ClickEquations Blog

It’s a tad early for year-end predictions, but I’ll make one anyway. 2009 will be the year search queries overtake ‘keywords’ as the focal point of interest among PPC managers.

Search queries, by way of definition, are the words and phrases user type into the search box before clicking the ‘Search’ button. They’re often and confusingly called ‘keywords, both in organic search and even within PPC.

In the paid search world we should pay close attention to search queries and the way they’re matched with the keywords we bid on – to determine how we can tune or keyword buys, match types, bids, text-ads, and landing pages.

Most paid search manager don’t have full access to every query for every click they pay for. Yahoo and MSN don’t provide them and Google Adwords provides only a very partial list and not matched at the keyword level.

Providing clear, complete, and detailed search query information is one of the great features in our ClickEquations paid search platform, and a few others provide query access as well.

Recently we’ve talked to a number of advertisers who’ve been mining queries to move to a much higher percentage of exact match keyword buys – a practice we’ve found to increase volumes and lower costs.

And this week Google introduced a new keyword expansion tool which can provide you with lists of actual search queries related to your keywords and your landing pages.

This is a great help. Both as a research tool, as additional insight into the algorithms google uses to contextually relate words and pages, and to get more people to think about the distinction between queries and keywords.

If you find the new Google Search-Based Keyword Tool useful, imagine how great it would be to see nearly every query for every click you’re paying for in your current search campaigns.

Following my post last week on Quality Score, I was invited by Bryan Eisenberg to participate with him as part of his Always Be Testing Seminar Series on Tuesday, November 25, 2008 @ 12:00pm EST.

Bryan and I will present and discuss important information on how understanding and managing Quality Score can transform you paid search programs.

Details are reprinted below, or see the post at the GrokDotCom blog.

Google Quality Score
Exposing the Secret Factor to PPC Success

Bryan Eisenberg, Co-Founder & EVP at FutureNow, and Craig Danuloff, Founder and President of Commerce360 Inc, a full service paid search management firm and developed the ClickEquations paid search software platform.

Quality Score is the PageRank of PPC. It’s a number Google assigns to your keywords which determines how much you have to bid, the position in which your ads appear, how often your ads are shown, and due to recent Adwords change it even determines if you can jump to the top of any search results page.

Understanding and managing Quality Score effects how you choose keywords, write text ads, and build landing pages. Knowing how your decisions impact Quality Score, and how Quality Score interacts with all the other controls you have in your accounts, can help you manage to greater PPC success

In this Webinar you’ll learn:

• exactly why the Quality Score in Adwords is so important,
• how Quality Score impacts the amount you spend and the amount you make from your PPC campaigns
• specific things you can do to drive the Quality Score higher for your keywords.

When: Tuesday, November 25, 2008 | 12:00pm EST

Where: Online, register here to receive your invitation

How much: It’s free, but space is limited so sign-up today!

The internet, as we all know, is the most trackable vehicle for marketing ever created. Everything that goes through these tubes can be perfectly tracked, traced, documented, and reported on.

Ya, right.

They never mention the two little requirements:

1. Every page must be properly tagged.
2. Every inbound/referring URL must be properly tagged.

(In the broader sense there is of course a third issue – I’m leaving aside for now the vast weaknesses of cookies and the role they play in online tracking/accuracy.)

Why Is Tagging So Hard?

By which I mean to ask two questions:

• Why do people find it so hard to add tags? The requirement (in the simplest cases) is to accurately cut-and-paste. (Yes there are more complex cases where parameters have to be passed, for now let’s leave those aside.) Yet in enterprise environments we often see multi-month waiting times, panels and commissions and committees who need to approve them, and all forms of insanity as prerequisite to getting 316 characters in a single text-block added to the universal footer of a website, or 75 characters appended to a URL.
• Why do the environments make tagging so complex? This is the other side of the coin. Web pages and URLs need tags. This may have been a requirement not foreseen in the mid ’90′s when core web technology was developed, but it has one for many years now. Yet neither web servers nor CMS systems nor email managers nor Google/Yahoo themselves have made tagging anywhere near as simple as they could.

Tagging – A System Requirement

While I’ll fully admit to having no understanding or appreciation for ‘IT Depts’ who can’t figure out how to allocate time to update page tags (and testing them thoroughly) on at worst a weekly or monthly basis, the more I think about this problem the more I think the root of the problem is in the technology layer itself.

