Months ago we were sharing our development progress with our advisor Avinash Kaushik and asking him for input in terms of the kinds of search analytics capabilities we should include in ClickEquations.
Avinash, like another charismatic leader, shared his passion for change.
“It’s fine to see the top 50 keywords by clicks, or the top 10 ad groups by revenue” he said, but what is much more powerful is to see what’s changed – the keywords making money today that were not making money yesterday. Or the campaigns that performed well last month, and are not performing well today.”
In our talk he went on to explain that “the top ten of anything rarely changes. With the Delta Reports you can truly see “what’s changed” and what changes is what’s actionable. If keywords were suddenly producing clicks or conversions, a marketer can and should go figure out why.”
This leads to another word you hear often when talking to Avinash – “insight”.
“When they go and figure out why a keyword or campaign is suddenly performing or failing to perform, they’ll likely learn something about their business or market. Something they can capitalize on.”
ClickEquations Analyst Delta Reports
The conversation inspired us to add a major new capability to our ClickEquations Analyst Excel Plug-in.
The Delta Report tables make it possible to request information about any PPC metric, and get back a sorted list based on the difference between two time periods.
Or as Avinash would say, we show you what’s changed:
- So you can request the top 100 keywords making more money this week than last.
- Or the 5 campaigns whose click-through-rates have dropped the most dramatically between January and last September.
- Or the worst 5 ad groups in terms of declining impressions.
- Or just about any period-to-period comparison pre-sorted by the amount of change.
See These Reports In Action
The Delta Report tables in ClickEquations Analyst allow you to request any data for any time-frame, and build whatever report or dashboard you need. They automatically compare the selected timeframe to the prior period (this week to last week, last month to the month before it) or you can specify any two arbitrary periods to compare.
This is very powerful.
But we include pre-built reports that are ready-to-use and take advantage of these features too.
One is our Growth/Decline Report which shows you twelve different views of your campaign and keywords based on the amount of growth and decline against a variety of metrics including clicks, revenue, and profit.
This report is the subject of the next video in our ‘ClickEquations in 90-Seconds’ series – which is now live on YouTube and below.
Video: ClickEquations In 90 Seconds – Growth/Decline Report
PS: Thanks to Avinash for the idea and inspiration.
Earlier we looked at the Google Adwords Impression Share metrics. These tell you if your ads are running when people type search queries that match the keywords you’re bidding on.
Very rarely will you find that the ads in your campaign are running anywhere near 100% of the time. Often you will find that they’re not running 25%, 50%, even 75% of the time when you probably expect that they’ll appear.
This will be shocking to some, and should be considered a huge problem.
The only reason to bid on keywords is if you want your ads to run when matching queries are typed. There is no logic to the idea that missing impression share is ‘ok’ because you don’t need the ‘extra impressions’.
- Isn’t it possible that the impressions you’re missing are the best – meaning highest converting – impressions? Or the most competitive impressions – those others are trying the hardest to take away from you? Do you really want to buy only the remnant impressions?
- Or it could be that you’re getting the best ones, and missing the worst impressions – particularly if you have much lower impression share than impression share exact match (and if you’re keywords are well chosen). It could be that you’re missing lots of wierdo-broad-match Google Gumbo queries that you wouldn’t want anyway.
The point is that lost impression share is an uncontrolled mystery.
If your campaigns have high amounts (say over 30%) lost impression share you’re letting Google decide how and when to advertise your site and spend your money.
Shouldn’t you decide?
Divide And Conquer
As discussed in post II in this series, your first step is to break down your campaigns into logical units for which IS becomes meaningful. IS metrics across campaigns with dozens of dis-similar ad-groups aren’t actionable.
Of course, re-organizing campaigns is a large and difficult process. Adwords Editor makes it possible in a simpler matter than before, but it’s still a lot of work.
At a minimum your ‘must win’ ad groups should be isolated in ways that give you good visibility into their IS performance. Your core brand terms, which we’ve written about before in terms of organization, are a good place to start.
Then I’d suggest creating a slum for your losers, misfits, and keywords of questionable origin. Every campaign has them, ad groups that are a bit of stretch, a test, perform terribly but are hung onto for sentimental value, whatever.
Get anything you really don’t care about, or know deep down isn’t likely to work moved out of your bread-and-butter campaigns and onto ‘short bus’ campaigns.
You can let them run there, work on improving them, ignore them, whatever. But they will no longer be mucking up the impression share metrics in your more meaningful campaigns.
