Since the dawn of time, paid search has been conceived of and managed based on four key components and common perceptions of their roles:

1. Keywords. Keywords define when your ads run. Choose keywords and phrases that people looking for your product or service would use.
2. Bids. Bids define how much you’re willing to pay when your ad appears for any particular keyword. Higher bids can help position your ads higher on the page and appear more frequently.
3. Text-Ads. Text-ads give you a headline and two lines of copy to attract and persuade searches to click and view your website.
4. Click/Conversion Reports. Basic reporting tells you how each keyword is doing, both individually and within campaigns, and ad-groups, in terms of clicks, conversion rates, and ROAS.

These four items remain important aspects of paid search today, but they’re not the most important variables, nor the best way to think about PPC.

We’re not operating in the same technical, competitive, or business environment as four or five years ago:

• In a world where changing definitions for match-type determine which queries cause your ads to run, worrying solely about keywords is inadequate.
• In a world where quality score has such a huge impact on where your ads run and how much you pay for them, worrying largely about bids is inadequate.
• In a world where a majority of your buyers visit your site multiple times before purchasing, text-ads remain important but must be considered in context of all user touchpoints.
• In a world where profitability is the real goal, then measuring intermediate metrics while ignoring the one that really matters - ROI - is illogical.

We’ve got a name for this old ‘keywords & bids’ view of the paid search world: ‘Low Resolution PPC’.

It was fine five or six years ago when the engines were simpler and the budgets smaller. It’s not fine anymore.

• Today you have to think about your user targeting based on the interaction of keywords, match types, and search queries.
• Today you have to think about your costs based on how quality scores and bids interact with match type keyword traps and negative keywords.
• Today you have to think about persuasion and conversion as a chain of events that starts with your text ad and continues through your landing page, your site, and the experience users have in your shopping cart.
• Today you have to think about analytics as a way to understand all of these variables and more.

It’s a high-def world, even in paid search marketing.

Introducing High Resolution PPC
But there’s more to High Resolution PPC than just a deeper consideration of the core mechanics of paid search.

We also want to shift the focus away from the mechanics of running paid ads and onto our relationship with the people to whom we’re advertising and how we manage that relationship.

We want to know who the people are conducting these searches, clarify why we want to talk to them, understand what will get their attention, and make sure to learn from our interactions with them so we can perform better in the future.

The Cornerstones of High Resolution PPC
In High Resolution PPC we manage our campaigns by using the options and controls in paid search to move clients through the marketing acquisition cycle.

Accordingly, we no longer think of search in terms of the four old cornerstones of paid search - keywords, bids, text ads, and operating reports - but instead in terms of the four stages of customer acquisition and management:

1. Target - To begin you define the focus of your efforts, using campaigns, ad-groups, and keywords to target specific groups of people who are asking the kinds of questions you want to answer with your paid search ads.
2. Value - Next you refine this focus within your target groups, using bids and match types and keyword negatives to properly value the different people who you want to attract to your website or landing pages.
3. Satisfy - With clearly targeted and properly valued searchers identified, the goal becomes delivering text ads, landing pages, and offers which satisfy user desires and advance them toward and through conversion.
4. Understand - Throughout this process we capture, analyze, and present meaningful and actionable information about each specific phase and the overall. Here you’ll apply improved reporting standards and metrics.

Each of these corresponds to specific tasks in the management of your paid search campaign, certain options that control your campaigns, and reports that provide metrics which guide the way or measure progress. (Watch future posts for details.)

Why the Change
The driving factor in moving to a High Resolution PPC approach is a desire for better returns on our investment of both time and money.

As with any other investment, we control risk by increasing the depth of our visibility and understanding, and then manipulating the options we have at our disposal.

With a High Resolution PPC approach, you regain control over your paid search campaigns, both in terms of having vastly better visibility into what is happening but also by understanding why specific results occur and how you can fix or improve them.

Next Time
In the next post on High Resolution PPC, I’ll dive a level or two deeper on the target-value-satisfy-understand process. This mental shift is the cornerstone, and once you start thinking about this logical flow in your paid search marketing, it becomes a lot easier to use the options in the engines more strategically.

{If you’re at shop.org in Las Vegas this week, stop by and say hello - we’re in Booth 115.}

What one piece of advice would I give to help improve a paid search campaign?

That was a question asked of our panel as SES in San Jose last week.

My answer: Make sure your brand keywords are fully segregated from all others.

Brand keywords - any keyword with your company name or variations in them - have completely different cost and performance characteristics than category or other other generic or product specific keywords.

These differences completely confuse the reporting for any campaigns and Ad-Groups if they’re co-mingled.

Separating Keywords and Queries
The first step is easy - every keyword you buy, regardless of its Match Type, should be in an Ad-Group if not a Campaign with only other keywords that contain the Brand name too.

Preferably, the brand terms are bucketed, with the ‘Pure’ Brand keywords in one group (those that represent just the name and variations itself), the navigational versions in another (www.brand.com, brand homepage, etc.) and the Brand-Plus keywords (Brand Sweatpants, Brand Coupons, etc.) in yet another, and so on.