Software that builds or serves web pages should have the ability to conditionally add ‘tracking pixels’ or ‘code snippets’ or ‘page tags’ or whatever you want to call them to each page, and provide a single management interface for controlling these included codes, defining the conditions on which they’re embedded, and even to make the parameter passing necessary in the most complicated cases, easier.

Software that creates or delivers URLs should similarly have the ability to simply and centrally administer the appending of tracking codes to those URLs.

In Adwords, for example, there should be Account, Campaign, and Ad-Group level parameters for tracking codes you want appended to every target URL. Why should it be necessary to manually insert them (150,000 times) at the ad-group or keyword level?

Let’s face it, they’re universal 99.9% of the time. Didn’t they teach me in High School that computers simplify repetitive tasks?

And Verify Please

On both sides – the site and the URL – these systems should validate and report on the presence and contents of these codes after they’re served.

Sometimes it seems like 25% of the man-hours of the entire online marketing industry is spent find those situations where pages or URLs were missing tags. And almost certainly a percentage of all our reports are incorrect based on places where these tags are missing and nobody detects it.

This Rant Sponsored By

As a marketing and paid search agency we’ve had our fair share of (which is to say more than humanly endurable) issues related to getting tracking pixels on client websites and managing the tracking codes that need to be placed into emails, affiliate promotions, and paid search ads.

Very often weeks or months of reporting was ruined, never to be corrected, by pending or incorrect tracking code issues. I know this is typical and true in online marketing deptments everywhere.

As we’re rolling out ClickEquations we’re now living through another aspect of this problem.

Clients and prospects that want to take full advantage of our system and use our ClickEquations tags, but they just can’t get their organizations or vendors to support them – at least in reasonable time frames. Or there’s a problem dealing with the complexity and delay involved in having all target URLs updated in the engines (although this can at least be automated via the APIs).

We’re working on ways to make tagging easier for our clients, but the universality of the problem suggests that it really needs to be solved down a few layers in the infrastructure.

I think it’s time the amount of pain and trouble this problem is causing got more organized visibility, so the creators of those lower level systems could start feeling the pressure to add the kind of tagging support we all need.

How have tagging problems or complexities impacted your online marketing reporting? How can we fix or improve this situation?

Will you be at SMX in New York this week? Stop by as see ClickEquations in the Exhibit Area.

One morning in San Francisco last week, the happy-time morning folks on one of the TV networks interviewed the whole original cast of Happy Days. Howard Cunningham, Ralph Malph, Fonzie – all of them who aren’t now as rich as Ron Howard.

One question the penetrating journalist just had to ask was about the phrase ‘jumping the shark’. Fonzie and Gary Marshall were quick to point out that the show was #1 for two years after that episode.

I guess they wanted to make it clear that they don’t even understand what ‘jumping the shark‘ means.

But later that day, after the 2nd day of the XChange analytics conference, where many of the WA Gurus and a lot of very prominent Analytics customers gathered to discuss their marketplace, it hit me:

I think high end of web analytics might have jumped the shark too. The money may flow for a while longer, but there are some real problems which may be irreparable.

What I Heard At XChange

With a unique conference format – all sessions except for a brief opening event are round-table discussions between 10-20 attendees – XChange is the perfect place to find out what’s really happening. Everybody gets their say, not just a few selected presenters.

And what they’re clearly expressing is frustration. The world’s most prominent web analytics thinkers and professionals seem to have five issues:

