In the ever competitive world of online marketing, setting your ad apart from the competition can make the difference between attracting online traffic and not. Targeting a person’s current location in your PPC ads is a proven way of capturing your customers’ interest.  It can help distinguish your ad from the rest, by providing searchers with a stronger, more personal connection to your ad, making them feel it was them whom you were targeting. Geo-targeted keywords also work to make regional or national brands look local, helping to attract more targeted traffic to your site and improve CTR and other key metrics. In this post, I’ll explore a relatively undocumented method of inserting geo-targeted keywords into your PPC ads that’s easy and time-efficient.

There are several documented ways to incorporate a searchers’ location into ads, but these can be very labor intensive. Usually, for each area that you want to target with location in the ad text, you have to create a new Ad Group. Very quickly you could have 100s of Ad Groups, which would quickly become unmanageable. What’s more, to further discriminate between targeting people who are currently in a location vs. people searching for a location (physical location v intent), you would have to create multiple ad campaigns.

Luckily, there’s another way! There’s a seemingly undocumented feature in AdWords that allows you to bend the boundaries of intent/physical location targeting.

In some cases Google will map search queries to geo-modified keywords based on the searchers location, e.g. if somebody in London does a search for ‘taxi’ Google will map that to ‘London taxi.’ For the keyword ‘london taxi’ we can have the following situations:

Using keyword insertion you can now get ‘london taxi’ into your ads dynamically. In fact, fill up your ad groups with enough geo-modified keywords and you can have ads saying ‘London Taxi’, ‘Manchester Taxi’, ‘Newcastle Taxi’, etc.

Figure 1: ‘London Taxi’ has been inserted but only ‘Taxi’ gets bold font.

Overriding intent

Sometimes location intent can be unhelpful. For somebody searching for ‘London taxi ‘ there is a reasonable chance that they are looking into the history of London taxis. We can deal with that though. Even if you have an Ad Group negative for ‘London’ Google will still match ‘taxi’ to ‘london taxi’ within London. In fact, that’s pretty much the only search query that keyword will match.

It can’t be that easy

There is 1 caveat to this approach. It appears that this only works with ‘Geography+single keyword’. ‘london taxi’ will work but ‘london taxi cab’ won’t.

What methods do you currently employ to incorporate geo-targeted keywords into your paid ads?

At SMX Advanced in Seattle last week, I attended a session entitled Quality Score: The Unwritten Manual For Google AdWords & Bing adCenter. While the buzz going into the session was about Bing’s new QS and its impact, the real meat of the session revolved around Google.

One of the featured speakers was Craig Danuloff, who has spent the last two years researching Quality Score for his recently released book, Quality Score in High Resolution.

Visible Quality Score & CTR

According to Craig, the Quality Score you see in the AdWords UI is just your “visible” QS – the QS when the query matches the keyword EXACTLY. So, while it’s a fairly good guide for exact match terms, it’s a very poor guide for broad terms, where a significant portion of the queries will not EXACTLY match your keyword.

Image: Craig Danuloff: SMX Advanced, Seattle, 2011

This means the QS you see for your exact match terms is actionable, but less so for phrase & broad match terms.  For accounts that bid on multiple match types, the goal is to have as few as possible “exactly matching” queries mapping to your broad & phrase match terms, so the QS you see would give you very little indication of the term’s true QS.

Craig mentioned that this “visible” QS can still be a good guide for determining where you should focus your optimization efforts.  He suggested looking at your Google Keyword/Ad Text pair CTR report and pausing any ad variations with significantly lower CTR than others.

Image: Craig Danuloff: SMX Advanced, Seattle, 2011

And, according to Craig, it all comes down to CTR. Tightly-themed ad groups and keywords in the ad text had very little impact on your QS.  If you have a compelling ad with no keywords in the text, and a high CTR, your QS is going to be better than if you’ve got the keyword all over the place and really tight ad groups. Tight ad groups & using the keyword in the text can be effective ways of increasing CTR (as best practices), but might vary by product & industry.

The Google Perspective

After Craig spoke, Frederick Vallaeys, Google Product Evangelist, talked about Quality Score from the company perspective, and suggested some tips for how to improve your CTR & QS.  His first note was about landing page quality, also known as landing page relevance – you either pass or fail.  If you’ve got keywords in your account at a QS 5 or higher, don’t look at landing page relevance first – you’ve already passed.  Frederick said bounce rate “might” factor into QS.  Page load time does.

On CTR, Frederick suggested using the new Google ad products, like Sitelinks, to improve your CTR.

