To answer these questions, marketers need to dig deep into their data to identify how their keywords are actually distributed. As a first step, I recommend pulling a keyword performance report for the last twelve months and then using a PivotTable to organize the data into a usable format.

My preferred method is to drag the Conversions into the Row Labels box and the Keywords and Cost into the Values box. Your Keywords will likely default to ‘Sum of Keywords’, so just click the dropdown arrow on the ‘Sum of Keywords’ menu, select ‘Value Field Settings’, and choose ‘Count’.

Once you’ve organized your PivotTable, sum the data based on the number of conversions to give yourself a sense of the distribution of head, torso, and tail terms. So what does this data tell you? Let’s look at a few examples.

In the table to the left, the advertiser has about 90,000 keywords in their account. They have a very long tail, but it is not delivering conversions. Over 93% of these keywords have had zero conversions in the past twelve months. Not good, but at least these non-converting keywords only accounted for about 14% of spend.

In this next example, the situation is more severe. Not only are 94% of keywords non-performing, but over 40% of the SEM spend was being wasted. What’s more surprising, the average non-performing keyword spent less than $5 per month.

This is a perverse version of the long tail; each of these keywords doesn’t generate enough volume to raise alarms, but collectively, the negative impact of these low-volume keywords is huge. This highlights the inherent tradeoff between granularity and transparency. The more volume that is derived from the long tail, the less data is available for managing each keyword. Taken to the extreme, conversion data can become nothing more than a series of 1s and 0s. By reducing the number of keywords, you can consolidate data into fewer elements, allowing for better decision making.

Based on this advertiser’s data, I aggressively cut back on the long-tail and paused all keywords that have had no conversions in the past twelve months. The result? ROI increased 5x, which freed up budget to reinvest in growing volume on performing terms.

The long tail can be a great source of efficient traffic and every account should have a healthy balance of head, torso, and tail terms, but marketers need to exercise caution. Here are a few steps to help better manage your campaigns:

• Periodically measure your campaigns to see how your keyword volume is distributed.
• Use caution with automated keyword expansion tools. No keyword should be added simply because someone searched on the term. Set criteria such as a minimum number of impressions or conversions before adding keywords to your account.
• When the tail starts wagging the dog, it’s time to cut. Consider pausing out non-performing long tail terms (even those with very little spend). If it hasn’t converted in a year, you probably don’t need it in your account.

1.       Social is difficult to classify. Here are the responses when asked the “best answer” for each of the participants:

• Survey Responses:
•  31% – To me, social is about being there, being a part of the discussion
• 38% – To me, social is a media exercise, but a top of the funnel media exercise
•  31% – To me, social’s best value is increasing engagement of direct response channels
• 0% – To me, social is a direct response channel

2.       Active moderation is the key to growing sustainable Social:

•   You want to gain new fans only as fast as your moderation supports them
•   Anecdotal stat: 95% of fan posts go answered in Facebook – a big opportunity
•   Building a compelling Facebook app along with the request for permission it entails allows a community manager to get more detailed data on fans and engage in customer reactivation activities
•   Breaking out statistics around “new customers” and “lifetime value” are the key to successfully valuing social

3.       Areas of thinking:

• Social is often split between organizational departments – paid DR, brand, community/communications.  Cross-communication is key but how is it best facilitated in divergent orgs?
• Paid ads perform better when pushing to internal Facebook pages, but they then exist for fan acquisition – how can activation exercises like sweepstakes and coupons move fans into CRM activity?
• Bob Evans is using social to build around focused keywords like “farmhouse feast” that have low CPCs in search – how can brands use search to build keyword ownership in low cost keywords and measure together with social?
• Advertisers have used focused Facebook pages like “Black Friday Sales” to build fan base and increase likes around specific concepts for later value – how can brands leverage this idea?
• Trueview ads on YouTube and pushing to a YouTube brand channel set up for remarketing can show a funnel with buying efficiencies right now (30 second views at $.21 in one example + remarketing data) – are more advertisers going to push into video?
• ZMOT introduces a way of thinking consistent with the panel note of “social as a research buy.” How can marketers best allocate budget for the important but difficult-to-measure research stage of the funnel?