• Data Collection - Analytics can only work if the right data is collected. Yet site tagging is hugely problematic because IT depts are slow and inflexible. Managing web analytics in this environment is like driving a race car where use of the gas and breaks requires a ‘request submission form’ that someone else will consider and implement, fully or partially, at some time of their choosing. You slam into a lot of walls this way. Of course, if you do get the site tagged, the circus that is cookies pretty much obliterates the data integrity anyway. Saying it’s the trends not the numbers only goes so far.
• Data Integration - Even if website-based tracking was perfect, the world is no longer website based. From social media to multi-channel to Flash, Flex, Ajax, Video, and Mobile, web analytics is a guard dog with a 10 mile territory and a 100-ft chain tied around its’ neck. That’s a lot of ground not covered.
• Core Capabilities - Supposed you had all the data you dream of – then you could analyze it as you wished right? Maybe not. There were no ‘I Love My Vendor’ buttons at this show – in fact the session on ‘When and How to Change Vendors’ confirmed only that the top analytics vendors have a lot in common with the airlines – everybody hates the one they use the most. The most common story was of executives wanting the cool reports and features they understood to be promised in the sales demos, and the analytics professionals having a hard time explaining why that was completely impossible.
• Competitive Environment - The party line at XChange was a professed distain for Google Analytics because it’s ‘limited and inflexible’, but they aren’t pleased with the growing lack of alternatives at the high end. Several still going concerns are assumed to be the walking dead, and the remaining green giant has a surprising lack of goodwill that would lead you to believe Microsoft had already bought them.
• Damn Customers - This is where the real trouble lies. Because of the issues listed above, analytics folks haven’t been able to educate or satisfy their customers – the managers, marketers, partners, and technical staff that need to consume the information and insights web analytics are supposed to produce. The stories clearly reveal users who want things they can’t have, don’t understand the things they get, ask for things they don’t need, don’t use the things they’re given, and remain therefore un-enlightened as to the behaviour and performance of their online assets. This is making it very hard for the analysts to tell them that what they really need is more time, more staff, and more money for new tools.

Is there success and satisfaction out there? Yes.

The most advanced of the practitioners are doing wonderful things. The smartest of them have generated huge wins from the tools they have. There are anticdotes aplenty. It’s not impossible.

But it’s not easy. Even those with clear wins aren’t living on easy street. Those without them seem nearly defeated. The barriors are just too high and too hard. The few wins are not worth the enormous costs.

It seems like high end Web Analytics is the new CRM, where companies used to spend hundreds of thousand, or even millions of dollars, only to find their sales staff secretly using ASK on their laptops.

What I Think It Means

And that’s the thought that got me. The high-end packages can out-perform Google Analytics in just about every way you can think of or discuss, except in the ease with which basic data and analysis is delivered.

Which leads to a paradox; the high end package can out perform Google Analytics only if they can be fully and properly configured, solve some very serious data integration problems, actually do most or all of what they promise, and become accessible to a very diverse set of end clients. But they’re failing at these four tasks which leaves most end-users getting only very simple reporting out of very complicated and expensive packages.

Wouldn’t they be better off just getting these simple reports from a simple and cheap (even free) package?

PostScript

I wish it weren’t true. I want the full promise of the high end. And it takes a lot to convince me that something possible is impractical.

But if the collective status of the smartest and best resourced analytics users is as it appeared at XChange, I think I just saw The Fonz water skiing in a leather jacket.

At the Search Engine Strategies Conference in San Jose this week I participated on a panel entitled “Identify, Analyze, Act: SEM by the Numbers”. Below are my presentation slides, and some accompanying thoughts and comments.

Of course, it’s great to see such a high-profile discussion of PPC Analytics. I’d love to dig in and talk about very specific strategies and tactics, metrics and applications, etc.

But with limited time, and with most paid search managers up against some real barriors to applying solid analytics to their campaigns, I decided to use the opportunity to talk about these challenges in the hopes that raising visibility would encourage the PPC community to start requesting the kinds of changes we need from the engines and tools vendors and the ‘best practices’ widely discussed across the paid search community in order to make meaningful analysis easier and more commonplace.

Invisibility
The first challenge to paid search analytics is that a lot of the data you’d want to analyze isn’t readily available.

The prime example, which I’ve covered in detail before on this blog is search query information. Google Adwords and Analytics (or Yahoo or MSN) don’t provide it at a keyword level, and neither to a great many web analytics or even PPC management tools.

Similarly, SKU-level margins which enable SKU-level profit to be calculated to enable ROI to replace ROAS, is not available in most tools. That has been discussed a length too.

But if we’re trying to measure how people who search react to our ads in terms of our profitability, it’s rather amazing that a clear view into their search and our profit isn’t a default condition of analysis.

Deception
Another challenge to PPC managers is data that isn’t what it appears to be – untrustworthy data. Some of it is inaccurate, some of it is simply misleading. None of it helps.

Averages are the backbone of most paid search reports – the average ROI/ROAS for a campaign, the average cost-per-click for an adgroup, the average order value for one engine vs another.

But averages mask as much as they summarize. Averages without awareness of the standard deviation (or dispersion) of their data should be considered of limited value. Yet millions of PPC reports are consumed every day leaving impressions and causing decisions based on the ‘feel’ of these averages.

