“We’re changing the design layout of our hotel, flight, credit card and bank account results, which help users complete actions such as booking flights quickly and easily,” a Google spokesperson told us in a statement. “We’ve always disclosed that Google may be paid when a user completes such an action; we want to be clear and consistent in how we do that.”

The New Look

The comparison units appear in the US when people do these types of searches:

• Flight search
• Hotel search
• Financial product search for checking accounts, savings accounts, credit cards or certificates of deposits

In the UK, the units only appear for financial products, specifically for current accounts, savings accounts and credit cards.

Again, the units aren’t new. They’ve existed for over a year for some products. They’re simply getting a new format. Below is an example of the old-style look, which some may still see now:

Here’s an example of how they are changing to over the coming days:

In the new format, the background color that’s used for Google’s traditional AdWords units is gone. The comparison units also carry a “Sponsored” disclaimer rather than an “Ads” one, as with AdWords ads. This seems part of Google’s positioning the new units as something different than ads.

Not Ads, Not Organic Listings But A “Third Kind Of Thing”

Indeed, even though Google’s called these “comparison ads” in the past, it pushed back on that label for them now. What are they called? We’ve yet to get a formal name for them. In talking with us about them today, Google referred to the units as a “third type of thing” — not organic listings, and not ads but something in between.

Clicking on the comparison link will take users to a results page in the relevant vertical search product, be it Google Hotel Finder, Google Flight Search or Google Advisor.

The distinction between these and ads, Google told us, was that advertisers control the keywords, the copy and the links in AdWords. In the results generated by the comparison units, Google decides what listings get displayed and how they get displayed, based on aggregate data that advertisers provide. In most cases, Google gets paid for leads it sends.

While the comparison ads site is still up and shows the program as in beta, participation isn’t open to any advertiser as with AdWords, not does it seem likely to be.

Google says those who want to be in the flight area, or the hotel area or in the financial products area will either already be approached by the right team in Google or know the team to contact. In short, if you don’t know the right place to talk to, apparently you aren’t the right company for these types of ads.

Organic, Paid Placement & Paid Inclusion Listings

This “third Kind of thing” will sound familiar to veteran search marketers. It’s paid inclusion. For those new to the concept, a refresher.

For the most part, Google (as well as Bing) has two different types of search listings. The first are “editorial” or “natural” or “organic” listings, the “main” listings that people tend to think of as the search engine’s results. Google doesn’t charge for people to show up in this space. Its search algorithms try to determine the most relevant sites to list for any particular search.

There are also paid listings, the listing powered by AdWords, where advertisers bid against each other to appear above or to the right of the organic listings. Because these ads grew out of advertisers trying to gain prominent placement, they’ve historically been called “paid placement” ads, even though with Google, advertisers can’t guarantee that their ads will rank well for any particular term, even if they’re willing to pay the most. An ad algorithm takes payment along with overall relevancy into account.

Paid inclusion was once a popular way that the major search engines like Yahoo or Bing’s predecessor MSN Search charged sites to help increase the odds they might perform well within organic search results.

You couldn’t buy a top ranking, but you could pay to ensure more of your pages were gathered up or revisited on a regular basis. It was kind of like buying more tickets for a lottery. You aren’t guaranteed to win, but you can buy more chances.

Google was long the major search engine that stood against paid inclusion, even calling out against paid inclusion as part of its 2004 IPO filing. Microsoft and Ask, feeling the pressure, dropped their paid inclusion programs that year. Yahoo — the last holdout — dropped its program in 2009.

So what’s up with paid inclusion happening at Google, which fought against it before?

Paid Inclusion In The Vertical Space

It’s important to note that paid inclusion is not happening in Google’s main web search results. At the time Google fought against paid inclusion, that was largely where it was happening. Since then, paid inclusion has moved into the province of smaller specialty search engines, where it remains common. Other search engines in vertical spaces, like Kayak.com and Mint.com, include data from companies with which they have financial relationships. Even Bing does this.

Google has come close to paid inclusion in the past with some mixture of sponsored listings in things like shopping and local results, but in talking today with the company, it seems it may be closer to this for some newer search products than ever before, if it’s not already there. I’d argue that it is.

To be clear, Google may have “free” information listed in any of these areas because of data feeds it pulls in or some crawling it does of the web. But it was clear the intention for these products is really to be building a way to compare between services from companies that Google has a commercial relationship with. That’s a fairly big departure from Google’s traditional search products. Google News, for example, doesn’t only feature newspapers that purchase inclusion. Nor does Google Shopping only list merchants that pay to be considered.

