees as a cash bonus.  (Distribute that on a Friday and watch people really spread the love around.)

When this was reported externally, some people misunderstood the corporate purpose of the Love Machine. Which is to say, some people thought it had no purpose at all, except as some gross hippy dippy vibe. Those people not only misunderstood the effect on morale, but also vastly underappreciated the value of the data from the Love Machine.

Think about it: as a manager, is it valuable to know which of your employees is appreciated, and which is not? The Love Machine is certainly not the sole source of information on this, but the data gives at least some general information on both specific and aggregate cases. You can see not only who is appreciated, but who gives appreciation. You can see which departments are praised and which toil without recognition. You can see who works across departmental boundaries and who only interacts upwards or downwards in their own reporting silos.

All this is phenomenally powerful data that gives factual indication of matters that you might otherwise pay significant amounts to guess about through expensive organizational consultants. I was convinced back then and I still am today that if HR departments and managers understood the power of this data, every company would have a Love Machine. Most would call it something else, but that part is just internal marketing.

Back to Google: as far as I know, they don’t have a Love Machine. But that doesn’t mean that they are feeding the best data they have into their Who’s Leaving? algorithm. Nearly every company has somewhat similar data in their email traffic, and certainly in a big data set, email traffic would work quite well. Now, I’m not saying that the email content should be analyzed – even though many companies actually do that, I find it unacceptably creepy. I just mean the fact of communication. Every manager knows that communication is a powerful indicator (and motivator) of job satisfaction.

Who’s talking to who? Who’s on how many email lists? What is the response rate and timing and length? Is communication cross-functional, up and down and across reporting levels, inside and outside the company?  All of that information is available in company email – I guess you could call it the email graph.

I would bet almost anything that this kind of data from email traffic provides a more powerful indicator of who’s leaving the company than the data from reviews and compensation. In fact, I’d bet that the email graph has better correlation to actual job performance than performance reviews!

Of course, the combination of those data sets could be even more powerful, and it’s entirely possible that Google is already doing this, though that wasn’t reported in the Journal piece.

apples to apples

Facebook turned 5 years old last week, and a couple of commentators took the opportunity to compare the company’s progress unfavorably to Google’s.

I understand the compulsion to compare every hot startup to the current media darling that literally put its name next to the definition of “Zeitgeist” – but still, I don’t believe there is any practical value in that exercise.  A meaningful comparison compares things of like kind, and comparing every company to the once-in-a-decade champion is not apples to apples.  Take a look at this list of companies, which I’d say are all the same kind of apple (of course one of them is literally an Apple):

Company Year Founded Year IPO Feb 09 Market Cap
HP 1939 1957 $87 B
Intel 1968 1971 $83 B
MSFT 1975 1986 $173 B
Apple 1976 1984 $91 B
Oracle 1977 1986 $91 B
Cisco 1984 1990 $99 B
Google 1998 2004 $119 B

These are the true giants of Silicon Valley (plus our favorite giant from up north), all companies that have spent a goodly amount of time with a market cap over $100 billion.  Comparing any private company to these monsters is a fool’s game; it’s like comparing a college basketball player to Michael Jordan.  Actually it’s worse than that – projecting athletic talent is considerably easier than projecting $100+ billion success for a company, because there are orders of magnitude more points in a company where externalities and luck play a tremendous factor.  (I always like to recall that Intel and Microsoft were initially made giants not by their own strategy, but by the strategic decision made by IBM when it chose to outsource production of its PC microprocessor and operating system.)  These true giants are Black Swans, by definition nearly impossible to predict, and useless as comparative points except when holding both points in retrospect.

If you must make comparisons, it’s more realistic to compare to the next tier, for example:

Company Year Founded Year IPO Feb 09 Market Cap
Sun 1982 1986 $4 B
Amazon 1994 1997 $44 B
Yahoo 1995 1996 $19 B
eBay 1995 1998 $18 B

I could put a dozen more on that list, but I’ll let you pick your own peer group.  Any one of those companies (yes, even that one that you think is irrelevant/dying/dead) could still take the multi-decade journey to giant-hood.  But even if they never do, they’ve accomplished something extraordinary in growing up from a tiny Silicon Valley startup (with one favorite from up north) to an independent company, a true a difference-maker in technology and the daily lives of millions upon millions of people.  Because they haven’t had such outstanding externalities and luck in their favor, they are a better basis for comparison.