We’ve noticed that you have some money that we’re not getting. This is naturally a situation which causes us grave concern, and so we’ve assigned a team of ambitious young engineers and MBA’s to the problem, and we’re excited to tell you what they’ve come up with: Automatic Budget Depletion.

Yes, it sounds too good to be true. The best part, you don’t even have to turn it on. We’ve done that for you!

From now on, you can rest assured you’re relieved of the burdonsome task of deciding where and how you want to advertise on Google. Don’t worry about what keywords are relevant to the search queries executed by the types of customers you’d like to attract. Forget about complicated matters like match type selection and bid determination. Crafting text-ads to match the desires and intents of your prospects – those days are gone!

From now on, just give us a few clues and we’ll take care of everything else. And that terrible end-of-the-month-oh-god-we-didn’t-blow-every-penny-we-had trama will be over forever. For while we still can’t tell you the true ROI of your campaigns, we can now assure you that you won’t know the return on your *entire* budget instead of having to suffer with only that kind of partial knowledge.

While we know this is exciting, we assure you that we aren’t finished improving your marketing experience yet. Right now our very own gmail engineers are busily porting over that feature which incrementally increases the size of your gmail storage so it can work on your campaign budgets too. Pretty soon you won’t even have to lift a finger to increase those. Just in time for summer vacation.

One small word of warning, however. We’ve decided that in the best interest of our users we’ll be forced to invoke quality score penalties on any landing pages which include clashing colors, dangling participles, or excessive metaphors. To ease your transition to these new rules for the next 18 months we will allow pages with these affronts to still receive clicks, but only if the CPC to Max Bid ratio was greater than .85 else these keywords will see new higher minimum bid requirements.

Thank you again for your continued blind and unquestioning support to our programs.

- The Adwords Team

As part of our continuing efforts to improve the user experience, we will soon incorporate an additional factor into Quality Score: landing page load time. Load time is the amount of time it takes for a user to see the landing page after clicking an ad.

Why are we doing this?
Two reasons: first, users have the best experience when they don’t have to wait a long time for landing pages to load. Interstitial pages, multiple redirects, excessively slow servers, and other things that can increase load times only keep users from getting what they want: information about your business. Second, users are more likely to abandon landing pages that load slowly, which can hurt your conversion rate.

My guess is that there is a third benefit – when people click ads and get quick loading pages, they can decide that they don’t like the page and then quickly get back to Google to click another ad. Too many slow landing pages and someone could tire of browsing and either buy something or just give up. Of course, the policy change isn’t being made for that reason.

Nor is it relevant that ads are what slow down tons of pages on the web. If only they could get people to stop putting ads on every single web page.

Maybe I’m a little cynical. But why don’t they just warn people that certain pages are slow loading by placing a tiny tortoise or hare icon just below each ad?

Oh ya. Unnecessary icons would slow page load times and ruin the user experience.

Why Match Types

Match types exist because it’s impractical to define or purchase keywords/phrases for every search query that users are going to use when looking for your goods or services. And in theory as well as reality it makes sense for the search engines to use both their deep data and algorithms to help us avoid having to specify 97 versions of ‘organic dog food’ in our keyword lists.

The problem is that the reality is not entirely as good as the theory. The Broad Match baby comes with a lot of bath water – queries that are too far from the keyword in meaning or intent and therefore are inevitably or too frequently unprofitable.

But most people don’t have the time, skill, or tools to drain the tub properly by reducing the use of Broad Match over time and expanding their use of Phrase, Exact, and Negative matches.

And that’s the problem. It’s not that Broad Match or even Ultra-Broad Match are bad per se (although Google could certainly continue to improve it in many ways) but that many advertisers rely on Broad Match too heavily for far too long.

How To Use Match Types

Broad Match (and possibly Ultra-Broad Match) should be used in many cases to test the waters. To find out which queries people are actually typing and determine how different queries convert. Armed with this information you can build a much more focused net of exact and phrase match keywords, and filter more specifically with negatives, so that the use and bids on broad match can be significantly reduced.