Google Hotel Finder, launched last year, appears to be a hotel search engine similar to how Google has a search engine for finding images or videos or web pages. But unlike those other search engines, from talking with Google, it seems most if not all the content in Google Hotel Finder is for companies that it has a commercial relationship with or hopes to have one with — a commercial relationship meaning Google gets paid for leads.

Google Flight Search which also launched last year seems the same situation. Google was unclear about whether businesses were listed for free within the area or why some airlines had booking options or not, if that was only for those with commercial arrangements.

As for Google Advisor which rolled up various financial product searching tools last year, individual sections, such as the credit card area, currently say that Google isn’t paid for offers shown. Yet this area powers the comparison units in Google that are expressly noted as sponsored. Google told us the wording in Google Advisor is being updated, after we pointed out this mismatch.

Will More Paid Inclusion Come To Google?

Even though paid inclusion is fairly commonplace in the vertical space, it still feels somewhat surprising for Google to be doing it. Having a search tool for financial products using paid inclusion even goes directly against what Google’s founders said they disliked back in 2004, as part of the IPO filing’s “Don’t Be Evil” section:

Google users trust our systems to help them with important decisions: medical, financial and many others. Our search results are the best we know how to produce. They are unbiased and objective, and we do not accept payment for them or for inclusion or more frequent updating.

It makes me wonder if future Google vertical search products will go down this route. I’ll be following-up more with Google about this in the near future.

Pamela Parker contributed to this story. 

Related Articles

• Yahoo To Drop Paid Inclusion Program
• Open Letter To Google: Do The Right Thing, Divest Yourself Of Performics
• Google Experiments With Paid Inclusion & Does “Promoted” Meet FTC Guidelines?
• Google Blurs The Line Between Paid & Unpaid Results Again
• Bing’s Travel Search & Kayak Favoritism Angers No One, While Google’s Gets Headline Attention From WSJ
• Google Goes Big For Financial Comparison Shopping, Launches “Google Advisor”
• Search For Hotels With Google Hotel Finder
• Google Travel Search Takes Flight With First ITA Travel Product

Covario, which serves mostly high-tech clients, says paid search in the Americas grew 15% in the first quarter, while Adobe’s Efficient Frontier, which serves clients in a variety of verticals, says it saw a 16% year-over-year increase in the U.S.. Meanwhile, the retail-heavy and U.S.-focused Rimm Kaufman Group (RKG) says search spend rose 30 percent as compared to the 2011 period.

All three companies recently released reports that give insight into how paid search did in the last quarter, and predict what spending might look like for the rest of the year to come.

Cost-per-click rates on Google continued to decline, according to Efficient Frontier. The company says Google CPCs fell by 5 percent year-over-year, and was down from the fourth quarter, as well. Still, by increasing clicks overall, the company has managed to hang onto its market share. It probably helps that Yahoo-Bing CPCs increased by 18% year-over-year, giving the Search Alliance less of an ROI advantage. According to Rimm Kaufman, Google CPCs fell 7% year-over-year in Q1, while CPCs on Yahoo-Bing rose 15% as compared to the year-ago period.

Image via Adobe's Efficient Frontier

Though Efficient Frontier said cost-per-click pricing was down in the automotive and finance sectors, the company found that CPCs dropped most precipitously (by 17%) in the retail sector.

Covario also noted a drop in CPCs, saying they declined 3% from Q4 ’11. The company’s analysts believe search engine algorithm changes are behind the decline and predict pricing will stabilize in the second half of the year.

The biggest trend noted by Efficient Frontier is paid search on smartphones and tablets. The company says spend on mobile devices in the U.S. represented 7.7% of all search spend in the first quarter, mostly driven by growth in spending on tablets. Tablet spend has grown from nearly zero in May of 2011 to 4.25% of all search ad spend by March of 2012. Spend on tablets is now greater than that on smartphones. The company predicts that overall mobile device spend will account for 15 to 20 percent of search spend by the end of this year.

Part of what’s driving the move to tablets especially is that conversions on tablets exceeds that of desktop devices, yet CPCs on tablets continue to be lower.

Image via Adobe's Efficient Frontier

Rimm Kaufman said it saw mobile traffic share at just under 14% at the end of the first quarter, which was nearly double 2011 levels. Tablets represented nearly 8% of paid search clicks and 57% of mobile clicks.

Image via Rimm Kaufman Group

Image by Rimm Kaufman Group

Google was the deliverer of much of that mobile traffic, and it continues to be the dominant player overall. The company commanded 78% market share in Q1, according to Covario. Spend on Google was up 1% from the typically-busy Q4 period, and up 23% over the year-ago period. The Yahoo-Bing Search Alliance showed 2% growth from the fourth quarter, but it’s still down 20% from the first quarter of 2011 and has 13% market share. Baidu, the leading player in China, grew 4% from last quarter and 142% year-over-year. The company rakes in 9% of global paid search spending.