Now Do Everything Right
Once you have reasonably tight campaigns, and clear IS metrics for these cleaned-up campaigns, you can start working on a fix to the real problem(s).
Except for one tiny problem: You can’t fix what’s causing lost impression share.
Lost impressions are a symptom of a much larger disease – the overall quality of just about every aspect of your campaigns design and performance.
So if you want to eliminate lost impression share, you’re just going to have to improve nearly every aspect of your campaigns:
- Build out your match type keyword traps. Increasing coverage of exact and phrase match terms, and bidding them properly, should garner more impressions for those terms for broad-match heavy campaigns.
- Harvest search queries to increase negatives and add new phrase/exact match keywords. Every step to remove excess and intelligently expand your keywords improves the value of the IS measurement and hopefully the number as well.
- Check and address quality score across your campaign. Ad Rank = bid x QS, and often QS isn’t thought of enough.
- Write and test more text ads. This is the most overlooked effort in PPC, can drive quality score which drives ad-rank, and more importantly can multiple CTR by many times which grows everything positive.
- Bid differently. As a component of ad-rank, which plays a huge role in Impression Share, bids are a factor. Notice that bids don’t have to be your first or only lever (And watch for our upcoming blog post series on bidding.)
Impression Share is an interesting, and perhaps unexpected, broad measure of the quality of our campaigns because of how it’s influenced by the wide range of factors suggested above. Paid search is way too complex, and still to opaque (and perhaps inconsistent and imperfect) to pretend that it’s a clear measure that will track ‘campaign quality’ in any precise way – but it is an indicator and one we can use in surprisingly far-reaching way.
Impression Share Wrap Up
A lot of the paid search process happens without enough feedback or context.
Any available metrics that help us understand and measure the funnel we’re trying to push people through, therefore, is very important.
Other than the laughably inaccurate traffic/click estimates in the keyword tool, impression share is our only way to get critical visibility into the size of the audience we’re aiming at and keep a scorecard of our progress toward reaching it.
Bonus Link: Watch our ‘ClickEquations in 90 Seconds’ video on how ClickEquations Analyst enables you to track and report on Impression Share.
What do you do to fix low Impression Share? That’s the question we were left with at the end of the last Impression Share post.
But before we get to that, there is something else about Impression Share that should be discussed.
Does It Matter?
Impression Share is only provided at the campaign level.
In most accounts, campaigns are roll-ups of many ad groups, and ad groups are roll ups of many keywords. Usually keywords and ad groups are not all of the same type or importance.
So before getting too flustered about missing impression share it’s worth stopping to decide if it matters, or more precisely if you can actually tell if it matters.
Suppose we have a campaign called ‘Bedroom Furnishings’ which contains 27 ad groups for everything from ‘nightstands’ to ‘sheets and pillow cases’. Within each ad group are 50 to 500 keywords, of various levels of importance and at various match types and bids.
For this business, suppose that within Bedroom Furnishings, 70% of sales are bedroom sets, 10% are headboards, 8% are lamps and the remainder are all kinds of little things. (assume all of these sales are profitable.)
In other words, only 3 of the 27 Ad Groups represent 88% of the company sales and profit.
In this case all the Impression Share metrics are useless.
The campaigns and ad groups are not organized in a way that allows us to use the IS information as it is provided.
There are too many different types of targets mixed into a single campaign. For some of the ad groups it contains we really want all the impressions we can get. For others, there are more firm ROI targets and beyond a certain point we can’t afford to bid. Still others just don’t matter much.
If we want to use and benefit from IS metrics, we need to reorganize so that one campaign holds the large volume (and profit) ad-groups, and within those ad-groups only the successful corresponding keywords.
Move the marginal keywords and ad-groups into their own campaign that can be tracked separately. And move all the other ad groups and keyword into a third campaign.
This is the minimum reorganization to make IS useful.
- At this point we can look at the IS metrics for our ‘large volume and profitable’ campaign and reasonably obsess about every % we miss.
- We can watch and work on the ‘marginal keywords and groups’ for these high profit categories, and make smart choices to improve them both in performance and IS.
- And we can watch the IS for all our other categories but probably not do too much about them.
A Bag of Rocks and Diamonds
Let me try and make the whole point another way.
Pretend you had a bag filled with 10,000 rocks and 100 diamonds.
If you knew the bag had a hole and a few dozen things had fallen out, you’d be concerned – but really not know how serious the problem was. Maybe all you lost was a few rocks.
