I was rather surprised when students started doing really well on tests – but when I verbally assessed their understanding of the logic, I was still greatly disappointed.

Furthermore, they completely fell flat on more complicated problems that require more than just plug and chug. Students were just cramming and memorizing and didn’t truly learn that much.

In paid search, there are a lot of parallels. I run across many account managers who believe in exact match and don’t believe in broad match. It’s a rule to them, in fact – an axiom in their minds. When prodding further by simply asking ‘why,’ I feel like a math teacher all over again. Ask ‘why’ and you get shallow half-answers that remind me of the plug-and-chug crew,

I’m not much of a paid search historian, but imagine it’s about 1997, and you’re part of the team developing the first pay-per-click platform. There’s no such thing as match type, so how do you create the concept? What question was “match types” trying to answer?

You can do better than these.

Well, to me (and assuming “keyword” was already a concept), it’s “how do we determine which sets of queries keywords are matched to?”

If you’re focusing on match types in paid search, you’re focusing on the ‘formulas’ – which means that a lot of the logic is being lost. To understand how to best use the ‘formulas’ of match types, you must understand the theory of sets.

First, we’ll define the match types based on sets. Then we’ll explore query-to-keyword mappings by match type – what works, what doesn’t, what we should strive to achieve.

Then, we’ll show how layers of complexity like negative keywords can create chaos within match types.

By the time we hit the conclusion, my hope is that you’ll have decided for yourself that simply managing accounts by match type leaves a lot to be desired – and a lot of ROI on the table.

Defining Match Types Based On Sets

Depending on the platform, the sets of queries matched to a particular keyword match type varies. Here’s the current AdWords definition of each match type. (Note that I’ve replaced the word ‘searches’ with the phrase ‘set of queries.’):

• Broad match allows your ad to show for the set of queries on similar phrases and relevant variations.
• Broad match modifier allows your ad to show for the set of queries that include your broad match keyword or close variations of your broad match keyword.
• Phrase match allows your ad to show only for the set of queries that include the exact phrase, or close variations of that exact phrase, and possibly other words as well.
• Exact match allows your ad to show only for the set of queries that use that exact phrase, or close variations of that exact phrase, and no other words.

By now we should have consensus that match types are not required to exist. They vary in quantity and definition across platforms and capture slightly different sets depending on current match type definitions. They are simply answers (they could be correct or incorrect answers, by the way) to the question of how to capture queries.

In PPC, we’re required to use match types; we don’t have any other choice. However, now that we understand that our actual question is ‘how do we deal with sets,’ our perspective on what makes a good SEM may change.

Mappings

When dealing with sets, you must also deal with mappings – in our case, query-to-keyword mappings. When studying mappings, you will find Google employs one-to-many mappings and many-to-one mappings, but it almost never uses one-to-one mappings.

The first image is a ‘one-to-one’ mapping, the second ‘many-to-one,’ and the last ‘one-to-many.’ The input (X), in this case, is the query, which is matched to the keyword, the output (Y).

Query/set mappings

Looking at these images, which do you think would be the most optimal way to run a PPC account?

Hopefully we all agree it’s better to be in control of mappings, and the one-to-one mapping is best in instances where there is enough actionable data to make use of it. Despite this, I don’t think I’ve ever seen a true one-to-one mapping, unless we force the one-to-one mapping with crafty account structure.

The other mappings are essentially the advertiser giving the platform liberty to control the mappings, and, when given the chance, the platform will do mass experimentation to maximize the SERPs eCPM.

To really nail PPC, optimization efforts must be made on consistent, hopefully non-overlapping sets. If the sets are constantly changing, then you are optimizing against a moving target.

Some may think that exact match creates one-to-one mappings, which is why so many people “believe in exact match, but not broad match.” However, at best, exact match creates a one-to-many relationship. This means that, yes, the exact match keyword can only be matched to one query, but the query can be matched to many other different keywords. It could, for example, be matched to the broad-match version of your exact match keyword if it exists.

Going back to our match type definitions, the query ‘wedding invitations’ could be matched to not only the exact match [wedding invitations] but also the phrase match, broad match, or modified broad match version. By viewing the search query report, you should see one query being matched to multiple different keywords at different times.