Remember before Y2K when the Information Superhighway was leveraging the New Economy outside the box toward a tipping point that would synergize a paradigm shift in which dot-com companies could gain enough traction to get eyeballs and harness mindshare?

Yeah, unfortunately, I remember that too. Though much else seems to have fallen by the wayside, the attitude that we’re operating on “internet time” – that there is something unique about our current pace of change – has endured. But amid all the Sturm und Drang, one thing seems to remain stable in pay-per-click advertising for most keywords: the ‘Bid vs. CPC’ relationship.

The stability of this relationship can be verified, both with actual performance data and with Google’s Bid Simulator (GBS). For each word that gets sufficient traffic (about 20 clicks per week), Google gives estimates for 3 to 7 possible bids of weekly Cost and weekly Click volume. By dividing these values, we can obtain an estimate of the CPC at each of those bids. These numbers are updated daily based on all of the previous 7 days’ searches related to that keyword, regardless of whether or not your ad was shown.

The graph below shows all of GBS’s estimates as orange squares of ‘CPC vs. bid’ for a single two-word broad-match keyword from August 1 through December 31, 2010 (153 days, for a total of almost 800 bid-CPC pairs). This period includes the launch of Google Instant (September 8, 2010) as well as Thanksgiving (November 25) and Christmas (December 25). It also shows the actual CPCs, as blue circles, for each day. (In cases where the bid changed during the course of the day, the bids have been averaged accounting for the fact that traffic volumes are higher at certain times of the day compared to others.)

(The gray line marks where the CPC is equal to the bid. Due to the second-price mechanism of Google’s ad placement system, CPCs must fall below this line.)

Notice that the actual CPCs tend to fall along the range of CPCs estimated for various bids by the GBS. Only about a dozen days were exceptions, including Christmas Day, although most of these “exceptional” days were fairly ordinary: Friday December 3rd is among them, as is Thursday August 12th, for example. Thanksgiving and the launch of Google Instant were not.

Fitting regression lines to each data set, we find that they are nearly identical.

The fact that Google happened to provide multiple estimates at a bid of $3.60 (which has created a vertical feature on the graph) allows us to easily gauge the range of CPCs possible at that bid, with the lowest being about $2.37/click and the highest being about $2.74. So, were we to bid $3.60 for this keyword and use the regression as a guideline, we’d likely know our CPC to within 10%. (In fact, the higher-end of this range occurred in August, the middle in September, and the lower end after that. So, if we took the current date into account as well, we’d be able to estimate our CPC even more accurately.)

Since the GBS’s estimates are derived from the previous 7 days’ search behavior, it might not be surprising that they would resemble actual performance so closely. A more interesting investigation would take Bid Simulator estimates from earlier in time and compare them to actual performance much later in time.

In the graph below, GBS estimates were taken on August 8 (covering the period from August 1st though the 7th) and a regression line fit through them. These GBS estimates covered a suggested bid range from just under $2/click to about $5/click (not shown). The keyword was a six-letter term ending in ‘s’, broad match, and could be interpreted as either a plural noun or a verb (like “dances” or “pilots”). Actual performance data is also plotted for the months of November and December, and a regression curve was fit through these points. Again, the two regressions are in close agreement. (In this case, Christmas and Thanksgiving Days were not exceptions.)

Please note that there is nothing special about the date in August I have selected nor the months of November and December. When all of the GBS estimates are plotted along with all of the performance data, the results look much like in the first graph. The lesson is: essentially any single set of GBS estimates of ‘CPC vs. bid’ from, say, the month of August is still a reasonable set several months later, despite major holidays and the launch of Google Instant.

In this case, there was a period of several days throughout the August to December period in which the bid was set at $2.50, which allows us to gauge the variability in CPCs for a constant bid. This bid resulted in CPCs from $1.61 in late October to $1.90 in early December. So again, when the regression relationship is known, selecting a given bid specifies the CPC to within 10% (and even closer when the specific date is taken into account).

