Pssst. Over there, next to the purple wing chair. Behind the foosball table and just beyond the 2002 vintage lava lamps. It’s a gaggle of digital marketers, munching on Pez, and screaming at the unfortunate Fortune 500 brand CEO who made the mistake of visiting this digital agency’s hip warehouse space conveniently located just steps from an eight-lane highway. “We want credit! We want credit!,” the digerati are chanting over the roar of trucks and stretch limos. A few, more impatient than the rest, begin pelting him with M&M’s. He’s being very slowly stoned to death. A member of the agency’s management team, clad in yoga pants and clutching a venti chai latte in one hand, uses amazing grip strength in her other forearm to bombard the stunned client from the left flank, with a powerful t-shirt cannon.

Sound realistic? Didn’t think so.

The fact is, digital marketers don’t have to tug on the CEO’s pant leg as much as they used to. And definitely no need to pelt the client with candy. A Forrester Research study for 2009, “The State of Retailing Online,” conducted in conjunction with Shop.org, continues to show paid search outstripping all other channels for perceived effectiveness. A whopping 83% of online retailers (including many of the largest) rate paid search as an “effective” acquisition channel, far ahead of #2, organic traffic, at 51%. “Offline advertising” only gets the nod from 20% of this group. “Banner ads” still draw derision. Only 9% of online retailers believe they’re effective in driving acquisition.

Little wonder that frightened spokespeople for the impact of offline spending and display ads on the effectiveness of that beloved paid search channel feel the need to circulate straw man arguments that accuse our side of skewing attribution.

For a relatively new channel, paid search is doing too well for the liking of traditional advertising people. The average large online retailer spent $5.9 million last year in the channel; that’s nearly three times what they spent even emailing their house list. Relative to their size, midsized companies in the space probably spent the most impressively, at $3.6 million. “Little guys” in the survey checked in at an impressive average of $202,000. A lot of these companies don’t spend a dime unless the cash registers are ringing as a result. If they’re paying for these clicks, chances are they’re working.

Sadly, some observers see this as a zero-sum game, and that’s one of the reasons we’re really debating attribution today.

Why this debate? Why now?

What type of knowledge do we have about our advertising investments, anyway? What type of knowledge? What? Huh?

Epistemology. You’re probably thankful you haven’t heard that term since undergraduate philosophy class, and not too pleased to be hearing it now. (I never took that class.)

But that word may be needed if we’re to understand what’s going on with today’s sudden interest in attribution.

There appear to be three major reasons people in our industry suddenly want to study more closely how to attribute “credit” to different media sources:

1. They’re selling all-encompassing solutions that promise to correctly explain every possible influence that goes into a consumer’s decision to buy;
2. Like the imaginary, immature digerati in the fake scenario of the opening paragraph, they want to claim “credit” to draw more budget to themselves in a zero-sum budget allocation game;
3. They just want to get a little better at optimizing their campaigns in their digital specialties, playing “small ball” to hit singles and doubles, and score predictably for their companies and clients.

Epistemologically, they’re all pretty far apart. #2 ranks at the level of the trivial interest-seeker. Economists call it rent-seeking behavior; political scientists call it “interest group politics”. Been there, seen that. Move on. There’s no argument here. Just self-interest.

Camp #1 is potentially very influential – and I worry that the well-meaning hope to unravel all the mysteries of response science will derail working marketers from pursuing the small successes that are within their reach.

On that: Grand Narratives have been out of favor for half a century or so, for good reason. When modernity started to stick in the craw of clever (mostly German or French) commentators in the last century, it wasn’t that we wanted to chuck science. It was that the “totalizing” narratives of capitalist and communist empires alike took things too far, claiming to explain everything.

In a world where the sizes and shapes of organizations keep changing and a steady stream of data serves to remind us that there is possibly more complexity to the economy than we can hope to tame, many of us have adopted an attitude to “take it as it comes”. It’s probably important to understand how a whole system works if we’re talking about global climate change, or DNA research. But outside of a few Dr. Evils at the largest of companies, depending on your job description, understanding how the whole system works is relatively unimportant in marketing. Doing well on the component parts can be very effective in creating more total profit.

This attitude may explain why a lot of people quietly admitted “me neither” when the unfashionable George H.W. Bush said: “I just don’t get the vision thing.”

To sum up: I’m proposing – for most of us – a sort of Goldilocks approach to this. Don’t claim to understand everything (too much science), but don’t just show up to the debate because you want more money for your department (spiteful science). We have, then:

(1)    Too Cold: The folks who want perfect attribution based on an all-encompassing system, impeccable measurement of all factors, even if – in order to accomplish their brave project – they need to invent tracking solutions that take data collection to new levels that surely violate the privacy expectations of users, if not the sheer letter of the many individual “Terms of Service” they agree to.

(2)    Too Hot: The hotheads who argue for more credit just for their own specialty, and less for the other guys, in a naked show of realpolitik. Today’s worst violators: the traditional agencies, engaged in a campaign called Defend the Spend. At all costs. And if it’s a zero-sum game, they’ll Defend the Spend even at the expense of emerging digital campaigns that (in our opinion) are doing very well, thank you. Are they right? We don’t think so. And some of us have t-shirt cannons and an attitude to back it up. Let’s hope we actually have numbers, too.

(3)    Just Right: The pragmatists who really just want to study the issue a bit more and solve their company’s or client’s digital media attribution problems a little better than they were being solved last year.

The data revolution is here to stay (but it has its limitations)

It was easier when we were mostly farmers, wasn’t it? No “multiple points of attribution.” Farmers could debate whether it was the weather, or someone’s work ethic, that contributed to crop yield that year. But it was mostly the weather.

Hold on, though. Farming’s probably no different from many other fields. It, like all the others, is suddenly going to be revolutionized by a wealth of data, and intervention from pesky outside number crunchers.

The mythology is already spreading. “The old baseball men” and their instincts and superstitions: replaced by statistics nuts. Moneyball.

Imperfect professional wine ratings by swishing, slurping, snobs: under threat from a Super Crunching computer model that predicts wine quality for a given year and region from a host of climate factors.

Weird bets by Hollywood studios, sometimes leading to expensive flops, are under threat from hedge-fund-backed Relativity Media, a production firm that has now backed hundreds of good films (though if you listen to its critics, few or no great ones). Relativity is known for its extensive number-crunching to bet better on which films will make money (and avoid losing it), but it can’t be accused of claiming to have all the answers.

Needless to say, we have to get better at number crunching in many fields, or we’ll be pushed into obsolescence, unless we have the budget of the New York Yankees.

But if it’s fashionable to do more number crunching, it’s also hard to tell the difference between the real deal and the snake oil salesmen.

Back to digital marketing. Despite the apparently huge differences in motivations to approach attribution from different angles, we can agree on this: attribution is broken. We know this much: a significant proportion of the inaccuracies and oversimplifications in giving due credit to online media sources comes down to the fact that we tend to count only the last click before the conversion (that is, if we’re counting at all, and closing the loop as we should be trying to do).

Now that we agree on the fact that last-click-only attribution stinks (for a competing view, see the previous column in this series), we can begin to come to terms with some fairly straightforward confusion in spend allocation in our online campaigns. Yes, Google AdWords conversion tracking as well as Google Analytics use last click attribution. And happily, that probably won’t last forever. And yes, we should look for ways of improving on this flawed model by augmenting our model with more sources of information.

But arguably, last-click-attribution leads, at worst, to minor data discrepancies. It often isn’t fatal to the project of attempting to correctly allocate budgets. And that’s really where the challenge – with a small c – lies for most of us practitioners. We’re trying to spend and bid accurately. So we want to act in a more enlightened way. We’re looking for additional clues that will help us to spend better.

Stop the double-counting

A related issue is that we’re trying to avoid getting ripped off, trying to avoid the “overspend” problem that can dog any media that makes inflated claims about its value. Jeff Greenfield and Mark Hughes, authors of a white paper, The Hushed Hidden Gaps of Online Media Tracking, examine such serious flaws in online measurement methods. They derive in large part from the imperfect user tracking technologies we use, including tagging and cookieing methods that lead to rampant double- and over-counting. Certainly, by continuing to tell tall tales about the effectiveness of particular segments of media, without sorting out the duplication in credit, it’s fair to say that we’re falling short of where we should be.

But are some of those tales taller than others? And might the de-duping be relatively simple to achieve, say, by looking askance at claims for the influence of view-throughs from display? Or by simply bidding less on YSM, Bizrate, and Google if our bottom line sales don’t agree with the inflated attribution claims in these channels?

I hope so, because I have a problem with the next step in the buy-in towards “comprehensive” attribution models.

(And I think so, because quite a few clients of ours focus mainly or exclusively on clicks from Google, so the threat of duplicate counting from display or Bizrate, say, simply does not exist.)

Some voices in the debate—like Greenfield and Hughes—will try to woo us into making this into a Big C “Challenge”. And lead us to believe that with their model, or by using a more sophisticated model, we can get closer to the Big T “Truth”. Be suspicious of such claims, in part because the technical tweaking needed to get closer to proper attribution is going to be well within our reach at low cost in the coming year or two, included with the platforms we already know and use. We won’t have to rely on “groundbreaking” technology to tell us how to count.

(Greenfield and Hughes refer to some tracking methodologies that aren’t unheard-of in the web analytics world today, to do with recording multiple attribution sources and “holding” these in a centralized report. But their term for it – a “synthetic cookie” – is not only non-standard, it isn’t in fact mentioned by anyone else, anywhere. It appears only in their white paper PDF. To prove it, I searched that exact phrase in combination with a whole variety of industry-specific terms (such as [“synthetic cookie” analytics]; [“synthetic cookie” attribution]; [“synthetic cookie” coupled with sterne, kaushik, peterson, novo, and so forth]). Arguably then, after this article is published, the term “synthetic cookie” will have only had a single mention in any legitimate source other than their own white paper – and that’s right here. I’m all for coining useful phrases, but analytics and measurement cry out for standards, not freelancing. Especially when they’re intertwined with the user’s rights, privacy policies, privacy legislation, and of course, claims for big-T Truth and big-E Enlightenment.)

