ClickEquations Blog

Paid Search Managers spend a lot of time analyzing clicks.

Which keywords got them? Did they convert? How much did they cost?

But how much time is spent thinking about the clicks you didn’t get? How much information do you have about those clicks anyway?

Earlier in this series I’ve discussed the idea that paid search marketers have a tough time getting a full and clear picture of what’s really going on in their accounts with the information currently provided by the engines, analytics programs, and PPC tools.

The last few posts discussed the lack of search query details as one example. Gaining insight into missing clicks is another.

Two Ways To Lose

There are two types of clicks you didn’t get. The first are those reflected in your click-through-rate; clicks that didn’t happen when your ad was shown. The count of these can be easily seen by comparing your impression count with the click count for any keyword.

The second type of missed click are those where the query was relevant (or interesting) to you but your ad wasn’t displayed. As Steve Forbert once said: (although I don’t think he was the first) you cannot win if you do not play.

Tracking Missed Impressions

Google has provided a series of Impression Share Metrics for over a year now, which provide important insights into the click missed because ad weren’t even displayed.

• Impression Share (IS): The percentage of times your ads were shown out of the total available impressions in the market you were targeting. This metric is available at the campaign and account level for search.
• Impression Share Exact Match. Impression Share Exact Match reports the impression share of your campaigns as if your keywords were set to ExactMatch.
• Lost IS. Your impression share + Lost IS (Budget) + Lost IS (Rank) = 100%.
• Lost IS (Rank): The percentage of impressions lost due to low Ad Rank (cost-per-click bid x Quality Score).
• Lost IS (Budget): The percentage of impressions lost due to budget constraints.

These are informative and critical reports. You should always know the IS numbers for your campaigns. There are times you can accept a low Impression Share, and times when you cannot.

It’s too bad it takes a trip into the reporting environment (or setting up an email report) to get them rather than having them ‘in line’ with other reporting metrics.

More importantly, this data is only available at the Campaign level, and we could really use it at the Ad-Group level. When you have a large campaign with many Ad-Groups is very possible that some have great Impression Share and a few have lousy Impression Share (or that the reasons why the number is what it is differ between Ad-Groups) and the Campaign-level roll up is of limited use.

In a future post we’ll dig deeper into the meaning and applications of these numbers.

Tracking Missed Clicks

There is less information, ironically, delivered about the clicks you miss when your ads do appear.

There are many reasons people don’t click (see this post for a good list). Many could not be translated into paid search metrics without qualitative research. But there more information that could be shared about these lost clicks.

For example, average click-through rates and various positions are known, both in absolute and relative terms. Given your position of your ads, how many more or less clicks occurred than should have been expected at that position?

And exactly how many clicks would each higher position garner, or lower position lose? This could be predicted with some degree of accuracy.

Since text-ads have their own click-through-rates, which have a massive effect on the CTR’s of keywords, another option is to look at which text-ads were displayed and calculate the number of clicks a keyword would have received if the best of them (CTR-wise) had run all the time.

So with a little work doing some calculations around the position and text-ad running for a keyword, we could start to know what our potential keyword CTR could be, if we just improved our position performance and text-ad copy.

Not Perfectly Clear

Paid search is the pursuit of clicks. The right clicks at the right price.

A clear picture of a paid search campaign would therefore tell us a lot about the clicks we got, and the clicks we didn’t get. Google’s Impression Share is a great start – it delivers actionable information and with the sub-metrics starts to break the main one apart so we can see how different factors are contributing to the remaining click opportunity.

Impression share needs to go mainstream – into the normal dynamic Adwords reports and the API.

And a comparable level of visibility should be given to the clicks we get and don’t get once our ad has been displayed.

• How much better could our CTR have been?
• How many clicks were missed because we under-performed our position?
• How many more were available at higher positions?
• How many were missed because text-ads were under-performing? (Within the text-ad itself, was it the headline or target URL that dragged us down?).
• Was there a specific competitor who took more share from us than another, over time?

These are just some of the things we should be able to know about our clicks.

Yesterday I noted that paid search managers face three challenges in trying to effectively manage paid search campaigns:

• A lack of clarity (reporting problems)
• Difficulty defining priorities (strategic and planning problems)
• Horrible inefficiencies (mechanical and processes problems)

I believe that these problems need to be solved in order to improve paid search management, both the profession and the results.