Above I have only shown two cases, but I have looked at literally hundreds and the results are essentially the same in each one (though there are cases where the CPC variability is larger). So, when it comes to the ‘bid-CPC’ relationship for bidding on keywords, “internet time” simply means that things are very stable over the period of weeks and months, despite holidays and major changes to Google’s algorithms and user interface.

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?

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.



But since the Cost-per-Acquisition (CPA) of an ad is measured in ‘dollars per conversion’ and the Conversion Rate (CR) is measured in ‘conversions per click’, we can multiply these factors together to determine our target CPC:

That is, if we should be willing to pay $10 per conversion and 20% of clicks result in conversions, then each click is worth $2.00 to us. We can see from the equation above that the only ways to reduce the target CPC to $0 are to have a target CPA of $0 (at which point an advertiser shouldn’t be willing to pay for even one click, which would be very bad for business) or to have a conversion rate of 0% (which would also be very bad for business), or both (which would be very, very bad for business).

Note in the equation above that Quality Score plays no role whatsoever in determining your target CPC. If there is an increase in one’s Quality Score, an account manager should not rejoice at the amount that the Cost-per-Click diminishes, but rather continue to pursue precisely the same target CPC, enjoying whatever increase is seen in the number of clicks.

One interesting thing to observe about the equation above is that any extent to which conversion rate optimization efforts are successful should result in higher target CPCs.  For example, if by designing new landing pages we manage to raise our conversion rate from 20% to 50%, then we should be willing to increase our target CPC proportionally from $2.00 to $5.00. That is, one measure of the success of landing page optimization efforts is the degree to which they increase the observed Cost-per-Click.

Looking at the equation above we can clearly see that AdWords account managers who wish to both increase their conversion rate and simultaneously decrease their average Cost-per-Click are pursuing counterposed goals.

A second interesting observation about the equation is that the judicious addition of negative terms to a keyword (in order to eliminate some low-value impressions) should concomitantly result in an increase in the ad’s conversion rate and, thus, increase the advertiser’s target CPC for that keyword.

Finally, it should be noted that the efforts by the search engines themselves to detect click fraud or to improve the relevance of a given ad to a given search query should also naturally result in increasing CPCs to whatever degree these changes enhance conversion rates. So, it should be expected that CPCs would have the tendency to inflate over the timescale of months or years as their algorithms improve.

For those who are unconvinced by theoretical arguments that successful online marketing efforts should tend to increase CPCs, we can also present hard-earned empirical evidence simply by looking at CPAs for various accounts.

For the month of March 2010, the graph below depicts the CPA for all of the non-brand-related keywords for a dating website which has a CPA target of about $100. (Naturally, brand-related terms tend to generate large numbers of conversions at dirt-cheap CPCs, so these terms have been eliminated from consideration for this analysis.) Collectively, these words cost just over $16,000 in March.

We can see from the graph that words whose CPC averaged $2.00 to $2.50 had a CPA less than $70 per conversion while words with an average CPC in the range of $0.00 to $0.50 collectively have a CPA of over $130 per conversion. It might be worthwhile, if the words with CPCs above $2 are limited by the account’s budget, for the manager to consider simply shutting off some words with CPCs below $0.50 in order to free up additional money for the high-CPC ads.

For ROI-based accounts, natural metrics to consider are the Profit per Impression and, of course, the Return on Investment. For one campaign in an ROI account that sells very expensive products, we again filtered out brand-related keywords. The remaining words in that campaign spent over $180,000 in March and the target ROI for this campaign was about 40%.

Again, the highest CPC keywords prove to be the most valuable, with ads whose CPCs averaged $10.00 to $12.00 bringing in an average profit of about $0.25 per impression and ads whose CPCs averaged $0.00 to $2.00 bringing in only about $0.01 in profit per impression. Not only did the lower-CPC keywords average less profit per impression, but they also had lower ROIs than higher-CPC words.

The point I’m making is that every ad occupies a small piece of visual real estate and real estate has value. Good real estate has a high value for a very simple reason – it’s worth it. That’s why you’ll find more Starbucks in downtown Manhattan than, say, the northern coast of Greenland. Low-CPC keywords tend to be low-CPC keywords precisely because they’re not worth much. (To be fair, though, in the course of this analysis I did find some quirky 3- and 4-term keywords that generated a lot of business at very low CPCs. It’s likely only a matter of time, though, before competitors discover them too.)