Stop the whining

The least constructive claims of all, it seems to me, are the players who believe that the whole reason we’re having this debate is so various stakeholders in a company or in the media buying industry should get “credit” for making their organization or their client money. This is the us-vs.- them style of argument that treats attribution debates neither as a scientific problem to be solved nor as a practical fact-finding mission to help in making small adjustments, but rather, a rhetorical type of exercise with all the sophistication of two six-year-olds arguing that the other kid got an outsized chunk of birthday cake. Where were they when they had the whole cake to themselves? Mighty quiet, I suspect.

In MediaPost, Dan Eggleston recently charged:

“Digital agencies generally do not take into account baseline brand awareness or brand equity or the impact of any offline media on online transactions. This is done despite the fact that studies on click-through rates for online display media show higher response when supported by a TV campaign.”

A glaring hole in Eggleston’s calculations is the actual cost of creating brand equity and buying offline media. And thus, the accompanying ROI calculation. A vague sense of higher “response” from a (relatively cheap) digital campaign supported by a (costly) TV campaign may be a start, but barely. We haven’t even reached the point of asking about sales conversions, but it’s nice we’re at least talking about response (clicks). Some actors in this debate don’t even like clicks, as I’ll review in Part 3 of this series next month.

This flavor of debate was bound to emerge at a crucial stage where traditional offline media budget costs (are we including the cost of the lavish creative?) are being questioned even by the most traditional CEO’s. In essence, the pockets of the bloated offline ad spending world that are ROI-negative can only be brought closer to break-even by stealing back “credit” from media that do work.

Eggleston claims (based on estimates from a study):  “Online media can be taking credit for as much as 15% of online conversions that were actually driven by offline marketing executions.”

All I can say is you can have credit for all of the sales if you want to take the blame for all of the spend. :)

Revenue attribution in digital media is indeed tough. The waters get muddier when the cynical enter the debate. Our t-shirt cannons are merely appropriate David-style responses to the claims of the traditional agency Goliath. Online retail represents only 6% of total US retail sales! If anything, its growth is slowing, even peaking. Attribution of offline sales (from online campaigns and online word of mouth, and all offline sources) is a much bigger attribution issue. So can we just be left alone to do our thing?

We don’t have to trust last clicks entirely, but we can use them. And we can build on them. And use a variety of methods and statistics to further inform campaign planning. No need for messy food fights. And yet, no need to back up the truck and spend $200,000 on an attribution system that claims it can help us find Ultimate Truth, either.

I’ll close with the simple words of Chris Anderson: “Google isn’t just taking more of the advertising pie—it’s also making a bigger pie.” Imagine that!

I went to a party last week. It was put on by a big online player, mainly to thank agencies who buy large gobs of display ads for their clients. And strangely, one of the hosts began apologizing to me (over the gurgling sounds of me thanking him for the free drink—how they let me in there, I’ll never know). “As an individual, between you and me, I’d never advocate a lot of this inventory. Hey, I come from the performance side. I worked [in a senior position for a search advertising platform].” But in his current role, he was doing a pretty good job; thanking the buyers of digital display media is a fun job, made easier by their rampant demand for it.

Search ads sell themselves with performance, to those who obsess about it. Venue: spreadsheet or analytics interface. Brain usage: left brain, mostly.

In a funny way, display also sells itself, to agencies and advertisers who—for other reasons—just plain want it. Venue: the social scene. Brain usage: right brain, mostly.

Are those two worlds about to collide? Maybe they are, and it could be the death of those thank-you parties. You guessed it. The display people are now armed with spreadsheets, analytics and attribution models. And while the search nerds go rock climbing to beef up the neglected studly parts of their personas, the guitar players, prom queens, and jocks at the agencies want into the nerds’ world. Armed with slick studies and recently-purchased designer specs, they’re banging on the door of performance marketing. It’s high school in reverse!

But are there really lenses in those glasses? And are the new attribution models favored by the display people making any sense?

One of the got-good-grades analytical types, Wister Walcott of Marin Software, doesn’t think so. At SMX East recently, he threw down the gauntlet. At 6-foot-8, Wister is a nerd you don’t want to mess with.

Leaving display aside for now, and focusing just on the paid search side of the equation: a lot of people now believe in the myth that for accurate bidding, you would need to look at clicks prior to the last click. Walcott, using data from brand etailers Marin Software works with, busts that myth.

Problem: 74% of purchases can only be attributed to a single click anyway. By definition, that’s the last click. Attempt to “attribute” that purchase to prior clicks or other factors? Good luck.

Beyond that, when multiple clicks can be tracked, you don’t find confirmation of the convenient “purchase path” fairy tale that people begin with one type of search (say, a generic term) and wind up with another (say, a specific branded term). Some buy cycle behavior may resemble this stereotypical model, but on the whole, the real picture of what influences people is much more complex than that.

So the next revelation: when you bucket keyword queries down into distinct types, only 7% of sales can attribute significant influence to some kind of click other than the last click.

Ergo: unless you’ve got some awfully compelling evidence to the contrary, paid search marketers, don’t be dislodged from your current practice. Which is, as you know, relying on performance data associated with the last click prior to purchase.

To be sure, there are probably several shortcomings to this case study. It makes no attempt to latch onto users over extended periods of time, doesn’t refer to a variety of potential influences such as email, direct navigation, display ads or even organic search.

But that scenario mirrors the real-world optimization environment you work in as a paid search marketer. The choice is clear: you can react as best as you can to (admittedly imperfect) data that is pretty close to 100% certain, or you can rebalance your keyword spend portfolio based on little more than hand-wringing and hearsay evidence.

So what might be “compelling evidence” of purchase behavior being influenced by various touchpoints? Don’t expect consensus here, but do stick to your instincts.

Former Canadian Prime Minister Jean Chretien perhaps said it best: “A proof is a proof. What kind of a proof? It’s a proof. A proof is a proof. And when you have a good proof, it’s because it’s proven.”

Incoherent babble, on one hand. But it aptly captures the determination to stick to your guns, for now, when you suspect you’re in the process of being bamboozled.

And how might we gather better evidence to better confirm our suspicions one way or the other? Two ways: by trusting complicated studies that aggregate other people’s results, or by gaining better tracking capability and looking at your own analytics data.

Most advertisers employ popular analytics solutions (and paid search platforms with reporting) that lack the capacity for multiple attribution. When current analytics tools make this new functionality available to the wider marketplace, you’ll get closer to being able to verify competing claims for yourself—as long as you understand how the technology works.

For maximum accuracy and comprehensiveness of user behavior tracking, these solutions are likely going to have to come from huge companies like Google that have access to more behavior data. Since the DoubleClick acquisition, and as their content networks and exchanges gain reach, their potential to develop a tracking standard has increased even more. That said, this will run up against privacy issues, especially in international markets.

I suspect it won’t be much longer that we’ll all be fumbling along using ecommerce attribution data from Google Analytics’ quirky means of tracking behavior from multiple sources, comparing it to our stats in AdWords, and wondering if there isn’t a better way to track things, and to customize our models.

Prediction: since some competing vendors have already raced ahead in this regard, then it won’t be much longer before Google plays catchup. Remember, buyers and sellers of media have often sought a mutually-agreed standard to audit performance: that’s what the “DoubleClick era” was all about back in the day. As with analytics generally, a shakeout is inevitable here. That shakeout will accelerate when Google releases products that address the new debates about multiple attribution.

I hope I’ve piqued your interest in this debate, but it’s just getting warmed up. Next time I’ll identify and assess the four major “camps” in the attribution wars.

Paid search accounts for about 50% of all digital advertising spending today in most major markets, give or take 10% (the UK clocks in as one of the most impressive English-speaking markets for paid search, clocking in at 58% of digital spend, according to econsultancy).

For we diehards, the reasons for running paid search campaigns are obvious. Believe it or not, though, many companies have failed to make the leap, or have only dipped a toe in the PPC waters. There is still much untapped potential in this channel.

So what’s holding people and organizations back? Perhaps it’s myths about click fraud, market size or performance. Perhaps weak real-life performance in pilot attempts. Perhaps a lack of in-house capability. Or (yes I said it) an ongoing SEO bias in the industry.

Whether or not you “do SEO,” you do SEO already by virtue of being indexed in Google and keeping track of whether that’s getting you any traffic or revenue. So in terms of incremental marketing dollars that need to be found in company budgets, clearly most of the untapped potential action is on the PPC side.

But the majority of the people doing SEM, and most of the industry chatter, Sphinns, etc. (80+%), are all over the organic search side of things: about links, redirects, how to deal with universal search, canonical-whatsitcalled and so on. It’s not unimportant, but I still think it’s disproportionate if you take all of that insider water cooler chatter at face value. That gang, and your developer or IT department, are probably going to tell management a lot of evocative tales about the power of organic search and analytics related to that. Think they’ll steal their own thunder by bringing up the urgent need for paid search? Don’t count on it.

Maybe, finally, it’s a lack of sizzle and excitement. Paid search still isn’t sexy for some reason. But profits are.

Let’s look at seven reasons launching (or revisiting) a PPC campaign is a must if you’re going put some sizzle back in your life. Or at least into your marketing campaigns as we anticipate an economic rebound.

1. Universal search will continue to play havoc with your SEO traffic assumptions. Do you want to “rank” in the theoretical world of an index of billions of pages, often winding up no better than below the fold or on page 2 or 3 of SERP’s, or drive additional, predictable traffic to offers you create directly, each and every day without fail?