Essentially, the factor that determines where the query is matched is ad rank (QS x bid). And, you’ll find that most of the time for similar keywords the QS is similar, so the match usually finds its way to the highest-bidded term.

With ‘near match,’ exact match can also become a many-to-one mapping, where multiple queries are matched to one keyword. This means that advertisers are not in control of much of anything, particularly if they follow the common best practice of ‘using all match types.’

I don’t want to simply make a blanket statement and say that one-to-one mappings are what all advertisers should strive for with every query. That’s impossible, and it’s also not optimal! I do, however, think it’s important to understand the mappings within an account and, based on what the advertiser has determine is optimal, create the desired mappings.

In general, however, one-to-one mappings are best (one query goes to one keyword every time), and many-to-one (many queries go to one keyword every time) are second-best. Worst is one-to-many mappings (one query goes to many different keywords) from an advertiser’s perspective because there is very little control, and data is very fluidly going one from keyword to the next.

From Google’s perspective, the opposite ordering is true, and they prefer one-to-many mappings. That is why Google sends so much traffic to your broad-match keywords! For Google, more chaos equals more money. Most of the time, advertisers have nothing but one-to-many mappings across their account.

Sets Influence Other Sets

Think of a negative keyword as a knife. It must be used with care, otherwise you’ll cut out some good stuff. But be too conservative with your knife, and you’ll be left with a lot of wasted spend.

What a negative is doing is removing queries from your reach. So, it’s taking one set, whether it is the set of queries matched to your campaign or the set matched to your ad group, and eliminating a sub-set.

This isn’t the most complex logic until you start thinking of how, in actuality, the removed sub-set actually impacts many sets at once. In the case of campaign negatives, it impacts the entire set of queries sent to any one of your keywords at any time.

Where this can get really scary is when people start to create account structure based off of match type considerations, and not sets. For example, it’s common to set up one campaign in exact match (no negatives), one in phrase (only exact negatives), and another in broad (phrase and exact negatives). Each campaign would have the same positive keywords.

Consider the set of queries an advertiser who sells iPads and iPad 2s would find relevant. The advertiser may conclude he/she only needs two keywords: iPad and iPad 2. Let’s agree with this assumption for the purpose of simplicity.

So, you’d have the following:

Campaign A – the set of queries ‘ipad’ and ‘ipad 2’:

[iPad]

[iPad 2]

Campaign B – the set of queries containing, but not equal to ‘ipad’ and ‘ipad 2’

“iPad”

“iPad 2”

-[iPad]

-[iPad 2]

Campaign C – the set of queries to whatever Google deems relevant but not containing or equal to ‘ipad’ or ‘ipad 2’

iPad

iPad 2

-[iPad]

-[iPad 2]

-“iPad”

-“iPad 2”

If a user then queries ‘buy ipad 2,’ where does the query get sent to? The answer is….somewhere in campaign B but I don’t know where. I think ‘ipad 2’ is more likely than ‘ipad’, but it depends on the ad rank!

This is starting to sound like quantum physics, and I don’t think it’s good to run accounts like quantum physics (set theory is hard enough).

Instead, consider the following:

Campaign A – The set of queries that contains ‘ipad’(or minor variation) but not ‘2’ AND (in a different ad group) the set of queries that contains the token ‘ipad’ (or minor variation) but in only conjunction with ‘2’.

Ad Group 1

+ipad

-“2”

Ad group 2

+ipad +2

Campaign B – The set of queries Google deems relevant but not those containing the phrase ‘ipad’.

Ad Group 1

iPad

Ad Group 2

iPad 2

Campaign negative

-“ipad” (feel free to add in -“ipad 2” if you’re a neat freak!)

In this structure, which is quite a bit simpler in my opinion, I’d know that any query containing ‘2’ and ‘ipad’ goes to the ad group for the ipad 2 in Campaign A.

That’s important because I have different ad copy for that ad group, and probably a different bid. Not to mention I have different margins for each product, and a different quantity in stock. So, I know when someone queries ‘buy ipad 2’ it goes to my ‘+ipad +2’ ad group.

Conclusion

I don’t think this is the type of an article that needs a conclusion. My hope is that you’ll say ‘hmmm,’ and ‘I’m not quite sure about that’ a whole bunch! The idea is simply for everyone to get their thinking caps on and start thinking about queries and query sets, and not keywords.

