ClickEquations Blog

We’re celebrating the release of Text Ad Zoom with an in-depth look at how you can optimize your text ads. In case you missed it last week, check out The Ultimate List of PPC Ad Testing Resources. It’s a huge collection of articles, videos and presentations on testing and writing text ads.

This week, we’re doing a 5 part Q&A series with the authors who wrote the articles featured in the list. Each day this week, we’ll have a different question and the answers.

Opinions vary and sometimes the authors disagree. Proof again that no matter how much experience you have, the data will win the day. Not every author answered each question. Finally, the answers are unedited straight from the authors, so draw your own conclusions and remember to test any ideas you read.

Read below for today’s question and answers (in no particular order).

Want to see Text Ad Zoom and all of the other great ClickEquations features in action? Request a demo or email sales@clickequations.com.

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Text Ad Optimization Question #1: What are some of the biggest mistakes people make in text ad testing (aside from only measuring CTR changes)?

Brad Geddes: I don’t think enough people focus on Profit Per Impression. Just by choosing the lowest CPA or highest converting rate ad, does not mean you will bring in the most revenue for your account. Another mistake is not having enough data before making decisions. There are too many online calculators where you can input some very low numbers (like 15 impressions and 1 click for one ad and 10 clicks and 15 impressions for another one) and the tool will tell you that you have a winner. Although, the number one mistake is not doing it at all. Ad copy testing is so easy that everyone should always be running a few tests at any one time.

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Andrew Goodman: I often hear: “test only one variable at a time.” Statistically, this really makes no sense, and more than that, it’s impractical. From a statistical standpoint, if you go in and try to isolate which of two calls to action are “better,” for starters, you’re ignoring variable interactions (once anything else you want to test has to be changed, you’re now assuming the winner from the previous test would interact most favorably with the changed conditions) and you’re ignoring the opportunity costs of the other tests you could be running. People will interpret this “test little things one at a time” maxim so literally, they will take forever to optimize properly. What this approach fails to see is how blinkered it makes you. “Is ‘buy now’ or ‘buy today’ a better call to action?” Maybe they’re about the same, or maybe what you’ve just done is rule out a different style of ad that took more room talking about pricing or a third party endorsement, or some other trigger. There is absolutely nothing wrong with bolder testing of three or four very different style of ad, to see if any of these create a significantly better response. For some reason, that sounds unscientific to some people, but you don’t create marketing results by spending your time in the wrong chapters of the wrong statistics textbooks.

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Jessica Niver:

1. Assuming they know what types of messaging appeal to their audience and not testing very different approaches against each other.

2. Completely ignoring CTR changes- though ultimately for a revenue or lead-based client you want the highest-conversion-rate ads, high CTR ads with lower conversion rates are informative. High CTR with lower conversion rate=people liked something about your ad but didn’t see a follow-through on your landing page, so it’s an opportunity to modify your landing page to match expectations and turn your high-CTR low-conversion ads into high-CTR high-conversion ads.

3. Completely disconnecting ad text testing and landing page testing (see above). One is the promise the other is supposed to deliver on, so even though it makes testing more complicated you can’t treat them as separate entities.

4. Running too many ads against one another for your traffic numbers. This just slows down testing and drags out poorly-performing tests. Let’s just figure out what works and move on to the next test, not watch something suck for two months until we’re 100% sure.

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Chad Summerhill: Not considering the cost of testing – You are just as likely (if not more likely) to lose than to win a test, so you want to eliminate losers quickly. Focusing on conversion rate only – If possible you should focus on conversion-per-impression or profit-per-impression.  The goal should be to maximize total conversions/profit.

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Amy Hoffman: People seem to tend to  get a little pause-happy, meaning, they tend to try to pick a winner before the test is statistically significant.  There are a few free tools online for determining statistical validity, which should be used to aid in the decision making process.

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Erin Sellnow: The two biggest mistakes I often see are people testing too many things at once (so it is difficult to isolate what really worked) or they don’t let ads accumulate enough data, and pause too quickly. While it is tough to wait it out, patience is important so you know you are making the correct decision.

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Pete Hall: I’d say people too often think that their new ads will crush the current iterations, so being overconfident with your ads and not properly A/B testing can an issue if you’re not careful. I’ve had numerous instances where I thought I’d written the perfect ad, built on successful elements of past ads, implemented it, and it tanked. So making sure to properly test your new ads against existing ads, even if you think it’s perfect is critical.

One other mistake is not setting ad delivery to rotate in an A/B test. AdWords likes to favor ads and this will skew your results, so ensuring delivery is set to rotate is key.

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Ryan Healy: Here are three common mistakes I see:

1. Writing an ad that gets a lot of clicks, but is not consistent with the messaging on the landing page. (This disconnect can hurt conversions and profitability.)

2. Writing a winning ad, then letting it run for months (or years) without ever writing a new ad to challenge it.

3. Writing two or three ads for an Ad Group, then letting them run for months (or years) without ever deleting the losing ads.

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Jeff Sexton: Well, perhaps the biggest mistake is NOT optimizing ad text – or doing some testing and then adopting a “set it and forget it” mindset.

But, assuming that people are actively testing their ad text, the next biggest mistakes is not thinking past the keywords to get at the searcher intention BEHIND those keywords.  Behind every set of keywords are people who are searching on those keywords in response to a need, problem, or question.  Optimizing ad text means writing ads that better speak to those people on the other end of the screen.

So you should be looking at actual searcher queries associated with those keywords, past test results, competitive ads and landing pages, etc. in order to actively seek out an understanding of searcher mindset.  Once you have that hypothesis you’ll be able to write ads on a more coherent basis and also able to interpret test results on a more scientific basis.  In other words, the proving or disproving of a hypothesis will give you a direction on “what to try next” after each test, whether winning or losing.  This will also allow you to more intelligently apply other ad writing best practices.

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Tom Demers:

1. Looking at the wrong sample size and/or deciding based on ”bad data” – Even though there are a lot of tools to help you identify whether you’ve reached statistical significance, people often ignore them and either end tests too soon or run them too long. Another variation on this theme is looking at “bad data” to draw conclusions about a test – basically you want to carefully catalog changes within your account so that you’re not lumping in data where a text ad is married to a different landing page or set of keywords. Those things can have a huge impact on ad performance, and may lead you to pick the wrong winner.