2. A huge chunk of Google’s resources goes right into developing the paid search marketing platform, features, systems and services. Why not use them? They include: keyword research tools;  customer support and even industry research support, depending on agency status or advertiser size; advanced delivery and segmentation features like dayparting and geotargeting; advanced filtering methods like IP blocking, site exclusion, negative keywords, and more.

3.   Custom reports and analytics out the wazoo. All free. Want to get a breakdown of the return on ad spend for your New York State geo-targeted campaign, or to see a breakout of the number of clicks and conversions that came in the content network from parked domains? Want to see which of eight ads pulled the best CTR? That’s all in the AdWords back end. Try getting such actionable analytics from the, uh, “organic SEO helpers” division of Google.

4.   Content targeting has gotten gradually better each month… for 36 months straight. Suddenly content’s an overnight success, but it didn’t get there by sketching it out on a napkin. It’s taken years of Google turning the flywheel, optimizing each part, to increase the program’s scale and to up the average value per content-targeting click paid for by the average advertiser. Another hugely convenient channel with more volume and less risk than in the past. And wait! There’s more! Google has entered a game-changing new world in display advertising, rolling out the DoubleClick Ad Exchange. That’s integrated with AdWords.

5.    Good guys get special favors. Are you a real company? You’re in luck.

• Google has taken steps to reduce the participation of some business models, like thin affiliates who compete with you for eyeballs on the same queries, or business owners with shady privacy policies;
• Google proactively filters click fraud, and actively polices and reduces payouts to rogue publishers in the content network;
• Display URLs and strong brand recognition help with Quality Score—Google seems to have tuned the system to help brands that consumers are more likely to deem “reliable” and “clickworthy.”

On the other hand, Google doesn’t bend over backward to protect your trademark, especially not if that reduces other advertisers’ choices. When it comes to the ecosystem, Google can be schizophrenic, but again, on the paid side at least you can pick up the phone and call someone.

6.   Transparency. You may not want all of it, but new tools like the Bid Simulator keep rolling out. The search engines are committed to giving you fuller information about many auction details. They’ve even rolled out a video of Google’s Chief Economist Hal Varian commenting on bid strategy. Think you can get an SEO position simulator tool out of the “organic SEO helpers” division of Google? (There isn’t one, remember, although to be fair, Google Webmaster Tools is pretty neat.)

7.  Rapid feedback and rapid results with less risk. There remains no better mechanism to gain rapid response to different ad copy variations, to gauge consumer keyword search patterns as they apply to your business, to learn about geo-specific buying patterns, etc. There is far less risk to testing offers, page layouts and marketing strategies rapidly—using paid search—than there is to tinkering with the nuts and bolts of your site’s content and architecture in the hopes of a medium to long term boost in organic traffic. SEO success is, for many companies, an after-the-fact pat on the back for years of a job well done. It is difficult to ramp up quickly. Paid search, by contrast, scales up quickly, and particular segments and tactics can be tested with relative ease.

Given the large spends now going into non-traditional promotion, paid search and digital marketing generally, we no longer need to advocate for dollars to “migrate” to these channels. Most big companies are in the process of figuring out how to migrate some dollars out of their bloated traditional ad spends. But in the process of ramping up paid search priorities can get mixed up, campaigns can languish, and no one around the company seems quite sure what needs fixing most urgently.

There are many hurdles to doing this right, which just makes the reward sweeter for those who do a lot better than their competition.

And for smaller companies, it sure is tempting to hold out hope that SEO and some mythical word-of-mouth can do it all for you. For most businesses in conventional industries that do a lot of their selling online, though, paid search can and should work. It’s about half of all digital ad spend for good reason. Trying to acquire those same customers through other channels, especially offline channels, will typically be costlier.

Paid search isn’t sexy. Nor is it tailor-made for penny-pinchers. It’s an ROI-driven direct response channel. But it’s worth the effort if you see it through! It beats just about anything short of magic.

Over the years, we’ve counseled advertisers to be wary of certain inflationary tendencies that are built into the very terminology and interface design of paid search. Experienced bidders know better but new advertisers can get tripped up.  Inflationary tendencies in paid search auctions include:

• New accounts being opted into content targeting at the same bid as search
• Bid estimators available at the setup phase
• The temptation to ego-bid as opposed to testing and optimizing
• Impatience in waiting out foolish competitors. (You want the house, but do you want it badly enough to pay $700,000 now, when down the street you’ll get the same one next week for $550,000?)
• The new “first page bid” notation in AdWords for lower-position keywords that suggests how high you should bid to stay on the first page of SERP’s (this could be set conservatively or aggressively. You need to take it case by case).

As market psychology shifts, these mechanisms have been less successful in holding up click prices. Advertisers are, in the end, free to bid lower. And, as we’ve seen recently with economic woes, many advertisers have gone into penny-pinching mode.

How can the bid simulator tool help?

It’s at that precise moment that the question arises: but just how low can I bid on a keyword while maintaining a decent ad position? I don’t really know what others are bidding, after all. Google’s new bid simulator tool is designed to answer this question. The tool obviously doesn’t show other advertisers’ bids, but provides relevant information that effectively answers the question indirectly.

Similarly, if you’re an advertiser seeking growth, and want to know how much extra it’s going to cost to make a significant jump in ad position, the tool has data in graphical form to help you answer that question. If it costs you $5.02 on average to stay in ad position 4, and you want to know what kind of price tag you’re looking at for position 2, the estimator will try to tell you (indirectly). It shows you lists of projected impressions at various price points, and you already know what your current ad position is. So, if you see that a significant increase in impressions will occur above $7.00, that may look like a decent tradeoff. If you see that a breakout in impressions and clicks (indicative of a significantly higher ad position) only occurs at $16.00, you’ll likely say “thank you very much, but I think I’ll stay where I am.”

The projections are based on the last 7 days’ worth of data for that keyword (on my wish list: longer time frames so low volume keywords can be eligible, or the ability to group keywords for projection purposes). Of course, past performance doesn’t predict future results, but by showing factually what would have occurred with a different bid, the prediction is likely to be in the ballpark. Here’s a screen shot from Google’s examples. It shows nothing dramatic, but you can see that jumping from the $2.10 per click range up to $6.36 or even $5.26 would be a pretty steep price to pay to gain a small amount of extra volume. Literally, it’s a steep pricing curve.

Real world examples: how pliable is the auction?

The next example, taken from a client’s account with keyword data disguised, shows the subtle tradeoffs involved in seeking the right bids and positions in a relatively competitive, high-cost auction. By spending 41.2%  more per click, we would have received 12.9% more impressions. But because those impressions would have been out of higher ad positions, the projected clicks rise more dramatically—by 44.4%. There’s no question that nearly any bid increase will lead to more costly CPA’s (lower ROI), but for those seeking increases in volume, the simulation can be helpful in gaining an understanding of just how pliable a particular auction is. And this information is richer than average ad position, in the sense that an average ad position of 1.9 could still be showing you in 4th and 5th spot more often than you realize, so your lower bid is costing you more volume than you might think.

Next, here’s a lower volume example from a less competitive auction. Not knowing what type of drop in volume we’d experience if we went with lower bids, we always kept to a relatively high bid, just to stay safe. The simulator tool tells us there is little buying support well below our current level, so economizing by dropping bids significantly is a good bet. In fact, at $2.21, the current bid looks kind of silly. The fact that the yellow dot, denoting our bid, is vertically on a y-axis above the next green dot in the plot on the graph, basically says we’ll see no volume decrease and no change in position until we drop to $1.54, and there, the drop is only slight. “Dude, just lower your bid to $1.54,” the tool seems to say. Thanks, dude.

Caveat: with low volume, we get an impressions prediction only, not clicks. So clicks could decline more dramatically than expected if you make big changes suddenly.

Finally, there are all too many examples of auctions that show significant opportunities to double and triple click volume, but the bid ranges to achieve that seem to stretch up and up and up, to ever-growing levels of insanity. From our current bid of $3, next stop $6. For a bit more traffic, next stop $8. Next stop after that, $13, and finally, you could really max out clicks by going over $19. All else being equal, that means someone’s probably chasing bids of $15 or more in an auction where we figure a click is worth around $2.50 with major pain starting at $4.00. Yikes! What are they doing? The bid simulator saves a lot of time here too. Experimenting over time to see how much money you’ll need to bid would be a big waste of time. Stand pat, or go up or down slightly, is the solution in many of these cases. There’s no point in chasing rainbows.

If you want to change your bid instantly while viewing the graph, you have the option of clicking on the presets and changing your bid, making either subtle or dramatic changes in positioning strategy. Or, you can enter your own bid if you don’t like what the presets say. I recommend often entering your own bid, because I like gradual bid moves. In general, projector tools don’t do a good job helping with real-world “walking up” and “walking down” methods that many of us have cautiously used for years (we make smaller bid changes over time, for various reasons).

Reasonably accurate projections—geared to the specific auction conditions and real time competitive bidding scenario in each keyword competition—are a great advantage over previous guessing methods. Before, it would simply take more time to gauge the impact of bid changes. You’d need to make the change and monitor the effect closely. With the bid simulator tool, you may be able to save time if you’re aware that there is little “buying or selling resistance” below or above your bid. In specific cases, advertisers can get away with making more dramatic changes.

Simulator beware

Back when I had more time, I used to play with a video flight simulator. Sometimes the simulator indicated I had successfully landed the plane. Even if I didn’t crash, under no circumstances would it have been wise for me to think to myself “say, I can fly a plane!” I’m no pilot.

Looking at a simulation is just that. Changing bids is, in the end, a serious business. Think about the information, and act intelligently on it. Don’t “believe” everything you see.