Of the three companies, only Covario and Efficient Frontier made predictions about the rest of the year. Covario forecasts 18 to 22% annualized growth globally in 2012, while the Adobe unit only gives a U.S. number, saying growth will come in between 10 to 15 percent this year.

“Let’s say this is your first day at Urbanspoon and I show you the following data. We’ve just launched an A/B test of that I’d like you to evaluate. [The example can be almost anything you want to test different results for – from almost any search element, PPC campaigns, email subject lines etc. In this case, I’m using a PPC example.] Imagine you are running two different ads on a campaign with 50 kewords. We’ve been running Ad A for a while and have 17,235 impressions and 272 clicks. I started running Ad B last week and that has received 41 clicks on 2,253 impressions. What would you do?”

I’m looking for an answer that goes beyond demonstration of pre-algebra skills and rudimentary familiarity with a calculator.

Obvious answers include splitting up 50 keywords into different groups, looking down stream to see differences in conversion rates, and technical answers around quality score. But what I’m really looking for is a theoretical understanding of statistics and the interplay between sample sizes, variability and confidence intervals.

Answers to the above theoretical question usually fall into one of three buckets:

1. I’d run as more Ad B’s so our impressions are equal and then compare the click through rates.  #FAIL
2. I’d run the Ads longer, you need at least 3 weeks of data to make a decision.  #FAIL
3. Ad B is better b/c the click through rate is higher.  #FAIL “and thanks for taking the time to talk with me, our HR department will be in touch . . .”

Turns out, you don’t need to have an equal number of impressions or a set amount of time to run this analysis. It’s actually a fairly simple concept that can ultimately then be mathematically defined:

The greater the difference between your A and B samples (drawn randomly from the same pool) the smaller the size your test needs to be in order to confidently assert that one performs better than another. Example – if we wanted to test if men were taller than women and we measured 100 men and 100 women  and the men averaged 7 feet tall and the women averaged 4 feet tall, you’d be fairly confident saying that men are taller than women.

Conversely, if the difference was 3 inches instead of 3 feet, you’d probably want to measure more men and women before confidently asserting men are taller than women.

In fact, it’s possible that your sample was misleading – as a population women are really taller than men, but your sample didn’t bear that out. This level of confidence can be mathematically expressed as a percentage – I’m 95% certain that A is better than B. (Meaning there is a 5% chance, or 1 out of every 20 times where you’ll unwittingly pick the underperformer.) The greater the level of confidence you want, the larger the sample size you need.

All of this can be calculated with innumerable free online tools. Larger sophisticated systems like Adwords and big ESPs build this statistical testing in to their testing methodology – but it’s easy for do-it-yourselfers too.

I like a tool called AB Tester, which allows you to measure up to three alternatives compared to a benchmark:

In the results above, I’ve done the analysis for our question . . . The “Confidence” column tells me there’s a 79.19% chance that B is better than our control A.

Watch how this Confidence grows when we add a zero to each column – keeping the CTR the same but increasing the sample size:

By increasing the size of the test tenfold, now there’s only 0.5% that A is really better than B.

Let’s go from theoretical to real. Here are results from an email test we did for our Hawaiian getaway promotion to Ludobites 9. (It’s over now, sorry.)

The first data column is sends, then delivered, then opens, then clicks. Assume we want to test three different content types to three different cities (now admittedly this is not a random sample – maybe people in San Francisco respond differently to content . . . )

Take the data from the 3/6 send and plug it in to A/B Tester. Note I’m comparing the CTR from Opened emails to isolate content as an impact to click through rate. Also note that while the sample sizes are similar, they don’t have to be the same.

My best performer here is the San Francisco content at a 5.5% CTR. I use that as a control and plug the other two into AB Tester:

This tells me there’s a 3.3% likelihood that the LA content might really outperform the winner (San Francisco). Additionally, there’s a 23.5% chance Seattle content is better than our “winner”. More testing necessary . . . .

Social Media Examiner announced the results of its fourth annual survey, which this year had replies from more than 3,800 social media marketers around the world.

When asked what other marketing channels they use, search engine optimization (SEO) was the No. 2 response behind e-mail marketing. Paid search — or “online ads” as the survey called it — was far down the list at number six. Sixty-five percent of social media marketers say they use SEO, compared to only 38 percent who use paid search advertising.

B2B social marketers were slightly more likely to do SEO (67 percent) than their B2C counterparts (62 percent).

Going forward, 68 percent of respondents said they’re planning to increase their SEO efforts — a number that’s down slightly from 71 percent in 2011. Only nine percent this year said they have no plans to use SEO.