2. Not testing enough – This is far and away the biggest mistake we see, particularly in larger campaigns. Across our network we see around a 30% lift in sales from continual optimizations made by our writers. This means for higher volume ad groups where you’re neglecting to test and iterate on ad copy, you’re leaving a lot on the table.

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Bradd Libby: ‘Only measuring CTR’ is a big one by itself. There’s at least one company, BoostCTR.com, named after doing this process wrong.

Here are some other mistakes:

1. Treating ad testing like it might be a quick cure for current performance problems. That is, waiting until some problematic performance is seen and then trying to use ad testing to improve results by the end of the month. Ad testing should be done continuously as a normal part of account management.

2. Not qualifying traffic prior to testing. It doesn’t do much good to test two ad creatives against each other on month, pick the winner, and then the next month add a bunch of negative keywords to the adgroup.

3. Misinterpreting the meaning of statistical significance. Confidence levels only state how likely results were to not have been obtained by chance.

4. Not repeating tests. Reproducibility is one of the hallmarks of good science. If ad ‘B’ wins in an A/B test, you should be able to repeat the test in 3 months and see ‘B’ beat ‘A’ again.

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Crosby Grant: And aside from not doing it at all!  Judgment errors in choosing the winner ad is a mistake that can be pretty costly and that happens often.  Using rigorous statistics is one part of the solution, but often requires more traffic, and thus time, than is reasonable or available.  A good example is with holiday ads.  You only get about a week each year to test each holiday ad version, which might not provide enough traffic.  To a lesser extent a similar dynamic happens with ads running year-round if you are imposing an artificial time horizon for your test cycles.  For example, if you want to complete a test every week, or every month.  Then of course there is the question of which metric(s) to optimize for.  Books could be written on that one.  My preference is for maximizing margin ((advertising revenue – advertising cost)/advertising revenue) because it takes all of the other metrics into account, and because at the end of the day, more money in your pocket is, well, more money in your pocket.  Of course, many advertisers don’t use rigorous statistics at all, and simply rely on judgment based on the metrics, whichever metric they choose.  I call that “business statistics.”  Statistics is not the whole solution though.  It is quite possible to have two identical ads with statistically significant variances in performance.  This is mostly due to the X-Factor of AdWords’ system assigning Quality Score based on limited data – which is another topic altogether.  So, another part of the solution is considering the content of the ads.  This is where human judgment comes in, and where experience really helps.  Choosing a test, and choosing a winner, then interpreting that to help you craft more ads that are also winners, is part of the art.  It works together with the science provided by the statistics.  Getting this part wrong is a potentially costly mistake that happens often, and that’s why it makes my list of one of the biggest mistakes people make in text ad testing.

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Rob Boyd: I feel the largest mistake is not creating ads with a purpose. When you get down to it, you can have all of your metrics and variables planned out perfectly but in the end it all comes down to the ad text. Is what your writing more effective at reaching your target audience then your existing ad? Is your ad focused on intent? As marketers, we don’t always write winners but I think the largest mistake is to throw darts blindfolded. If you aren’t truly getting into the mind of your audience you are stacking the deck against yourself. Plus, when you do write a winner, it’s all the more satisfying. In my opinion, the second largest mistake in ad testing is not keeping your account pace in mind. What I mean by that is, you have to test in relation to the spend or click level of the account. If each ad group is only generating a handful of clicks a day and you are testing 5 ads, it could take months over months to gather statistically relevant data. Testing in relation to your data gathering ability is important because it will allow you to make actionable decisions more frequently, which should result in more consistent incremental improvements over time.

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Greg Meyers: Many Advertisers tend to test too many elements all at once, so there is no clear understanding of what was the deciding factor in identifying a winner vs. loser. Another key mistake that happens is figuring what elements make up the test. Typically, the 1^st level test should be either a specific CTA (Call to Action) or to a different Audience. The idea of testing a single word would be a waste of time and would not “move the needle” Other common mistakes would be insufficient length of testing time which could lead to misinterpretation of results.

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Bonnie Schwartz:

A. Testing too many variables at once, which makes it difficult to pin down what actually led to the winning ad.

B. Testing too many ad copy variations at once, which makes getting enough data to make statistically significant data difficult

C. Going along with B, not basing decisions off of statistical significance

D. Not Testing at All!

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John Lee: Advertisers make a wide variety of mistakes when testing text ads. The biggest, and most obvious, is simply NOT testing at all. But more specifically, advertisers frequently test too many ads at once. This can slow down testing, complicate determining results, etc. Test a smaller number of ads, 2-3 is best, with concrete testing variables in each.

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Jon Rognerud: Firstly, testing with too little data. In other words, they make a decision to pause or delete an ad before understanding or knowing that it actually works. Secondly, just copying what others are doing – assuming that it will work for them.

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Joe Kerschbaum: Testing too many variations at once. Testing variations that are too similar; I’ve seen too many tests where the ads are basically the same except for perhaps a punctuation mark. Test big ideas and see what works.

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Learn More About The Authors

• Brad Geddes – Certified Knowledge
• Andrew Goodman – PageZero
• Jessica Niver – Hanapin Marketing
• Chad Summerhill – PPC Prospector
• Amy Hoffman – Hanapin Marketing
• Erin Sellnow – Hanapin Marketing
• Pete Hall – Room 214, a social media agency
• Ryan Healy – BoostCTR / RyanHealy.com
• Jeff Sexton – BoostCTR / JeffSextonWrites.com
• Tom Demers – BoostCTR / MeasuredSEM
• Bradd Libby – The Search Agents
• Crosby Grant – Stone Temple Consulting
• Rob Boyd – Hanapin Marketing
• Greg Meyers – SEMGeek / iGesso
• Bonnie Schwartz – SEER Interactive
• John Lee – Clix Marketing
• John Rognerud – JonRognerud.com
• Joe Kerschbaum – Clix Marketing

Steve Baker at epiphany put up a very interesting post this week, in which he analyzed some quality score data to try and answer three questions:

1. How high is a high click through rate?
2. What is a decent click through rate for a given position?
3. How do you know if your Quality Score is being dragged down by the Account Quality Score or your adverts?