Is Google shooting itself in the foot?

Industry-watchers never tire of citing the flaws in AdWords. But anytime you go over the wish lists we’ve put forward over the years, you can easily point to the many feature requests where Google not only obliged, but exceeded the asked-for functionality. The bid simulator is a pretty radical step, in that it takes an apparently unfixable shift in the auction methodology (from a straight auction under GoTo, with all bids public) to a proprietary algorithm that includes bids as well as relevancy factors under a sealed-auction scenario, and appears to do a decent job of providing usable competitor information without revealing anyone’s private info.

It’s worth noting, on that front, that this feature first surfaced in a similar simulation offering released by Yahoo Search Marketing with its Panama release. Credit Yahoo with that first attempt at auction transparency.

By being so transparent in a recession, though, isn’t Google practically begging us to fix inefficient bids? If we see limited buying resistance below us, won’t we accelerate our bid decreases? Is Google trying to ruin its Q4?

Google would likely not persist with any feature or auction mechanism that was particularly biased against Google’s profitability in the medium to long term. In fast-moving retail, especially as the recovery unfolds in 2010, many advertisers will probably use the bid simulator to figure out the best way to raise their bids, not lower them.

So like most observers, I won’t worry too much about Google. In the end, only a small percentage of advertisers will actively use the simulator. And as Google diversifies its revenue stream, they know better than you and I whether their next few quarters of growth are looking smooth. Growth in average CPC’s may not be in the cards near term, but overall revenue growth is likely to elevate gradually from “flat” to “steady.” And that is of course from an incredibly high base that’s currently north of $20 billion per annum.

Joseph Heath, a philosophy professor at the University of Toronto, has published the timely Filthy Lucre: Economics for People Who Hate Capitalism (HarperCollins, 2009), a clever romp through formal economic theory. As even-handed as any intellectual inheritor of The Simpsons sensibility, it debunks six economic myths of the right, and six of the left. Heath writes like a Gen-X’er, coolly interjecting phrases like “Holy crap!” into formal academic commentary.

Logical leaps being my specialty, let’s jump right into paid search.

Heath’s book happens to provide brilliant tools for grasping the amazing efficiency of the Google AdWords keyword auction. But it also helps us push past platitudes about its “invisible hand” workings, to reach a better understanding of why and how the “free market” auction must be regulated.

Paid search pricing revisited

Aside from the problem of herding searchers into searching directly at a search engine made up solely of paid links as GoTo did in its 1998 launch, the click auction was a brilliant step forward in terms of efficiently pricing digital advertising. The core of it was that the highest bidder on a keyword ranked highest on the page. Bid too little, and no one sees your ad.

There was now no fixed pricing or fixed number of impressions or clicks you could force the media company to sell you by placing an ad “buy”. The pricing was set by the level of competition, and the volume dependent on users clicking. In the long term, the model led to windfall profits for Overture (now Yahoo! Search Marketing) and Google, not only due to bidding wars for high-priced clicks, but due to the marketplace’s efficiency in monetizing lower-priced inventory.

But there would be many wrinkles along the way.

The failure of fixed pricing

Google actually took a step backwards in late 2000 when it introduced the first version of AdWords, with fixed rates based on a CPM model. One problem discovered by advertisers unfamiliar with the young medium was that prices could be sky-high, especially on a per-click basis. It was back to the drawing board. Google would later bone up on the concept of a Vickrey auction, significantly altering (but borrowing substantially from) GoTo/Overture’s invention. They rolled out the “real” version of AdWords in 2002. Where keyword competition was light, advertisers found bargains.

As proof mounted that the auction model was more efficient, a certain reverence developed for it. And so into the discourse crept some of the excessive reverence for the abstract benefits of the free market often seen in the field of economics: people worshipping classroom concepts while failing to take note of their evident conflicts with not only empirical reality, but often, logic itself.

Many buyers, one market maker, one primary seller

The fact that there are all these moving parts, freedom to bid, and something resembling a high number of competitors, if breathed in by someone who inhaled a bit too much during Econ 101, might smell a little like the never-existing “perfect competition” often used as a precept by economics professors.

The search engine industry, of course, was far closer to “perfect competition” at a certain point in the 1990’s, with six or seven viable contenders for searcher eyeballs. But in the current Google-centric world, it’s indisputable that many buyers are buying media through primary market maker (platform builder). Now, nearly 50% of the product sold by that market maker is in fact the market maker’s own media. So for half of Google’s revenues, the transaction is all about “many buyers, and one seller.”

In such a scenario, you can use either classic economics or simple logic to tell you that the seller has an incentive to take away “freedoms” in the auction such as making it easy for some buyers to pick up inventory (less popular keywords) at rock bottom prices.

As it turns out, Google has a double incentive to crank up prices. Searchers like white space. On purely informational queries, searcher satisfaction goes up when the page is less cluttered with commercial messages. Google likes to either exact a decent effective CPM rate, or zero in exchange for serving no ads.

Thus far, we’ve only talked about how Google stacks up against some classical economic theory in the economic sense – roughly, as a player that behaves as you might expect a monopolist would.

But Google behaves as both a powerful player of the game, and (in a sense) its regulator. In a number of areas, discovered as the auction ran like a massive simulation game over the years, Google now intervenes in a manner similar to a government. That’s not necessarily a bad thing. Indeed, it is utterly unavoidable.

“Night watchman” at least

As Heath points out, even what are today called “conservative” economists (sometimes called libertarians) are forced by logical imperative to admit some role for government. The logic works this way: if you walk through all of the commercial transactions taking place in today’s complex capitalist economy, and tote up the cost just of enforcing contracts, avoiding obvious harm, and the like, this transactional machinery already constitutes a significant component of GDP.

In the click auction, the libertarian model broke down early on, in areas fairly basic to the conduct of a fair process. The fact that a “click” could be generated by a malicious person or a bot, first off, necessitated strong “state” (auctioneer) intervention.

Darwinism and maladaptive effects

Some of the areas a Google might consider intervening in are relatively harmless, but interesting nonetheless. Let’s focus on how common it is for seemingly self-organizing systems to break down.

“Invisible hand” thinking sometimes blithely assumes that evolutionary forces always turn out to help organisms (or social organizations) to successfully adapt.

But right in the Darwinian literature are basic examples of “maladaptive effects,” Heath writes. Taken from the standpoint of a species, an animal might develop characteristics that help it prosper or thrive in finding a mate. But as that characteristic is replicated and exaggerated, the whole species might suffer and eventually die off.

A peacock’s tail feathers are a relatively innocuous example of this. The Irish elk and its “grotesquely impractical antlers spanning 13 feet” would be a better example of a flashy trait leading to species-wide doom. “Individuals with such traits are able to survive,” Heath writes, “only because the structure of the trait is such that as they go down, they are able to take every other member of the species down with them.”

The AdWords auction is rife with such mechanisms. Under AdWords (and its contemporaries, Yahoo Search Marketing and Microsoft adCenter), there are a host of things advertisers could try to do with their ads to stand out. The worst forms of showboating might include making false statements, excessive use of caps and punctuation, and so on. Google began policing many of these things from the outset of the ad program in 2002. Google’s introduction of Landing Page and Website Quality guidelines in late 2005 was simply a continuation of this role, but the fact that the enforcement was expressed coolly in the form of a numerical score was a significant twist.

Quality Score: cyber-guvernment?

Google is not a real government – hence the coined term “guvernment” (that just happens to coincide with the name of Toronto nightclub, The Guvernment).

If it’s so obvious that Google can and must regulate the ad system, what’s the big deal?

Well, it’s less and less obvious to many advertisers, because this regulation is not being called that. By burying many policies within a numerical score that is applied inside accounts, Google behaves like a regulator vis-à-vis the auction, minus the accompanying degree of transparency and due process that we typically associate with that role.

Rank on the page, and thus your advertising economics, are determined by a Quality Score, assigned by keyword. The fact that increasingly baffling theories are circulated in the industry about how Quality Score works is due in part to the usual snake oil approach of some would-be experts, in part due to poor reading comprehension, but also in part due to Google’s opaque formula and administration of the formula.

Calls for transparency were heard loudly from the advertiser community throughout the rollout of Quality-Based bidding, accelerating Google’s response on several fronts. Google has published extensive sets of rules and guidelines. It has upped the precision of keyword quality score to a published ten-point scale. And, a diagnostic page informs the advertiser about whether keyword relevancy or landing page quality may be to blame for a low score on a given keyword. These measures fall short of the disclosure that some advertisers seek, however.

There was no perfect way to do this

For whatever reason, Google often uses technical language to describe their regulatory role in Quality-Based Bidding. Yet if advertisers are being regulated in some fashion with each and every action they take in the system, it’s only natural that they would want that function described in the traditional way (we’re applying rules to this market system we’ve built) as opposed to the current language (the system as a whole rests on global concepts like prices and quality, and all are expressed with numbers).

Continued advertiser calls for transparency and due process seem inevitable. They would be inevitable with any large system. Google has in the millions of advertisers, and many times more users. No matter how they build it or express it, the rulebook is going to seem strange or confusing to somebody. Hiding it in a scoring mechanism produces different flavors of puzzlement and controversy, is all.

Government OK, monopoly bad?

When you look carefully, Google’s role as a potentially monopolistic “single seller” is more threatening to advertisers’ bottom lines than its role as (often hidden) “guvernor” of the auction. Don’t be swayed by anyone claiming that these kinds of systems can work responsibly and efficiently without regulation. They can’t. It’s just extra puzzling because the terminology (“your keyword isn’t showing because of your ‘website quality’”; you’ve probably broken a rule but we can’t tell you which one) is otherworldly.