Wouldn’t you feel better though if you could put the diamonds in their own little bag and really make sure that nothing fell out?
Keywords and ad groups are the same way. You can’t take great care of the good ones when they’re mixed in with all the junk. Separate and segregate.
A little bit of a big topic for another time, but the use of Impression Share highlights the need.
I’ve written about the problem of averages before. Impression Share is another place that getting average data for a disparate set of things can greatly diminish the value of the information. It’s up to you to organize so that the metrics provided are useful.
End of Part II
In the next post we’ll leave this issue behind and assume you’ve organized your campaigns in ways that make the IS metrics meaningful, and talk about what to do to fix what you find.
What if your ads didn’t run?
You picked the keywords, placed the bids, people searched, but your ads didn’t show up?
It happens every day. In almost every one of your campaigns.
It’s documented in a metric called Impression Share (in Google Adwords, no MSN or Yahoo equivalent yet.)
Impression Share displays the percentage of the time that your ads were displayed to people who entered search queries which match your keywords (at their specified match types).
100 minus Impression Share is the percentage of the time your ads didn’t run when you thought they would.
If your campaigns are profitable, the missing impressions are missing profit. Who can afford missing profit these days?
Three things stand between you and this extra profit:
- Getting your Impression Share metrics.
- Knowing what they mean.
- Taking the steps necessary to drive Impression Share up.
Finding Impression Share
To get an impression share report most people have to go to the Reports tab in Adwords, build a Campaign report, and edit the fields to include IS, Lost IS (Budget), Lost IS (Rank), and Exact Match IS. You can’t access these metrics at the AdGroup level (a shame we’ll decry another time).
Impression Share Options in Google Adwords Report Configuration
Impression Share Metrics in ClickEquations
Understanding Impression Share
There are four Impression Share Metrics. IS, IS Budget, IS Rank, and IS Exact. The first three are relatively straight forward. The last is a bit confusing.
- Impression Share = The percentage of the time your ads where shown (for this campaign) out of the times it was eligible to be shown. Eligible means the search matched your keyword, your account was active, the geo-targeting and day-parting and other settings were right, etc.
The next two metrics explain the Impression Share you didn’t get. If your Impression Share is 70%, then your Lost Impression Share is 30%. But why didn’t your ads run those times? The next two metrics tell you:
- Lost IS (Budget) = The percentage of impressions lost due to budget constraints
- Lost IS (Rank) = The percentage of impressions lost due to low Ad Rank (cost-per-click bid x Quality Score).
So Impression Share + Lost IS (Budget) + Lost IS (Rank) = 100%. These tell you what you got and what you didn’t get, and why.
The last one is trickier. For that reason I don’t think it gets the attention it deserves. And I’ll admit that I didn’t understand it until today when I started digging into this topic while doing some analysis work.
- Exact Match IS = The impression share of your campaigns as if your keywords were set to Exact Match. That’s the official Google definition – the one that seems generally misunderstood.
So let’s try it a different way. Exact Match IS tells you the percentage of the time when your ads were displayed for search queries that exactly match the keywords in your campaign.
One minus Exact Match IS is the percentage of the time when someone typed EXACTLY your keywords in as their search query and Google still didn’t show them your ad.
Using Impression Share
The IS metrics are great because they tell you things you could otherwise never know about your campaigns.
Foremost, they tell what you’re getting and what you’re missing in terms of impressions – and from there the calculation of missing clicks, conversions, and even revenue/profit is rather simple (see chart 2 below).
This is huge. We can finally at least partially answer the perennial question ‘How much more could I make from my paid search campaigns?’.
Start With Exact Match IS
Although it somehow seems offered as an afterthought metric, I’d recommend starting by looking at your Exact Match IS.
This simplifies the world and if you’re buying anything near the right keywords provides a sense of how you’re doing in terms of getting shown to the people looking for you.
If your Exact Match IS isn’t high (as usual there’s not simple way to say what that means, but let’s go with 70% or higher) then you really need to work your way down the list and think about your keywords, bids, quality score, ad copy etc.
Think about it this way: if Google doesn’t think it’s worth their while to show your ads to people typing in exactly the keywords you’re buying, how can you expect them to think running your ads is worth it for search queries you aren’t even directly buying?
Now Look At Impression Share
Let’s assume you have good to great Exact Match IS (you worked that out over the last 90 seconds right?). Now look at regular old Impression Share.