First you need to see what’s happening, then you’ll want to decide what needs to be done, and then you can hopefully get it done with a reasonable amount of effort.

That doesn’t sound like too much to ask.

But 4-5-6 years into explosive growth in paid search and we’re hardly out of the starting gate. Today I’ll expand on the issues regarding reporting and clarity, and in future posts dive more deeply into the problems of setting priorities and executing paid search tasks.

What Paid Search Reports Don’t Tell You

Paid search is about answering questions. People type queries and search engines return results, which are lists of possible answers to the questions they believe are being posed. I want to structure my campaigns as tightly as possible around those search queries.

Every search engine tells you how many impressions your ads had, and how many clicks you got. They have to I suppose, since the CPC is what drives your billing. What I really want to know is what did I miss? And why? Then I can set goals and define strategies or tactics (or at least design tests) to do better.

Each conversion hopefully generates more revenue than it cost to cause that conversion, which is reflected in the rather innane ROAS metric. Being impressed with a good ROAS seems akin to believing you’ve saved money by buying something you didn’t want when it was on sale. Goods or services have costs (COGS) and the only metric that matters is ROI taking account (at least) both direct-marketing and goods/services expenses.

When my clicks do generate revenues, I’d like to know which ones. Then I can make wise decisions about future investment and effort around certain keywords and queries.

Unreasonable Demands?

So I’d like to know which search queries generated which results, how many clicks I didn’t get and why, the actual amount of profit made on each transaction (and from each keyword, query, and click).

Do any of these sound unreasonable? Far-fetched? Demanding?

Yet these desires are not generally or specifically fulfilled through the paid search reporting capabilities provided by the search engines, popular web analytics software, or even specialized PPC management tools.

Surprised? The devil is certainly in the details, and some of the information defined is available in some packages/places, but generally with huge compromises and limitations that disqualifies or invalidates them as actual or sufficient information.

Really? Yes to the best of my knowledge, as the next post will review in somewhat excruciating detail. I’m happy to learn new facts or discuss this further in the comments – significant corrections will be appended to that post.

User search queries, accurate revenue & expense allocation and matching, and ROI reporting are just three of the ways that the current generation of PPC reporting generally fail paid search advertisers and managers.

The fact that these problems/limitations are seemingly not well known, frequently discussed, and therefore clammored for as improvements is one of the things that has to change to move the business/market forward.

Managing paid search campaigns is hard.

But why?

I’ve come to the conclusion that there are three primary reasons why it’s so hard to manage paid search campaigns efficiently and effectively:

1. There is a lack of clarity. It is amazingly difficult to get accurate and complete data on campaign performance and results. Much of the data you need to see is scattered across three to five different tools and interfaces. Other data is presented in formats or based on calculations that just aren’t right. (they’re wrong.) Still other information is seemingly unavailable. There is no quick and accurate way to get reports which are satisfying.
2. It’s tough to assign priority to possible actions. This is directly related to the clarity problem in many ways, but given the size of today’s campaigns, and the information provided by both the engines and even with leading analytics and paid search tools, it’s hard to know what is the most important next step to take. There are so many choices and the functional and mathematical basis for clearly making these decisions are just not available.
3. Actual paid search management is horribly inefficient. A huge number of the things one needs to do to manage campaigns are dreadfully difficult to accomplish. Many involve potential campaign reorganizations. Some depend on keyword expansion or match type filtering. Others require bid modifications or target landing page testing. Almost all are about 100x harder to accomplish at the scale they need to be done than I wish they were or anyone has time to complete.

These three issues – clarity, priority, and efficiency – are holding back the paid search industry. Perhaps not in terms of pure industry spend – because fear is still driving a lot of rather uninformed dollars into the game – but certainly success and returns from the advertisers point of view are suffering greatly.

While I don’t think they’ve been identified or considered in quite this way, the overall feeling of being ‘out of control’ or ‘without control’ pervades the comments I’ve received in recent discussions with both practitioners and executives responsible for paid search.

When was the last time a search marketer told you how in-control of their campaigns they felt? How sure they were that both expenses and revenues were where they should be?

Does anyone feel this way?

Over the next few posts I’ll dive deeper into each of these problems, attempting to clarify the issues and try to start identifying solutions.