Only 43 percent of the social media marketers surveyed say they’re planning to increase their use of PPC advertising.

As you’d expect, Facebook was the No. 1 social networking site with 92 percent adoption. Google+ was a distant sixth, but most respondents say they’re planning to use Google+ more in the future. I’ve written up more about that aspect of the study on Marketing Land: Survey: Only 40 Percent Of Marketers Are Using Google+, But Many Plan To Change That.

(Stock image via Shutterstock.com. Used under license.)

Feeds define the data that may be changing regularly, like e-commerce inventory, store locations, etc. Readers may be able to generalize these terms to apply to existing rules-based tool if one is currently in use. First, a word about where this approach fits in to the broader picture.

The Broader Landscape of Managing Keywords & Negatives

There are many approaches for discovering keywords and adding them to a PPC Account, and many tools to support the various approaches. This article presents a rules-based approach and some tools that support it.

A good rules-based approach such as this often separates homegrown PPC Accounts from their more-mature competitors. This approach could be part of an overall strategy for managing Keywords and Negatives throughout the lifetime of a PPC account.

A Rules-Based Approach

A good rules-based approach is part of an overall Keywords and Negatives strategy. Most advertisers know how to manually add Keywords, many Advertisers know how to effectively use Negatives, and even How to Use Search Term View.

Quite often, efforts to add keywords in this way end up mostly manual, and occur something like: “Well, what about THIS keyword, or THAT one? Yeah, they would be good… But do don’t we have to go back and add that every where else, too?”

Good advertisers will often take that one step further and actually measure the value of their efforts by monitoring the value of those keywords over time. Some Advertisers know how to use other keyword research and generation tools such as SpyFu, Compete.com, or Wordstream.com and the like.

Some advertisers mine their SEO search queries for PPC keywords (and vice-versa), and some advertisers define rules for their automated tools that automatically add keywords based on performance (e.g.: for every broad match keyword with more than 100 clicks in a month, add a phrase match for the same keyword), or remove keywords based on lack of traffic (e.g.: remove keywords that have zero impressions after 3 months of eligibility). There are many components to managing Keywords and Negatives.

These are all healthy, productive components of a complete Keywords and Negatives strategy. A good rules-based approach helps advertisers leverage the good ideas by building them out across accounts.

• Dictionaries: Define the various groups of words that are relevant to your account
• Grammar: Define how to combine Dictionaries into actual Keywords, within your Account structure of Accounts, Campaigns, and AdGroups
• Feeds: Define keywords that change over time, as with available inventory, prices, etc.

Dictionaries

Consider a mythical PPC client, the ACME Bike Store. A previous Search Engine Land article demonstrating Stone Temple’s PPC Permutator uses a similar example. Today’s article builds on the ideas in the Permutator article. ACME’s Grammar might look like this:

That is to say, the Dictionary defines [terrain], [bike], and [product], each as a set of tokens that can be used in a Keyword Grammar to create Keywords.

Grammar

ACME’s Grammar might look like this:

That is to say, the Grammar defines meaningful ways to combine the tokens defined in the Dictionary, to create Keywords. For example, the combination of the Dictionary and Grammar so far would produce:

The list expands quickly from here. This is one of the values of this approach: Generate a lot of useful Keywords for a minimum of creative effort. However, this benefit can also be a drawback.

It is not productive to produce an excess of keywords that generate zero impressions. It can actually be counter-productive, and negatively impact Quality Score. Managing Zero Impression Keywords and Quality Score are both integral parts of any complete PPC effort, and beyond the scope of this entry.

Regardless, there is no need to over-tax those processes by wantonly adding keywords.

Grammar With Match Types

A Grammar can also define the Match Types for each Grammar entry. By defining the Match Types for each entry, the Advertiser can avoid repeating the entry for each Match Type wanted. The above Grammar definition might now look like this:

Advertisers should choose the right combination of Match Types to create a list that is full of keywords that are specific enough, but not so specific that the Grammar generates an excess of Keywords with zero traffic.

That brings up another good practice beyond the scope of this article, which would be to devise a system for automatically expanding Keywords on new Match Types when they get enough search volume. Another day…

Grammar With Modified Broad Match Support

Advertisers may choose to support “Modified”, as in the example below. One way to do that is to include the “+” sign wherever it might apply in the Grammar definition for the Keyword. Keywords being added as “Modified” retain the “+” in the output, and any output for other Match Types simply remove them.

This way, the Grammar is expressive enough to address the requirements of “Modified”, without requiring additional entries. That is one worthy goal when designing these tools; minimize the amount of effort required of the Advertiser, by maximizing the expressive power and flexibility of the Grammar Syntax.

