Buying the same keyword more than once, with different match type settings, is an idea we like, as explained in our Match Type Keyword Trap series.

But this practice begs the question – should the same keyword appear more than once in the same ad group, or should you split them into different ad groups?
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Separate But Equal
In terms of the effectiveness of the keywords at their match types it doesn’t matter. Google will match them appropriately no matter where you put them.

But I favor splitting them into separate ad groups for five reasons.

1. It’s easier to match search queries to text ads. This is the name of the game, and each keyword will attract different queries based on the different match types. So can you write better ads knowing that some of these queries will be exact, some will use the phrase, and some will be all over the broad-match-place? Probably.
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2. Reporting is easier to digest (pt 1). If you’re a search query freak like me, and have a great tool like ClickEquations that shows you nearly every search query, it’s easier to scan the queries in an ad group to see if they’re all appropriate and uniform in content and nearly so in performance if they’re segregated by match type.
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3. Reporting is easier to digest (pt 2). The roll-up data and averages of any ad group are only as worthwhile as the consistency of the performance of the keywords that make it up. Diverse keyword groups produce statisics-of-questionable-value (SOQV as it’s known in the trade). Broad match keywords perform very differently than exact match keywords and I don’t find it useful to see the average CTRs or CPCs or CPAs of them rolled-up together.
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4. Quality Score should be better. By the letter of the law on QS, we want high-as-possible CTRs and tight query-keyword-adgroup-landing page relevance. Both should be slightly better with segregated ad groups – although as with all quality score details, there is no way to prove this!
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5. Reporting is easier to produce. Google does not provide a macro to automatically tell you the match type of a keyword as part of the destination URL. This is one of the few areas where Yahoo and MSN have something Adwords does not (intentionally on the part of Google we can be sure). Therefore if you want to track, measure, report on the performance differeces of your various match types, it’s a lot easier if they’re in separate ad groups. There are other solutions, but this one is the simplest and most robust.

This is not a big deal. For many people, or even in certain situations within a campaign, repeating the keyword in a single ad group makes sense. But if and when possible, I split them out.

Note: This post was inspired by comments made on a recent PPC Rockstars with David @Szetela Podcast. These shows have become a regular part of my commute, and I recommend them highly! (Even the occasional ones when I’m an guest.)

If someone who has never been to your website before does a paid search, clicks an ad caused by a keyword you bought, then makes a purchase, the attribution is easy. They keyword that you paid for to get the click, should get credit for the revenue generated by the purchase.

But very often, this is not the scenario that leads to conversions which take place on your site.

• People come multiple times before purchasing.
• They often come from different sources each time the come, occasionally repeating sources along the way.
• They sometimes make a series of purchases, either after all of their visits or interlaced among their visits.

These are just a small sampling of the issues and don’t begin to define or describe the complexities.

And this is not the post where I’ll try to do either. (Those will come.)

But was we work to sort out the right way to handle revenue attribution within ClickEquations, we’re capturing some data that I thought it would be interesting to share.

The following images document real-life ‘click-chains’ – sequences of visits to a website with resulting or intersperced conversions. They are a tiny tiny fraction of the sequences found in one account in a 30 day period.

• Rows with green ‘P’ cells are visits that came from paid search keyword clicks.
• Rows with white ‘N’ cells are visits from organic search, email, affiliate links or other sources.
• Rows with yellow ‘C’ cells are conversion events.
• The number in the first column represents the visit number for that person over all time.
• Only visits within a 30 day window are included although the visit count may have begun far earlier.

And if you’re into this kind of thing, they’re very interesting.

Click-Chain Histories

#1 – Our first contestant is a frequent visitor (note we’re starting with visit 37), loves those paid search ads, but does buy at least occasionally.

Four more after the jump (as they say)

#2 builds up a head of steam with a bunch of Dec 30th visits and a purchase, then wham-wham, two more buys with no more paid-click-charges over the next few days.

#3 is another guy an online marketer could love. One paid search, three purchases in a row.

#4 is a jack-rabbit, clicking six paid searches over two days, but coming through with the purchase in the end.

#5 is another consistent visitor and shopper, with purchases before and after their two paid clicks.

For each of these, assuming for a moment that we’re only concerned with paid search, how would you allocate the (yellow) revenue to the (green) keywords?

Now make it harder – assume that for each (white) ‘N’ we can tell you if it was a bookmark or email or affiliate link. How would you allocate revenue across all the visits?

Hint: There are no right answers. Much more later.

This is true largely because the broadly used ‘last click’ allocation model (where the last keyword gets 100% of the revenue credit) is really inaccurate and inadequate.

There are several other attribution models, including first click, linear, weighted, and other hybrids. The pros/cons of each are worthy of extensive discussion. That’s not the topic here however. We have a related question and would like to get some feedback.