Here you’re likely to see something ranging from confusion (some highs and some lows) to a real bloodbath (all lows or at least no highs).
The reason these aren’t all 98.7%? The only easy answer is if you’re lucky enough to have some %’s in the IS Lost Budget column. And I say lucky only because that column is at least definitive. You can spend more and get those impressions.
Lost IS (Rank) theoretically explains the rest, but really it doesn’t explain very much.
Rank means Ad-Rank. Ad-Rank is Bid x Quality Score. Bid can simply be an insane fee you pay despite what something is worth, you probably don’t want to ‘win’ that way. Quality Score is determined by many things, as we’ve been over.
So Impression Share provides an easy way to see something, and know something that is very important to know. But it doesn’t provide a magical simple path to improving the problems it helps you find.
End of Part I
To solve our problems we’ll have to follow the path through our campaigns.
Impression Share forces us, if we look at it hard enough, to understand the roles of both bids and quality score, to think about our match type strategies, to organize our campaigns more effectively, to include the right keywords not just the most keywords, and to broadly see how interconnected the many options really are in a paid search campaign.
We’ll dig into that work in a follow up post later this week.
Target, Value, Satisfy, Understand. That’s the mantra of High Resolution PPC.
The idea is to stop thinking about mechanical components like keywords and bids, and instead focus on a logical marketing progression.
We want the tools to support our work process instead of having to build a work process that serves the tools.
The First Step is Targeting
Targeting means showing your ads to the right people. Paid search ads are delivered as answers to questions. People type in a search query and you pay for the privilege of having your ad be one potential answer to that question.
So you must know:
- What questions do you want to answer?
- What answers do you plan on giving to those questions.
Campaigns, Ad-Groups, and Keywords are your targeting tools.
Keep in mind that they’re called ad-groups, not keyword-groups. The goal is to segregate keywords, controlled using the match-type option, so that all the queries attracted by a single ad-group are questions answered by the text-ads in the ad-group.
In other words, you want every searcher to see a a text-ad that is directly relevant to their search. To do that, you must organize your ad-groups around the search queries they attract, not the keywords they contain. Every search query that causes your text ads to be displayed, should be highly relevant to the text ad that is displayed.
Let’s illustrate with an example.
Supposed you knew that all of the following search queries would be coming into your account, and you could hand match them to appropriate text-ads before the results page was delivered to the searcher.
- Discount Dyson Vacuum
- Dyson Vacuum Features
- Dyson Vacuum Coupons
- Compare Dyson Vacuums
- Cheap Dyson Vacuum
- Dyson Extra Cyclone
Wouldn’t you want the 3 price-related queries to get a price focused text ad, and the three feature related queries to get a feature-related text ad? Doesn’t it make sense that this would produce the highest click-through-rates and the highest ROI?
Yes, of course.
This is why you have to think about queries not just keywords, and use ad-groups to target the groups of people you want to talk to.
The Second Step is Valuing
Once we’ve targeted the right people using different ad-groups, we can then look inside the ad-group and take advantage of the fact that we don’t have to place the same value on everyone in that group.
Match-Types, Negative Keywords, and Bids are some the core valuing tools.
Extending our previous example, suppose experience tells us that people who search for ‘Cheap Dyson Vacuum’ just don’t buy from us (we’re not that cheap). That has no value, so we add ‘cheap’ or ‘cheap dyson vacuum’ as a negative. But ‘Dyson Extra Cyclone’ is a very specific feature so people who search on that are far into the buying process, we see that query frequently with a high conversion rate. Make that an exact match and bid it up.
You get the idea. By correctly using these tools, watching our search queries and continually refining our campaigns, we can group queries within an ad-group, value them appropriately, and manage both budgets and returns.
The Third Step is Satisfying
People decide how well our paid search advertising does. They decide how to formulate queries which trigger our ads (or not) and they click (or don’t) and buy (or not).
Text-Ads, Landing Pages, and ultimately your offers, website, and checkout process are your satisfaction tools.
When we’re targeting accurately, and valuing properly, we have the ability to focus on satisfying those who see our ads and visit our site. Trying to do so before we’ve completed these steps means, by definition, that we’ve got too wide a range of people coming to really have a fair shot at measuring the results of any attempts at improvement.
There is little doubt that text-ad writing, let alone testing, is the paid search option that gets the least attention and effort as compared to its importance and potential impact. Rewriting a text ad and doubling performance – in terms of CTR which even if it does not improve conversion rate can proportionally increase revenue – is common. We’ve seen many ad re-writes produce 10x-20x CTR improvements. Try that with a better bid.