There’s always a lot of talk about the ‘future of search’ but it usually focuses on the searchers or the search engines. I’d like to try and think about it relative to the future of search managers and search management tools.

NOTE: This is part of a post series. It’s available as a single post for easier reading: The Match Type Series.
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In several earlier posts in this series I’ve discussed the how’s and why’s of buying the same or similar terms at the same time with different Match Type settings.

I outlined in one post the details of creating a Match Type Keyword Trap to filter certain search queries into specific match types. Buying multiple terms and multiple levels – when done correctly – has the ability to give you control over which queries are caught at which price.

Exact-Phrase-Broad

One reason this works is because the engines (generally) execute the match types sequentially.

In other words, if you are bidding on the same keyword, or two keywords that would both match for one particular query, an Exact Match should take precedence over a Phrase Match which should take precedence over a Broad Match.

So even though a particular query is technical a match for both one Broad Match keyword and another Phrase Match keyword, the Phrase Match should always ‘win’ and catch that query.

I should hasten to point out, this will not always be true. If you carefully watch query reports for your keywords you will see queries that were exact matches against a keyword you had set to Exact Match, yet the query lands in a Broad Match group. But in our experience these are rare in the sub 1% range of all queries.

Emphasis the Match Type Setting with Higher Bids

You can and should add punch to this precedence by ALWAYS placing rather substantially higher bids on your Exact Match vs Phrase Match, and Phrase Match vs Broad Match when they’re stacked in targeting the same terms.

And make the differences between the bids significant – it generally won’t help to bid $0.05 more for Exact Match than Broad Match. When bidding it’s easy to look at your Max CPCs (since that’s the option used to set the bid) but since your actual and average CPC is usually just a fraction of the Max you really can’t base your decision on those. Look instead at average CPC’s being reported and then set the Max’s at large enough intervals to create real steps between the different keyword/match type combinations.

By placing a substantially higher bid on the match type differentiated keywords, you’re providing another algorithmic reason for the engine to match exact match queries to your Exact Match keywords. Of course, it should also be true that you want generally higher position and higher impression share for the keywords you’re bidding on Exact Match.

A Simple Match

At the start of this series I mentioned that Match Type was a powerful and often under-utilized option. I hope these five posts so far have covered some of the ways you can get more out of these options. Time for a break from Match Type, however. Watch for a new series starting soon.

How many keywords should you place in one ad-group?

It’s an age-old question for paid search marketers.Traditionally ad-groups have been considered organizing baskets for keywords. All the variations on a particular keyword, and/or all the keywords driving to a particular product or product category, are often placed into a single ad-group.

It’s common to see campaign and ad-group structures which mirror product categories and sub-categories, for example.

When the question of ‘how many’ comes up, the answer is often given as a number. I’ve never understood this. Why does the number of keywords matter?

Even the Yahoo Smart Start Guide (PDF) suggests “While there isn’t a magic number of keywords to include, you may want to start with no more than 20 – paired with two or more ads – and adjust from there.”

Consider The Text-Ads

Here’s another way to think about it. What you’re really organizing with ad-groups is text-ads, not keywords.

Each text-ad in an ad-group should be a different attempt to answer the same questions, or attract people with the same interests. It doesn’t matter how many keywords you place in an ad-group as long as the queries each of them is likely to be matched with are all appropriately served by those ads.

You’re playing Carnac, writing answers to questions that are going to come along later. The keywords you put in that ad-group are your only chance to ensure that your answers are going to be relevant to their questions.

If there are 20 keywords then there are 20. If there are 100 then there are 100.

When To Split Ad-Groups

Leaving the numbers aside, there are two smart reasons to subdivide the keywords in an ad-group. Both are based on the idea of matching your text-ads more closely to the search queries and keywords.

One is to separate keywords by subject terminology – a focus primarily on nouns – so that the specific keywords are repeated in your text-ads and on your landing pages. This is done primarily in service to the gods (or slave drivers) of quality-score.

The other is to separate keywords by qualifiers – verbs, adjectives, or other modifiers. This is done primarily to better align your text-ads with the expressed or implied intent of the users. In most cases it also brings along the quality score benefit too.

Would you want to present the same text-ad to someone looking to ‘buy a house’ as someone trying to ‘sell a house’? How about someone wanting ‘bell bottom jeans’ vs one looking for ‘stone washed jeans’. ‘discount headphones’ vs ’3-driver stereo headphones’?