The take-home here is: Don’t put your ads in Greenland.

So, if your marketing efforts are going well, with improving conversion rates and good negative terms in place and someone asks you how your account is performing, be sure to hold your head high and say “My CPCs keep going up!”

The decline in 2009 is entirely attributable to the financial panic and resulting recession. The trend is still upwards in the long run. This price inflation is a direct result of several factors:

• Moderating growth in US search query volume: According to Comscore, while worldwide search query volume grew 46% in 2009, US growth is less than half that, 22%. In addition, again according to Comscore, the US figure is largely unchanged from the year earlier rate of 21%.
• More advertiser dollars are moving from other media to Search, with projections of doubled Search Marketing industry revenue: Forrester estimates US Paid Search Marketing to grow from $15.4 billion in 2009 to $31.6 billion in 2014. When some of this “new money” enters the market, bad spending decisions are inevitably made as experience is slowly gained and strategies adjusted to account for the different medium.
• Search is building more mindshare as the runaway success of Google generates awareness of its platform: According to Neilsen Media Research, the Percent of Time spent in Media versus Percent of Ad Spend in Media is 3.6 for the Internet, and just 0.4 for newspapers, thus showing an over-allocation to newspapers and a similar under-allocation to the Internet. This dramatic imbalance sets up print media for a massive loss of revenue as advertisers find new ways to spend their marketing budgets. In fact, as a sign of things to come, Kantar Media confirms this in a recent study that shows advertising spend in traditional media like TV, newspapers, and radio plunging by 10%, 20%, and 22% in 2009. On the other hand, Internet display grew by 7%.

Sure enough, the high prices are starting to bite: Forrester Research says that two-thirds of marketers report that high keyword prices are their biggest challenge in paid search. In the very recent past, the easy answer was better and more sophisticated bidding. Many agencies and vendors built their practice on the sensible notion that a smarter bidding mousetrap would catch more search traffic. But that approach is now so widely used that it alone is inadequate to move the needle. So, what’s a savvy Search Marketer to do?

Though not entirely new to Search Marketers, they will have to sharpen their skills on keyword management, keyword testing and targeting. Agency partners will have to develop a deep understanding of the client’s business objectives, generate keywords that can assist in achieving those objectives, and then organize them into optimal allocations. Testing the efficacy of these keywords is the next course of action, followed by continuous refinement and alignment of the resulting campaigns with overall business objectives and strategy. When managing a small number of keywords, this management and optimization task is daunting but doable; at large scale, it is out of the question without powerful tools and sound methodologies that support an experimentation and feedback model.

Understanding and optimizing for Quality Score will also be an essential tool for the successful Search Marketer. Such an approach creates a virtuous cycle—the better a score, the cheaper the click, which liberates more marketing budget for increased ROI. Mastering and implementing a range of tactics are critical in improving Quality Score:

• Effective keyword selection
• Use of negatives
• Match type refinement
• Demographic targeting
• Geographic targeting, particularly beyond just local

Again, at small scale all of these tactics can be implemented by a determined Search Marketer who has the self-discipline to tend to his or her campaigns relentlessly. But, at large scale, tools and effective methodologies along with best practices in the hands of seasoned professionals are the only way to address this problem.

Finally, a clearer and deeper insight about the targeted customer is essential in driving effective Search Marketing. Every business that bids on a given keyword in Search will likely have a different business model and varying ways of monetizing any resulting clicks. One way to attain competitive advantage in the marketplace is for a business to really understand the value of a customer, particularly if there is residual or lifetime value beyond the immediate transaction. Developing this persistent customer intelligence can assist in building better attribution models and multi-faceted ROI models. In turn, this understanding of customer value can help the Search Marketer refine the keyword strategy to further improve performance and reduce marketing costs. While conceptually straightforward, implementing Search Marketing campaigns that are informed by an end-to-end customer intelligence requires considerable skill and expertise among the business owners, agencies, and technologists.