He also mentioned that, in the QS calculations, Google normalizes for position – there is higher CTR expectation in the higher positions. He also reiterated the old standby – smaller, tighter ad groups and more keywords in the ad text, as the main way an advertiser can impact their QS.

Overall, I’m inclined to lean towards Craig’s view of Quality Score. It really may be that simple – that keeping an eye on your CTR, as the key QS metric, just might be the Quality Score magic we’re all looking for.

For example, currently the URL for Fritzy’s Pet Care Pros appears as <www.FritzysPetCarePros.com>, after the change the URL will appear as <www.fritzyspetcarepros.com>.

Google maintains that the change should have a positive impact for advertisers: “In any given month, we experiment with hundreds of subtle variations of the Google search results page, testing everything from font sizes and colors to layouts and spacing, as well as dozens of other variables. Recently, we found that by standardizing the look of the URLs on the page, we were able to improve many of our user metrics, including ad clickthrough rates.” (our bolding)

But more clicks doesn’t necessarily mean more revenue. Without the ability to customize the appearance of the display URL,  PPC managers now have one less optimization lever at their disposal.    Patrick McCarthy, SEM Senior Manager at The Search Agency, explains that Google’s change could further increase competition for top positions: “As Google shifts to a more uniform ad format across paid and organic listings, testing of headlines, descriptions and vanity URLs becomes that much more important. The value of Ad Sitelinks and the additional real estate that comes with the Ad Extension, increases as well. Expect CPCs to rise as advertisers compete for top positions to ensure Sitelinks display.”

With all the changes Google is making to its results page, marketers must become even more diligent in testing new ad variations and measuring the overall impact.  As Mike Jarvinen, VP of Marketing Strategy at The Search Agency explains, “As an agency competing in competitive verticals, we are always testing every possible component of an ad to try and increase performance. The display URL and headline elements hold the strongest weight in ad performance and we are actively paying attention to this marketplace change both in defining the impact for our advertisers and isolating potential opportunities to increase performance for individual advertisers.”

Our representatives at Google have informed us that the changes should come into effect over the next two weeks. Have you already seen the reformatting on Google? How do you expect the change to impact your campaigns?

Google confirmed having rolled out this change on English language domains November 4.

And a recent study form Harvard Business School Professor Benjamin Edelman suggests that such a change could have a dramatic impact on search marketing performance.  Edelman conducted an online experiment in which he measured a random collection of users’ interactions with search engines, both in the standard configuration, as well as in an altered version in which he changes the advertisement disclosure label.   For half the subjects in the study, Edelman’s team replaced the “Sponsored Links” label with “Paid Advertisements and the other half were served the standard results page.  Across all subjects in the study, Edelman found that users exposed to the “paid advertisement” label clicked on 25 to 33% fewer advertisements than the control group which saw “Sponsored Links.”

Our Research

A 25 to 33% reduction in CTR would be significant for most advertisers.  Now that the “Ads” disclosure label has been rolled out in the U.S. for more than a week, our research team set out  to measure the actual impact of this change on advertising performance.

Methodology

Our analysts looked at AdWords data for all clients of The Search Agency running campaigns in the United States from October 28 – November 10.  We excluded any campaigns that significantly increased or decreased their budget during this time, as well as any campaigns in which our Account Managers had implemented a keyword expansion or culling.

This data set includes over 80 million impressions and 1.5 million clicks and for both business-to-consumer and business-to-business advertisers across a wide range of industry verticals.

To determine the impact of Google’s change on our clients’ performance, our analysts conducted a pre/post analysis — comparing six key metrics for the last 7 days of “Sponsored Links” to the first 7 days of “Ads”.

Results

Across all AdWords campaigns in our sample, we found the following changes in account performance:

• Average position increased 1.5%
• Total impressions decreased 1.5%
• Total clicks increased 9.7%
• Average Click-Through Rate (CTR) increased 11.4%
• Average cost per click (CPC) dropped 2.3%
• Total cost increased 7.1%

Contrary to what we may have expected, CTR has increased 11.4% since Google went to the “Ads” disclosure label, with no significant shift in average position or CPC.

Conclusion

After reviewing Edelman’s research, no one here predicted that CTR would increase by double digits.  Rather, our team expected the change to have little to no effect.  Our hypothesis was that the average search user has become so familiar with the general layout of the Google results page, that a change in wording on a small label would likely go unnoticed.

As Edelman points out in his study, the disclosure labels are, in fact, the smallest text on the page.  And while one could argue that the term “Ads” is more clear and explicit than “Sponsored Links,” Google has in fact deleted 11 characters; thereby making the already minuscule label that much more inconspicuous.