But writing is hard. Writing is subjective. Writing takes quite a lot of time. None of these make it less important.
All the same is true-er for landing pages, website experiences, and shopping carts. This all very hard, time consuming, and costly work. But it is ultimately directly responsible for the success or lack thereof of paid search campaigns. Even within whatever limitations exist, it should be considered, managed, and measured.
The Final Step is Understanding
Even in this greatly summarized view of the paid search process, there are a lot of moving parts. Each exists by the hundreds, thousands, or hundreds-of-thousands in typical campaigns. They occur tens-of-thousands of times every day as impression and click counts increment. And we have weeks and months of history for all of this to consider and trend.
Paid search can only be managed effectively if you can learn from this data – look into it and find information.
Website and Search Analytics are your tools for understanding.
This means knowing which metrics are important. And when trends are really trends. And how all the numbers affect each other.
It also means that you need the ability to get at the data that can inform you, and easily produce the reports and dashboards that will do so for both you and your colleages or managers.
The key is continuous improvement. Paid search campaigns are never perfect. And they exist in highly dynamic environments. Only through hard work to understand the campaign and know the best move to make next to improve it can you really drive great results.
The shift into the T-V-S-U mindset is a big one. It changes the process of managing paid search and the way you think about and use the options and tools the search engines provide. More importantly, it aligns your search and marketing goals, and makes it easier to prioritize your PPC efforts and measure your results along the way.
In future posts we’ll dig into each stage and step of this process in more detail. Have questions before then? I’d love to hear them, or your comments.
This post is part of a series on High Resolution PPC, a framework for understanding and managing paid search advertising.
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.
Yesterday I noted that paid search managers face three challenges in trying to effectively manage paid search campaigns:
- A lack of clarity (reporting problems)
- Difficulty defining priorities (strategic and planning problems)
- Horrible inefficiencies (mechanical and processes problems)
I believe that these problems need to be solved in order to improve paid search management, both the profession and the results.
First you need to see what’s happening, then you’ll want to decide what needs to be done, and then you can hopefully get it done with a reasonable amount of effort.
That doesn’t sound like too much to ask.
But 4-5-6 years into explosive growth in paid search and we’re hardly out of the starting gate. Today I’ll expand on the issues regarding reporting and clarity, and in future posts dive more deeply into the problems of setting priorities and executing paid search tasks.
What Paid Search Reports Don’t Tell You
Paid search is about answering questions. People type queries and search engines return results, which are lists of possible answers to the questions they believe are being posed. I want to structure my campaigns as tightly as possible around those search queries.
Every search engine tells you how many impressions your ads had, and how many clicks you got. They have to I suppose, since the CPC is what drives your billing. What I really want to know is what did I miss? And why? Then I can set goals and define strategies or tactics (or at least design tests) to do better.
Each conversion hopefully generates more revenue than it cost to cause that conversion, which is reflected in the rather innane ROAS metric. Being impressed with a good ROAS seems akin to believing you’ve saved money by buying something you didn’t want when it was on sale. Goods or services have costs (COGS) and the only metric that matters is ROI taking account (at least) both direct-marketing and goods/services expenses.
When my clicks do generate revenues, I’d like to know which ones. Then I can make wise decisions about future investment and effort around certain keywords and queries.
So I’d like to know which search queries generated which results, how many clicks I didn’t get and why, the actual amount of profit made on each transaction (and from each keyword, query, and click).
Do any of these sound unreasonable? Far-fetched? Demanding?
Yet these desires are not generally or specifically fulfilled through the paid search reporting capabilities provided by the search engines, popular web analytics software, or even specialized PPC management tools.
Surprised? The devil is certainly in the details, and some of the information defined is available in some packages/places, but generally with huge compromises and limitations that disqualifies or invalidates them as actual or sufficient information.
Really? Yes to the best of my knowledge, as the next post will review in somewhat excruciating detail. I’m happy to learn new facts or discuss this further in the comments – significant corrections will be appended to that post.
User search queries, accurate revenue & expense allocation and matching, and ROI reporting are just three of the ways that the current generation of PPC reporting generally fail paid search advertisers and managers.
The fact that these problems/limitations are seemingly not well known, frequently discussed, and therefore clammored for as improvements is one of the things that has to change to move the business/market forward.