ClickEquations Blog

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.

Paid Search Managers spend a lot of time analyzing clicks.

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

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

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

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

Two Ways To Lose

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

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

Tracking Missed Impressions

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

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

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

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

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

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

Tracking Missed Clicks

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

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

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

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

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

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

Not Perfectly Clear

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

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

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

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

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

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