Conspiracy theorists have plenty of fodder with this most recent innovation.  Did Google make the change in response to increasing pressure from the Federal Trade Commission?  In testing this change, did Google see a similar jump in CTR and accelerate its implementation?

Bottom Line:  Google’s switch from “Sponsored Links” to “Ads” has not had a negative impact on advertiser performance.  In fact, it may have had just the opposite effect – searchers have clicked on a higher percentage of ads since Google made the switch.

With all the recent changes Google has made to the results page, however, who knows if there’s any real correlation between this latest modification and an 11.4% increase in CTR?  Either way, we’ll be watching this trend very closely over the coming weeks.  In the meantime, what type of results are you seeing in your accounts?   Leave a comment and let us know.

Without much fanfare, Google has replaced the label “Sponsored Links” with the word “Ads” above both the top 3 paid results as well as the right-hand rail:

Google made this change on November 4 and confirmed the roll-out to Barry Schwartz at Search Engine Land.  Google has officially retired the term “Sponsored Links” on all English language domains, and will be rolling it out to other domains and languages in the near future.  At the time of this writing, “Sponsored Links” is still being used on Gmail, Google Maps, and Google Shopping.

With all the tweaks Google has made recently to its standard results page, what impact could a change from “Sponsored Links” to “Ads” really have?  Long before Google made the switch, Ben Edelman and his research team set out to answer this question.

Edelman conducted an online experiment in which he asked a random group of internet users to answer two questions based on research using a search engine.  The subjects were directed to use a custom URL provided by the research team.   For half the subjects in the study, each reference to “Sponsored Links” in the Google results page was replaced by “Paid Advertisements” as shown here:

Edelman’s study included questions pertaining to “commercial research” (searching for a mattress retailer) as well as more “informational research” (researching various cancer treatments).  Across the entire sample and all types of searches, Edelman estimates that a change from “Sponsored Links” to “Paid Advertisements” yielded a 25 to 33% reduction in clicks on advertisements.

The study has a sound methodology and the entire article is definitely worth a read.  It includes a detailed overview of the prior research into this topic as well as the role of the Federal Trade Commission (FTC) in regulating pay-per-click advertising.

Edelman maintains that Google has not gone far enough with this recent change.  The FTC has called for advertising disclosures to be both “clear” and “conspicuous.”   I certainly consider the term “Ads” to be more clear than “Sponsored Links,” but Edelman provides a convincing argument for why the term “Ads” will not be nearly as effective as “Paid Advertisements” in distinguishing the sponsored from organic results.

What do you think of Google’s latest change?  Have they gone far enough with this disclosure?  Had you even noticed Google’s new advertising label?   And what impact (if any) do you believe it will have on search behavior?  Leave a comment and let us know.

For this case, I grouped the campaign traffic based on position. This grouping method would tell us how many impressions and clicks our campaign got at position 1-3, 3-5 and so on, and at what cost.

This table looks good at first glance, but what does it tell us? It tells us that positions 1-3 gave us the maximum traffic and cost more. But if we really want to compare the performance based on positions, then we need to look at CTR and CPC.

Let’s take a look:

According to this table, whenever ads in the campaign showed up at Position 5 or lower, I actually had a higher CTR and lower CPC. Strange, but true. According to this data, we should look only at showing our 5+, right? It’s still too early to make that decision.

Lets first look at percentage of traffic (clicks):

This makes things a little clearer. I spent 78% of my cost on ads appearing in positions 1-3 which gave me 64% of my traffic. This isn’t too efficient, but it’s still driving a large volume of traffic.  But traffic in Positions 3-5 also seem to be doing well in terms of CPC, so how do we decide what the ideal position is for this campaign?

That, in my opinion, would depend on the goal of the campaign. If the aim is drive more traffic, and in return awareness, then CTR is the metric we want to optimize.  The above analysis was done for a brand advertiser for a small sample size, but a similar analysis could be run for performance based campaigns, in which conversion data could provide further insight.

For advertisers running performance/conversion campaigns, there are several possible scenarios. For example, clicks from a higher position could result in more conversions per click, in which case a higher position would be positive. But if the conversion rate is found to be fairly uniform across positions, then it might make sense to go for the least CPC position. Of course, this is assuming that we have enough impressions at the lower position.

If you are a marketer who regularly sets bids based on position to maximize coverage and/or conversions, this type of analysis can provide further insights into which position is best for a specific campaign.  With this information you can start improving the efficiency of your campaign- rather than chasing the ‘holy’ position 1.