These are things we’d all like to know, and his results are interesting, but I have some concerns about whether or not they really answer any of these questions in any way we can rely on. To be clear, I’m not sure – so I’m posting my thoughts here to hopefully further the discussion.

If you haven’t please go read his entire post.

There three thing that concern me about the methodology and the conclusions:

1. A mistake concerning the idea that ‘quality score is only calculated on Exact Match’.
2. The assumption that ‘visible quality score’ is quality score.
3. The treatment of the relationship between quality score and bids and position.

Quality Score and Match Type

As discussed at length last week, visible quality score only takes into account the performance of past impressions where search query was identical to keyword, regardless of match type. Using a data set comprised only of Exact Match keywords is certainly a study of its own, but may very well not be representative of how all keywords of all match types perform or behave. Since AdWords already disregards non-identical queries, given the other assumptions this analysis would be equally accurate with all match types included.

Analyzing Visible Quality Score

It’s very hard not to conflate quality score and visible quality score, as Google themselves use the one name ‘quality score’ to refer to both – but they’re very different and I think as search managers we need to begin to really understand that these two things are very different and using them interchangably will lead us to a lot of very inaccurate conclusions. I wrote about the differences in a guest post last week on PPC Hero.

The complexity is that if you’re only analyzing queries which are identical to keywords, as visible quality score does, when in fact all non-identical queries are earning potentially very distinct quality scores for those same keywords, then there is no way to know how valid any conclusions really are. In effect, it’s taking a non-random sampling of the available data (only the identical queries, which represents an unknown % of the data) and ignoring the rest. We might assume that the identical queries have higher CTRs and therefore represent the best quality scores of the bunch – but it is literally impossible to know.

Of course, visible quality score is all we’ve got. Therefore it’s entirely natural to analyze this data and try to understand it and learn from it and draw conclusions. I’m not arguing against it. But I am suggesting that the characteristics of that data have to be acknowledged and considered along with any conclusions.

Quality Score, Bid, and Position

We all know that bid x quality score = ad rank, which determines the position in which any ad appears. In this case, quality score is not visible quality score but a version I’ve taken to calling ‘quality score for ad rank’ that includes a number of factors ignored in visible quality score.

In his post, Steve supplies some very nice charts showing the relationship between position and quality score from his data set. He’s found keywords with visible quality scores of 10 that live in nearly every position from 1 down to 8, for example. Actually his post includes similar charts from many different quality scores.

Chart from Steve Baker @ epiphany

Steve’s draws several conclusions from this data:

• “It appears that Google expect the click through rate in any position to be about 65% of the next position up. So where position 1.0 has an average click through rate of 34%, position 2 has an average click through rate of 22.1%”
• “This appears to be Google’s estimate of what ‘should’ happen to your click through rate every time you drop a position – you lose just over 1/3 of your clicks.”
• “Using this, you can potentially ‘health check’ your account. If you have a click through rate of 4.5% in position 4, you should have a Quality Score of around 7 or so. If you are getting less than the predicted Quality Score across the bulk of your keywords (excluding brand, on Google only, on Exact Match), then it’s a sign that your account has other issues, possibly with the landing page, keyword relevance or the overall account quality.”

Ignoring for a moment the issues about match type and visible quality score, I just can’t quite see how these conclusions are valid. My concern is that the impact of bid on determining the position a keyword earns isn’t considered or reflected – it isn’t just quality score that is driving these positions.

I’ve only had a few moments over the past 24 hours to really think about this, but I’d love to hear from the many smart readers we’ve got what they think of this analysis.

It would be great to have ANY answers to the original questions, and Steve has done a great job of collecting data and presenting it to us with some interesting potential conclusions. I hope he doesn’t mind if we try to crowdsource some additional work on his data.

UPDATE: I realize re-reading this that I didn’t comment directly on the three questions Steve set out to answer. They’re great questions, and there is a lot we know about the answers outside of the data being discussed here. I’ll take these up in a future post.

Quality Score in High Resolution

New 225-pg paperback
by Craig Danuloff

Learn more and order your copy today.

Welcome to week two of my mea culpa tour. Last week I revealed an error from an earlier post on how quality score takes search queries into account. Today I’ll talk about some new facts regarding the most popular post I’ve ever written – The Economics of Quality Score.

An Economist Walks Into A Bar…

The original Economics of Quality Score post describes the impact of quality score on CPC. What was interesting about it, I think, is that it included two tables that purported to quantify the actual economic impact of quality score – how much CPC decreases when a keyword earns a 10 and how much extra you pay if a keyword only gets a 3, for example.

The original calculation was based on visual quality score (see the guest post I recently wrote about visual quality score over at PPC Hero). Doing the math while assuming that quality scores are really whole numbers between 1 and 10 produced the tables included in the original post.

Working with these numbers resulted in dramatic results and a powerful graphic that has been borrowed and republished in many blog posts and used in quality score seminars. The story the numbers told was that earning a quality score 10 gets you a 30% discount on every click, while suffering with a quality score for costs you a 75% CPC premium – to take just two examples.

This calculation made the risks and rewards of quality score very clear. Or so it seamed.

It didn’t take too long after the original post went up, to realize the mistake in these calculations. Quality score isn’t really a whole number between 1 and 10. So these results must be inaccurate. Oops.

A disclaimer was added to the original post.

The disclaimer explained the mistaken assumption, and concluded by saying that while the actual numbers in the chart were undoubtedly wrong, the point remains true – the positive and negative effects of qualty score did apply – and ‘hopefully the numbers are roughly proportional’.

Which brings us to the new information. They’re not proportional.

Quadratic, I Didn’t Even Factor

There are many differences between visible quality score and the quality score number used to calculate CPC. Visible quality score is a whole number between 1 and 10. Quality score for CPC is a real number and the scale is non-linear.

The premise of the calculations I did in the ‘economics’ post was that the distance between the numbers was known and constant, and if you divide any number by 7 and then divide that same number by 10, you will always get a 30% difference in your answer. This was intended to be revealing in terms of quality score.

But since the math that drives your CPC involves numbers that aren’t between 1 and 10, and don’t have a predictible relationship to each other – and are a secret held inside a big blue safe in Building 47 on the Google campus – it turns out we can’t reasonably calculate or estimate the actual impact of quality score in CPC.