Rising keyword prices are likely a fact of life for the modern Search Marketer. There is just too much value in those keywords for them to go cheaply! But by going back to the root of the business problem, and focusing on keyword management, Quality Score, and a holistic view of the customer, a marketer can bring business insights into play to reduce the effective keyword costs.

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.

In the film, Dr. Varian describes a 4-step process for finding an optimal bid:

1. determine your maximum profitable CPA,

2. determine your conversion rate,

3. calculate your value per click, and

4. adjust your bid so that the value per click equals the incremental cost per click.

The specific example he gives is of an online retailer of digital cameras where each camera sells for $300 and costs the retailer $200 (for a profit to the retailer of $100) and where each click has a 5% chance of converting into a sale. The basic question he addresses is: What is the retailer’s optimal bid (per click)?

Since the retailer can afford to spend up to $100 to acquire each new conversion and there’s a 5% chance any click will convert, he should be willing to spend up to $5.00 for each click, at the absolute most. At time 2:40 Dr. Varian says: “If you were to pay $5.00 for each click you would expect to just break-even on your marketing investment.” Obviously, then, having a cost-per-click (CPC) more than $5.00, which corresponds to a cost-per-acquisition (CPA) greater than $100, results in a loss to the retailer.

Since we now know that a $5.00 CPC results in no profit, Dr. Varian then begins to consider which bid does maximize profit. “While bidding at your value per click would generally lead to profitable results,” he says at time 3:20, “it may not produce the maximum possible profit for your marketing investment.”

He demonstrates this with a specific example, using sample values similar to the information that Google’s new Bid Simulator feature provides. For several reasonable bid values, we see the number of clicks expected per week, the total expected cost per week and the average CPC. Dr. Varian suggests calculating the expected revenue by taking the number of clicks and multiplying by the value per click and also calculating the expected profit by subtracting the ad cost from the revenue.

Figure 1. A screenshot from time 4:58 in 'Google AdWords Bidding Tutorial'

Doing this, we can see that a bid of $5.00 truly does not maximize the expected profit, since there is a higher expected profit if the bid is $4.00. At time 7:26, he says: “Actually, in this example, you probably want to bid a little bit more than $4.00 so you can get your ICC [incremental cost-per-click] as close as possible to the $5.00 value per click.”

The incremental cost-per-click from one bid to the next is simply the additional cost incurred divided by the additional number of clicks you receive. (Often, the ICC can be a large number, Dr. Varian explains, because raising a bid raises the CPC of all the clicks you’ll receive, not just the incremental ones.) To bid optimally, it is important to know the ICC, Dr. Varian says. “Whenever your value per click is less than the incremental cost per click, it will pay 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.”

Even though Dr. Varian’s video is jam-packed with useful information about optimal bidding, it must be pointed out that he has also left out a great deal and made numerous simplifications and assumptions. Naturally, some simplifications are necessary for the purpose of maintaining comprehensibility (As the British writer Hector Hugh Munro once said, “A little inaccuracy sometimes saves tons of explanations”). However, some of the concepts that Dr. Varian skimmed over are so critical to determining optimal bids that they simply cannot be ignored when actually bidding on Google AdWords. I liken Dr. Varian’s remarkable new video to a power table saw – useful in skilled hands, but dangerous to unskilled ones. And it is only by knowing the limits of Dr. Varian’s description of optimal bidding, and how to make the most use of what he does describe, that search engine marketing managers can get the best performance out of their accounts at the lowest risk of causing themselves grievous financial harm.

The first limit is probably also the most obvious: to use Google’s Bid Simulator (GBS) for a given keyword requires it to be active for that keyword. Presently, the GBS only returns Click and Cost estimates for keywords that have gotten about 25 or more clicks in the past 7 days. For words that have gotten fewer clicks (which typically constitute the vast majority of the words in an account) you get an estimate only of the number of Impressions. And if a word recently was receiving Click and Cost estimates, but then fell below the minimum click traffic level, the Click and Cost estimates will be terminated, leaving you without a way to intelligently change the bid after that. In other words, Google’s Bid Simulator can be a helpful supplement to your primary means of calculating optimal bids, but you can’t rely on it to always return estimates when you need them.