In search marketing, our mission is not to seek out new life and new civilizations, but rather the largest number of profitable dollars. For many online enterprises, this prime directive boils down to just:

That’s it. The simple truth is that the only ways for most companies to increase monthly profit are to increase the number of orders placed each month or the average profit per order (or both). The number of online orders per month is the number of searches times your impression share (IS) multiplied by the clickthrough rate (CTR) multiplied by your conversion rate (CR), so:

I call this The Drake Equation of Search Marketing. To some extent, it represents the actual mechanics of an online conversion. First, a search occurs. Some fraction (IS) of these result in an impression. Another fraction (CTR) of those impressions result in clicks, and some other fraction (CR) of those result in orders.

Every action we take as online marketers is intended to increase, directly or indirectly, one or more of these variables (without detrimentally impacting any of the others). But, of these factors, only the last two happen at your website. There, marketers apply landing page optimization (LPO), the purpose of which is typically considered: “to maximize the conversion rate (CR)”. However, we can see from the equation that there’s another facet of LPO whose purpose should be: “to maximize the average profit per order”. (Alternatively, one can simply combine these two factors, CR times ‘average profit per order’, to get the ‘average profit per click’, which is the overall metric by which the efficacy of various LPO efforts should be compared.)

The first three of the steps in this equation (searches, impressions, and clicks) occur on the search engine, where we do have some influence. Many people know that rank (a.k.a., position) strongly affects CTR, but so does the relative attractiveness of a listing.

PPC marketers experiment with the title, description text, and display URL in ad creatives to boost the CTR [Though again, they should really be maximizing CTR X 'profit per visit', which is 'profit per impression']. Similarly, SEOs influence organic results, since the title of a natural listing is often taken from the page’s title tag and the description from the meta tag or from text on the page. Online marketers often define the purpose of SEO to be something like “to get pages ranked as highly as possible for terms that are key to a site’s business”, but from the graphic above we can see that another facet to SEO is to make natural listings more attractive to searchers irrespective of any efforts to affect the listing’s position.

The primary factor which determines position in natural listings is ‘relevance’, which is partially found at the time of the query based on the page content’s quality and congruence with the search intent, but which is also partially determined by measures of the page’s ‘popularity’ which are calculated long prior to the query. We desire inbound links since each inbound link can be thought of, in some sense, as a vote by other website authors for our page, which boosts our PageRank. However, clicks at a search engine can also be viewed as votes for the relevance of our site, in this case, votes by the users of the search engine. Thus, click traffic becomes a self-reinforcing cycle, with highly relevant pages receiving more clicks (thus, a higher historical CTR) and therefore being treated as relevant in future searches.

The variables we probably influence the least are those at the beginning of the chain: the number of searches per month and our impression share. A massive off-line advertising campaign might drive additional searches for key terms, but as Frank Lee pointed out in ‘2012: We Weren’t Warned, Only Teased‘, even this is not guaranteed to produce traffic, let alone conversions. And, adding unique content to our site could increase our impression share, since it makes us more relevant to additional queries. But other than those, our best options are simply to promote good architecture to make our site easy to navigate (read: easy for search engines to spider) and to avoid hosting malware, or appear spammy or any of the multitude of reasons for search engines to switch our impressions off entirely.

This creates a problem for advertisers who have good-quality ads whenever poor-quality ads appear in the same set of results, especially in cases where those poor-quality ads are in higher positions. For example, I entered a search query into Google that, to the best of my knowledge, I had never used previously: [designer shoes].  Look closely at the ads that appeared on the right-hand side of the screen:

The first ad starts “Sale on Designer Shoes”. So far, so good, even though the text itself looks like it was written by someone who had chewing gum stuck on their keyboard. No big deal though, I’ll just skip that ad and go onto the next listing for “kate spade” shoes. Well, I’m male and katespade.com sounds like they probably sell women’s shoes, so I’ll skip that one as well. The next ad, however, looks a bit like a train wreck – no unique text beyond my own search query, a basically unparsable domain name, and something that looks like an attempt at Dynamic Keyword Insertion that, even if it had been done properly, would still have only inserted the words “designer shoes” one more time.

At this point, any reasonable person would simply stop looking at the ad listings (which is a real shame for ShopHousingWorks.com or Bloomingdales.com, which are further down on the page but might actually have what I’m looking for). This is what online marketers term The Pollution Effect: if some of the ads look really bad, searchers might not be willing to take the time to judge each ad on its own merits, since they might assume that ads which are placed below a bad ad are probably just as bad, if not worse.

Economists call this effect an ‘externality‘, which simply means that bad ads might impose a cost on other ads near them (in the form of a reduced click volume) by polluting the space with irrelevant or otherwise unappealing listings. On any given search the presence of one or more ads which are so bad that they cause searchers to cease examining the ads entirely (what I call “showstopper ads“) could therefore cause relevant ads to be unfairly disregarded.