We can’t calculate or estimate how much a 10 saves you vs a 7. We can’t calculate or estimate how much extra you pay for keywords with poor quality scores such as 3. Google hasn’t shared enough information for us to know.

Why Did The Quality Score Cross The Road?

There are at least three morals to this story.

The first is that we still don’t know how any increase or decrease in quality score economically impacts your account.

I suppose we could track individual keywords and try to find instances where quality score goes from X to Y while position remains constant and calculate the size of that change, and then after doing this a great many times build a new table based on observation. Of course, there are so many other variables in the system (different search queries, geographies, competitors, etc.) that it would take a huge amount of data to even have a chance at accuracy and in the end we’d never know.

The second is that I should better verify the veracity of the information I post. I’ll work on that.

The third is that Google is really good at hiding their secrets.

The ability to actually know the amount of money a change in quality score was worth seemed like such a big deal because it represented a rare bit of clarity in the sea of uncertainty. We orient well around something as clear and familiar as a 1-10 rating system, but when we stop and think about it:

• We don’t know the CTR’s that achieve any given ranking,
• We don’t know how many auctions we’re being ruled ineligible for because of our score,
• It’s extremely hard to know how queries or geography or ad performance impacts our score, and
• While we know ‘higher is better and lower is worse’ we have no way of knowing how much better or how much worse.

It’s like the perfect carnival game – it seems like getting the coin to land on the plate is easy and the variables are within our control…

So in the end, another mystery not solved.

I promise that the new book does get to the bottom of a few.

Quality Score in High Resolution

New 250-pg paperback
by Craig Danuloff

Pre-Publication
Learn more and order your copy today.

Searchers never see your keywords, match types or bids. They do see their own search query and your text ad. Your text ad is the first opportunity you have to attract a potential customers.

Not surprisingly, your ability to write effective text ads plays a dramatic role in determining how many people you can reach and whether they’re the right type of customers. Yet, even the most seasoned marketers would fail at guessing which of their ads will be successful. That’s why PPC ad testing is fundamentally linked to profitable campaigns.

We’re big fans of testing and data based decisions, which is why we released Text Ad Zoom. Instead of relying on instinct and guesswork,  Text Ad Zoom lets you pick the best performing ads based on statistically significant data.

To celebrate the release of Text Ad Zoom, I’ve scoured the web to create the Ultimate List of PPC Ad Testing Resources. It’s one stop for advice test design, measurement and a healthy dose of copywriting ideas, so you have something to test.

While I tried to be as exhaustive as possible, I’m sure that I’ve missed some great resources. Please add your favorite articles, videos and white papers/ebooks in the comments section and I’ll add them to the list.

Finally, if you’d like to see Text Ad Zoom in action, and all of the other great ClickEquations features, request a demo or email sales@clickequations.com

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PPC Ad Testing & Measurement

There is an art and science to PPC ad testing. These posts cover the ins and outs of campaign settings, test design, ideas for testing and how to analyze the results.

• Basics
  • Learn the Basics of Testing Ad Copy: A PPC Primer by John Lee
  • Want to Double Conversions in One Month? Split Those Ad Groups by David Szetela
  • Measuring a Text Ad’s Effectiveness by David Szetela
  • Ad Text Testing – a PPC Manager’s Constant Friend and Companion by John Lee
  • Paid Search Ad Testing: Manage To ROI Or CTR? by Andrew Goodman
  • PPC Testing Part 1: The Ground Rules by Josh Dreller
  • Never Odd Or Even: PPC Ad Text Testing Made Easy by Jessica Niver
  • Simple PPC Ad Tests Can Drive Up Your Click-Throughs and Conversions by Andrew Rudnick
  • How To Write and Effectively Test Your PPC Ad Texts by Greg Meyers
  • How to Execute a Flawless Ad Copy Test in AdWords by Ryan Woolley
  • An Easy-To Follow Method For Ad Optimization & Testing by Crosby Grant
• Advanced
  • Secret Truth Series #18: Effective Text Ad Testing by Craig Danuloff
  • The Pitfalls Of A/B Ad Split Testing, Part 1 by Matt Van Wagner
  • The Pitfalls Of A/B Ad Split Testing, Part 2 by Matt Van Wagner
  • The Pitfalls Of A/B Ad Split Testing, Part 3 by Matt Van Wagner
  • Step-by-Step Instructions For Testing Low Volume Ad Copy by Brad Geddes
  • Statistical Significance: Not Just for Geeks Anymore by Brad Libby
  • Visualizing your Ad Test Results to Boost Confidence by Chad Summerhill
  • Best Practices and Text Ad Testing by Craig Danuloff
• Testing Ideas
  • 5 Ideas for A/B Testing Your PPC Ad Text by Amber Speer
  • Ad Text Optimization -- What To Test (And When)? by Dave Greenbaum
  • Ad Text Display URL – What to Test by Dave Greenbaum
  • Google Changes Ad Display Rules & Creates a New Testing Opportunity by Brad Geddes
• AdWords Settings and Features
  • Step by Step Guide for Using Adwords’ Campaign Experiments (ACE) for Ad Copy Testing by Bonnie Schwarz
  • To Optimize or Not To Optimize, Google’s New Ad Setting by Amy Hoffman

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PPC Ad Copywriting

Effective PPC ad copywriting distills every direct marketing principle into a few lines and a far too few characters. These posts were a bit harder to organize. I pulled all of the articles specifically focused on copywriting for beginners into one group. Lists of tips and mistakes to avoid were a popular set of headlines, though they’re not particularly different from the overview & advanced group, though easier to scan. If you’re looking for PPC ad inspiration, there’s not shortage in this list.