To be clear, there are many very valid uses of Broad Match; I’m not at all suggesting that it’s reasonable or desirable to eliminate all use of Broad Match.

I am suggesting that Exact and Phrase are vastly under-utilized, and that the bidding structure around multiple purchases of the same keywords are almost always out-of-whack. (More on the bidding implications of Match Type in a future post.)

Expanding Match Types

It’s good to see Google expanding the ways that they use their data and algorithms to drive traffic. Unlike many in the PPC community I’m not against innovations like ‘Automatic Keyword Matching’ – there are new and small advertisers for whom this type of capability is a good match.

But I do think that for larger and more advanced advertisers Google should go the other direction – exposing more options within the Match Type controls.

• It would be great to have 3-5 grades of Broad Match with some control or indication of what kinds of matches are supported.
• It would be great to have a non-linear Phrase Match (to purchase and query that includes the exact words you buy but not necessarily in that order or without extraneous words).
• It’s a crime that Match Type is not available as a variable to be handed back in the target URL string so better analysis on the impact of Match Type can be done in search analytics.

Match types are one of the most important and least effectively utilized of the controls available in paid search. Google needs to do more with them, but so do most paid search managers.

It’s therefore vital to intelligently manage and organize branded keywords. It’s also very important, and a completely separate task, to control how branded queries are matched with other keywords in your PPC programs.

Why Branded Terms Are Different

Brand term click-through-rates and conversion rates generally exceed those of non-branded terms by quite some margin. CTR’s are often 3x, 5x or higher than their non-branded counterparts. ROI for branded keywords is commonly measured in thousands-of-percentage points.

If brand terms are mixed into ad-groups across your PPC program, the results you see at either an ad-group or campaign level will show the blended averages of the branded and non-branded terms; this does not provide an accurate view of reality and therefore makes the numbers effectively worthless.

A client for whom we recently started work, for example, had an ad-group deeply mixed between branded and category terms. Looking at a campaign report you’d see that the ad-group had a 3.90% CTR a 0.86% conversion rate – about half of other comparable ad-groups within that campaign.

A closer look inside the ad-group, however, revealed a far different picture.

The branded terms had a 16.61% CTR and 1.67% conversion rate, while the non-branded terms had only a 2.48% CTR and a 0.26% conversion rate.

Clearly these numbers give three very different impressions of how the campaign is performing and of the type and urgency of changes that may need to be made.

Organizing Branded Keywords

To gain a more clear and accurate view of campaign performance, and more actionable data, pull all branded keywords out of the ad-groups they’re scattered across, and put them into branded-only ad-groups within a purely branded campaign.

These groups should produce terrific results. Take advantage of the opportunity to really work on testing text-ads for your branded terms, and since these groups/ads usually gain great quality score rankings you should be able to cost effectively attain strong top or near-top positions.

Another way to leverage this new focus is to keep a close eye on the impression share numbers in Google as you really don’t want to miss potential inventory with this campaign. It may even be necessary to raise bids not because you want to change your average position (although that may be the results) but instead because you want ensure that your close to or achieve 100% impression share.

Managing the Resulting Non-Branded Ad-Groups

If your brand terms were outperforming (which is virtually always true) the reported results for the ad-groups from which the branded terms have been removed will usually decrease. This can be quite shocking at first, but I’ve never seen anything but positive long term results from the process.

But by seeing the remaining keywords for what they are you can make much better decisions about the work these ad-groups need moving forward.

• You may realize that working a lot harder on the text-ads is necessary to see if it’s possible to drive the CTR’s up.
• You may need to revisit and either expand or contract the keyword lists, or tune the match-types.
• It’s very possible that you’ll decide certain keywords just aren’t working and never will, and pause or delete them.