In future columns, I’ll focus back on the real threat to your bottom line – Google’s power to exact high click prices, which will inevitably return in an economic recovery – and what, if anything, you can do about it.

If you spend enough time sitting at your desk in summer, you can go slightly insane. That aside, daydreaming sometimes leads to back-door observations, studies show.

Glancing out the window, it’s evident that it takes a lot of resources and coordination to run a neighborhood. Schools, crossing guards, street sweepers, garbage collection, public pool maintenance, snow removal, sewage, plumbing, hydro, zoning bylaws, parking permits, and the list goes on. Except, as a homeowner or resident, you don’t have to worry about any of these things. Just (directly or indirectly) pay your property tax bill, and presto - it’s all done.

It’s pocketbook management! Running a city is pretty easy — for you. Just pay your bills.

Wouldn’t it be that nice if paid search platforms worked that way? Unfortunately, the dynamic, competitive auctions are far from pay-and-forget. They require constant input from both the left and right sides of your brain. While larger companies must budget for in-house analysts and agency expertise (greatly reducing the hazards and heat blisters associated with “repairing your own roof”), small to midsized companies are left with a serious time and expertise deficit. How to manage all this complexity as a one-person show?

Shortcuts aren’t just for “losers,” but it depends on the shortcut. Putting the appropriate level of effort (not too much, and not too little) into different parts of the exercise should help you maximize efficiency.

The Long Tail revisited

In digerati circles, Chris Anderson’s fascinating study of the potential unlocked by “onesies and twosies” in digital content downloads got a conversation started. Before long, search marketers became fixated on the keyword frequency long tail, sometimes for good reason. Content-rich businesses can rank well organically on long tail terms, if they play their cards right. Paid search marketers may miss sales if they’re not working the full extent of the universe of potential keywords.

But keyword search frequency is only one issue. Its significance is easily misinterpreted. On the face of it, perhaps due to the length of the tail (or its sexiness), you might think you should actually spend *more effort* managing and scrutinizing long tail keywords. In reality, it’s less. They generally make up 2-5% of overall account spend and resulting sales.

Shouldn’t we be looking at another long tail graph to give us cues as to workflow and decision-making? Namely, the Long Tail of Account Effort? Many account managers get it backwards, and put in punishing hours of busywork and over-analysis trying to eke out pennies’ worth of efficiency, while the Benjamins are slipping through the cracks.

High, medium, and low priority tasks

To fully flesh this out would probably leak some kind of patentable secret sauce, so these are just examples and high-level thoughts. What are some of the activities that should really grab your attention? Which are less important? While I don’t cover this here, you’ll also need to decide in which sequence, or how frequently, each of these should be addressed.

HIGH:

• Account settings. In particular, distribution and geographic settings options, to name a few.

• Stopping major leaks. Short of frequent account-checking, some type of automation – if only alert-based, is a must to help you quickly yank the status quo on parts of the account that are melting down for whatever reason (seasonality, website or inventory issues, competition, or just plain unknown).

• Correct use of matching options.

• Establishing goals and understanding strategy.

• Establishing consumer responses through rigorous initial ad testing.

• Quality Score hygiene in initial account builds.

• Understanding everything there is to know about the highest-volume parts of any campaign, specifically the highest-volume ads and the top 20 or so keywords. This includes bidding tests and re-evaluations of strategy and competitive strategy.

MEDIUM:

• Bidding accurately in the torso (medium frequency) parts of accounts.

• Full keyword coverage in torso.

• Assessing anomalies in performance week to week or biweekly depending on volume. (Single day or two-day spikes up or down generally don’t lend themselves to accurate diagnosis; sometimes events are indeed random.)

• Quality Score awareness and keyword intent savvy to inform ongoing decisions.

• Savvy use of analytics and custom segments to inject new efficiencies into accounts (dayparting, demographics, ad copy, on-page copy changes, etc.).

• Establishing soft/secondary KPI’s to inform decisions on long-sales-cycle products and services.

• Expansion to other engines and sources once AdWords learnings are rock solid; syncing up the accounts using a campaign platform tool.

• Volume expansion experiments.

• Multivariate testing on high-volume ad groups.

LOW:

• Precise, testing on medium-to-low-dollar-volume parts of the account.

• Replicating learnings throughout very low-frequency, long tail groups. (Important, yes, but relatively low dollar impact.)

HIGH/MEDIUM/LOW:

• Bid adjustments on keyword groups, ads, segments, content, etc.? Exclusions of nonperforming segments?

For smaller accounts or verticals where competitors aren’t all that savvy, you might be able to get away with even less scrutiny than some of this implies. For example, you could put multivariate ad testing aside completely, or make a few mistakes on keyword matching options, without hurting yourself too much.

Note the confusion that arises in the final item, regarding the focus and methodology that needs to go into something like bid adjustments and responses to segment reporting. There are ongoing disagreements about these matters. This is for good reason. As I’ll attempt to show briefly below, approaches to decision-making vary by situation, and decisions on the whole require sound input in the form of various kinds of information.

If bidding accurately and conducting and concluding tests decisively sometimes feel like trying to solve a complex math equation where there are too many unknowns to provide a single, definitive answer, that’s probably because they are. (Related SEL item: Offline Conversions – How to Get SEM Credit.)

Decision theory and imperfect information

If we had time for one as busy marketers, a review of different eras of research in decision theory would uncover some shocking truths about decision-making environments, whether in the case of firms responding to marketing information or much more complex public policy decisions.

Most of what experts know and attempt to uncover about the process of decision is sharply at odds with the glib confidence of many busy paid search marketers, who may gratefully sop up rah-rah search-engine-speak and vendor pitches that oversell simplicity in automation.

The reality is that “perfect” decisions in any social environment only get made in hypothetical totalitarian societies with a tiny handful of perfectly defined goals, based on 100% perfect information.

We can force such situations for the sake of expediency, so we can always program a computer to perform certain routinized calculations, and train ourselves to respond in semi-robotic ways to the information we have at hand. But there can remain flawed assumptions throughout this process. For example, a bid automation tool likes nothing more than to work with a fixed “cost per acquisition target.” Yet the preponderance of company owners are shy about fixing a monolithic number on what “a lead” or “a customer” is worth.

Until recently, most people in the advertising industry understood that they operate in a climate of  politics-like uncertainty; only a small cult of direct marketing gurus felt they could truly tame the data beast (much like hubristic Masters of the Universe trading derivatives on Wall Street).

The old ad people understood that you needed meetings with various stakeholders to address multiple directions and goals. They understood that consumer attitude research informed how ad campaigns were conceived, but that there was always an element of mystery to product development and marketing to shifting consumer tastes. They knew that you couldn’t always attribute every sale correctly to a given ad impression or combination of influences. They trusted that at scale, word of mouth and brand effects would take effect. And they knew that they didn’t know a lot about the details.

The downside to that era was that it was all too easy to ignore data completely and to dodge key, embarrassing facts. Today’s era of advertising is a bit more enlightened, but we also need to be sensitive to its limitations.

The apparent precision of current marketing data can mislead us into believing we can make decisions with the cool certainty of that mythical policymaker in a totalitarian society aiming at a single “KPI” based on perfect information (gained, no doubt, from spying on everyone 24-7 in “1984-esque” fashion).

Since that is rarely the case, I’m proposing two general attitudes that will help real-world managers move real-world campaigns towards a state of improved efficiency:

• Understanding the Long Tail of Account Effort, focus on high-priority items and use the appropriate combination of automated and human brain mechanisms to address them on a regular basis.
• Recognize that much of the information we look at is less complete and less accurate than we suspect, and always open to interpretation. Even an introductory-level statistics course should be enough to persuade anyone that huge mistakes can be made in inferences; statistics are routinely misinterpreted in all fields, from finance to medicine. So why would marketing be any different? Some account mechanisms work relatively reliably, but sometimes it’s simply important to note the degree of uncertainty and to wait for either more information or more consensus-building activity that might aid in weighting decision criteria.

Based on the above considerations, it’s certainly easier for a solo business owner (or dedicated consultant given free rein) to seamlessly “work” an account; using whatever work style they choose and whatever data interpretation method they feel is appropriate, no one will be there to second-guess.

In any situation involving multiple stakeholders, though, something funny arises — something that likely wasn’t anticipated by many of the coders creating these bid auctions. You have to discuss and debate the statistics. You have to look at the customer’s needs and online behavior from a variety of angles. You need to decide on the right way to decide, and move forward in an environment populated by yes (gasp!) other people.

To cite Bryan Eisenberg’s pithy description of this state of affairs (on a recent Orion Panel at SES Toronto): “It’s called marketing.”

If that’s your job, that totalitarian society with a single goal and perfect information based on 24-7 surveillance and a massive R&D budget isn’t looking so bad, is it?

Prioritize and collaborate for improved results

Whether you’re working in fiercely independent fashion, or nicely with others, today’s number one takeaway is: prioritize. Takeaway number two:  in non-totalitarian societies (i.e. any most of us would want to live in), collaboration is generally a good way to get better (not perfect) information upon which to base decisions.

“Horror” is an apt term for how many experienced paid search practitioners respond to the newbie’s mistake of optimizing paid search ads solely to CTR.

Before getting to the nuances of ad testing, though, it’s important to emphasize yet again that rapid setup of ad rotation, for testing purposes, was the gift given to us by the Google Gods in 2002 when they rolled out what was then called AdWords Select. Seven years later, many marketers have not fully accepted the gift.

Although few drivers are as key to performance as the ad copy, many campaign managers have a tendency to neglect ad testing to this day. There is still much more industry “noise” in the day-to-day tactical chatter about two other levers: (1) keywords and (2) bids. In those areas, the newbie’s attention is easily grabbed by vendors who have nothing to sell but more keyword discovery, and more frequent bid changes.