• Basics
  • Paid Search Ad Copy: Five Things Newbies Don’t Know by Andrew Goodman
  • A Beginners Checklist for Writing Benefit-Focused Ad Texts by Pete Hall
  • PPC 101 Writing Successful Creatives by Frank Watson
• Tips & Mistakes to Avoid
  • The 4 Keys to Writing a Paid Search Masterpiece by Jeremy Hull
  • 9 Mistakes When Creating Ad Text by Amber Speer
  • 9 Tips to Write Effective Google AdWords Copy by Saad Kamal
  • 7 Ways to Be Sure You’re Writing the Best Ads Possible by Amber Speer
  • You Will Never Write the Perfect Ad Text, but Here Are 2 Ways To Keep Getting Closer by Joseph Kerschbaum
  • The 6 Next Steps After Your AdWords Content Optimization by Joseph Kerschbaum
  • Paid Search Ad Copywriting: 7 Heavenly Virtues by Andrew Goodman
  • 8 Essentials For Crafting Killer Paid Search Ad Copy by Alan Rimm-Kaufman
• Overview & Advanced
  • Secret Truth Series #17: Lament Of The Text Ad Copywriter by Craig Danuloff
  • Effective PPC Ad Copy, Part 1 by Ron Jones
  • Effective PPC Ad Copy, Part 2 by Ron Jones
  • Paid Search Ad Copy: Kicking It Up A Notch by Robert J. Murray
  • Two Advanced Tactics for PPC Copywriting by Josh Dreller
  • The Right Brain Of Paid Search Ads: Tips & Tricks For Creative Ad Copywriting by Josh Dreller
  • Tailor Your Ad Copy to Buy Cycle Stages by Mona Eleseseily
  • Killer PPC Ads: The Final Word by David Szetela
  • PPC Advertising: Art or Science? by David Szetela
  • The Secret Formula for Writing Mesmerizing Headlines by Sherice Jacob
  • How to Train PPC Copywriters: Five Pay-Per Click Copywriting Best Practices by Dave Greenbaum
  • Creating Killer Ads Using Bing Dynamic Text by Jen Barrett
  • Why and How You Should Rethink Ad Writing and Bidding Strategies by Bryon Watson
  • Writing Ads: Bust out of a Rut by Perry Marshall
  • How Can I Make it Better? Part 5: Improving Ad Text by Rob Boyd
  • Kick Your Quality Scores Back Into Gear with a Few Ad Text Changes by Amber Speer
  • Using Ad Text to Find the Right Clicks by Andrew Rudnick
  • Stop Spending Thousands of Dollars on Unqualified Traffic by Writing Smarter Ad Texts by Amber Speer
  • Write PPC Ads that Scream and Stand Out From the Pack by Joseph Kerschbaum
  • How to Write Ad Texts that Promise and Landing Pages that Deliver by John Lee
  • Catch the BEST Leads, Not the MOST Leads by Using Qualifiers in Your Ad Text by Amber Speer
  • The Benefits of Using Benefit-Driven Ad Texts: What’s in Your Tackle Box? by Joseph Kerschbaum
  • Catch More Clicks and Conversions with Better Ad Text: Use the Right Lures! by Joseph Kerschbaum
  • Ad Text Evolution: 5 Changes to Your AdWords Ads & How They Affect Your Bottom Line by David Greenbaum
  • Ad Text Tips and Tricks for the Content Network by Erin Sellnow
  • What Your Text Ads Say About You by Erin Sellnow

Videos, Webinars & Slide Presentations

If your brain is numb from reading about PPC ad testing and copywriting, check out these videos and presentations instead.

• Write Killer PPC Text Ads
  
• PPC Text Ad Statistical Analysis
  
• PPC Ad Text Best Practices from PPC Hero
  
• How You Can Write Killer Ad Copy


• The Van Wagner Ad Sets Optimization Model


• Test That Ad (Panel presentation at SMX)


• Writing & Testing Ad Copy -- Connecting With Your Customers From The Very Start
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• Require Registration
  • Paid Search Testing & Experimentation: Optimizing for Superior Results
  • Writing Killer Text Ad Copy: 1001 Secrets for Success
  • Beyond the Text: Getting the Most from Your PPC Ads
  • Breakthrough in PPC Ad Testing and Optimization: Using Ad Sets to Improve Even Your Best PPC Ad Campaign
  • PPC Testing & Optimization 2010: Best Practices

It was exactly two years ago today that I posted the first bit of what I thought was going to be a quick series of posts to dispense with this issue of quality score once and for all.

The idea seemed reasonable enough (they all do when they pop into my head): write a chapter (post) a day for two weeks or so and nail a clear definition and tactical plan for all things quality score.

Two years later and I’m pleased to say I’ve nearly got it. Six chapters are back from the copy editor, four more are in her hands, and the last couple will get there soon.

What took so long?

There were a number of factors:

• It’s a big topic. Quality score is not one simple thing nor does it have one simple impact. It’s complex and pervasive in AdWords. Way moreso than it first appears.
  
• Google hasn’t fully explained it. They’ve said a lot about it, but they had left a lot of big obvious questions unanswered.
  
• It’s subtle and complicated – Therefore it’s a bit difficult to explain clearly. This was probably the biggest time sink, trying to boil down the material to be comprehensive and not confusing or just plain dull.
  
• This isn’t my full time job. Life at ClickEquations and outside of it takes up a little time.
  
• My attention wanders. Bob Dylan has played 188 shows since that first post was written, for example.

Why It Matters

Of course, none of that matters. What matters is that every time someone does a search where one of your keywords might match and one of your ads might be shown, quality score determines if it’s shown, where it’s positioned, and how much you pay. Your success in paid search is literally defined by how effectively you earn good or great qualty scores.

It’s something we all should understand.

The role of quality score in paid search is unique: it is both grading your past (quality score is a measure of the success of any keyword) and at the same time influencing your future (quality score is a prediction of future success which it then plays a role in making come true).

Anything that accurately tells you how well you’ve done and then determines how well you’re going to do should be paid a lot of attention. I argue in the book that quality score should drive which keywords are in your account and which ones get deleted. It should drive the organizational structure of your ad groups. It should drive the copy in your text ads. And it certainly has a lot to do with how you’ll have to bid.

It doesn’t make sense to spending tens or hundreds of thousands of dollars per month advertising via paid search and not deeply understand such a core aspect of the paid search process.

Imagine playing a game every day – for money – and not knowing all the rules?

Yet this is how most people have effectively been forced to play. The basics of quality score are well known, and that’s a big improvement over 2 or 3 years ago when the subject was almost universally ignored. But as soon as you get past the basics, past the generalities, into the land of ‘what exactly should I do with this keyword’, you get to a place where even the very best in the business (and I’m lucky enough to talk to a great many of them with some frequency) just haven’t been sure what to do. The facts, at that level of detail, haven’t been available.