In these brand focused Ad-Groups, you have to use Broad and Advanced match very sparingly and carefully, and eventually almost entirely eliminate them. If you leave them, you’ll get too many non-brand queries matching and diluting the intent of these highly focused Ad-Groups.

The other side of this Broad/Advanced Match coin is that you’ll also want to add your brand as a negative in all the remaining non-branded Campaigns and Ad-Groups. Otherwise the engines will match brand-inclusive queries against your non-brand targeted keywords.

This can be and feel dangerous, if you’re not completely sure that your Brand campaigns are complete, bid properly, running the full range of Match-Types (with of course the Match Type Keyword Traps fully configured and loaded.)

It’s probably a good idea to skip this step of adding the brand as negatives in the non-branded campaigns for a few days to ensure that there aren’t certain query formulations that your new Brand targeted Campaigns are missing.

Watch the query reports carefully, and add variations to the brand campaigns, and ultimately more negatives to both the brand the non-brand campaigns.

The Payoff
Immediately upon starting this process, especially if your campaigns had brand terms and lots of broad match scattered throughout, you’ll see radical shifts in your search reports.

• You may be amazed how much revenue is coming from and and how little cost is going into your pure brand campaigns. That’s the good news.
• You may be shocked at how much money and how little revenue is coming from your now-strictly-non-brand ad-groups. That’s the bad news. Or the opportunity, depending on how you look at it.

In any case, you’ll have a new level of clarity about the performance and activity in your PPC campaigns.

Coming Up
I’ll share more thoughts on the execution of full brand segregation, and the implications of the changes it makes to your reported results, in future posts. This is another one that may take 3-4 posts to just scratch the surface of.

In the meantime, questions and comments are encouraged. Are your brand terms separated into ad-groups? Does that help you better understand the way your PPC budgets are spent? What problems have you seen trying to control brand via Match Types? Any other ideas?

Thus far in this series we’ve talked about the difficulty of getting a clear view of paid search performance, of deciding the most urgent risks and opportunities amidst the volumes of data that you do have.

Now we come to the third issue: the productivity (or lack thereof) in making changes or improvements to your paid search account.

Possible Changes To Improve Search Campaigns

There are a limited number of things you might want/need to do to your paid search campaigns. Most of them aren’t too difficult when required on a small scale. But there’s not much in PPC campaigns that really happens on a small scale, which is where the frustration begins.

You might want to add keywords. It’s not hard to generate a large list of incremental keywords, and there are tools to help you do it. You can even harvest search queries, scape competitor websites, or get lists from Compete or Hitwise of terms driving traffic for others.

But to effectively apply a list of keywords they need to be expanded and parsed into versions and phrases and synonyms and layered across match types and segregated into ad-groups and campaigns and matched with bids and text-ads. The ideal environment for this would both facilitate the process as a whole and provide suggestions based on a learning algorithm which watched your style of division and targeting.

You could see the need to modify match types based on your search queries to build more effective match type keyword traps. This requires versioning keywords, segregating them into Ad-groups, pyramiding bids, and making sure the net is wide and lacks gaps or overlaps. Software could visualize this process and make it ‘drag and drop’ and even ‘bionic’ if someone put a little effort in.

Your bids may need to be changed, and of course this is the one task to which some substantial software automation development effort has been placed. This is a big topic I’ll save for a future series of in-depth posts.

You might need to substantially reorganize your campaigns. This happens for all kinds of reasons, many having to do with the impact of organization on the roll-up summary numbers as presented, some having to do with quality score management, the issue of match-type control and reporting, issues of geo-segmentation, and of course good old logical segmentation.

The technology provided for campaign reorganization today - cut and paste - is getting a little dated and I feel confident that a more elegant and productive solution could be conceived and developed.

The text-ads you’re running may need to be altered. While the idea of presenting four blank boxes and allowing unlimited freedom (with the constraints of available character limits) is powerful, perhaps there would be some advantage in tracking and analyzing the different ‘recipes’ used in various ads, building up repositories of different synonyms for important concepts and then making it easy to re-use effective ones and tracking how they perform both individually and as groups based on their relative position in the ad, in the ad as it runs at different positions or on different days, etc.

Lastly there is a chance that you’ll need to test different landing pages (leaving alone for now the implications of testing various designs within a single landing page). From the typical home page vs category page vs item page variances, it may be wise to consider user personas based on the keywords and queries and other factors as well.

Here again the current ‘type-anything-you-want’ technology could be enhanced by allowing simple meta data to be entered and tracked (how are item pages doing in terms of conversion vs category pages vs the home page) and enabling automated testing of these variations. And it doesn’t have to be limited to just a simple ‘which page’ consideration - performance may vary by the length of the query or number of works in the keyword phrase?, time of day, day of week, visit number, or many other factors. Software could track and optimize this.

Working In A Coal Mine

The common element in the current state of paid search management is that only one of the steps in even the most simplified version of the process has progressed even one iota in the last five or more years in terms of automation.

Tens of thousands of people are being treated as migrant-search-workers standing in the hot sun every day harvesting keywords and clicks.

And for the moment we’re not talking about the chisels and stones they’re given to bang out reports and dashboards.