In effect, now you have to do the hard work of managing a non-branded campaign. Central to your success is a willingness to test and manipulate all of the controls which are available to you.

Query Management

When your branded keywords are all segregated within selected campaigns and ad-groups, the next challenge is making sure the search engines don’t go around you with their broad/standard matching rules.

It’s not uncommon at all to see user searches for a brand name matched to category and product names which are set on broad/standard match even though that same brand name has an exact match keyword setup in a different campaign.

There are many problems with this. The first is that you’re no longer controlling your messaging but instead letting the engine ‘decide’ which of the ad-groups and keywords (and corresponding text ads, bids, and landing pages) are the best to apply to queries containing your branded terms.

In this case, you’ve lost control of the user experience both inside the engine and on your website.

In addition, since the branded queries still click-through and convert at much higher rates than non-branded searches, your search results are once again polluted (or at least unfairly shifted) by the performance of branded terms.

Another Approach

For the more sophisticated, either in terms of thinking or tools, it should be pointed out that it is possible to classify keywords using certain reporting packages, and gain the insight and clarity of brand-segregation without completing the physical separation process.

The downside of this approach is that it may limit your ability to target your creative, monitor your impression share, and limit queries to matching only the actual branded terms via the use of negatives.

Summary

In the world of search, brand terms are from venus and all other keywords are from mars. Configuring campaigns to force their separation produces better control, better reporting clarity, and better bottom line results.

I wish could trust these numbers. But I really don’t. They could be high or low, and as usual the averages don’t matter.

There is an interesting back and forth on the ClickFraudNetwork blog between these guys and Shuman Ghosemajumder from Google.

• I personally have no doubt there is an issue.
• Or that it’s very hard to track/detect.
• Or that the search engines aren’t being as transparent and forthcoming as they should/could be.
• Or that Click Forensics is both sincere and at the same time trying to promote a business.
• Or that if the engines believed it was as small a problem as they say, they could be much more convincing.

I applaud Click Forensics and their CEO Tom Cuthbert for the initiative and effort of setting up the ClickFraud Network. But until it’s cut loose to be a truly independent entity run by a consortium of vendors and advertisers, it’s credibility is legitimately in question, even before you get to the obvious technical problems/limitations.

At some point the industry needs to face this. Thus far it’s buried in the growth and all the other changes and issues. Perhaps it will stay that way for another few years. With so money at stake that would be unfortunate.

Take a look at this report snippet from onlynaturalpet.com. As part of our initial work on the account we’ve been cleaning up the organization of many ad-groups, so since early December there has been quite a lot of campaign and ad-group reorganization going on.

Weeks later reviewing a portion of the January results, we see clearly the impact of the way latent orders are handled – a bunch of ad-groups with zero spend, zero clicks, but orders and revenues.

Of course this only shows half the story. Each of those ad-groups has been recreated in a different location within the campaign, so there is a corresponding ad-group elsewhere in the report which shows both clicks and revenue for this same time frame, but as compared to peer ad-groups that weren’t part of the clean-up, the new ones suffer from much lower revenue (because all their trailing revenue is still being reported here in the old groups).

For beyond the fact that reporting both clicks and revenues when they happen as opposed to attributing revenue to the click that provoked it distorts reports by assuming that costs and revenues are constant when they’re not (see the earlier post for a better explanation), we have the problem that after making organizational changes your reports have severe limitations (they’re sort of worthless) until many weeks after the changes.

This post is part of a case-study series on the Commerce360 management of paid search campaigns for onlynaturalpets.com. It is being done with the kind permission of Only Natural Pet Store, and some data has been changed to keep PetSmart guessing. For your convenience, we’re keeping a list of all posts in the series.

That’s a very nice additional CTR and obviously a huge average improvement in conversion rate.