Similarly, raw performance data is presented in all kinds of PPC reports without regard for the statistical significance or margin-of-error of that data. Snap decisions to turn of text-ads, pause keywords, or inversely let them run can be based on data which would tell a very different story if reviewed slighly later in the process.

Why don’t the tools use conditional formatting or some other method to warn you that ‘there isn’t enough sample sizes to evaluate these numbers yet?’

And lastly I didn’t get the chance to speak on one last but very important point. And there isn’t enough time to go into it in detail here, but just about every revenue or profit number used to report or analyze paid search is ‘wrong’ or at least ‘suspect’ based on huge limitations in how keywords/clicks are matched with revenue over time.

Everyone knows that many people visit sites a number of times before they buy, and the question of which visit and driving method gets ‘credit’ for the sale is frequently discussed. Most search and analytics programs use the last-visit standard, although a few allow a first-visit option and some even enable the choice of a linear allocation. On the SES show floor I even learned of one package that does a ‘reverse time-decay’ allocation IF the last keyword is a brand term.

All of these have issues, but what’s more important is that they skew the performance of different types of keywords, and it’s hard to imagine that influence is fully considered when the eventual keyword and bidding decisions are made. (MUCH more about this later).

Unlimited Power and Resources
A few more challenges to taking advantage of search analytics. One is the shear size of the data we’re reviewing. Huge campaigns with hundreds of thousands of keywords, sometimes hundreds of Campaigns and Ad-Groups, and time-marches-on producing a wide range of time frames and performance trends. Plus of course the engines change, businesses have seasonality, competitors keep moving, etc.

Taking this all in, keeping the factors in mind, and driving to meaningful conclusions is not easy.

Within this world, and due to many of these factors, we’re making a near constant stream of changes to our campaigns. But are we making these changes carefully?

When changes are made, the current crop of tools don’t help us to record the time and date of the change, watch what happens over a significant number of days/clicks/conversions, and then remind us to confirm, extend, or roll-back the change. We’re conducting tests without any test plan, test scoring, or final review.

On a related note, most of the changes we’re making – adding keywords, shifting bids, modifying Match-Types, rewriting text ads – have impacts which ripple through our campaigns and certainly reduce the clarity of our reports. If we look at a two week period when 457 changes were made throughout the account in clumps throughout that time frame, what are we really learning about either what happened or what we should do next?

Lessons To Learn
As the above hopefully suggests, the core issues in being more analytical with paid search campaigns are not simply to produce and review more reports.

We have to get clarity and accuracy from our data first, apply sensible practices to the construction and management of our campaigns, and raise the bar for both the tools we demand and use and the way we understand and use the reports they provide.

We don’t need perfect solutions to any of these problems. Those aren’t coming soon and aren’t really necessary. But we should take steps to both understand and factor in these issues as we work to better learn what’s really going on within our accounts and how we can use that information to make better decisions and drive better results moving forward.

We also have to understand these issues and demand that the engines and tool vendors work towards handling them in more comprehensive and reasonable ways. Both sides have ignored these issues for too long.

The Match Type Keyword Trap
To include at least one practical element, I included a slide summarizing the several posts that live earlier in this blog about how to target queries more specifically using Match Types.

Final Thoughts
The feedback after the session was great, although I probably and characteristically bit off more than the time allowed. I hope this exposition helps those who were there and those who were not. As always, I’d be happy to answer questions or discuss it further in the comments.

The session I participated in at SES on Tuesday got a write-up by Avi Wilensky (who clearly takes notes very quickly).

It’s a good summary of my presentation and those of the other panelists. I’ll add some more thoughts and a copy of my slide deck shortly.

Update: Another from Lisa Barone at Bruce Clay Inc.

2nd Update: A full review of my presentation with the slides.

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)

Amazingly, I just finished the slide deck for my presentation a SES in San Jose next week. I’m not sure I’ve ever done a deck a full week in advance before!

The session topic is terrific: “Identify, Analyze, Act: SEM by the Numbers”, and so is the panel:

• Brian Cosgrove, Site-Side Analytics Engineer, AvenueA / Razorfish
• Heather Dougherty, Analyst, Hitwise
• Michael Stebbins, CEO & Founder, Market Motive
• Brett Crosby, Senior Manager, Google Analytics, Google

I’ve decided to use my presentation time to run quickly through the epic battle for truth and justice in the world of PPC. It’s my first PowerPoint that includes images of Mephisto, Darth Vader, and all the Superfriends.