Or they’ve been shattered and scattered into different pieces in different places, with pockets of incorrect info mixed liberally throughout just to make it fun.

Is this any way to conduct business or spend millions of dollars? It really isn’t.

I have some fun analogies in the book about what other businesses would be like used similar terms & conditions and communication strategies. Nobody would accept it and nobody would do business with them. But who can stop advertising on Google just because they’re not getting all of their questions answered?

Google’s Role

At which point we have to stop and praise the truly amazing copywriters at Google. There are dozens of extremely well written posts in the AdWords help system, and blog posts and responses in the help forum, and still they managed to not explain to us precisely how we’re being rated or what we should do to score higher and do better. The broad strokes are extremely clear, but the details are entirely lacking.

To be fair, their job is to provide an overview to millions of advertisers, the vast majority of whom need exactly the level of detail they get. And I genuinely believe their writing is incredibly accurate, clear and concise. But it doesn’t go as far as serious advertisers spending real money need or I think deserve.

Some of the key people at AdWords agreed that ‘advanced users’ desired and required a different kind of information. And they were willing to share – in very large part – the information that was necessary to produce this new resource. They were really open and really helpful.

By all appearances they are happy to have a more complete picture of quality score out there, but the opportunity or the format hadn’t presented itself, or maybe nobody had every found the right way to ask them about it before.

Get Yours

In any case, I believe and hope this book will be useful to all of us who who have been craving more depth and details about how quality score is calculated, how it impacts the account, and how to manage it more effectively.

ClickEquations clients will be getting a free copy.

Learn more and order your copy today.

If the wind holds up, books will be out in time for SMX Advanced in Seattle. Maybe we’ll have a little QS-Geek party.

I was wrong. A couple of times.

The subject was ‘how quality score works’. And in both cases I wrote long detailed posts on this very blog, and I have come to learn that these particular posts were not accurate.

My revised world view was provided courtesy of our friends at Google. They have been kind enough to help me to better understand quality score – the gory details and the dark recesses – over the past six months or so, and I’m going to share some of what I’ve learned.

Actually, I’m going to share all of what I’ve learned, but only some of it will be here on the blog. For the full story, you’ll have to get yourself a copy of my upcoming book ‘Quality Score in High Resolution‘ which will be out in June.

If you’re a ClickEquations client, you’ll be getting a courtesy copy.

Otherwise you can pre-order your own copy for a limited time at a 46% discount off the not-so-tiny retail price.

Now on to my most recent mistake.
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The Actual Truth About Quality Score and Search Queries

A few months ago I wrote a post called ‘Match Types & Quality Score – The Truth At Last‘. It turns out the title probably should have been ‘Match Types & Quality Score – More Confusion and Inaccuracy’. I thought I had it right, but my source was reading between the lines in the detailed study of the official word and various conversations over the past few years.

Now this is embarrasing for many reasons. Chief among them is the fact that I’m not a fan of all the how freely incorrect information and poor advice flows through blogs and tweets and even from the conference podium in this market. I generally work hard to know my song well before I start singing, as some old man once said when he was younger than he’s now. But I blew it on this one (and at least one other which I’ll admit next Monday.)

I am glad that I get to correct the error. I wrote this book to clear up the many mistaken assumptions and recommendations I regularly see passed off as quality score information and ‘tips’. The fact that the research process exposed some of my own errors is a fair price to pay to set the overall record straight.

When I sat down with Google to ask for their help in research and tech-editing the book, I told them that I really didn’t want to do all this work and get it wrong. But I knew there were many specific points that I couldn’t be sure of, because the published material wasn’t detailed enough. I was very pleased and excited when they agreed to help. Over time they answered every question I asked, and only rarely with a ‘no comment’.  This includes responding to the ’11 Hard Questions About Qualty Score‘ I posted a few months ago.

The Details of My Mistake

The crux of the mistake I made concerning the role of search queries was taking the fact that google says ‘quality score is calculated based on keyword performance only when a keyword perfectly matches a search query’ too literally.

One of the things I learned while writing the book and trying to follow all the threads presented in the AdWords help files and official information, is that whenever someone (including Google) says ‘quality score’ you’d better quickly ask ‘which one’ (the book lists eight of them).

In this case I fell down the very easiest hole – the statement above refers to what I call ‘visible quality score’ the number we all see next to our keywords in the AdWords interface. Visible quality score differs from the versions of quality score used to calculate important things like ad rank and CPC in a number of ways.

The statement above is entirely true in terms of visible quality score – the numbers you see are only impacted by queries that equal the keywords – but that does not mean, as I claimed in the earlier blog post, that the quality score from queries identical to a keywords is used to make decisions or calculations about queries that are not identical to the keyword.

To be fair and complete (and slightly mysterious) there is a second cause of my error. This one is based on what I think is an intentional misdirection Google uses when talking broadly about quality score. Google is expert at shaping our perceptions and expectations, and one of the ways they do this is by creating impressions that aren’t literaly true but serve some other purpose – sometimes even for our own good.

Suffice it to say that when that blog post was written I still held some nieve (although almost universally held) beliefs and I am no longer so afflicted.

The third element of my mistake, this is more of a proof or an error-of-oversight, is the fact that the core description of the calculation of qualty score includes ‘the relevance of the keyword and the matched ad to the search query’. So Google had in fact already definitively confirmed that search query was considered. I knew that but overlooked it’s implication when writing that post.

How Search Queries Influence Quality Score

When quality score is being calculated, after a query has been made and before the advertisers and pricing has been decided, AdWords looks at a wide range of factors to assign your keyword a quality score. One of those factors is relationship between the current search query and the current keyword. That relationship can dramaticaly impact the resulting quality score, which means that different search queries matched to one keyword may see significantly different rankings and significantly different CPCs even if they achieve the same ranking.

Search queries are a part of what determines quality score, just not the quality score you see in your account every day.

Doubling Down and Getting It Half Right

I compounded my error by going on to say that the solution to the problem of search queries not impacting quality score, was to create new keywords in order to give each query what amounted to ‘access’ to its own quality score.

The point I was making may have been wrong, but the idea still has merit. By adding a new keyword from what was once just a search query, you do gain the ability to see the quality score for that query – because now it will be identical to the keyword.