Making great accounts is about more than great copy, great landing pages, and great products; it’s also about great query mappings and valid data based on consistent sets. Many paid search accounts are like whack-a-mole, where fixing a problem one place just results in it popping up elsewhere. If you can create a solution to the whack-a-mole problem, then you’ve got a great PPC account.

All right, “class,” this week’s homework assignment is to tell me what is wrong with my proposed campaign structure!

That being said, adCenter has some really neat features that Google hasn’t adopted, no matter how obvious they might be. Here’s how to use them to your advantage.

Search Partner Exclusion

Most of the new accounts I run across have search partners shut off (on both adCenter and AdWords). With AdWords, I can certainly see how that it’s an ‘all or nothing’ scenario. adCenter, on the hand, offers search partner exclusion, much in the way AdWords offers content partner exclusions.

Search partner exclusion is pretty simple. Within campaign settings, simply copy your list of search exclusions to ‘exclusions’ under advanced settings.

You may also apply search exclusions in bulk via the editor tool, or the UI’s “make bulk changes” feature.

In order to create your list, use the ‘publisher performance’ report in the ‘reports’ tab. Here you’ll get a list of the adCenter search partners, and you can determine which to make negatives.

Keep in mind this is still search traffic, so must of the time your performance on these sites should be pretty good; however, there are certainly a fair amount of borderline scam search partners operating with a fair amount of volume.

Ad-Group-Level Settings

All right, I admit it: most of the time, dealing with ad-group-level settings is incredibly ‘clunky.’ But there are some really cool use cases.

Ad rotation certainly stands out. I’m certainly in the minority, but with a fair degree of regularity I use AdWords ‘optimize for clicks’ or ‘optimize for conversions’ (usually the latter) rotation settings. As such, when I want to run a test in only one ad group, it creates a bit of a quandary, because I’m forced to switch everything to ‘rotate.’

Not so for adCenter, which offers ad rotation as an ad-group-level attribute.

While ad rotation is the setting that stands out the most, essentially every setting can be applied at the ad group level on adCenter. Outside of negatives, and the new ‘flexible reach’ options, AdWords doesn’t really offer unique ad-group-level customization.

Branded Search Terms

A few months ago our heralded CEO wrote about protecting brand terms. As you might expect, opinions were a bit divided – should only TM owners be able to bid on their own branded terms, or should it be an open auction system? I can see both sides of the coin, and apparently so can adCenter. The answer seems to Rich Ads in Search (RAIS).

With RAIS, bidders apply to have ‘rich ads’ for their branded queries. The result is guaranteed sole top position, and a bunch cool of add-ons and different formats. With this, competitor terms are relegated to the right rail, so if someone is searching for a brand term to find that brand’s competitors, they’ll find them off to the side.

If someone is searching for a brand term in order to end up on that brand’s website, they can’t miss the ad, and they won’t be distracted or tricked into clicking on a competitor’s.

Customer Service

It’s hard to argue that Google has poor customer service, but at the minimum I can say that it’s inconsistent. The same can be said for adCenter. If you’ve been given a ‘Microsoft’ rep, my condolences. However, if you’ve been given a Yahoo! Rep, take a moment to moment to give thanks. Yahoo! Reps crush AdWords reps time and time again.

Not only will Yahoo! do a great job on reactive issues – ‘billing,’ ‘disapprovals,’ etc. – but they’re more than willing to jump into the nitty gritty. Perhaps it’s a result of the product itself being pretty poor, but the reps are great at creating, editing, and adjusting campaigns for those who ask.

In addition to helping you manage your campaigns, they’ve got a lot of internal flexibilities. Our reps will help us create QBRs for clients, do research on the keywords competitors are buying, and provide share-of-voice information.

Best of all, Yahoo! reps aren’t salesmen. Google reps, on the other hand, certainly are. Certainly Yahoo! reps are there to help you grow your spend; at the minimum, they’ll take the time to understand your business objectives. Google extends no such curiosity, and their reps simply push around the latest buzzwords in order to help rack up your bill.

Pixel Length Variation

This is a huge problem with the AdWords pixel. AdWords pixels have 30-day cookie windows, and there are no other options available (though you can adjust the time frame for which they look for view-through conversions).