For the sake of simplicity, the remainder of this article will use examples without the “+”.

Grammar With Negative Match Types

Similarly, a Grammar can define Negative Keywords and Negative Match Types. This helps to craft Airtight AdGroups, which is a highly recommended best practice.

For example, an Airtight AdGroup approach would prevent searches for “mountain bike” from matching for the AdGroup containing the Keyword “mountain bike tires.” To handle that case, add a Negative Exact Match “mountain bike” to the “mountain bike tires” AdGroup.

Grammar With Account Structure

It will be necessary to specify where to put all of these Keywords in the PPC Account. This is one of the final pieces needed for a complete Keyword Grammar: Account, Campaign, and AdGroup.

In addition to naming the structure elements, using a good naming convention will be valuable for many reasons; one convention to consider would be to name the group from the Dictionary, then embed the actual token used within parenthesis.

Example: Group(token), as in the table below.

The Grammar definition above defines a new AdGroup for each token defined in the Dictionary for [terrain] and [product], e.g.:

• Terrain(mountain) Product(tire)
• Terrain(mountain) Product(tube)
• Terrain(road) Product(tire)
• etc.

Defining a naming convention enables the Advertiser to report or take action in bulk based on the name of the Account Structure elements, such as Campaign and ADGroup. Example: generate a report showing the performance for all “Product” AdGroups, vs. other AdGroups to find out if customers convert differently when they have a specific product in mind.

Advanced & Optional Considerations For Grammars

There are some optional components to include in this approach. Named Sets allow advertisers to define specific groups within the Dictionary. For example, a Grammar Entry for [product:tire], which might output the set of keywords {tire, tires}.

Similarly, advertisers could implement Named Sets with Magic Words. The “Each” Magic Word directs the Grammar to expand the Grammar Entry once for “each” token in the Dictionary. Advertisers could consider more complex rules, such as [product:singles]. This could expand one line for each of the Dictionary entries in the “product” group that are single words (vs. plurals).

One worthy enrichment is the “Other” Magic Word. This is really useful for Negatives, allowing the addition of every “Other” token the in Dictionary as a Negative. For example, add a Manufacturer(Puffy) AdGroup, and add a {Manufacturer:Other} Keyword as a Negative Phrase Match to prevent this AdGroup from picking up searches from any other Manufacturer.

Advertisers can enrich the Dictionary with whatever meta-data works in a given application, and use Syntax in the Grammar, like Named Sets and Magic Words, to consume that meta-data. The simplest version of these special cases that extend the capabilities beyond the Dictionary is the use of “raw text.”

Grammar Special Case: “Raw Text”

Grammars should be capable of using “raw text” to create names, as with “ACME Bikes” to define the Account Names in the Grammar above. Similarly, Advertisers could use “raw text” in a Keyword definition to combine with Dictionary tokens, or in even in lieu of them.

A good time to use this is when starting with an existing keyword list. Just add the current Keywords do the Grammar, and then continue to add new definitions using the Dictionary. This way the Grammar describes an entire Account structure, which can be quite valuable.

Feeds

Use Feeds to define data that changes over time, such as available inventory. For example, ACME carries Puffy, Hwinn, and sometimes Sianchi bikes. A Feed might contain the following data:

Using Feeds In A Grammar

In order for a Grammar to consume the Feed above, the tool needs to understand a Syntax to express it. One could use curly-braces, e.g.: {Manufacturer}, {Category}, etc.

If Named Sets and Magic Words are implemented, consider enabling them to work with Feed elements as well. This provides the ability to expand Feed-sourced elements in a Grammar, like so:

Separate Grammar & Feed

Separating Dictionaries and Feeds provides the flexibility to adjust one while still leveraging the other. For example, an Advertiser can add a new Bike synonym to the Dictionary.

The next time the Account is generated, the Grammar will expand the new Bike-derived keywords for everything in the feed. Another example would be to add or remove a particular Manufacturer. The next time the Account is generated, the Manufacturer would be added or removed, respectively.

One note on scalability and the underlying implementation. Depending on the application, Advertisers may choose to expand the Dictionary first, or expand the Grammar first. Either way, the results should be deduped. Consider which approach will minimize the computing resources needed to address the exponential growth of the output.

Finally, it is worth noting that a similar approach, and much of the same data, can be used to produce Ads, as well as much of the rest of the information required to build a complete PPC Account.

A similar approach works for adding Geo-Targeting, with {lat} and {lon} for each {Location} provided in the Feed, and consumed in a Campaign Grammar supporting geo-targeting settings. The approach can be expanded to include whatever is needed in a given environment.