In the video, Dr. Varian suggests performing tests of higher and lower bids on your own to see how Clicks and Cost change with the bid, but this too is problematic, since testing high bids is often expensive (with higher bids incurring greater total ad cost and lower bids forgoing potential conversions) and typically involves collecting data for more than 7 days (and those days shouldn’t be weekends or holidays or days when your tracking crashes etc., etc.)

The second point where careless bidders could harm themselves comes about by confusing your ‘maximum profitable CPA’ with your ‘target CPA’. Early in the video, when Dr. Varian introduces the example where the retailer sells a digital camera for $300 which has a wholesale cost of $200, he says, “Therefore a conversion for a user who buys a camera on your site generates $100 worth of revenue for you.” This $100 figure is labeled ‘max profitable CPA’. Just afterwards, at time 1:05, he says, “That $100 is your maximum profitable cost per acquisition, or ‘CPA’. You can pay up to $100 per conversion and still make a profit on the sale.”  Dr. Varian’s calculation is 100% correct – your maximum profitable CPA is your profit per conversion multiplied by your conversion rate (conversions per click).

However, please pause for a moment to ponder the difference in meaning between the phrases “maximum profitable CPA” and “CPA of maximum profitability”. The first refers to the CPA at which you just barely break even. To have even a slightly higher CPA means to lose money. The second phrase, ‘CPA of maximum profitability’ means the CPA at which you make the most profit per click, not just barely break even. This is your target CPA. Your target CPA (not your maximum profitable CPA) multiplied by the keyword’s conversion rate gives you your target cost-per-click (CPC), the amount you should attempt to pay per click.  Pay above this amount and you will spend more per click than you receive in additional profit. Pay less than this amount and you will refuse clicks whose value to you exceeds the amount you expect to receive from them.

A third area where viewers might encounter some confusion comes from the atypical way Dr. Varian describes the financial goals of the fictitious retailer in his example. Search engine marketers tend to classify their accounts as either ‘CPA-targeted’ or ‘ROI-targeted’ (return on investment) and CPA-targeted and ROI-targeted accounts, though generally very similar, are not identical. In Dr. Varian’s example, the retailer sells products online and apparently is able to track the number of items sold in each purchase, the price of the products, the cost of the goods sold (COGS) and, therefore, the profit per conversion, and so forth. Such accounts are typically classified as ‘ROI-targeted’, since their explicit objective is to maximize the total net margin (that is, profit after COGS, ad costs and all other costs have been accounted for) that they receive per day. In an abstract sense, the goal of all economic activity is to maximize the profit generated, but in cases where an account cannot track the number of items sold, value per item, etc. at a very granular level, the account manager is forced to resort to a proxy measure of profitability. With these accounts, called ‘CPA-targeted’ accounts, account managers often select a single type of event to call a conversion (as Dr. Varian lists, “the sale of a product, a new lead, a sign-up, or getting users to download or view some material on your site”) and then attempt to maximize the number of conversions received for each amount of spending.

It’s strange that Dr. Varian gives an example where the retailer has the ability to track conversions, revenue per conversion, COGS per conversion, profit and so forth, but then chooses to attempt to optimize to a target CPA, rather than to the target ROI that maximizes profit.

A fourth issue that Dr. Varian simply glosses over, most likely for the purpose of saving “tons of explanations”, is the fact that in his example he talks about a digital camera that brings in $100 in gross margin per sale, but that on Google AdWords, advertisers don’t bid on products, they bid on keywords. Dr. Varian admits this himself when he says, “In general, however, you don’t bid by CPA on Google; you bid by CPC, or ‘cost per click’.” So, the advertiser would not bid on each sale of that particular digital camera, but rather, each click on the ad associated with, say, the keyword “digital camera retailer” / exact match. A searcher might purchase the $300 camera after typing in that search query, giving the retailer a $100 gross margin (before ad cost). But the next person to search for that term might just purchase a $10 carrying case for a digital camera. Or, the searcher might be a professional photographer whose studio was recently robbed and spends $10,000 on a wide variety of equipment in one order.  Therefore, the advertiser has no way to know a priori when setting the bid whether the next click will bring in $10 in business or $10,000.  So, a profit-maximizing advertiser must estimate the expected Revenue per Conversion (RPC) and the expected Cost of Goods Sold per Conversion (COGSPC), two numbers that Dr. Varian discusses as if they are easy to calculate, but which in the real world are often known only to a certain level of accuracy.