Suppose you bid on the broad match keyword ‘dog food’ and it was frequently getting matched to the search queries ‘organic dog food’ and ‘cheap dog food’ among many others. Now further suppose that when AdWords looked at the ‘relationship between these queries and the keyword’ what they saw was, relative to the query ‘dog food’ itself, very positive for the query ‘organic dog food’ and fairly negative for the query ‘cheap dog food’.

In that case, the quality score visible in the account would reflect the performance of the ‘dog food’ search queries. But the queries ‘organic dog food’ and ‘cheap dog food’ would get real-time qualty score calculations, and the resulting impression counts, positions, and costs, based on their own merits. But you would never be able to see those differences.

If on the other hand, you added ‘organic dog food’ and ‘cheap dog food’ as their own keywords (probably in phrase or exact match, but that really doesn’t matter) then the visible quality scores that would appear for these keywords would (ultimately perhaps not immediately) reflect the full detail of their performance and value as AdWords saw it.

By splitting them out you’d be able to make their ‘invisible’ quality scores visible.

A Lot of Shadows In A Short Hallway

I hope this post clarifies the facts about search queries and quality score. I regret and apologize for the original mistake.

This episode highlights a lot about the complexity of quality score – both in terms of how it works and how we as paid search managers get information about it. The complexity of both of thse is one of the main reasons I took up the task of figuring this stuff out and writing this book. This post has turned out long enough, so I’ll say more about that in the near future.

In the meantime, if you’d like to support this project, please consider taking advantage of the pre-sale pricing and offers.

In the first blog post of this year, I talked about some of the challenges paid search managers face in terms of managing tests in their accounts.

The issues, as that post discussed at length, include:

• There are a lot of interacting variables – it’s a complex system
• Critical data is hidden from us – we can’t see what’s happened.
• Our tools don’t facilitate testing – which is just crazy.

And the fact that we face two statistics problems.

• The math we need isn’t trivial
• The data we usually have is sparse and dirty

These are issues we think that good paid search software should address, and in the April 2011 release of ClickEquations we’ve begun with the introduction of our Text Ad Zoom feature.

Text Ad Zoom

Text Ad Zoom makes it easy to see an accurate analysis of the statistical confidence you can have in the CTR or conversion rate metrics reported for your ads.

For years paid search managers have had to ‘eyeball’ or ‘estimate’ the statistical signficance of these numbers. Some have relied on ‘rules of thumb’ like ‘wait for at least 100 impressions’ or ‘wait for at least 10 clicks’ but most of the time we just take the numbers at their face value and if one ad has what seems like a dramatically lower CTR or Conversion rate than it’s peers, we pause it or rewrite it.

With Text Ad Zoom the guesswork has been removed.

By simply double-clicking on any text ad in ClickEquations Manager you’ll be presented with the current metrics for all ads in the ad group – using the data from the currently selected date range. The best performing ad is automatically highlighted at the top of the Text Ad Zoom dialog box, with all of the current alternative ads in the ad group listed below.

Click To See Full Size

Next to the CTR and Conversion Rate metrics is a simple rating for whether or not the reported metrics are significant. The ratings are Yes, No, and Maybe.

• When the significance rating is YES, the results are statistically significant.  A ‘Yes’ rating means >95% confidence. In this case you can trust the numbers and take action on them. If the ads keep running the future results are likely to be consistent with the past results..
• When the significance rating is NO, the results are not statistically significant. A ‘No’ rating means confidence is <90%. In this case you should not make decisions on this ad based on the current data – there has not yet been enough data collected to trust that they reflect an accurate representation of what will happen in the future..
• When the significance rating is MAYBE, the confidence is between 90% and 95%, and you can probably trust the numbers and take action on them (it is likely that the current trends will continue) but if you want to be more certain then you should let the ads run to gather additional data.

More To Come

A built-in statistical significance test is just one of the ways we can make it easier to test ad copy and better manage your campaigns. Watch for more to come in future releases of ClickEquations.

BTW, Text Ad Zoom is just one of the new features we added to the April 2011 Release of ClickEquations. We’ll look at other cool new features in upcoming posts.

This is a repost of my monthly column at Search Engine Watch.

When you’re dealing with a niche and high value product, competition for active buyers in paid search can be fierce. And expensive. Consider this example from enterprise security:

It’s basic economics: the smaller the universe and higher the value of click, the higher the CPC. If you’re in B2B or marketing another complex purchase, you likely need to be in front of this audience.

But, it’s not the only way to reach them. And you can pay 90 percent less.

Target Prospects When They’re Ready to Learn

Buying isn’t a linear process, but prospects generally go through some various phases of consideration before they purchase. The classic marketing AIDA model is a good way to think of the phases of search activity. It stands for Awareness, Interest, Desire, and Action and looks a little like this:

Somebody searching for “networks security software,” for example, is likely in the Action phase. Their search goals are focused around the execution of their purchase vs. research about the problem.

That same person would like search “how to improve network security” or “network security advice” well before they’re ready to buy. When prospects are in the awareness stage, they’re looking for education, not a sales pitch.

They’re also much cheaper to target.

I know this from personal experience. I manage the paid search campaign for ClickEquations, a SaaS platform to help people measure and manage their paid search advertising more effectively. As you can imagine, reaching Action searchers is pricey.

However, that same audience often searches for education about how to manage paid search efficiently. You can offer them a white paper. Educational clicks are 90 percent less expensive:

Ready to Learn

Ready to Buy

In addition to reducing your cost, you get the benefit of reaching customers before the competition and the ability to establish yourself as a thought leader.

Successfully targeting learners vs. buyers requires a different approach. Here are three tips for reach your prospects when they’re ready to learn.

Tip 1: Get Their Contact Information Without Asking For It

It’s common knowledge that adding form fields decreases conversion rate and increases your cost-per-conversion in most cases. But, how much do you really give up?

Jon Miller, co-founder and head of marketing at Marketo (disclosure: I’m a customer), wrote a really interesting article where he quantified the cost of adding more fields to his landing pages:

Conversion rate decreased 30 percent and cost went up $10 per lead! Not surprisingly, the most expensive field to add was phone number.