Build Your Own, Or Use An Existing Tool

This rules-based approach to Keyword generation, using Dictionaries, Grammars, and Feeds, provides a practical means of powerfully expressing new Keyword ideas into massively scaled PPC Account buildouts. The approach was presented generically in order to provide a basis for Advertisers to incorporate the ideas into tools and processes.

There are also commercial PPC tools available that already incorporate their own versions of some of the same concepts. Kenshoo offers “Real Time Campaigns”. Enterprise Travel advertisers have been using similar home-grown systems for almost a decade now. Of course Stone Temple offers The Permutator for free, and we have our own internal tools we use during Client engagements (Disclaimer: The Author is associated with Stone Temple Consulting.)

Stone Temple has built accounts with 10s of millions of Keywords, Negatives, and Ads for a client with over 800 locations throughout the United States, and uses the same approach to manage Keywords with the changing inventories of various e-commerce clients’.

These ideas can be implemented readily using home-brew or off-the-shelf tools, or by engaging with Consultants who have access to them. On a personal note, I eagerly anticipate the day when Search Engines adopt these ideas and make them available to Advertisers.

How would a rules-based Keyword Generation approach impact your PPC efforts?

Six-to-One Impact in Offline Stores

Based on two years of research conducted by retail marketing firm RevTrax, the study discovered that “for every $1 of e-commerce revenue generated from paid search, marketers can expect to see approximately another $6 of in-store revenue.”

In other words, paid search has 6:1 impact on offline sales over e-commerce. Because of the challenges of tracking consumer behavior online to offline, most of this has been invisible to marketers. Only now with the rise of smartphones and other methodologies is online-to-offline tracking becoming more widely available.

Between August 2009 and August 2011 RevTrax monitored millions of paid-search ads and consequent sales for its retail clients. To track in-store sales accurately RevTrax used landing pages with coupons and unique IDs:

• A paid search ad was displayed to a consumer
• The paid search ad led the consumer to a printable or mobile landing page displaying a coupon with a unique barcode
• The consumer redeemed the coupon inside a brick & mortar store
• Each coupon was tracked back to the online search (and the keyword)

Average Paid Click Worth $15 in Store

Using this methodology, RevTrax could conclusively determine in-store sales affected by paid search ads. Here’s how RevTrax’s findings illuminated the “value of a click” (where the average transaction size was under $200):

• The average click on a paid search ad generated approximately $15 of in-store revenue, with some merchants seeing as much as $28 of in-store revenue.
• Approximately 9% of clicks on a paid search ad generated an in-store sale, with some merchants seeing up to 26% of clicks on a paid search ad generating an in-store sale.

Again, what the company found was that paid search drove $6 in offline sales for every $1 in sales online. RevTrax thus argues that multichannel merchants who do not include in-store sales into the ROI calculation are potentially “undervaluing the paid search channel by as much as 85 percent.”

This is the first study to conclusively show the offline impact of paid search at this kind of scale, based on actual behavior rather than consumer surveys and self-reported data. The findings are pretty radical, with broad implications for search marketers and the industry as a whole.

Postscript: Here’s a bit more color and explanation from Seth Sarelson, COO of RevTrax, in response to a couple of questions I received about the study:

Clients are measuring these paid search campaigns at the keyword level and looking at brand keywords, competitive keywords, categories/products, etc. and the study uses a mix all these different types of executions to come to these results. I’m not sure that it’s clear to the person commenting that we’re talking about paid search only, not organic, so there’s certainly no guarantee that a brand is at the top of the paid search results for any keywords in a particular category.

One of the things that the study also mentioned is that many clients are reporting that 40-50% of customers acquired via paid search were new. This is big as it shows that this isn’t just an example of existing customers searching on branded terms looking for a deal.

It would certainly make sense for us to do a follow up that compares brand vs non-brand to address this specifically, as it’s an important point of distinction. I’m going to push for this internally.

As for PC vs Mobile, we’re working on a later study that will address these results, but most of what we’re looking at here is printable coupons from a PC.

Stock image from Shutterstock, used under license.

Conceptually none of this is entirely new. There are a range of “analog” companies and methods that measure retail foot traffic. However Euclid tries to bring online analytics’ sophistication to offline locations with smartphone tracking.

Offline Tracking with Smartphones

At least since 2008 people have been talking about using smartphones to track retail foot traffic patterns. And many companies are now trying to connect online and offline behavior with smartphones in various ways (e.g., check-ins, digital coupon redemptions).

Each physical venue or store location using Euclid installs a sensor that tracks people with smartphones entering the store. The company then provides a familiar type of dashboard, showing the various metrics it’s tracking. And yes, not everyone has a smartphone. But while smartphones are still not in the majority (43%), they will be in short order.