Should the allocation of credit ever extend beyond a single conversion?

Two examples below further frame the question. In both cases, assume there is a user-defined tracking period which applies. In other words if you’ve set a ’30-day tracking period’ in any case the look back for events would only extend back 30 days.

CASE 1

Search on KW1 –> Search on WK2 –> Conversion 1 –> Search on KW3 –> Conversion 2

Question: Should KW1 and KW2 be part of the allocation chain for Conversion 2? If so KW1 and KW2 could get partial revenue credit for both Conversion 1 and Conversion 2.

Or does Conversion 2 only allocate back to KW3?

CASE 2

Search on KW1 –> Search on KW2 –> Conversion 1 –> Conversion 2

Question: Should KW1 and KW2 be part of the allocation chain for Conversion 2?

Or should Conversion 2 be seen as an independent event (perhaps inspired by a follow on email or other interaction after Conversion 1.)

FYI, Google Adwords does allocate revenue to keywords from multiple conversions if no search happens between them.

Your Opinion?

We’d love to hear your opinion on this. Please take the survey below, and leave any other thoughts in the comments.

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Everyone has, and PPCProz is building a list of the zaniest, so we can all laugh our way to the poor house.

There will always be broad match in every campaign. But if huge portions of your traffic/revenue are coming through broad match you (or your agency) are not working hard enough.

And if you have a lot of broad match it’s certain that you’re:

• Paying too much per click
• Missing a lot of impressions
• Getting useless clicks
• Wasting money

I’ve written extensively about Match Types in the past, and proposed the ‘Match Type Keyword Trap‘ as a guiding principle of how and when to use them and most importantly transition your search queries into phrase and exact keywords, with appropriate bidding.

Broad match is an important tool – it saves time and energy, and provides a place to start.

But mostly – although not entirely – it is a training wheels set you should get beyond for the lion’s share of your PPC spend and revenue.

Many people covet or chase higher positions, and there are several possible reasons:

1. The assumption that ads in higher positions get more clicks simply because they’re in higher positions.
2. As we all know ‘higher is always better’ – especially when it costs more.
3. And of course, eye tracking studies prove, um, er, that people look higher more often.

(The empirical and anecdotal evidence I’ve seen suggests that the power of higher positions is much less than most people seem to imagine. In a future post I’ll go into this in great detail. This is not the real subject of this post.)

As a result, there is a lot of attention paid to the Average Position metric. And a LOT of money is spent on upward bid changes made because of the number this metric reports.

So how good is this number? Probably not very good.

Let’s take one tiny little case study to demonstrate.

The keyword is ‘cat treatment’. And on Saturday Jan 3rd it produced about 25 clicks in one of our accounts. The average position for the term (in broad match) was listed as 4.55. This is the average of all the positions in which it appeared during the 1543 impressions it enjoyed that day.

Now be honest, despite all you know about averages (including the fact that it could have appeared in position #1 760 times, and in position 8 783 times) when you see that 4.55 was the average it makes you think it spent the day bouncing between position 4 and position 5. Right?

But did it?

Let’s look at Google Analytics’ handy Keyword Position report for this keyword on that day. This shows the position the keyword was in when it earned its 25 clicks.

Yowza!  This keyword covered more ground than Paris Hilton in NYC on Saturday night. (I always wanted to see how much Google traffic a single Paris Hilton reference caused.)

It was in all three top positions, and everywhere on the right side from position 1 to position 6.

Keep in mind that this is a map of clicks, not impressions. So maybe the impressions did cluster closely around the 4.55 average and the few stray impressions way up to top 1 and down to position 6 just all got clicks. Or maybe the actual impression distribution was extremely broad and the 4.55 average, while it is true, is really not useful to us in terms of analyzing keyword performance or making bidding decisions.

At this point only two things are really clear;

1. We really need better information. If the search engines won’t provide the actual impression and click position distributions, and/or make the the position-at-time-of-click a macro that can be delivered in the target URL, they should at least provide the standard deviation for the average position so we have some idea of what it really means.
2. We should resist the urge to put much faith, or make too serious of decisions, based on the reported Average Position of any keyword.

One of our clients bought a keyword, got a click, and made a sale. So far so good.

Actually they made three sales, to the same person who clicked on that one keyword.

• One conversion was during the visit following the click.
• One was made an hour later when they returned and bought some more.
• And then again 7 days later and bought even more.

Here’s the surprising part: Google Adwords took revenue credit for all three sales as one conversion and applied the total as revenue on the day of the click.

The second conversion could have come from click on a follow-on offer in the first purchase confirmation email. The third one could have come from a click on a banner ad the person saw 6 days later. Should that first keyword get the revenue credit for all three sales?

There is no easy answer.

But this does end the year where I think we’ll spend a lot of time next year – improving both the understanding and practice of revenue allocation.