The more narrowly you can segment your user queries, which you control via keywords and match types, the better your click through and conversion rates will be.

Divide and Conquer

The topic of organizing campaigns is one I hope to cover extensively in the coming weeks. This post was inspired by one over at PPC Hero talking about the benefits of breaking down ad-groups.

NOTE: This is part of a post series. It’s available as a single post for easier reading: The Match Type Series.
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The first and second posts of this series introduced and expanded on the concept of the Match Type Keyword Trap. In the first, it said:

Over time, by watching the queries that each keyword attracts we can tune this system quite precisely, not only filtering unwanted queries with new negative keywords, but expanding our total volume through quality score and bidding improvements and tailoring the ROI of different query classes.

And promised to expand and elaborate. So here it is:

Tuning Options

When you buy the same keyword at different match types, or different keywords and phrases at different match types in a coordinated effort to properly target and value queries, your initial settings will be less than perfect.

You don’t know what queries you’ll see, the price you’ll pay for each, or how they’ll perform in terms of conversions.

But you can monitor and measure each of these over time, and make adjustments to create a more effective trap.

There are three controls you’ll primarily use to tune the trap:

• Negative Keywords - Without question your campaigns will see (and you’ll pay for) queries that are either undesirable or prove to be poor performers. You should continually review query reports and add keywords as negatives either to all appropriate ad-groups, or to those which have bids above the keyword value.
• Add or Move Keywords - As you review the queries caught by each keyword and ad-group, and the performance of both queries and keywords, there will be interesting or well performing keywords which should be moved up the match type & bid hierarchy.

  If a query is performing exceptionally well against the Phrase Match option, for example, you might want to create an Exact Match copy of that keyword and give it a higher bid. This should cause that query to be grabbed by your new Exact Match and yet let other matches to that Phrase Match keyword keep matching there.

  Well performing keywords in the Broad Match group (which is usually bid particularly low) are especially good candidates to be ‘promoted’ into the higher-bids & more targeted environments of the Phrase Match or Exact Match ad-groups.

• Raise or Lower Bids – Based on your goals (revenue or CPA or ROI or whatever) and reflecting the measured performance of the purchased keywords, find the right shape of the pyramid by bidding good Exact Match performance up and cutting Broad Match bids as you negative out losers and promote winners.

I should have pointed out somewhere earlier, that by far the best way to configure the MTKT is to separately each keyword group with different Match Types into separate Ad-Groups. This makes reporting and measurement easier, and allows you to control negatives at the right level.

As a naming convention , we end each Ad-Group name with a (E) if it holds Exact Match keyword, (P) if it holds Phrase Match keywords, and (B) for Broad Match. This makes is much easier when visually inspecting reports or making account changes.

Measuring Progress

Success with your MTKT is achieved when you’re attracting only desirable queries and have maximized ROI by setting bids according to conversion profitability.

Reviewing the queries on an ad-group by ad-group basis is the cornerstone of the process.  The Exact Match keywords should be clear and profitable. The Phrase Matches should be on target or quickly either promoted to Exact or made into negatives. And the Broad Match should also winnow down in many cases (but not always) through promotion or negative creation.

In some cases the Broad Match ad-groups are ultimately turned off, or left running with extra low bids just to capture any potentially new and interesting queries.

Results are harder to summarize, although as pointed out in the previous post, what you normally shouldn’t see is great variation between the ROI (or ROAS if you must still use that horrid metric) for the different Match Type divided ad-groups.

There are exceptions, but they should be positive ones where Exact Match, or more rarely Phrase Match groups are extremely profitable while others are just normally so. But very low or negative returns are a sign that either the queries being attracted just don’t have potential, or else something later in the chain is wrong – ad-text, landing page, offer, checkout process etc.

Next Up

In the next post, unless something else comes up we’ll finally cover that Rock Scissors Paper game I promised to disclose.

NOTE: This is part of a post series. It’s available as a single post for easier reading: The Match Type Series.
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The previous post introduced the idea of building a Match Type Keyword Trap. This layering of keyword & match type combinations provides control over which, where, and how queries are attracted, and therefore their cost-per-click.