“Well, so what?”, you might think. If 5% of ads are showstoppers, they should only reduce traffic by about 5% at most, right? Besides, Google has measures in place to weed out bad ads, so showstopper ads should never be a big problem. It turns out, neither of these seems to be the case.

I’ve estimated the effect of differing percentages of showstopper ads on the clickthrough rate (CTR) assuming that the ads are randomly placed, that searchers tend to examine higher-placed ads before lower-placed ads, and that encountering a showstopper ad causes the searcher to stop looking at any further ads. For each position, the ratio of the clickthrough rate at that position with some percentage of showstopper ads is compared to the CTR at that position when there are no showstopper ads.

Surprisingly, having just 5% of the ads be showstoppers likely causes a significant drop in traffic, with the ad in position #1 averaging a 5% drop, but with the ad in position #10 seeing its traffic drop by about 40%. And, the total number of clicks received by all of the ads combined drops by about 15%…all from just 5% of the listings being showstopper ads!

If 30% of the ads are showstoppers, the effects are even worse, with the ad in position #10 seeing its traffic drop by over 97% and the click traffic to all of the ads combined dropping by about 60%. Given these ratios, it’s understandable why search engines are so vigilant about weeding out irrelevant ads and those that violate style guidelines.

But recent changes to how Google serves broad-match ads in search results might make showstopper ads soon be much more common, rather than less so. I’ve noticed that Google has started carrying over ads from older searches into the ads for a new search, regardless of whether or not one is logged into a Google account at the time and also even if one has specifically turned off Google’s Web History feature and cleared the search history.

For example, I recently did a search for [buy authentic polish flag], getting ads from FlagsImporter.com, United-States-Flag.com and AmericanFlagsExpress.com in the results (among others). Immediately afterward, I did a search for [buy barometer], the results of which are shown below:

Look! There’s an AmericanFlagsExpress.com ad in there at the top of the right-hand sponsored links, and several other flag ads throughout the right-hand side. In total, this particular search shows five carryover ads (the most I’ve seen so far), representing 50% of the sponsored links on the page. As my calculations above showed, it probably doesn’t take too many low-relevance ads like these for click volume to suffer appreciably. Woe be unto you, BarometersPlus.com.

I’m certain that Google has their reasons for starting to show carryover ads, and no place more so than the internet can brilliance sometimes be indistinguishable from insanity, but frankly this practice does not seem to me to be in the users’ best interest. Performing similar searches on Bing.com doesn’t turn up carryover ads like this, so whatever’s going through Google’s head doesn’t seem to be happening elsewhere.

The lessons for marketers, I think, are threefold: First, on key terms, check to see that your ads do not rank below showstoppers or else you might be sacrificing good-quality traffic without even knowing it. Second, if Google persists in showing ads across search queries like this, know that the analysis of ad performance is going to get much more difficult, since your ad for flags might start showing in searches for barometers. (In fact, the Zappo’s leather jacket ad appeared in the search I did for “designer shoes” came just after doing a search for “leather jackets”). Unfortunately, Google’s Search Query reports might not be helpful in identifying cases where this occurs, since they include the catch-all category of ‘X other unique queries’ in the list of queries that prompted impressions. (I’ve seen reports that say ’1 other unique queries’. Why not just show me that 1 other unique query? Could it be because it was the equivalent of ‘buy barometer’ in a flag-seller’s account?) And, finally, recognize that if this practice of showing carryover ads becomes more common, then the top ad positions will become more valuable, since outranking showstopper ads might be the only way to avoid their toxic effect on clickthrough rates.  So, be prepared to pay more for placing your ads in top positions (which actually might be the reason why Google is showing carryover ads in the first place…)

Do you see carryover ads between unrelated search queries?  If so, please let me know.

Quality Score (QS) is a value reported by Google that is likely closely tied to an ad’s clickthrough rate (CTR).  I’ve described why this is so in a previous post of mine called ‘Why is Clickthrough Rate the Main Factor in Quality Score‘. In a sense then, Quality Score acts like a multiplier whereby Google treats dollars from some advertisers as worth more than dollars from other advertisers.  If your Quality Score is double mine, then I need to bid $2 for every $1 that you bid.  The actual cost-per-click (CPC) that an advertiser pays is simply the minimum amount they would need to have bid to beat the ad located below them:

So, if your AdRank (that is, your bid multiplied by your Quality Score) is 6.000 and I have a Quality Score of 2, I must bid $3.01 to beat you.  If I raise my Quality Score to 3, then I need to only bid $2.01 to beat you.  And if I manage, by testing lots of different ad copy, to find a version that gets a great clickthrough rate which results in a Quality Score of 6, then I need to only bid $1.01 to beat you.