Asking for a phone number is often a sticking point for sales and marketing. Sales wants and needs the number to reach out to prospects, but, for early stage, educational content in particular, prospects are reluctant to disclose it and you miss a chance to establish a relationship.

As Miller points out in the article, however, there are other ways to get the data.

One solution to consider is Jigsaw Enterprise. It’s a Salesforce company that’s directly integrated into the platform. What distinguishes it from others is their data source: it’s crowdsourced as people enter contact information they have to credits for the info they want.

It’s also automatic. You can set up the system to auto-append information that isn’t associated with the record (which I recommend rather than over-writing something a prospect or sales rep has entered):

The data, which is appended every 12 hours, isn’t perfect or complete and the system isn’t priced for small businesses. But it does offer an automated way to increase conversions without losing all of the contact information your sales team needs.

Tip 2: Integrate Offline Conversion Tracking

Finding the keywords and setting up a landing page are relatively straightforward processes when you’re targeting learners. As you would expect, these people need to be nurtured and marketed to over time before they move into the Desire and Action phases.

That exacerbates an underlying issue with complex sales: long sales cycles and offline conversions. Not every educational prospect will be equally valuable. That is say: not every lead is qualified.

Ultimately, we want to adjust our marketing mix and paid search buys based on what closes, not just who flirts with us.

From a PPC perspective, however, that presents a challenge as we have to tie together three systems:

• Search engine data
• Our PPC tool
• Business outcomes from our customer relationship management (CRM) database

The trick to connecting the three systems together is the use of an external ID for each keyword. That unique key allows you connect front end data (clicks, cost, CPC) with online conversions (form submissions) and the later stage activity (opportunities, sales, retention).

A key can be any alphanumeric value, for example extid=18MQGH1MGKKQAG3T0.

You need four things to use this approach:

1. System To Generate External IDs: This can be as simple as a spreadsheet or a more sophisticated platform that creates them automatically. Each key has to be specific to the keyword.
2. Hidden Form Field to Capture External IDs: The IDs will be appended to your destination URL as a parameter. A hidden form field on your landing page will grab that ID from your URL. If you’ve ever setup a Salesforce form and added in a campaign or lead source variable, it’s the same approach.
3. Method to Export External IDs with Values from a CRM: You need to be able to create a report or list that associates each external ID with a latent conversion and value from your system (e.g., “1 Opportunity” and “$500″).
4. Tool to Connect Paid Search Data with External Conversions: Once you have an ID for a keyword and a value for that ID, you need technology to stitch together the two so you can measure, report, optimize, and bid on business outcomes.

Note that outcomes data, by its very nature, will be sparser than micro-conversions. You can optimize on earlier actions and do a biweekly or monthly review with the later stage data.

Tip 3: Retarget Prospects on the Display Network

It used to be that search marketers only got one, maybe two, chances to convert someone on a landing page before we lost them.

That changed with the introduction of retargeting, or remarketing as Google calls it. Briefly: remarketing allows you to show display ads only to an audience of people who have been to your site and exhibited some desirable behavior.

A classic business-to-consumer example is shopping cart recovery, or the targeting of buyers who added items to their shopping cart but who did not check out.

There is a parallel in B2B marketing. Prospects who visit your landing page, but don’t fill out your form are the most likely to convert from a display campaign and worth chasing with ads for a few days at least.

Setting up a remarketing campaign is relatively straightforward.

First, create what Google calls “Audience.” Set up a separate campaign for retargeting. Go to Campaigns > Audiences > Add Audience. If you don’t see the Audience tab, select the drop down arrow to add it.

In the “Create and manage lists” section, you’ll need to create three lists:

1. People who visited your landing page.
2. Those who visiting your thank you page (i.e., converted).
3. A custom combination of list 1 and not list 2 (i.e., those who visited, but didn’t convert)

For each list (1 and 2), you’ll have a tag to put on your landing page (list 1) and your thank you page (list 2). This allows Google to cookie your audiences appropriately.

There is a lot of strategy in how to design and prioritize remarketing lists. I recommend you read Brad Geddes excellent article on the topic for more detail.

To be read for a remarketing campaign you’ll need:

• Banner ads to run on the display network. You’ll want text ads too, as some sites don’t accept banner ads.
• Separate campaigns for better budgeting and bidding.
• Proper tracking and attribution. Last click will only make it look like your retargeting campaign deserves all of the credit.

And We’re Only Just Getting Started…

Complex sales require marketing that supports how buyers purchase at every stage. Paid search is an integral part of that mix. If you’re locked out of active buyers because of CPCs or simply looking for a way to expand your PPC buy, targeting prospects when they need education is a great strategy.

To learn more about B2B paid search, I recommend you read The Buyersphere Project by Mediative (formerly Enquiro) and Ryan DeShazer’s recent column, “The Reinvention of B2B Paid Search.

I was recently on a panel at Search Marketing Expo (SMX) where we talked about the idea of man vs. machine. Matt Van Wagner baited me the question: Who is better at PPC, people or technology?

It what might have been a surprising answer for a guy marketing ppc technology, I voted for people.

At the end of the day, you’ve got to have talented people driving the ship, regardless of what technology you use. Strategy and creativity are fundamentally human talents.

But, as anyone who has worked in search knows, creating a great PPC team is easier said than done.

That’s why I’m excited that ClickEquations is sponsoring the upcoming American Marketing Association webinar: How to Build a World Class PPC Team.

Here’s what you’ll learn:

A great paid search team can make the difference between okay results and rapid business growth. But how are today’s CMOs and VPs of Marketing are supposed to build highly effective and accountable teams when they face intense challenges? We know that:

• Paid search talent is hard to find
• Paid search is changing at an incredible pace
• It’s hard to know if a campaign is truly optimized. Could the ROI be higher? Could volume increase?

In this webinar, you’ll hear two case studies of companies who have created world class paid search teams and, most importantly, improved profit:

• Ian Lurie – President, Portent Interactive
• Drew Fortin – Senior Manager, Internet Marketing, Compete

You’ll learn why it’s critical to:

• Hire a complementary mix of creatives, technologists and analysts to meet the demands
• Build cross-functional teams with complementary skills
• Create an infrastructure of tools and technologies to leverage the team

The webinar is tomorrow (Tuesday), 3/29 at 1 EST / 10 PST. Register right here!