In the simplest case, you’d buy one keyword (say ‘Whaazooh’) three times in one campaign – once on Exact Match, once on Phrase Match, and once on Broad Match.

The goal is to catch all queries which are literally ‘Whaazooh’ with the Exact Match keyword, all queries which are ‘Whaazooh’ plus some word(s) before or after it with the Phrase Match, and all other related queries with the Broad Match.

Why?

Because in almost every case where many different queries exist for a single word or topic, some of those queries are very valuable, some are mildly valuable, and many are not valuable (or at least not valuable enough). We want to segregate these queries by their value to us so we can pay highly for the high value ones and less so for those less valuable.

In the simple cases (I have to keep saying that because not all cases are simple, there are many complex variants of this) we’ll do better by trapping the best ones with the most specific Match Types (Exact if possible or Phrase if not) and using Broad Match to harvest winners and losers which are acted upon accordingly.

Winners are promoted (to Phrase Match or Exact Match). Losers are demoted via lower bids or even made into negative keywords.

We do better not because of the place they’re trapped, but because by segregating them we control the bid (as well as the text-ad, landing page, etc.)

Forcing The Stack

Buying the same keyword three times at different match types does not itself bait the trap. If the same word is purchased at both Exact and Broad, and has the same bid and earns the same quality score, chances are good a related query with be matched sometimes to one and other times to the other.

To force the trap to work you have to stack the bids – higher for the Exact Match versions and sequentially lower for the Phrase and Broad Match versions. This gives the Exact Match keyword multiple reasons to attract and win the Exact Match queries; it is a better match and it is bid higher (which is good in itself and factors into quality score).

When you do this, leave enough room between the various bids. The Average CPC the engines report are averages, so expect a range of bids in each and leave enough room so the ranges don’t overlap.

In this example, we bid higher for several terms that have proven great performers, setting them on Exact Match and bidding $1.25. Several others that are good performers and perhaps come in some variations are set at Phrase Match for $0.65. A larger collection of phrases and concepts are bid Broad Match at $0.15. Over time we shift, add, put in more negatives, and generally take control over how we pay for and catch queries.

Equalizing Return

How do you know if it’s working?

In theory you’ll normalize the ROI (or ROAS if you must) for your Exact, Phrase, and Broad Match keywords. In other words, you’ll raise bids for your Exact Match keywords to maximize profits. You’ll set accordingly lower bids on Phrase and Broad Match keywords until they produce the same return as the Exact Match does – so their lower conversion rates and ROI are compensated for with proportionally lower bids.

They get the bid they deserve.

To Be Continued

Again there are many exceptions and details left out of the above descriptions for the sake of time and length, but I’ll move into examples in future posts which should illuminate the concept. In the meantime, if you have any questions about this leave a comment and I’ll elaborate.

NOTE: This is part of a post series. It’s available as a single post for easier reading: The Match Type Series.
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Match Type is the PPC option which has perhaps the highest impact, is the least understood, and is most often under-utilized.

In this and the next few posts, I’ll take a long look at the Match Type option and how and why you should use it to improve your paid search campaigns and results.

Match Game

The Match Type option is the primary connector between your keywords and the search queries users actually enter into the search engines. Each keyword has a Match Type associate with it, which defines how the keyword is connected to queries. On Google we have these options:

• The EXACT Match Type turns the keyword into a rifle. It will only cause your ads to be displayed when the query is identical to the keyword. (At least in theory, we’ll cover some real world exceptions later).
• The PHRASE Match Type turns your keyword into a shotgun. It will hit anything surrounding the keyword as long as the query contains your purchased keyword(s) with anything before or after them.
• The BROAD Match Type turns your keyword into a bomb. It will explode in all directions and send debris and shrapnel farther and wider than you had ever imagined. In other words, the keyword can match pretty much any query the search engine decides is even tangentially related. (There will definitely be more on this later).

In addition to these Google now offers ‘Automatic Matching’ as we’ve written about previously.

The theory of these basic Match Type definitions are easily understandable – but in practice deciding the right Match Type isn’t always easy.

The problem is that each Match Type is a filter of sorts, letting certain queries through and stopping (or reducing the probability of) your ad from showing for other queries.

But these are rather coarse filters, and when considered against the massive diversity of search queries that users type when looking for something, plus the impact of other factors such as bids, quality score, and competitors, any Match Type choice becomes a pretty large compromise.