Since each ad is only required to pay the minimum cost-per-click necessary to beat the AdRank of the ad located below it, the naïve perspective is that it is always beneficial to increase QS.  Optimization, in this view, is synonymous with maximization.

Unfortunately, it can easily be shown that it is not always in an advertiser’s best interest to have the highest Quality Score possible.  This is why it was disturbing to see a recent post by George Michie, consistently one of the most lucid voices on subjects related to pay-per-click advertising, saying the exact opposite recently at the Rimm-Kaufman Group’s blog.  Like most of Mr. Michie’s posts, this one is worth reading in its entirely, but to quote the passage most relevant to this discussion:

“…there is no complexity involved with QS strategy. You want the QS to be as high as possible always. That doesn’t vary by season, or by time of day, or by category. It doesn’t depend on stock positions, margin structures or return rates. Higher is better, and the mechanisms for making improvements are obvious.”

If only it were so simple.  But it can be made clear by example that higher Quality Scores are not always better.  Consider an advertiser who is faced with showing two different ads: one that’s generic enough to attract many clicks and another that’s specifically targeted so that it gets few clicks, but the clicks that it does get are from users who are likely to actually purchase the product.  (In advertising parlance, this is called ‘qualifying’ potential customers.)  The numbers below are fictitious, but realistic:

We can see that by showing the generic ad 1000 times, we attract 100 clicks, resulting in 15 conversions.  If each conversion brings in $100, then our revenue is $1,500.  If each click costs $5, then our profit is $1,000 (or $1.00 per impression), the clickthrough rate (CTR) for the ad is 10% and the Quality Score might be something like 10.  In contrast, the targeted ad might also be shown 1000 times, but get only 30 clicks, among whom are the same 15 people who would have clicked on the generic ad.  So, our revenue is still $1,500, but the cost (at $5 per click) is now only $150.  Thus, our profit is $1,350 (or $1.35 per impression).

Of course, because the clickthrough rate is now only 3%, the Quality Score is likely to be lower (let’s call it a value of 3, even though the Quality Score value that Google reports is probably not a direct, linear function of CTR).  But that doesn’t matter to the advertiser, because the profit is higher.  Mr. Michie said: “You want the QS to be as high as possible always”, but in fact the purpose of all economic activity is to maximize profit, not clickthrough rate nor Quality Score.  So, when performing tests of competing ad creatives, we should judge them by the differences in profit they generate per impression, irrespective of the Quality Score values that Google reports.

This article is a follow-up to ‘Optimal Bidding, Part 1. Behind the Scenes of ‘Google AdWords Bidding Tutorial’

There used to be this TV game show called “Card Sharks” where, in one portion, contestants were shown a playing card and then asked to guess whether the next card was going to be higher or lower in value.  (This wasn’t high-brow entertainment.)  The best strategy is obvious – when the card shown is low, guess that the next card will be higher, and when the card shown is high, guess that the next card will be lower.

In the new video ‘Google AdWords Bidding Tutorial‘, Google’s chief economist, Dr. Hal Varian, explains how advertisers can use Google’s Bid Simulator (GBS) to determine a bid that is “at or near the profit-maximizing level for each of your keywords.”  His method involves calculating the value per click, and then comparing that value to the GBS’s estimated incremental cost per click (ICC), as seen in this screenshot:

The specific example he gives is of an online retailer of digital cameras where each camera sells for $300 and costs the retailer $200 and where each click has a 5% chance of converting into a sale.  (In this case, since each click has a 5% chance of bringing in $100, the value per click is $5.00.)  Dr. Varian says, “Using data from Bid Simulator, or from your own experiments, we can see how many clicks we could have potentially received at different bids and how much those clicks would have cost.”  These are the first three columns in the screenshot from time 7:33 below.

Since each click brings the retailer $5 (before ad costs are considered), we can multiply the number of clicks by $5 to find the amount of money the retailer expects to make per week at each bid level (Revenue).  Subtracting the ad Cost from that amount tells us the net Profit per week.

The incremental cost per click (ICC) is simply the change in cost between any two bids divided by the change in clicks between those bids.  So, in Dr. Varian’s example, going from a bid of $3.50 to $4.00 costs an extra $97.29 (that is, $407.02 – $309.73) and brings in 21 more clicks (154 clicks – 133 clicks), for an ICC of $97.29 / 21 = $4.63.  However, each click brings the retailer $5.00, so it’s in this advertiser’s best interest to raise the bid and get more clicks at (or just above) a price of $4.63.  At time 5:52, Dr. Varian says, “Whenever your value per click is less than the incremental cost per click it will pay for you to lower your bid in order to reduce your cost.  Conversely, if your value per click is higher than your incremental cost per click, you should increase your bid.  You can see the ICC at our $4.00 bid is the closest value to our $5.00 value per click without going over.  So it’s going to bring in the highest profit.  Actually, in this example,” he says, “you probably want to bid a little bit more than $4.00.”  How much higher?  He doesn’t say.