All paid search campaigns are an effort to gain some type of return; we spend in hopes that we get back more, either in terms of gross revenue or net profit. If we can’t measure how much we get back, and/or if we can’t easily and accurately associate that revenue with the keywords or other sources of traffic to the site, we can’t measure our return correctly. And if we can’t measure return correctly we can’t come to conclusions about our current efforts or make decisions about what to do next.

It’s a giant problem and the stakes in PPC have risen to the point that we can’t ignore it anymore. I know this blog will devote a lot of attention to it in 2009 and I believe it will become a common theme in the industry.

But I think Ad-Rank deserves a little attention first.

Ad-Rank doesn’t get very much attention – certainly a lot less than bidding, and even a lot less than Quality Score. But Ad-Rank determines your position, and to some degree whether your ads display at all.

Ad-Rank Defined
According to Google, “Ads are positioned on search and content pages based on their Ad Rank. The ad with the highest Ad Rank appears in the first position, and so on down the page.”

Ad-Rank = CPC bid (Max CPC) × Quality Score

So we bid to gain Ad-Rank.

And we care about Quality Score because (among other things) it helps us achieve Ad-Rank.

Ad-Rank and Quality Score
Quality Score is important because it is weighted equally with your bid in determining where/if your ads run. The two factors are intertwined and the result is interdependent.

As the chart at right shows, you can get the same Ad-Rank with a lot lower bid by improving your quality score.

And as Quality Score gets more important, bidding gets less important. Not unimportant, but less important. It’s a zero-sum game.

Ad-Rank and Bidding
Yet while Quality Score is getting more visibility and mind-share than ever before, I’m not sure its ascent is being considered when thinking and acting on bidding.

Creating bidding strategies and running bidding rules or using bidding algorithms that don’t take QS into account at all, seems strange and seriously sub-optimal.

Traditionally bids are decided in an effort to impact position, and often the assumption is made that when an increased bid resulted in a higher ROAS or ROI, it was the change to the bid that was the direct cause – but I don’t know of a rule or algorithm in any PPC software today that either a) checks the actual position impact or b) checks to see if a Quality Score change was a mitigating factor.

It should of course now be noted that both bid and quality score increases have other impacts beyond their influence on Ad-Rank; Google has said that minimum bid and quality score thresholds are set for achieving Top (as opposed to Right Column) positioning, for example. So there are cases where it’s wise to increase your bid regardless of Quality Score issues.

Ad-Rank and You
The core tenant of High Resolution PPC is that we’ve all been lulled into an over-simplified view of how paid search works, both to accelerate our adoption, simplify our understanding, and keep us from complaining about really unfair or opaque aspects of the system that is taking our money.

Ad-Rank is an open secret. It’s well documented, easy to understand, extremely important, and almost never discussed. Time to change that.

This is generally defined as a ‘branding’ requirement, although it may be more accurately described as a form of vanity bidding.

In preparing for next Tuesday’s Quality Score seminar with Bryan Eisenberg, I’ve started thinking about the impact of Quality Score on Bid-to-Position.

It’s easy to think about Bid-to-Position is based on the out-dated thinking of PPC as a pure auction. The strategy itself implies a willingness to pay ‘whatever it takes’ to attain a certain position in the rankings.

(Or at least pay an amount more than economically justifiable – many Bid-to-Position rules do allow you to set a MaxCPC over which the desire for a certain position will yield to some economic reality.)

But as the role and impact of Quality Score increases the ability to bid your way into a position gets harder and harder, and in many cases ultimately impossible.

Position is not driven solely by bid anymore. And in many cases bid won’t even be the largest influencing factor.

We can see this by looking at Google’s new ‘First Page Minimum Bid’ which, according to Google is “based on the exact match version of the keyword, the ad’s Quality Score, and current advertiser competition on that keyword.”

If that’s what it takes to get on the first page, then this obviously has a lot of implications to anyone hoping to ‘Bid-to-Position’.

1. First it reinforces the notion that all of your keyword work should be striving toward Exact Match, because Exact beats Phrase which beats Broad.
2. Second it says you had better really worry about the Quality Score of the keywords you’re trying to position. A lousy QS will sink your chances of attaining any position, let alone a top one.
3. Bids are only important in the context of these first two.

On Tuesday Bryan and I will dive deeply into Quality Score and how you can and why you need to focus on improving it for the keywords in your campaigns. This ‘secret formula’ has impact on every dollar you spend, and every click you get – or don’t get.

The impact of Quality Score on various bid strategies and campaign goals is just one of the topics we’ll cover. Please join us if you can.

The idea is to stop thinking about mechanical components like keywords and bids, and instead focus on a logical marketing progression.

We want the tools to support our work process instead of having to build a work process that serves the tools.