In the end good triumphs over evil, and hopefully I’ll reveal the some of the secrets that smart search marketers can use to protect themselves and their budgets from the dark forces.

If you’re going to be in San Jose at SES, please try to attend on Tuesday at 4pm. After the show I’ll make the full deck and details available here for anyone who can’t make it.

It’s Monday morning. You manage a large paid search account. What should you do today?

The answer depends, of course, on the status and situation of your account. But let’s think and talk generically.

And let’s assume, for this one brief moment, that you have fast and easy access to any reports or dashboards that you might wish to see to help drive your decision. All you have to do is decide what you’d like to see and the information magically appears on your screen or printer.

Day Planner

The first candidate is rather obvious; if our campaigns are incomplete, improperly constructed, or if our business or competitive environment has changed, then a clear priority should be to adjust the campaigns accordingly.

This might mean campaign and adgroup re-organization, keyword and match-type adjustments, changes to our bids and text-ads or landing pages.

But if there’s none of that type of work to do either (I know, that’s my second nearly-impossible assumption of this post thus far) then what?

What if you have an on-target campaign with properly valued keywords and queries producing a generally acceptable number and ratio of sales and revenues?

Or at least, if you can’t quite stretch your imagination that far, one in which there are no major gaping holes or areas of total embarrassment. What do you do then?

Opportunities and Risks

In a solid and stable campaign there is still a lot going on. The world is still changing – in terms of seasonality, user behavior, competitive and non-competitive bidders, the keyword matching algorithms and search engine rules, etc.

In this environment effort and emphasis should be placed on opportunities and risks. Areas where you could do better, and areas where you’re not doing so well.

A lot of the opportunities and risks in a paid search campaign can be identified using comparisons and calculations.

Comparisons

Comparisons tell us how things (Ad-Groups, Keywords, Products) are doing in relation to both other similar things, or in relation to another time frame.

Reports telling us the top and bottom 5 Ad-Groups by ROI (or ROAS) or the top and bottom 50 keywords by Gross or Net Profit can help you quickly spot winners you should let run, or losers that you should fix or kill.

Even better are reports listing the most improved or most deteriorated performance – which Ad-Groups or keywords are making (or losing) money this week/month when they did the opposite last month. Our friend and advisor Avinash Kaushik has talked about the power of these reports for web analytics in general and reminds us that they have great power in PPC too.

Calculations

Simple calculations can tell how components of our campaigns are performing against either goals or averages or medians – and often make it very clear where time or attention should be placed.

If you have Ad-Groups or Keywords which under-perform your goals on 30-60-90 day moving average basis, aren’t those ripe for review?

If certain Ad-Groups have an exceptionally wide performance distribution (on one of several possible metrics, such as CTR or Conv. Rate), doesn’t that suggest that the text-ads or the landing pages aren’t well matched to the queries?

More sophisticated calculations can reveal where bids are under or over their optimal settings, when ad position or text-ad CTRs are causing under performance, and even when certain keywords should be moved out of their current Ad-Groups.

Signposts

All of these examples suggest a world where the right information is collected by your search analytics software and then processed and formatted in a way that helps you know where your valuable time and energy should be placed.

Thus far, however, neither the search engines, web analytics providers, or even paid search management tools have really provided very much of this type of prioritization assistance.

What we’re getting is a lot closer to raw data than useful information.  And in the face of this raw data quite a few campaigns are managed on instinct rather than insights.

Of course, some paid search managers do the kinds of comparisons and calculations I’m describing either manually, in their heads, or via extensive efforts with a lot of data exports and Excel worksheets. But it takes a lot of work – time that itself is not used managing the account – and there are limits to how much post-processing and manual calculations can be done.

The Long Week Ahead

In a series of earlier posts I argued that we need better clarity into our paid search accounts. Today I’m suggesting that even when we get the right data there is a need for technology and automation to help make sense of it, to translate the data into actionable information.

As far as this post-series goes, that leaves only the last of the three challenges facing paid search managers – the efficiency of implementing changes once you figure out what’s required. I’ll tackle that one in an upcoming post.

(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)