Where is the Eli Whitney of PPC?

(Upcoming Events: I’ll be at the Semphonic XChange Conference in San Francisco on Aug 17-19, and am Speaking on “Identify, Analyze, Act: SEM by the Numbers” at Search Engine Strategies in San Jose on August 19th)

In my ‘Three Challenges’ Post I wrote the following to describe one of the fundamental reasons why I think the process of managing paid search needs to be improved:

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.

Since then I’ve written four posts in an attempt to explain and expand. But I’m not sure I captured it.

To manage something effectively it’s necessary to see cause and effect. The paid search networks use such complicated rules and hide certain key data elements which make this impossible.

Search queries, which are the primary driver of search success, are a key example. But it’s really the full relationship between queries and keywords and match types and quality score and text-ads and landing pages. The truth lies in that matrix somewhere, but nobody is letting you see it.

You see a pile of queries over here (partially, sometimes). A bunch of keywords over there. Some ads further off in the distance. Want to understand the relationships? Put them together in your own head.

Clues are great in a mystery. Not in a business transaction.

One thought I wasn’t able to put in the last post about missing and misleading click data, was about keyword click-through-rates.

Do keywords really have click-through-rates?

Objectively they do because the engines report them. But does that make sense?

If A Keyword Falls In The Forest, And The User Doesn’t See It…

The user doesn’t even know the keyword exists. The user typed a query (which in some small percentage of searches was exactly matched to the keyword, but far more often was only related to the keyword) and was shown (if they even saw it) a text-ad (containing some specific copy) in some position on the page in relation to a number of other text-ads (not to mention the organic search results.)

What portion of the influence in that click, or lack thereof, did the keyword have?

• We know different text-ad copy produces different CTRs.
• We know different positions result in different CTRs.
• We know that the presence or absence of specific competitive adds produce different CTRs.
• We know different queries that may match to the same keyword in broad or phrase match type have different CTRs.
• We can assume that CTRs vary by time and the geography of the user.
• There must be a couple of other factors I’m not thinking of right now… (comments?)

So does the keyword really have a CTR, or do the combinations really have CTRs? Clearly the Keyword CTR is the average of a range of different situations and conditions.

The Average Average is Only So-So

There are a lot of averages presented in search analytics. That’s necessary as we can’t handle all the granules, but close attention must be paid to the composition of these averages, lest they be less than clear or useful.

If the campaign is reasonably constructed in terms of organization and match type application, and are being reasonably run (meaning the text-ads and bids have both logic and dilligence being regularly applied to them), then the average CTR as reported for keywords can be useful. If any of these elements are missing, the utility dwindles rapidly.

As with most averages in PPC reports, if you aren’t sure dive down and look at the components - the more performance diversity you find inside the less weight you should place on the average.

Know Your Metrics

Just another example of the fact that even the simple metrics of paid search have more to or behind them than you might realize, and how some understanding and healthy skepticism can help you get closer to truly understanding what’s happening in your campaigns.

(Credit where it’s due: The idea of questioning KW CTRs, and many other ideas you’ll find in this blog from time to time, was first suggested by Bruce Ernst)

(Upcoming Events: I’ll be at the Semphonic XChange Conference in San Francisco on Aug 17-19, and am Speaking on “Identify, Analyze, Act: SEM by the Numbers” at Search Engine Strategies in San Jose on August 19th)

Another lawsuit aimed and preventing the use of trademarked keywords was dropped this week. This time it was American Airlines who had filed a lawsuit against Google for allowing other to use their name to trigger the display of competitive ads.

According to Bloomberg:

American claimed Google violated its trademark by allowing competing airlines to bid on keyword searches that generate “sponsored link” ads on search-results Web pages. The ads take advantage of the American brand’s popularity, even if the name isn’t used in the ad, the carrier said.

Google settled similar suits by other U.S. companies before the untested area of trademark law could be addressed by a judge or jury. Foreign lawsuits still pose challenges to the advertising practice, part of Google’s AdWords program.

Courts in France have held Google liable for allowing advertisers to select trademarked terms as keywords, according to U.S. regulatory filings. Google, based in Mountain View, California, said it is handling or recently resolved similar cases in Germany, Israel, Italy, Austria and Australia.

Google had argued that its “invisible” use of trademarks isn’t technically “trademark use” under U.S. law. Google compared the program to practices such as placing generic drugs next to name brands in pharmacies and buying billboard ads next to those of competitors.

This last point is the reason I’ve never understood the merits of these suits. Trademark law is designed, in my very simple understanding, to prevent one company from confusing customers with a name that is similar (or identical) to another company.

Does buying a trademarked term as a keyword provide one company benefit from the name and reputation of another? Certainly. But isn’t that why all the car dealers rent space on the same block? Doesn’t it happen when magazines review all the products in one category together?

Every company in the world wants to steal customers and prospects from their competitors. Their efforts to do so yield better features, better pricing, and loads of other consumer benefits.

Using trademark protection to limit confusion benefits consumers. Using it to try and limit consumers knowledge and awareness of competition harms consumers, and should itself be illegal. Great to see a lawsuit go the right way.

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.

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.

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!