While certainly imperfect, the adCenter pixel lets users select from 7-, 15-, 30- or 45-day windows. Of course, a custom time frame would best, but at least adCenter offers some flexibility.

Conclusion

In an ideal word, adCenter and AdWords would be essentially mirrors of one another. It seems like adCenter finally has gotten the message by introducing site links, modified broad match, negative match-type options, and a multitude of other features that Google had previously rolled out.

As an advertiser, that’s a huge win. Not only does it improve our ability to manage across platforms, but it improves the likelihood that we can mirror structures across platforms without sacrificing optimization efficiency.

So far Google, for its part, has failed to take note of the areas where adCenter is winning – though I’d wager that Google will eventually decide to take notice and mirror the additional functionality that adCenter has created. This type of competition certainly benefits the end user, but it remains to be seen if Google will admit where they’ve been beaten to the punch – and do something about it.

As long as we’re on the same page, and already slinging the clichés, this post will help you “work smarter, not harder” by helping you identify where your accounts lie on the head-tail spectrum – and how to use that knowledge to prioritize tasks like ad testing, keyword optimization, negative keywords, and more.

First: Identify If Your Accounts Skew Towards ‘Head’ Or ‘Tail’

Head accounts are those where non-branded KPIs (primarily conversion volume) are condensed within a relatively small query pool. These are the accounts that follow the Pareto principal – that is, 80% of your output (sales, revenue, etc.) is generated from 20% of your account (in this case, queries).

Even this, however, tends to overstate the situation, as 20% of queries by count is still extremely large for a true ‘head’ account. In fact, as few as 1 or 2% of unique queries and/or keywords can generate up to 80% of sales in true head accounts!

In these accounts, each individual top non-branded query can be responsible for 20% or more of non-branded conversions.

Tail accounts are those where no individual non-branded query makes up more than about 2% of total non-branded query conversions. These are accounts where your top non-branded query can fall off the map completely, and odds are you wouldn’t notice it.

In these accounts, you’ll find thousands of unique converting non-branded queries, and the list of converting queries would be extremely large.

In the graph below, I’ve sorted the top 50 non-branded queries by conversion volume from one head account and one tail account. Then, I chose to graph the cumulative percentage of conversions brought in by these query sets relative to the entire set of non-branded queries.

To get an even better sense of how dramatic the difference between Head and Tail account is, in the head account the top 5 non-brand queries make up 44.25% of non-branded conversions, while the tail’s top 5 make up just 5.01%.

If I were to continue the graph of the ‘tail’ account, it would take roughly 7,000 queries before we approach the percentage of conversions brought in by the ‘head’ account’s top 50.

Based on these differences, it starts becoming apparent that a one-size-fits-all approach to paid search isn’t optimal. Throw in things like management of branded queries, as well as the entire Google Display Network, and it’s impossible to come up with a list of ranked prioritization across the breadth of accounts in different verticals.

Going off of these benchmarks, you should be able to identify if your account skews towards a head account or towards a tail account. While the accounts that I’ve chosen are close to upper and lower bounds, I’d actually expect that most well-built accounts look similar to my examples.

That is, few accounts would fall directly between a head or tail account. Even so, accounts will run the gamut, so, instead of classifying an account as ‘head’ or ‘tail,’ it’s typically more appropriate to consider the degree to which an account is head or tail.

So what use do we make of the classification?

Well, while each and every optimization effort is important for accounts, the prioritization of them differs depending on whether or not the account in question is ‘head’ or ‘tail.’

Note: Remember that an account can be classified as ‘head’ or ‘tail’ only after reasonable keyword expansion has taken place (to avoid false ‘head’ accounts, which don’t have enough keywords built out to capture long-tail queries), and the account has consistent distributions over an adequately long time frame.

Now, on to the tasks – and how to prioritize them based on head/tail categorization.

Ad Testing

Typically, ad testing gets flagged as the most important aspect of PPC. That makes sense, since it’s all that users see on the SERPs. However, the impact of ad testing much more easily felt on head accounts.

On a head account, an ad test that results in 20% improvement in CVI (conversions per impression) to the top 50 queries nets out 8.8% non-brand improvement – not bad for a day’s work.

On the other hand, the same 20% improvement on the top 50 queries of the tail nets out only about 1%. Couple that with the fact that ad testing is slower in tail accounts, since the clicks accrue much slower for the same quantity of queries, and ad testing is a much more important task for head accounts.