A fifth factor with which the online advertiser must contend, but which Dr. Varian also assumes to be obvious, is the conversion rate for any given keyword is usually not known precisely.  At time 1:44, he says, “Your conversion rate is the number of conversions completed on your site, divided by the total number of ad clicks to your site.”   It is true that this quantity is your observed account-wide conversion rate.  However, advertisers bid at the keyword/matchtype level, not at the account level. Knowing the total number of conversions divided by the total number of clicks for your entire account is simply not good enough – you need this information for every individual keyword on which you intend to bid.  For very high traffic keywords (and only for very high traffic keywords) simply dividing the number of conversions by the number of clicks can give you a fairly decent estimate of the conversion rate.  But as any experienced online marketer can tell you, most keywords fall into ‘long tail’ traffic-levels, where there are simply not enough clicks and conversions per keyword to be able to pin a nice simple number like ‘5%’ to the conversion rate.

And, finally, a sixth issue which Dr. Varian glosses over is really an entire collection of related problems: keywords don’t exist in a vacuum.  He only talks about optimally picking one bid for one ad as if all the information necessary to set that bid is contained within the performance data of that keyword and that keyword only.  But optimally picking a set of bids for a set of ads requires considering account-level and campaign-level budget limits, cross-keyword attribution (that is, the fact that a non-converting click on an ad might prompt a later search on a different term which then does convert), conversion latency (which Dr. Varian addresses briefly at time 2:50 when he says that “you might want to bump this value [your assessed value per click] to reflect the fact the visitor might not convert on this particular visit but may return in the future to buy something”), cross-channel and cross-media influences, data errors, holidays, hour-of-day and day-of-week periodicity, seasonality and a gamut of other issues that would run the length of your arm.

The short point is, the topics Dr. Varian does not talk about in his new video are at least as important to optimal bidding as the ones he does, and if you take his advice at direct face value, you might get hurt.  Fortunately, The Search Agency (and AdMax, our online marketing platform) are here to maximize the return on all of your online marketing efforts, regardless of your level of expertise.

This post is continued in ‘Behind the Scenes of ‘Google AdWords Bidding Tutorial’, Part 2‘.

When managing PPC search auction bids on a weekly basis, it’s fairly common to look at each keyword’s performance data for a recent time period (a week, or longer) and compare it to the performance the previous time period.  The idea is to make a fair comparison of the keyword’s recent performance to the prior performance so that you can make good bid management decisions.

If you see that a keyword’s CPA is well above its target, you might have the tendency to reduce the bid.  Too low, you might increase it.  Near the target, you might leave that bid alone.  I call this ‘Goldilocks Bid Management’ and while there are many variations of that folk tale, remember that in the version by Roald Dahl (author of Charlie and the Chocolate Factory), Goldilocks gets eaten in the end.  (Even in the versions suitable for younger children, Goldilocks still winds up running away screaming, which is probably what you’ve been inclined to do after looking at endless spreadsheets for too long.)

The difficulty in getting optimal performance with this style of bid management is not with you, dear reader, but rather with the nature of that beast called CPA.  Perhaps I can prove it to you by example.  Rather than bore you with actual performance data, I can just as easily bore you with totally made-up performance data.  So, let’s consider the simplest possible case, a fictitious keyword that acts like a metronome.  Every day, our keyword gets 10 clicks for a $1 Cost per Click (CPC).  Exactly every 50 clicks (that is, every 5 days) like clockwork it gets 1 conversion.  $50 in spend every 5 days.  10 clicks per day.  $1 per click.  1 conversion for every $50.  Here’s a very simple question for you:  What is this keyword’s CPA?