I call this the ‘Card Sharks Approach to Bid Management’ and it might work well for people who have all the time and money in the world to ‘experiment’ with various bids, as Dr. Varian suggests, or for those who have the luxury to only know their best bid to the nearest $0.50 (or whichever other increment Google chooses to display) or who are willing to just guess some amount between the GBS’s tested values.  My question is: Since Dr. Varian shows that we have all the information we need to determine an optimal (or near-optimal) bid, why test different bids at all?  Why not just directly calculate (to the best extent possible) the single optimal bid and then just use that bid immediately, instead of nudging bids up and down until we hit some sort of observable sweet spot?

Personally, I think that showing AdWords’ users how to directly calculate a profit-maximizing bid is one of Dr. Varian’s ultimate goals and the simplified description he provides in this video is just a waypoint on that journey.  So, rather than wait for him to get to it on his own, I am going to describe for you a simple, visual means for determining your optimal bid from Google’s Bid Simulator.  Then, I’ll use some straightforward math to show you how to calculate the optimal bid (and CPA and ROI) for Dr. Varian’s example.

The screenshot from time 7:33 (Figure 2, above) contains some of the information found in Google’s Bid Simulator, but it also lacks a key component.  If you actually look at the Bid Simulator for a word in your account, you’ll notice that for high-traffic words, in addition to the columns of numbers, the dialog box also contains a graph that looks something like:

Each green point on this graph comes from a row on the spreadsheet – the point furthest to the right from the highest bid (the top row of numbers) and each next point to the left from the next row down.  (I’ve added the bid labels to each point for clarity, but they are not shown in Google’s actual Bid Simulator.)  Since it is obvious that at a bid of $0.00 the word will get 0 clicks at $0 cost, I have also added that point to the graph.

A recent blog post by Google which states that Hal Varian’s research indicates conversion rate (CR) does not vary much with position is very interesting, in part because this also implies that CRs do not change as a result of changing the bid. So, each word has a value per click (in effect, a rate at which an advertiser is willing to trade dollars for clicks) that does not depend on the bid, position, or number of clicks already obtained.  Therefore, we can draw a straight line on the ‘Cost vs. Clicks’ graph whose slope is the value per click and slide that line until it just barely touches the Bid Simulator’s estimates.  The point on the Bid Simulator’s estimates where the two lines meet (in this case, just over $4.00) is the optimal bid.

For bids lower than (that is, to the left of) this point, the advertiser should be willing to increase the bid because the slope of the ‘Cost vs. Clicks’ curve is less than the rate at which the advertiser is willing to trade dollars for clicks.  For bids higher than this level, the advertiser should be willing to forgo (too-expensive) clicks to save those dollars.  The point where the straight line crosses 0 clicks (in this case, about -$370) is the negative value of the expected Profit per week, which you can confirm in Figure 2 above.

None of this should be surprising to anyone who has read Dr. Varian’s article called ‘Position Auctions’ (International Journal of Industrial Organization, vol. 25, iss. 6, Dec 2007, p. 1163-1178) and all of my description from above is taken directly from that article.  It seems perfectly reasonable to me that Google might add this functionality to their Bid Simulator at some point.  (In fact, it surprises me that they haven’t done this already.) The advertiser could simply enter the ‘value per click’ in an input box, and the GBS could plot the line, find the optimal bid and determine the estimated profit per week in the blink of an eye.

However, if you know the relationships for ‘Clicks vs bid’ and ‘avg CPC vs bid’, it is also possible to just calculate the optimal bid directly on your own, without fiddling with the Bid Simulator and taking the Card Sharks Approach.  For Dr. Varian’s example, we can plot ‘Clicks vs bid’ and ‘avg CPC vs bid’ and find that, for this simple demonstration case, they are both basically straight lines:

That is, Clicks follows the line ‘m bid + b’, and avg CPC  follows the line ‘n bid + g’, where m, n, b and g are parameters that can be found by least-squares fitting (i.e., the ‘trendline’ feature in Microsoft Excel).  For this case, m is about 47.865, n about 0.676, b about -33.514, and g about -0.0357.  Oddly, the points corresponding to a bid of $4.50/click seem to have been adjusted upwards from a linear relationship, perhaps to make the results in Dr. Varian’s demonstration clearer.  (It’s mildly disturbing that he might have fiddled with the numbers, even for demonstration purposes, since it makes one wonder to what extent the estimates provided by the Bid Simulator itself might be manipulated.)