The First Step is Targeting

Targeting means showing your ads to the right people. Paid search ads are delivered as answers to questions. People type in a search query and you pay for the privilege of having your ad be one potential answer to that question.

So you must know:

• What questions do you want to answer?
• What answers do you plan on giving to those questions.

Campaigns, Ad-Groups, and Keywords are your targeting tools.

Keep in mind that they’re called ad-groups, not keyword-groups. The goal is to segregate keywords, controlled using the match-type option, so that all the queries attracted by a single ad-group are questions answered by the text-ads in the ad-group.

In other words, you want every searcher to see a a text-ad that is directly relevant to their search. To do that, you must organize your ad-groups around the search queries they attract, not the keywords they contain. Every search query that causes your text ads to be displayed, should be highly relevant to the text ad that is displayed.

Let’s illustrate with an example.

Supposed you knew that all of the following search queries would be coming into your account, and you could hand match them to appropriate text-ads before the results page was delivered to the searcher.

• Discount Dyson Vacuum
• Dyson Vacuum Features
• Dyson Vacuum Coupons
• Compare Dyson Vacuums
• Cheap Dyson Vacuum
• Dyson Extra Cyclone

Wouldn’t you want the 3 price-related queries to get a price focused text ad, and the three feature related queries to get a feature-related text ad? Doesn’t it make sense that this would produce the highest click-through-rates and the highest ROI?

Yes, of course.

This is why you have to think about queries not just keywords, and use ad-groups to target the groups of people you want to talk to.

The Second Step is Valuing

Once we’ve targeted the right people using different ad-groups, we can then look inside the ad-group and take advantage of the fact that we don’t have to place the same value on everyone in that group.

Match-Types, Negative Keywords, and Bids are some the core valuing tools.

Extending our previous example, suppose experience tells us that people who search for ‘Cheap Dyson Vacuum’ just don’t buy from us (we’re not that cheap). That has no value, so we add ‘cheap’ or ‘cheap dyson vacuum’ as a negative. But ‘Dyson Extra Cyclone’ is a very specific feature so people who search on that are far into the buying process, we see that query frequently with a high conversion rate. Make that an exact match and bid it up.

You get the idea. By correctly using these tools, watching our search queries and continually refining our campaigns, we can group queries within an ad-group, value them appropriately, and manage both budgets and returns.

The Third Step is Satisfying

People decide how well our paid search advertising does. They decide how to formulate queries which trigger our ads (or not) and they click (or don’t) and buy (or not).

Text-Ads, Landing Pages, and ultimately your offers, website, and checkout process are your satisfaction tools.

When we’re targeting accurately, and valuing properly, we have the ability to focus on satisfying those who see our ads and visit our site. Trying to do so before we’ve completed these steps means, by definition, that we’ve got too wide a range of people coming to really have a fair shot at measuring the results of any attempts at improvement.

There is little doubt that text-ad writing, let alone testing, is the paid search option that gets the least attention and effort as compared to its importance and potential impact. Rewriting a text ad and doubling performance – in terms of CTR which even if it does not improve conversion rate can proportionally increase revenue – is common. We’ve seen many ad re-writes produce 10x-20x CTR improvements. Try that with a better bid.

But writing is hard. Writing is subjective. Writing takes quite a lot of time. None of these make it less important.

All the same is true-er for landing pages, website experiences, and shopping carts. This all very hard, time consuming, and costly work. But it is ultimately directly responsible for the success or lack thereof of paid search campaigns. Even within whatever limitations exist, it should be considered, managed, and measured.

The Final Step is Understanding

Even in this greatly summarized view of the paid search process, there are a lot of moving parts. Each exists by the hundreds, thousands, or hundreds-of-thousands in typical campaigns. They occur tens-of-thousands of times every day as impression and click counts increment. And we have weeks and months of history for all of this to consider and trend.

Paid search can only be managed effectively if you can learn from this data – look into it and find information.

Website and Search Analytics are your tools for understanding.

This means knowing which metrics are important. And when trends are really trends. And how all the numbers affect each other.

It also means that you need the ability to get at the data that can inform you, and easily produce the reports and dashboards that will do so for both you and your colleages or managers.

The key is continuous improvement. Paid search campaigns are never perfect. And they exist in highly dynamic environments. Only through hard work to understand the campaign and know the best move to make next to improve it can you really drive great results.

Summary

The shift into the T-V-S-U mindset is a big one. It changes the process of managing paid search and the way you think about and use the options and tools the search engines provide. More importantly, it aligns your search and marketing goals, and makes it easier to prioritize your PPC efforts and measure your results along the way.

In future posts we’ll dig into each stage and step of this process in more detail. Have questions before then? I’d love to hear them, or your comments.

This post is part of a series on High Resolution PPC, a framework for understanding and managing paid search advertising.

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.

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.

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.

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!