Euclid doesn’t track individual users. They see and deliver data in the aggregate.

Online to Offline: There’s a Larger Story

Euclid may or may not gain traction. But what Euclid represents is more important: a larger movement to connect online ads or promotions and offline behavior.

The venerable paper coupon or traditional call tracking have been the two primary methods historically used to measure the efficacy of advertising on in-store sales or ads-to-store behavior. More recently call tracking has been expanded to online advertising and digital coupons redemptions or check-ins (to a lesser degree) are starting to provide more visibility into the impact of online promotions on offline sales.

As another example of the online-to-offline tracking trend, search and social marketing platform provider Kenshoo is now monitoring Facebook and Foursquare check-ins (via APIs) to see if particular search or social ads produced in-store lift. The methodology isn’t connected to keywords but at some point you’ll probably be able to track paid-search keywords to in-store conversions.

Big Changes Coming with New Data

Credit card issuers and point-of-sale vendors are also starting to measure in-store conversions and tie those back to online promotions. There are some scary plans around credit card purchase tracking and the sale of personal data. And mobile payments/wallets will also eventually yield this “closed loop” data too — with varying degrees of anonymity.

Indeed, privacy will have to be negotiated in all these scenarios; and some of the “more aggressive” ones will probably need to be restrained by legislation. However the Euclid solution doesn’t fall into that privacy danger zone because it’s anonymous and aggregated.

The larger point is that within three years, lets say, the in-store success of online and mobile advertising will be much more visible than it is today. Indeed, the “offline” data becoming available to digital marketers via smartphones, credit-card matching and POS data is going to dramatically alter online marketing.

The numbers come from the 2011 State of Digital Marketing Report, which was compiled by Webmarketing123, a California-based online marketing agency. The company surveyed more than 500 U.S. online marketers in August and September; about two-thirds of all respondents identified themselves as B2B marketers.

Whether B2B or B2C, both groups of marketers agree that SEO has the biggest impact on lead generation. 57 percent of B2B marketers credit SEO as their primary source of generating leads, while 41 percent of B2C marketers said the same thing.

Both types of marketers say that website traffic is the primary way they measure the success of online marketing efforts. Brand awareness was at the bottom of the list for measuring success by both B2B and B2C respondents.

The survey asked a number of budget-related questions, including one about which channels get the majority of the marketers’ budgets. On the B2B side, one-third indicated that SEO gets the majority of their budget. But on the B2C side, more than 42 percent say that PPC gets the majority of their budget — about double the number of B2C marketers who said SEO is their top budget allocation.

Overall, 60 percent of respondents said they plan to increase their budget for social media marketing in 2012; 53 percent plan to increase their budget for SEO and 40 percent will increase their PPC budget.

Those increases in social media spending are likely field by another couple statistics from the survey: 68 percent say they’ve generated leads from either Facebook, Twitter or LinkedIn, while 55 percent have closed deals from social media leads.

The 2011 State of Digital Marketing Report can be downloaded from Webmarketing123′s website. You’ll need to provide name and contact information. There’s also an associated infographic with many statistic, which we’ve posted separately here: Infographic: Digital Marketer Views On SEO, PPC & Social Media.

But is there an opportunity to create the same sort of asset beyond search? Absolutely. Search retargeting can provide marketers with a totally transparent, multi-factor means to leveraging keyword expertise. And automation can play a big role in making it happen.

Evolution: Search Retargeting Today

Search retargeting has come a long way. While early attempts offered limited inventory, few keywords, incomplete reporting, inconsistent results, and scant optimization options, today’s advanced breed of retargeting solutions offers completely new optimization techniques and keyword management tools that both drive improved performance and provide insights for future marketing efforts.

All can be accomplished in a transparent, granular, multi-faceted manner to support high-volume, real-time bidding environments. In other words, today’s keyword decisions are happening at the speed of real-time bidding and at the massive scale of display advertising.

Why Search Marketers Should Care

This enormous scale and ability to optimize at the keyword-level appeals to the search marketer who already buys into optimization, and who seeks to scale their efforts beyond text-based search.

It takes them outside the silos of search and into their extended marketing and media mix, where they can leverage keyword intelligence in a much more holistic way.

Optimization Basics: Why They Matter In Search Retargeting 

To best leverage your keyword optimization knowledge when delving into search retargeting, you should tap into the basic building blocks of optimization that you leverage for search.

Doing so will go a long way to boosting the results of your retargeting efforts.

Just as in search, be sure to start with a broad set of keywords that are a reasonable reflection of your brand, your market, or your relative consumer demand.