Since higher CTR drives ‘quality score’ and should raise average position, or allow you to maintain a position at a lower bid, Google is delivering quite an incentive to put at least some of your payment business through G-Checkout. I’m going to see if our case-study client at OnlyNaturalPet.com would be willing to add Google-Checkout support so we can see the impact for ourselves.

While they clearly want to drive those transactions, this does seem a bit unfair to advertisers spending hundreds of thousands or millions to run paid ads but who don’t want to adopt Google Checkout, and now find themselves effectively penalized as a result (for those keywords where competitors have the Checkout icon).

If you look at a page of results with the Google-Checkout enhanced listings it’s clear they draw extra attention. Once there are a bunch of them on the page I think they actually make it harder to read the text ads. I wonder whether these icons will last for enternity or only until the Google Checkout program reaches some target adoption level.

Even more interesting, what if Google let you ‘enhance’ your text-ad with a little graphic icon, either from a library or of your own choosing, for some small incremental fee? Sort of like eBay does with listing enhancements. Certainly would clutter and de-beautify the page, but if they’re willing to do it for Checkout, how about giving us all a shot?

OnlyNaturalPet.com is very interested in the number of new customers being driven via paid search. So I popped into D2 and quickly segmented units and sales for paid search by all visitors vs first time visitors.

Sure enough, we’re getting a pretty high count and percentage of new website visitors via our PPC programs. I can’t find first time buyers/customers yet, but I think that data may be here somewhere.

I was also reminded of the great drill-down benefit of D2 as I easily made my way through the layers of data.

As the screen shot above shows (click to enlarge) I can clearly display an entire path: Engine-Campaign-AdGroup-Keyword-Query (as covered in several recent blog posts) and see the actual products purchased for each query. That is amazing detail.

I should also point out that moving up-down-left-right within Discover is very fast. I hadn’t though about D2 last week when writing the post on how tough and slow navigation was for PPC. I’m not yet sure I can get to all the metrics I’d need for daily use here (more on that in a later post), but D2 sure allows fast and fluid omni-directional navigation.

Next, I’m going to rebuild my most favoritests calculated metrics in D2, and find/refind everything else this thing can do.

This post is part of a case-study series on the Commerce360 management of paid search campaigns for onlynaturalpets.com. It is being done with the kind permission of Only Natural Pet Store, and some data has been changed to keep PetSmart guessing. For your convenience, we’re keeping a list of all posts in the series.

The following numbers are based on data CI collects from hundreds of manufacturers, retailers, and shopping comparison engines. From within that world, these are the number of people who used different shopping channels/sources before their purchase.

1. One Channel Before Purchase – 37%
2. Two Channels Before Purchase – 25%
3. Three Channels Before Purchase – 16%
4. Four Channels Before Purchase – 8%
5. Five or More Channels Before Purchase – 14%

Two things to note:

• Each visit through an email, organic or paid search, shopping comparison engine, or retailer site counts as a channel visit. In other words, if you came through two different shopping engines those are counted as two different channels in the above list even though they’re the same channel.
• This data does not document the number of visits to a single website. A user who went through four channels to four different sites before purchasing is in the ‘Four Channels’ data.

This is really interesting data, and generates a lot of thoughts on the issue of attribution. More thoughts on that in a future post.

The paid search engine tracking tools and major web analytics packages can (generally) track these delayed purchases. They assign credit for this delayed revenue back to the paid search channel and even the engine, campaign, ad-group, and keyword responsible for the click.

Most use a 30-day timeframe during which delayed purchases are credited properly. Some allow you to specify the timeframe – SiteCatalyst/SearchCenter for example allows you to track delayed purchases for up to one year. (Set the evar expire date in the Admin control panel.)

But there is a timing problem in PPC reports. They show the clicks/expenses for the requested period, and the orders/revenue that took place in the requested period. But there is no requirement that the reported clicks cause the reported revenue. In fact, it’s an open secret that in many cases they don’t.

(There are other issues causing inaccuracies in these reports too, as posted earlier.)