Every bid manager knows that’s there’s no such thing as keyword that behaves this nicely.  Most are much nastier creatures.  But even so, I want to know this keyword’s CPA, so, like any good analyst, I’ll make a spreadsheet.  Let’s say that I do my bid management on Mondays.  This word got a conversion the previous Monday and 1 conversion every 5 days thereafter.  (So therefore, it also got a conversion 2 days ago, on Saturday, and will get one 5 days after that – that is, this coming Thursday.)  Here’s a sample of my data:

Today is Monday the 1st.  Last week (Monday to Sunday), this word spent $70 and got 2 conversions (one last Monday and one Saturday).  So, it has a 7-day CPA of $35.  On Tuesday the 2nd, the word spent $70 the previous 7 days (as it does every single 7-day period), but got only 1 conversion in the past 7 days (Saturday’s conversion).  So, the 7-day CPA jumps to $70.  It stays there until Friday, when there are again 2 conversions in the past 7 days and it drops back down to $35.

To answer my very simple question, “What is this keyword’s CPA?”, we all know is that it’s actually $50, but the CPA that you see depends on when you measure it, and for which duration.

The actual CPA is $50.  It must be, because there’s $50 in spend for every 1 conversion.  But look at the graph – our average CPA is never $50.  Some days we see $70 and others we see $35.  (For this particular example, a repeating pattern of three days of $70, followed by two of $35.)  I call this behavior ‘The CPA Sawtooth’, and those people who don’t know about the tendency of CPA data to act like this can get hurt.

Unfortunately, if you try to avoid this problem by looking only at daily data, you won’t find refuge.  On the days in which a conversion occurs, the CPA those days is $10.  But on days when there are no conversions, your spreadsheet won’t even display a number for the CPA that day.  (This isn’t a bug in Excel, it’s a bug in mathematics.  If I spent $10 today, but go no conversions, then my CPA wasn’t $10, it’s actually not a mathematically defined number.)  If you filter out all that days where conversions are zero from your spreadsheet and only look at days where a conversion occurred (to try to get a feel for that keyword’s CPA on any given day, it will look like the CPA is only $10 (even though it’s actually $50!).

If I’m the type of manager who compares keyword-level performance data week over week, then on the 1st I’ll see a 7-day CPA of $35.  A week later, on the 8th, it will be $70.  On the 15th, back down to $35.  On the 22nd, back up to $70.  A simple alternating pattern, right?  On the 29th, will it go back down to $35?  No!  CPA is more feral than that.  On Monday the 29th, the 7-day CPA stays at $70.  It only goes back down to $35 the following week.

But, keep in mind, the underlying CPA stayed constant at $50 the entire time.  By adjusting your bids based on the Goldilocks approach, you might wind up increasing your bids some weeks and decreasing them other weeks for absolutely no underlying change in performance.

Of course, no real keyword is this well-behaved.  If instead of having 1 conversion happen every 5 days we instead say that on any given day, there is a 20% chance a conversion will happen, then on average there will still be 1 conversion every 5 days (for an average CPA of $50).  But this time, a graph of the 7-day CPA won’t be so predictable.  (This isn’t the method I personally would use to generate realistic data, but it’s good enough for the purposes of this demonstration.)

Obviously, the results depend on the specific sequence of converting days and non-converting days.  If a conversion happens on day 1, then there still a 20% chance that one will happen on day 2, and a 20% chance after that that a conversion will happen on day 3, guaranteeing at least 3 conversions this week.  So, we could go several days in a row with conversions, rather than 1 conversion every 5 days.  (Conversely, since there’s an 80% chance of not converting on any given day, we could go many days without a conversion.  If we go at least 7 days without one, then the 7-day CPA that week will actually be an undefined number.)

But, in general, the results will look as crazy as the ones shown in the graph below.  Some days, the 7-day CPA could be well below the $50 average.  Some days, it might spike above.  In some periods, it will change quickly.  In others, it will stay steady for days at a time.  In other times, it might not be possible to calculate the CPA at all!

And this is for a word that still, on average, generates 1 conversion every 5 days.  Throw in some variation in click traffic, changes in the CPC and maybe a data error or two, and the CPA will be even more variable.

The important lesson here is that the CPA you see on your screen depends in part on how you choose the timeframe and when you do you your analysis.  If you take the numbers at face value, you’re liable to get bitten.