A Brand New Example

Let’s consider what might appear to be the simplest of all Match Type situations; your company name. Suppose that you’re running the paid search campaign for the well-known excess-capacity auctioneer Whaazooh.com.

What Match Type should you place on the brand name keyword ‘Whaazooh’?

• If you buy ‘Whaazooh’ on Exact Match, your ad is eligible to run only when the search query is ‘Whaazooh’ (or ‘whaazooh’) but miss every other direct variation (‘whaazooh.com’, ‘whaazooh inc’, ‘Whaazooh acutioneers’ as well as the mis-spellings ‘waazoo’. Of course, you also don’t get any of the contextual but not literal search queries either – you’ll miss ‘liquidation auctioneer in Palookaville WI’ and thousands of other searches who were intentionally or conceptually asking a question that your ad could have answered.
• If you buy ‘Whaazooh’ on Phrase Match, your ad is eligible to run for ‘Whaazooh’ or ‘whaazooh’ and direct variations that include ‘whaazooh’ such as ‘whaazooh.com’, ‘whaazooh inc’, ‘Whaazooh acutioneers’, ‘shop at whaazooh’ or even ‘whaazooh sucks and you should never do business with them’. You’ll still not be running (at least due to this keyword) for any conceptually related searches.
• If you buy ‘Whaazooh’ on Broad Match - the default and most popular match type, everything is potentially covered. You’re ad is eligible to run for ‘whazzooh inc.’ and ‘whaazooh reviews’ and even ‘excess diamond tip drill bit dealers’. You’ve officially cast a wide net.

Each step from Exact to Phrase to Broad opens you up to a larger quantity of (generally) less specific search queries. Some of these incremental queries are relevant and will prove profitable, but many will be  irrelevant, or at least low converting.

Buying ‘Whaazooh’ on Phrase match means you could easily pay for the click of someone who searched ‘Boycott Whaazooh’. And on Broad Match you almost certainly will pay for the clicks of people who searched for things which are 100% unrelated to your company, products, and industry.

So deciding the right Match Type requires balancing the benefits of progressively more diverse query matches against the risks of progressively more diverse query matches.

But for most keywords there is no perfect balance. You’re left to try and find the most acceptable compromise between volume and profitability.

Building Filters With Keywords and Match Types

The problem is actually somewhat easier to solve if we think about it in terms of a group of keywords all working to attract a set of related queries.

This is also more akin to your real world ad-groups, where there are many related words and phrases and each, depending on the Match Type could attract queries related to the same subject or using the same terms. Often even the same keyword will be purchased multiple times within one campaign, setting the Match Type differently in each instance.

In this way you can build a layered keyword trap, using the Match Type option (along with our Bid and several other controls) to specifically capture certain queries at the Exact Match level, others at the Phrase Match Level, and still more at the Broad Match level.

Considering the ‘whaazooh’ brand for example, we can buy the keyword and some related phrases separately at Exact, Phrase and Broad Match Types, and (assuming proper bidding and quality scores) we’ll catch specifically targeted queries at each layer while letting others fall through to be caught (or not) by the levels below.

Over time, by watching the queries that each keyword attracts we can tune this system quite precisely, not only filtering unwanted queries with new negative keywords, but expanding our total volume through quality score and bidding improvements and tailoring the ROI of different query classes.

In a later post we’ll take a detailed look at this tuning process.

Taking Control

The great benefit of this model is that it lets us take pretty significant control over the keyword to query matching process back from the search engines.

Rather than just buying Broad Match keywords and letting the engine decide which queries are important, or buying just Phrase or Exact Match keywords and missing out on a lot of volume, we set the stage to have the best of all worlds.

And with proper Ad-Group and Campaign configurations and good tracking software we’ll have amazing visibility into our progress, so we can understand things clearly and tune rapidly.

To Be Continued

This has been a long post, but covers only a fraction of the issues and side-bars and related topics and option settings necessary for really effect use of the Match Type options. In the next post we’ll look at this Match Type Filter Set more closely, and review the associated bidding strategy that makes it work and drive profitability.

I’ll also discuss in more detail why this works due to a secret game of Rock Scissors Paper going on deep in the data centers of Google.

Questions or Comments about Match Type? Please leave them in the comments!