By contrast, ad refinement requires know-how, experience, and a testing methodology. It’s the guts of this little response engine you’re building. Don’t neglect it.

Optimize for maximum clickthrough rate (CTR)?

I’ll call it CTR for short, but it may include “other relevancy factors.” Today’s paid search systems no longer use CTR alone as a proxy for relevance and quality, for ranking purposes. They use “quality” measures that focus primarily on CTR. One of the reasons CTR is so important to the engines is revenue.

As such, many of the built-in tools in the campaign management platforms have a high-CTR bias.

When you enable a new AdWords account, your ad rotation setting is defaulted to “Optimize.” If you’re taking advantage of ad rotation, the system will automatically favor “winning” ads that have a high CTR. You need to change this to “Rotate” if you want to take control back.

Dynamic keyword insertion is a trendy tool that can help you match the ad title or body copy elements to what the user typed into the search engine. Typically, this raises CTR’s. It’s very popular, but again, should be used for special purposes only.

The agency world—at least the more cynical side of it—also has a pro-CTR bias. If the client fails to ask performance-based questions, and is treating paid search more like a media buy than a “tweakable lead generation machine,” it’ll be tempting for some agencies to turn on the above tools and to optimize for higher CTR especially if they’re paid as a percentage of spend. And some in-house managers might also want the line item for search to go up, as opposed to improving ROI. Higher CTR means more clicks, and therefore a higher spend.

(For the same reasons, they might overbid the campaign into an ad position that is higher than economically desirable.)

By extension, although they’re certainly going to say otherwise if you ask intelligent questions about filtering, analytics, negative keywords, and the like, Google’s default advice will carry the same bias. Why wouldn’t it? Higher CTR’s mean more clicks which mean more revenue for Google. And meanwhile, they can make more users happy.

That’s fine if you’re not concerned about paying higher costs per acquisition for the extra volume. But optimizing strictly to CTR is generally seen as a rookie mistake by more performance-oriented search marketers.

Optimize for maximum ROI?

So now what? Just test the ads for a few weeks and an apparently statistically-significant number of sales conversions or leads, and kill the ones with the worst cost-per-sale or cost-per lead numbers, right? After all, that cost per conversion number is right there in your AdWords interface as long as you have AdWords Conversion Tracker installed. And pausing or deleting the non-performing ads is just a click away.

If your approach is cautious and a “pristine” ROI (as opposed to total profit) is paramount among your objectives, that’s fine. But it’s often the wrong move.

What if you happened to have two ads in your test (of four or five ads, say) that tied for the lead in ROI, but one had a significantly higher CTR? It wouldn’t hurt to favor the higher CTR one, would it? Even if your criteria were solely internal to your company. Assuming profitability to begin with, total profit would be higher if you chose the high ROI ad that also got more clicks than the ad it was tied with for highest ROI.

Unfortunately, that only rarely happens, but it illustrates the potential you could be missing out on.

Go for the “double win”

If you’re rewarding yourself (but not The Google) with lower CTR ads all the time, the economics of that can hurt you because the system’s tuned in the house’s favor.

In addition to potentially hurting your ad position and therefore click volume, an ROI-only approach to testing ads will actually hurt the ROI itself. If you want to regain the lost volume, you’ll have to increase bids above where many competitors are bidding. That’ll cut into the “better ROI” you temporarily achieved. The house wins.  :(

Why? Google, in particular, places such a huge weight on keyword CTR in its ad ranking algorithm, that you could be hurting keyword Quality Scores if you’re always overfiltering and settling for nice high ROI ads that also have poor CTR.

In other words, if optimizing for pure CTR is just plain careless, optimizing for high ROI alone can be the “easy way out” that hurts total profit and also eats into ROI itself. When you’ve refined to the point where you’ve got ads with higher ROI, you need to hold out for further discovery so that you can find—among contending high-ROI ads—the high-ROI ad among those that has the highest CTR possible. I call this a “double win.”

That’s not easy. But your chances of finding one of these are increased if you’ve been through a staged ad testing process and are now doing some kind of proprietary multivariate ad testing. A partial factorial approach to testing from a potential pool of 64 ads, say, gives you a greater chance of stumbling on that “genetic freak” of an ad that just happens to do a little better on both counts than all the rest.

Note: most of the literature you’ll read on multivariate testing applies to landing pages. Most advertisers have not yet thought to apply this to the ads. And in the ad testing field, we have certain advantages if we’re conducting the tests with our own methods or tools. Luckily, tools like Google Website Optimizer introduced a “pruning” feature to allow advertisers to arbitrarily give up on certain losing elements or combinations prior to test completion to reduce testing time. In the ad testing field, advertisers can “prune” a losing ad based on judgment, at any time.

Avoid subjectivity where you can

The above brief notes on methodology suggest that we can do a lot for the economics of our campaigns by sticking to a plan and trying various methods and various creative theories to achieve superior consumer response. Yet oftentimes we don’t get there because we don’t experiment enough. Either we’re content to stop too soon, or someone in authority (the famous HIPPO’s) vetoes an effective ad element because they deem it wrong somehow.

What about other considerations?

To be sure, there may be budget issues, brand or feel considerations, to say nothing of regulatory, seasonal, or factual concerns that stop you from testing everything to the ideal degree.

On the whole, many advertisers have a long way to go before their ad testing strategy is up to par. The first step for many is to ward off the industry-wide CTR-only bias, and the second step is to look again at the importance of boosting CTR if ROI has become the only benchmark for ad performance. Further refinement is possible with focus, patience, and a strong methodology.

The keyword long tail is dead. Long live advertising.

The first shot (heard ’round most of the search marketing world) in this war against busywork was actually fired in 2002 by Google, when they introduced powerful matching options for AdWords. Too bad some folks haven’t yet heard that shot.

This may be an exaggeration for effect. But if you’re like me, an experienced paid search pilot with somewhere close to your 10,000 hours in flight time, you’ve probably had enough of bloated ad groups containing hundreds of pointless, longshot, low-to-no-volume keywords.

Some experts in the field are suddenly joining me, jumping on this Long Tail Keyword List Critique bandwagon. One, who used to work in business development for a campaign management platform which shall remain nameless, up until recently sung the praises of the Long Tail to the sky. Until he changed jobs. Now, he says that’s so 2005. The top 10% of your keywords create 99% of sales volume. Or if you make really long keyword lists, the top 1% of your keywords create 99% of your sales volume. Let’s get real.

If I had my choice, this would be my shortest column ever! Maybe I’ve been tweeting too much. Forgetting prepositions. PPC superhero tired. Many keywords. Dragging him down.

First, the requisite disclaimers. Accounts vary. Campaigns can benefit from a lot of keywords, sometimes, if managed correctly.

But the situation I ran across recently was typical. Let’s say the product (name changed to disguise client identity) is “balsa wood sailboats.”

Inside the ad group someone had diligently built years ago, I found hundreds of phrases: [green balsa wood sailboats]. [balsa wood sailboats midwest]. [how to make a balsa wood sailboat]. And my particular favorite: [pinkbalsawoodsailboat]—perhaps a translation from the original German. And hundreds of others. You might think that being so thorough is good campaign management. I don’t think so.

Not to overcomplicate my reaction: I have a pretty good feel for these things. And these pages and pages of keywords were really starting to piss me off.

Part the reason for this visceral reaction boils down to keyword intent. Googler Nick Fox has recently been quoted as saying that focusing Quality Score so heavily on CTR is kind of like using the wisdom of crowds to tell you what is or isn’t relevant. CTR isn’t the only relevancy factor, but if there is a huge disparity from e-commerce industry norms, that should be speaking volumes. So the high-intent words were generating really good CTR’s, in the 3% range. And some of the words that might be more informational or DIY in nature, were eking along at 0.7% or so.

Perhaps rather than asking for each and every low-volume keyword, whether its presence might help a campaign, ask if there is much downside to getting rid of it. If it’s very low volume, and the same search might be covered by a broad or phrase match in the campaign (adding appropriate negatives when time allows), then hyper-efficiency isn’t really doing you much good. And indeed, the presence of such keywords can be hurting the account more than helping (though the impact should be marginal either way): they drag down overall Quality Score, and they could be making the job of analysis just a bit more difficult.

Granularity is good, of course. The ability to tightly target very specific search intents is why we paid search marketers get off on what we do. But you can overdo it. By letting these “passenger” keywords take up too much mental bandwidth, you forget that your job is to make the bold moves—to get the big important things right.

The historical data for the past year of this particular case study campaign backed up my theory.

Only the top 80 keywords or so had been clicked even once or more in the past year. Out of 116 conversions, only one came from the bottom 30 of those; in other words, from an economic standpoint we would have been OK if we’d cut the list off at 50.

104 of 116 conversions came from the top 15 keyphrases.

A few of those farther down the list that seemed promising, like [organic balsa wood sailboats], received few clicks and zero conversions for the year. The “organic” keyword got 7 clicks in the year, but didn’t convert. The CTR was adequate, at 2.5%. I guess I’ll let it run for another year. Of course, by then, it’ll only have around 14 clicks—far from statistical significance—but it’s less annoying than some of the others (the ones with zero clicks, especially).

After my deletions, there remain 200 active keywords in the group. I’d like to see another 100 or so eliminated.

Perhaps I’ve been doing this too long, if certain keywords irritate me so much that I want to squash them under my foot like ants. If you prefer to treat hundreds of stray, low-volume keywords as precious larvae just waiting to become beautiful, conversion-boosting butterflies, that’s your call. They just happen to make me ornery.

My approach might be altered slightly if attribution models were more forgiving. Some of these could be counted as “research” keywords that contribute to further searches down the road, and eventually, sales. But given that we’re generally evaluated based on strict ROAS criteria, and Quality Score punishes us for low CTR, that’s a nice theory that doesn’t reward campaign managers today. Performance that takes more than a few days to be measurable isn’t something that most clients and bosses are able to reward, especially if they’ve drunk the AdWords-as-Direct-Marketing Kool-Aid.