The initial set can be based on search engine and campaign data, and include other terms that are likely to draw the consumer demand you seek.

In addition, be sure to include competitive terms as well as high-funnel category terms that may be cost prohibitive in search engine marketing. And remember to consider multiple aspects of the consumer path to conversion.

Option One: Manual Optimization

Once you’ve covered optimization basics, your search retargeting efforts will be on solid ground, ready for refinement. Significant performance improvements, whether measured in CTR, CPC, or CPA, can be made with manual optimization, which typically refines elements one at a time, without full consideration of how various elements interact with each other.

An example of single element manual optimization would be eliminating keywords that do not deliver the desired CTR, CPC, or CPA results. Other examples would be eliminating poorly performing sites, ad positions, creatives, times of day, etc. In addition to eliminating poorly performing keywords, additional keywords can be added to categories that are working especially well.

For example, if competitive terms are working well in a campaign, additional competitive terms can be researched and added. Finally, adjusting recency from 30 days to an instant might reduce volume, but also significantly decreases CPC / CPA.

The main advantage of manual optimization is that it is quick and easy. However, this method makes it difficult to account for the interaction of various campaign elements.

Option Two: Multi-Dimensional Automated Optimization

Automated optimization can provide you with multi-element approaches to refine and evolve retargeting efforts in real-time.

So what does it look like? Imagine that you are now able to change individual elements of any campaign – starting and stopping, yanking terms on the fly, in real time, based on observed performance of titles and descriptions, ad placement, exchange, bid, creative, frequency, per-period impression limits, etc.

However, because it’s automated, you don’t have to do all that work – the system does it for you. It essentially looks at variables that are impacting performance and decides whether or not to bid, how much to bid, and which ad to serve. And automation does it all in 30 milliseconds or less. In short, it will help you be quicker on your feet than ever before.

Below are just a few ways automated optimization can help: 

1. Maximize Performance. Because multi-dimensional automated optimization looks at the interaction of many campaign elements, performance can be improved over manual, single dimensional optimization techniques.
2. Improved Delivery. Single dimensional optimization requires more of a blunt instrument approach, in which some potentially valuable impressions are eliminated from consideration. For example, impressions targeted to a particular keyword may work well on some domains and not on others. Automated multi-dimensional optimization will allow continued delivery on the impressions with the keyword/domain combinations that are not performing well.
3. Save Time. Automated optimization eliminates the time required to manually pull reports, analyze them, and implement changes.
4. Dig Deeper. When you’ve attained a foundational understanding of your performance factors and your optimization outlook, you can then venture further into performance analysis. Doing so will allow you to look more deeply at combinations of individual components to identify weaknesses and opportunities for future efforts. Taking a more predictive approach here, you spot the correlations to help drive performance and help plan, budget, and manage future efforts.

Overall, marketers have a tremendous opportunity to parlay their keyword optimization knowledge into search retargeting.  The best way to successfully grow your efforts should include leveraging the power and scale of automation.

It will enable you to have aggregate learnings and cross-media trending data on your keyword assets at your fingertips — putting you in the best possible position to immediately and constantly tune your ROI, and model your path forward. It’s as close to predicting campaign success as you can come.

From the OneRiot blog post:

We’re delighted to announce that OneRiot has been acquired by Walmart.

The OneRiot team will now be joining @Walmartlabs – Walmart’s hub for the creation of new technologies and business models integrating social, mobile and retail for the next-generation of e-commerce.

And from the WalmartLabs post:

A few weeks ago, a small team from @WalmartLabs visited the offices of OneRiot in beautiful Boulder, Colorado. OneRiot has developed some pretty nifty technology that analyzes social media signals from popular networks like Twitter and Facebook to deliver ads that are relevant to consumers’ interests. As our teams debated the finer points of Big Data, Fast Data, and machine learning technologies, it became clear to us that we could find no better colleagues than the guys at OneRiot.

As a part of Walmart, we’re continuing to work with the intensity of a technology startup. Today I’m pumped to share the news that, within 30 days of that first meeting, we have closed a transaction to acquire the key assets of OneRiot. The technology team at OneRiot will move to Silicon Valley and become part of @WalmartLabs in September.

As I have written before, here at @WalmartLabs we’re doing some amazingly interesting and impactful work at the intersection of social, mobile, and retail. The technology at the core of what we do is the Social Genome, which enables us to connect millions of consumers with the best products based on their interests at any given moment. The OneRiot technology will enrich the Social Genome, and the OneRiot team adds to the already deep expertise we have around social data analysis.

In April, Walmart acquired the Kosmix search engine for WalmartLabs: Walmart Buys Former Search Engine Kosmix To Power Social & Mobile Shopping.