Nevertheless, our goal as advertisers is to maximize the amount of net profit made per week (after ad costs are considered).  Since net profit = Revenue – COGS – AdCost, our goal is simply to find the bid where d(net profit)/d(bid) = 0.

Revenue = Clicks x CR x RevPerConv

COGS = Clicks x CR x COGSPerConv

and

AdCost = Clicks x CPC

Thus:

We know from Dr. Varian’s research on conversion rates that CR is not a function of bid, and d(Clicks)/d(bid) = m, so if we say ProfitPerConv = RevPerConv – COGSPerConv, then the equation reduces to:

Multiplying the equations for Clicks and CPC together and differentiating with respect to the bid gives:

The first two terms, ProfitPerConv x CR, is simply the value per click (VPC), so:

(Note: this equation is only true for the specific ‘Clicks vs bid’ and ‘CPC vs bid’ relationships used in Dr. Varian’s example.) That is, for the specific example where Clicks and CPC are linear functions of the bid and have parameters equal to the values listed above, the optimal bid (which Dr. Varian called “a little bit more than $4.00”) is actually a little more than $4.07.  (If the Click and CPC relationships are assumed to be 2nd-order polynomial, rather than linear, the optimal bid turns out to be essentially the same, $4.08.)  So, there’s no need to use the Card Sharks Approach when you can just calculate the optimal value directly.

What is the CPA at which profitability is maximized?  If you recall, Dr. Varian calculated the ‘maximum profitable CPA’ in his video, but the ‘maximum profitable CPA’ is the CPA above which the advertiser’s profit is negative.  In other words, it is the CPA at which the expected profit is equal to zero.  When bidding, our target CPA is the ‘CPA of maximum profitability’, not the ‘maximum profitable CPA’.  We can see the difference between the two in the diagram below, which plots net profit vs. CPA.  Profit reaches a peak at a bid a little bit higher than $4.00 and declines from there until reaching 0 at a CPA of $100, when the bid is nearly $7.50 ($7.45, actually).  The ‘maximum profitable CPA’ therefore is $100, but the CPA of maximum profitability is much less.

(Notice that the position of the point corresponding to a bid of $4.50 has perhaps been moved by the possible adjustment made to the Bid Simulator’s numbers at that point.)

We can actually find the CPA of maximum profitability (that is, the target CPA) quite easily from what we already know.  CPA = Cost / Conversions, therefore:

It’s simple algebra to multiply the optimal bid, shown in an equation above, by ‘n’ and then add ‘g’.  Thus:

(Again: this equation is only true for the specific ‘Clicks vs bid’ and ‘CPC vs bid’ relationships used in Dr. Varian’s example.)  For the particular parameters that fit the sample data best, this optimal CPA is approximately $54.38.

It’s remarkable to see how easy in Dr. Varian’s example it appears to be to make a profit on AdWords. In his example, any bid in the range of $0.70 to $7.54 turns a profit.  A bid of about $4.07 yields the most, but any bid from about $3.32 to $4.82 gives an expected profit that’s within 95% of the maximum.  In other words, even though the purpose of Dr. Varian’s video was to demonstrate how to determine an optimal (or near-optimal) bid using Google’s Bid Simulator, one of the interesting lessons of the specific example he crafted is that (for this particular example only) you can bid anywhere in a $1.50-wide range surrounding the optimal bid and still basically be maximizing your profit.

Many account managers say that they would like to push down their CPAs as low as possible.  But another interesting lesson from this example is that in addition to a maximum profitable CPA ($100, where the advertiser makes no profit), there is also a minimum profitable CPA (in this case, about $8.75, corresponding to a bid of about $0.70, below which the advertiser also makes no profit).  So, account managers who are too successful in pushing down their CPAs might also be pushing down their profits, perhaps without even realizing it!

Unfortunately, determining the conversion rate, revenue per conversion, COGS per conversion, the relationships for ‘Clicks vs bid’ and ‘CPC vs bid’, and the CPA (or ROI) of maximum profitability in most real-world examples is not as simple as Dr. Varian’s example.  Therefore, The Search Agency (and the AdMax online marketing platform) are here to help you maximize the return on all of your online marketing efforts.  Please don’t hesitate to contact us if you need assistance with your online marketing efforts.

Thanks to Eric Sodomka of Brown University for examining the 2nd-order polynomial Click and CPC models.