Beyond a certain point, more keywords won’t lead to more sales. Remember the purpose of testing and improving paid search campaigns: better CPA’s, and increased volume and total profit. If the long tail stuff isn’t helping with that, isn’t it time to focus back on what will help you achieve those objectives?

You’re not off the hook quite yet. Although the long, shot-in-the-dark, or overly specific phrases were not helping the account, that doesn’t diminish the importance of keyword planning and keyword discovery efforts. At a high level, there are probably concepts you (and maybe the tools you use) are missing.

If there are some regional variations on the name for “balsa wood” (hypothetical example: “scatterwood”), or different ways for saying “sailboat” (obviously there are), then you’ll need to incorporate keywords using those concepts, perhaps by testing them in different groups with their own ad copy. It’s a matter of feel: how many significant variations in concept are worth representing in the same ad group, or different ad groups?

But do refer back to the core point here as you do this: the excessive granularity often counseled by busywork-loving “best-practices-robot-people” can actually throw a wrench in the response testing process, requiring you to wait ever longer for a statistically significant conclusion. Just as there is such a thing as too many ads in an ad group, there is such a thing as too many campaigns and too many ad groups. And as I’ve tried to show, too many keywords.

That’s all for this month. Gotta run. Spidey senses tingling. PPC-man, away! Off on another important mission.

If you’re a diehard search marketer, you probably find web analytics conferences a bit strange. Many of the speakers are versed in the nuances of customizing third-party analytics platforms like Google Analytics, Omniture, and ClickTracks. But many of them seemed to have learned to sprint before they learned how to walk. I’ve been struck by how often search marketers have been told to pay close attention to some arcane point about an approach to data interpretation by people who have rarely, if ever, lived and died by the performance of an actual client paid search campaign.

While the “practitioner’s approach” may seem a bit more instinctive and less learned, the many detailed campaign improvements and tests that are the staple of day-to-day full time paid search marketing require the “instinctive practitioner” to be intimately familiar with the core—as opposed to exotic—workings of analytics and stats offerings. Thus they’re far less likely than “the Quants” to have gaping holes in their practical knowledge of the most obvious areas for improvement that need to be attacked on a regular basis.

For the sake of entertainment, let’s call the practitioners The Muddy Ones, after their tendency to get down and dirty. The legion of analytics experts with PhD’s or similar credentials: let’s simply call them the Quants. They’re both important to the profession, and Muddy Ones who fail to pay adequate heed to the power of formal math are bound to be overmatched in their professional challenges eventually. That said, it’s surprising how often the Quants are wrong about stuff, and how infrequently they focus on “quick wins” and “core tasks” of the sort that delight most clients and bosses.

Build a powerful campaign

The hallmark of performance-driven marketing is to manage campaigns to key metrics. But management isn’t marketing. So the first background point to keep in mind is that it’s all too easy to go through performance numbers and suggest responses. But how is that campaign built in the first place? What creative and planning processes go into it? Does the person building an AdWords campaign, for example, understand the way to architect a campaign correctly, in stages, so that management of that campaign becomes a meaningful exercise? There’s nothing worse than trying to “tighten up” the performance of an uninspiring, weakly-built campaign. No matter what you do, it’ll limp along at low volume at best.

A powerful campaign, as I define it, is characterized by (just to name a few):

• An understanding of Quality Score fundamentals: CTR and relevancy
• A model of how to test ads in different situations, and a plan for injecting response-inducing creative into the testing process
• A good understanding of filtering options and campaign settings
• A superior understanding of keyword research and keyword matching options
• Understanding ad distribution and reach

Managing everything to ROI

The convenience of tracking software like Google AdWords Conversion Tracker, Google Analytics, and (less convenient but also powerful) third party platforms like ClickTracks and Omniture means that there’s a consensus among the Muddy Ones and Ministry of Math alike that performance based marketing means reacting to conversion performance (or other KPI performance) and adjusting campaigns accordingly.

So that means you can adjust campaigns to keep any aspect or segment—a campaign, ad group, ad, individual keyword, segment of the content network, a time of day, and so on—within your target ROI range.

It doesn’t matter whether you’re a Muddy One or a Quant, this is where the debate actually gets rich in many cases. By ROI, what do we mean?

Muddy Ones know that rapid iteration is easiest within AdWords and similar platforms if you look at a target CPA (cost per acquisition) figure such as cost-per-order, cost-per-lead, cost-per-registration, etc.

But most of us would agree that on some other (perhaps looser) time frame, we need to look at ROAS (return on ad spend), involving real revenue or arbitrarily assigned revenue in the equation. That can lead to interesting questions related to data loss, time frames, repeat business, and lifetime value. Certainly, the initial customer acquisition should be given revenue credit in long-term planning. So by looking at longer-term data on true revenue associated with keywords and other segments, we gain added insight. The much shorter cookie life of Google AdWords Conversion Tracker means we can’t rely on it alone.

Assists are influential, but to what extent?

And of course attribution takes on even more “art-like” overtones when we start measuring assists. More and more, we’ll be asked to discount the weight of last-click keyword searches (such as a navigational seeming search for the brand’s website) and to give proper attribution to the banner impressions and research keywords that precede it in the buying cycle.

It’s easy to generate smooth talk about that type of issue, but for Muddy Ones, much harder to actually act on the information—especially when we rarely have full assist information today. Just because someone was exposed to some aspect of your multi-channel, many-keyword campaigns doesn’t mean that aspect contributed much to the sale. Does a keyword assist get 15% of the credit? 30%? One-off studies using control groups may have promising information (Quants love these because they are “walk-away” proof of concept), but to put it bluntly, they have little to do with the patterns of causation in the campaign you’re working on now. That’s someone else’s data, and you can’t verify either the consumer behavior or the related company financials. Nicholas Nassim Taleb would say that overreliance on other people’s studies is another example of The Luddic Fallacy (nerd fallacy, loosely translated).

Muddy Ones seem pretty instinctive in their behavior, but necessarily so. Because it’s so hard to see what’s “really” going on with every touchpoint, I’ve always felt that beyond the core CPA and ROAS numbers that we work with as a rough guide, attitude is everything. If you’re pro-advertising and bullish on the impact of your brand and website, then more exposure is good as long as it’s decently targeted. I think companies should be afraid of just stopping their advertising on their “on the fence, maybe helpful” keywords, just as they’ve always been afraid of the unseen effects of shutting down broad-based ad campaigns.

CPA tunnel vision: avoid

For rapid campaign iteration there’s no question that you should focus on something like a CPA number broken down by the appropriate segment, such as ad group or ad. Assuming you’ve created something that is worthwhile in the first place, iteration (bid management, ad selection) helps you fix what’s broken and choose the best paths.

But there are three problems related to this.

First, a powerful campaign requires experimentation to find right answers to questions. That includes ad testing and keyword experimentation. Overcautious management to CPA targets will lower volume and may thwart experimentation.

Second: more than lowering volume, there is a problem if your cautious CPA targets create much lower CTR’s than your competitors are generating. Plain and simple, the AdWords ranking algorithm loves high CTR’s. All else being equal, we need to try to build up CTR’s in an account whether we like it or not.

Third: Google’s black-box Conversion Optimizer product is one that automates the process of reaching target CPA’s. But it hampers hands-on tuning and learning on individual keywords. Advanced users should consider using Conversion Optimizer for set-and-forget functionality only once they’ve completed their own intensive process of gauging the performance of many individual keywords and matching options, to say nothing of ads. Third-party, alert-based bid-to-CPA tools are a wise use of automation as they allow the analyst to stay on top of wasteful situations while continuing to get down and dirty (Muddy) with hands-on optimization.

The most important point above was #2. Contrary to popular belief, CTR is a perfectly acceptable stat to include on your short list of core paid search metrics. Indeed, it’s vital, because it’s the most important component of Quality Score and will lead to higher rankings and improved volume for less money per click; thus, not only improved ROI, but more total profit.

CTR boosters

Muddy Ones like to focus on campaign activities that tend to increase relevance and targeting. So when they use techniques to boost CTR on ads, segments of content, groups of keywords, etc., they use powerful techniques that ideally don’t harm ROI.

1. Improve matching options. Although Quality Score ostensibly normalizes for match type, I still feel that de-emphasizing broad matches and dialing up the emphasis on phrase matches is going to help your campaign on a number of levels. Not only will your CTR go up, but so should your conversion rate. Win-win.

2. Add negative keywords. Judicious use of negative keywords always improves both CTR and ROI. Always. Talk about a win-win. The only risk is damaging volume and profit by over-negativing. Don’t assume too much, just filter the obviously untargeted searchers and those with low buying intent.

3. Test ads religiously. We use a two-stage process that starts with “big wins” in terms of discovering behavior triggers for a particular customer base. At the refinement phase the idea is to use multivariate testing or other methods that will leave you open to discovering ads that create a “double win”—that is, ads that have significantly higher CTR, but harm ROI less than similar high-CTR ads (or in rare cases, actually improve ROI).

4. Find excuses to geotarget. Geotargeting is one of the hottest ways to target even more closely. You might even want to think of excuses to geotarget so you can raise account-wide Quality Score.

In my next posts on this subject, I’ll take a closer look at how to deal with imperfect conversion data, interesting segments to look at, and other core analytics functions that you can access without looking any farther than the Google AdWords interface. I’ll reinforce the above point about CTR’s vital role in successful campaigns. Finally, I’ll look at some fun ways to get more out of Google Analytics without having to pretzelize yourself into a Quant.