Now, if a PPC manager could find a test that works across keywords and product lines in a ‘tail account,’ then the successful test can touch a higher percentage of the non-brand portfolio than just the top 50 queries.

In tail accounts, however, you are typically dealing with many different competitors, auction conditions, margin goals, etc., so it’s rather unlikely that an account-wide roll-out of a successful ad test in a tail account will have the same results on the rest of the non-brand portfolio.

Additionally, the data on ad copy tests in head accounts is much cleaner. In fact, you can do ad copy tests for each head query and still end up with enough data to make a statistically significant choice!

The skinny: critical for head accounts; important for tail accounts.

Keyword Expansion

Keyword expansion is primarily a tool for capturing new queries. Since there is a much larger pool of unique queries in tail accounts, it’s much more important to focus on expansion here.

This idea extends to match types as well. Since head accounts have such a high concentration of conversions occurring in a small pool of queries, it’s feasible to run less aggressive match types, such as exact and phrase match. Conversely, since there are always more conversions to be found in ‘tail’ accounts, it’s advisable to run broad and modified broad match with regularity.

If asked the question ‘What are you failing to capture?’ in a head account, it’s unlikely there would be much of anything. Even if only half of the tail were being captured in a ‘head’ account, that’d only leave a small percent of new conversions to be found.

In a tail account, there are tons and tons of unique converting queries. As such, it’s feasible that there are new veins of traffic to be found, and that if enough of them are found, it could result in a very tangible traffic increase. So, at the minimum, tail accounts have the potential for growth via keyword expansion, whereas head accounts have little potential for growth.

Semantically, it’s also much more plausible that the account manager simply ‘forgot’ a keyword in a tail account, as opposed to a head account, where there is likely only on product or service being offered.

The skinny: critical for tail accounts; not very important for head accounts.

Negative Keywords

Much like positive keywords, negative keywords are a more important task for tail accounts. Since more queries are being captured in tail accounts, the likelihood of more poor queries being captured also exists. While this speaks to the quantity of negatives, high-volume negatives will almost certainly exist in both types of accounts.

Finding the high-volume negatives is a rather finite task, so early on each account will have equal dependence on negatives. Going forward – that is, as a recurring task – tail accounts will consistently find new low- to mid-volume negatives, whereas head accounts may find nothing at all over time.

For either account, however, it’s important to have as much of an impact on the account as possible. That is, instead of negging out a 1-impression 1-click query, identify the token that is irrelevant, and apply it across the account (as a general rule).

The skinny: critical for tail accounts as both initial and ongoing task; critical for head accounts as an initial task but not very important as a recurring task.

Bidding

Bidding as a whole is very important for head accounts and tail accounts. However, the integrity of bids on head accounts is substantially higher. That is, it’s easier to determine a ‘true’ bid for a query with high volume than to determine a bid for a cluster of queries in the form of an ad group-level bid or broad match keyword.

Additionally, since there are fewer optimization levers for ‘head’ accounts, the relative importance may well be higher there, but the absolute influence will be similar for both accounts.

The skinny: critical for head accounts; important for tail accounts.

Making Heads Or Tails Of It All

Head accounts and tail accounts are very different. As a general rule, when considering an optimization effort, I think ‘What percentage of my conversions is this action likely to influence, by how much, and in which direction?’ This type of sanity check really keeps good PPCers from getting into a one-size-fits-all mentality.

Now, after reading this, you might wonder “Would I rather work on a head account or a tail account?”

If you’re a creative person who writes killer copy and obsesses over landing-page mirroring and minor product tweaks, go for the head account. Make sure you’ve got a keen attention to detail, in the form of precise ad copy tests and minute bid changes.

Also, don’t be surprised if one day you wake up and some crazy competitor increased the auction prices by 30% on your top 10 queries! Your top 10 are also most likely their top 10, there are no surprises or ‘hidden gems’ in head accounts. Perfectionists prefer head accounts.

If you’re a grinder who doesn’t mind bulk keyword builds and you aren’t paralyzed into always waiting for statistically significant data to make changes, go for a tail account. Tail accounts can always find an extra point or two, and the level of ‘depth’ required on an individual query is less important. Go for a tail account if you’re always starting new tasks before perfecting the last one.