There are three sources for the data in a paid search report:

• Left-Over Transactions. Revenue from clicks/visits that occurred prior to the initial reporting date. The clicks and expense of these clicks is not included on the report.

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• In-Period Transactions. Clicks and their resulting revenue when both occurred inside the reporting dates.

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• Incomplete Transactions. Clicks and their expense for which some of the orders and revenue occur after the final reporting date. These orders and revenue are not included in the report.

So for a report on paid search ‘last month’ in a company which has trailing sales for about three weeks after the initial click (not at all atypical), the report would include:

• A stream of left-over transaction revenue, starting with 21-days worth the first day of the month stretching all the way into the third week of the month.

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• A good number of in-period transactions where the click occurred on or after the 1^st of the month and the sale closed by the last day of the month

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• The full expenses but an unknown amount of revenue for incomplete transactions from every day except the first week of the month. It should be noted that the revenue from these incomplete transactions never ‘fills-in’ for the ‘last month report’. Even if you wait the full 21 days and then run it, it will report the same revenue total it did on the 1^st of the new month.

The theory or justification for allowing this sloppy mix of data to pass for an actionable report is this: the left over transaction revenue is probably equal to the incomplete transaction revenue. So on-balance the report is probably pretty accurate.

And in some cases that’s probably true.

Unless your business has seasonality. Or product line changes. Or sales/promotions by either you or your competitors. Or impact from weather or other external major events. Or unless you add, delete, or modify your keywords. Or change your bids. Or test new ad-creative. Or work on your landing pages. Or in any other way change anything that might impact your customers, market, business, campaigns, or website.

Depending upon the degree to which you do any/all of those things, you’re going to need to take these reports with a proportionately large grain of salt.

• If you’re working aggressively on your PPC campaigns, trimming keywords (because the reports you now know you maybe shouldn’t trust told you that they weren’t making you any money – ah the irony), narrowing match types, and lowering bids, for example, you’ll see an exaggerated improvement in the following period as left-over revenue continues to come in from your old running keywords, higher-priced bids, and broader matches.

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• Seasonality such as that we experience with a number of major apparel manufacturers, causes an artificial boost in some campaigns as a new season kicks in, as sales pickup aggressively from all the pre-season shopping that went on in the preceeding weeks. Another side effect from the current reporting method is that we don’t gain accurate visibility into when starting pre-season advertising is a good idea. With true-attribution we’d know.

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• As promotions or seasons end, you get the opposite effect with exaggerated swings down. Now fewer latent conversion occur due to pricing or inventory issues, making a lot more of last periods click-cost become a lot less profitable (and perhaps even pushing into the red) more than any report will show.

Why Not Do It Right?

First let’s define who we’re talking about. As far as I know, everybody creates their paid search reports using the erroneous methods described above. Google, Yahoo, MSN, Google Analytics, IndexTools, and Omniture SiteCatalyst/SearchCenter are the ones I’m familiar with. Please leave comments if you know of any package that can properly relate clicks to revenue.

The obvious question is why. Why don’t paid search reports match clicks to their resulting revenue? Why doesn’t last month’s report include only the clicks that took place during that month and the sales that resulted from those clicks?

The answer seems to just be that this is how it’s always been done. It’s undoubtedly easier computationally. There are interface issues to doing it the way I’m suggesting.

Since the same software doing this reporting (the engines or your analytics package) knows both the average number of days for your latent conversions and the number of days for which they’re configured to allocate sales (as well as their allocation rules relative to multiple visits, but we’re leaving that complexity out of this discussion) a ‘last month’ report could include a simple flag disclaiming that ‘Report Incomplete – Additional Revenue Expected for 17 more Days’ or whatever.

Using accounting terms, it’s time paid search reporting moved from a cash to an accrual basis.

I don’t know why. I’ll see if I can’t get some folks from the engines or analytics vendors who might be willing to talk about this ‘on the record’ for a future post.