In the previous two columns, I covered questionable assumptions and inconvenient truths related to bid management automation in paid search. With all those reservations and concerns out of the way, if you’re in the market for a solution vendor, you still have to form a relationship with one that meets your needs. Let’s look at some criteria that might help you choose.

Requisite full disclosure

There are many viable bid automation options. At this stage, it’s important to reveal the author’s biases and business relationships, if any. I’ve done my best to try out such tools over the years. The fact that the field changes quickly confers an advantage to vendors with the resources to update their software to reflect changing paid search platforms and changing tactical requirements. It also confers an advantage to “late movers” who have studied buyers’ needs and are in the process of building out their tools today. I’m on the Advisory Boards of two such vendors: (1) Clickable, a New-York-based paid search campaign management vendor that has closed significant rounds of venture capital funding led by the likes of Union Square Ventures; (2) Acquisio, a Montreal-based firm that gained its chops as a search marketing agency but today focuses on software. Acquisio has some private investors and is nearing the close of a Series A round of venture capital funding.

I’ve heard many good things about Click Equations, which boasts Avinash Kaushik as an advocate and adviser; and Adapt, recently acquired by WebVisible. Kenshoo is another hard-charging new entrant into the space, with recent funding from the likes of Sequoia Capital. I have no formal ties to these or any other vendors in the bid management space.

Where are the happy customers?

Speaking of bias, it’s hard to get neutral answers about this technology from users in the field. Informal surveys of real users don’t tend to get you positive mentions at all. Many users I’ve run across offer only lukewarm support for their bid management vendor, and many times when you ask, users will signal that they intend to switch vendors or simply stop using third-party tools. There are many reasons for this, but I would cite a lack of trust and customer support, as well as unimpressive ROI, as common reasons for discontinuing use of legacy bid management tools. Cumbersome usability would be another.

Despite repeated efforts to probe for neutral contributions to the “pro-automation” side of this debate, the preponderance of those who speak up on this subject take a like-minded position to the one I’ve held for years: automation can hurt more than it helps, and talented, passionate marketers and analysts are in such short supply that this human element is the main variable that will make or break your campaign. Lance Loveday echoed the sentiments of many with a satirical take on “push-button marketing” in his recent SEL column. An observer of the UK search marketing scene recently posted to the SEM 2.0 group : “Human skill involved in search is undersold, is undervalued. The importance of the people that run the campaigns in the search equation is more important than ever and all I seem to hear from agencies, aside from those above and some US agencies ~ last 24 months are that they save us money or they bring technical proprietary to the table.”

As I mentioned in a previous column, if you couple that sentiment with the fact that those talented and passionate marketers will have plenty of support and automation offered to them from the first parties (search engines) themselves, third-party sellers of automated tools must make their case in an increasingly crowded and skeptical marketplace.

But assuming you understand all that, automation can save time and money. So I hope this list of general criteria offer food for thought in your upcoming vendor selection efforts.

Criteria for choosing a bid management automation vendor

I’ll divide these into two categories: general or corporate characteristics, and features. The general points about your business relationship with the vendor are actually more important, given that feature sets change rapidly.

General criteria

Before zooming in to look at individual vendors, it pays to consider these broader issues first:

Appropriateness. Your business needs to be buying enough paid search clicks, and facing certain key challenges with the lack of available automation, to make it even worthwhile to use some of the sophisticated third-party tools out there. Very sophisticated tools that make frequent bid changes, and do other Stupid Non-Human Tricks, may only be suitable for 1-5% of the market: typically, high volume, low-margin retailers in fast-moving markets. Even with more appropriate and less expensive tools, sometimes the cost and learning curve may outweigh the benefit if your spend is small. The classic target market for many of these tools is an agency that manages multiple accounts. But some vendors in the space are targeting a large number of small to midsized businesses. I’m not sure that’s going to work out.

Trust, independence, and privacy policies. In this space, I tend to favor pure software plays over those that are tethered to the service operations of an agency. From the agency’s standpoint, automated tools can be a powerful selling point, and a powerful barrier to switching. From your standpoint, though: if you’re an agency, it’s inevitable that using or recommending one of these tools is going to offer a competing agency the chance to poach your clients, or at the very least study their behavior. If you’re the client, you may find that there is a fear factor in discontinuing your contract with the agency that promotes its own automation tools, so you’re overly dependent on the agency.

The flip side to that concern: a well-integrated service and solution offering can be the best of both worlds, providing a smooth and convenient process. Independence is an issue in general. Few of these vendors are built to last. Acquisitions are not always disruptive, but if the acquirer is a big search engine company, then this may defeat the purpose of using a third-party, independent tool. There’s no question that in using paid search automation you’re handing your back end business data over to yet another vendor. In this regard, the general as well as personal characteristics of vendors are important. If the vendor has a track record of respecting privacy policies, and not seeming “snoopy,” that’s good.

Cost, total cost of ownership, and Kool-Aid threshold. Don’t put off the question of how much the tool and associated services cost. Ask early, and often, and ask if you will pay extra if you go over an allotted amount of account activity. Then ask yourself if the learning curve to become proficient with the tool is going to pay for itself in the next 6-12 months. Less strictly speaking, if it seems like the relationship with the vendor will be too high on your agenda and get in the way of other work, you might decide it just isn’t worth the hassle. Remember, you’re trying to simplify and optimize your own business, not join someone else’s cult.

Momentum and standards. No technology is forever, but most of us out here are looking to ride periods of continuity in standards for all they’re worth until such time as we need to jump on something else. Being constantly in evaluation mode is disruptive. We want to be productive. So if something bills itself as a “platform,” it had better turn out to be just that, not some kind of techno-wobble-board that sends you flying after a few short months. In the web analytics space, for example, a tiny minority of vendors have been big or long-lived enough to evolve almost into “the” standard: WebTrends and Google Analytics qualify here. In ad serving, enough agencies and practitioners worked with their specific technologies, that DoubleClick and their DART became accepted as a standard. Enough people used third-party paid search bid management system Atlas that their URL tagging system became close to a standard for awhile.

My confidence level in a paid search bid platform as a long term play with momentum depends in part on who comes out on top of a messy struggle for supremacy. And that in turn likely rests on continued growth in the overall dollars being invested in the channel, and these tools’ adaptability to the long-term challenge of buying and managing other auction-based media. I think all of this will come true: the number of dollars being spent by the typical company through the AdWords-style auction environment may still have a long way to run (say, a five or tenfold increase from where we are today); and one or two third-party platforms will emerge from the heap as standards many of us rely on to facilitate the tasks at hand.

Features and benefits

Here are some high level criteria to look for. This doesn’t drill down to every detail.

Workflow. No one got into the SEM industry so they could feel like they were doing a rote job, akin to working in the bowels of a bank processing center. This sentiment of course is neatly encapsulated in Acquisio’s famed t-shirts that read “I HATE DOING THIS SHIT.” The “stuff” (like managing individual bids, doing global changes to ads, etc.) that account directors hate doing is more fun when automation speeds that process rather than making you an extension of the machine. Clickable, for their part, has contributed fresh thinking to our industry by focusing on an intuitive user interface rather than forcing users to shape their workflow to the demands of a spreadsheet-era tool.

Today’s best newer-generation tools combine the concept of “alert based” or “decision support” automation with priority-setting. So to take the Clickable example, the human analyst, not the system, is still running the campaign. The analyst’s decision making is supported by a prioritized alert system that means the human doesn’t have to pore through long lists of data and recommendations. I’d be remiss here if I didn’t add that the third party tools need to be better than free Google AdWords Editor at all this. They should provide helpful aids such as competitive intelligence and ad copy support, built right into the interface. This type of functionality is going to continue to evolve, but only a handful of vendors have the diverse product development chops to make it all come together.

Sophistication where appropriate. If possible, look for aspects of sophisticated computation methods that would be a natural extension of your own campaign strategy. Some of this might include improved grouping, and predictive models that help you bid accurately on low-volume keywords based on a portfolio approach (only a few vendors are likely to have the sophistication to actually pull this off). Another set of cool features should be interesting bid rules that might come in handy to an experienced campaign manager, above and beyond the simple “target CPA” bid rule and other simple features that are already built into the search engines’ own interfaces.

Acquisio is currently rolling out several innovative bid rules. Some vendors in the space now offer enhancements like better attribution and other analytics features you just can’t get with free products like Google Analytics. That’s gotta be a plus. One or two vendors offer proper support for designing multivariate tests for ads. Given that my agency was one of a handful that pioneered structured multivariate testing of rotating paid search ads (as opposed to landing pages, the more common form of multivariate testing in the digital marketing space to this day), all we can say is: what took you so long?

These ideas just scratch the surface. Overall, you shouldn’t be blown away by general technology claims (of the “same materials used in space shuttles!” variety). But smart is good, especially if it helps to plug holes left by free offerings.

To wrap up, my personal curiosity about technology platforms in the field of accountable digital advertising leads me to wonder about a couple of other criteria or factors driving change in this sector. The first is about platform scalability: are too many vendors essentially hacking out semi-robust demos based on their development team’s initial technology recommendations, using languages and protocols that won’t scale? (But people have said the same about everything from AOL to Twitter. Usually if you get big enough, you can adapt.)

The other question in my mind is about consolidation. I mentioned standards earlier. While not quite winner-take-all, the sector cries out for leadership, and said leadership will mean that also-rans fall out of the race. Clearly two things have to happen in this space for the benefit of end users. First, some of the current vendors need to merge or some need to die off by attrition. Second, vendors will need to sort themselves out so that they target different subsets of the market. That scenario is already well underway, and it points towards the Occam’s Razor of more consolidation but maintaining some diversity of solutions for different needs.

We’re not quite done yet! Next time, I’ll review a bid management horror story. It’s a real page-turner.