The system is credited with a 60% drop in major crime and has subsequently been adopted by major cities including Washington DC, Los Angeles, Baltimore, and Philadelphia. Interestingly, all that really sparked the change was the acquisition of information—trend insights into neighborhood crime patterns over time.

In the 1999 interview, “Betting on Intelligence,” CompStat creator Jack Maple outlines the fundamental principles behind CompStat:

1. Accurate, timely intelligence clearly communicated to all.
2. A rapid deployment that is concentrated, synchronized and focused.
3. Effective tactics and strategies.
4. Relentless follow-up and assessment.

A phenomenon that follows a similar arc of intelligence acquisition and process is occurring today in the natural search industry.

Shift From Limited View To Landscape View

In much the same way the NYPD moved from examining individual incidents to mapping crimes and compiling data to expose crime trends, the search industry is moving from an ‘incident by incident’ (keyword-by-keyword) view to a technology-powered ‘neighborhood by neighborhood’ (search landscape) view.

Driven by mature technology that gathers, stores and analyzes natural search data over time and allows for segmentation on a ‘neighborhood-by-neighborhood basis’, this information empowers SEO professionals to discern trends and communicate, report and act on these trends with maximum efficiency.

Specifically, technology-enabled collection and analysis of ‘big data’ in the SEO industry has enabled marketers to take a new, broad neighborhood view of their natural search landscape as opposed to the keyword by keyword perspective they were previously limited to (see screenshot below for more on how technology is enabling that view).

The new standard for search marketers as Police Chiefs of their online cities is to understand what is occurring on a holistic, global basis, thus arming themselves to respond to both opportunity and threat in their own online ‘neighborhood.’

Technology enables a landscape view over time

Rethinking The Approach In Search Marketing

Aside from making you aware of trends taking place in our industry, why does this matter to you as a Search Marketer?

For many still operating in the SERPs with a ‘pre-CompStat’ (pre-enterprise SEO technology) mindset, the approach is still an ‘incident by incident’ one. Many may not be aware that a ‘CompStat’ broad, data-driven view that empowers SEOs to be the informed Police Chief of their online city is even possible.

Others may need to actively change their thinking from that of Police Chief with an incident-by-incident view to one of a Police Chief who has clear insight into the unique segments of their city. This might include establishing similar principles as Jack Maple did for CompStat.

In SEO, these guidelines may be formed by asking yourself:

•  Given access to natural search trend data, segmented by the unique ‘neighborhood’ topography of your online city, how would your practice of SEO change?
•  Would you approach opportunity discovery any differently?
•  Given your newfound ability to measure, would your willingness to try new things change?
•  How about your approach to major projects such as a site redesign?

Technology In SEO Is The New Norm

In the late 1980s before he invented CompStat, Jack Maple manually plotted crimes across subway stations on a 55-foot-long wall map. Graduating to technology as a solution that kept track of crimes, not only by location but over time, allowed for analysis of crimes by neighborhood, effective modification of tactics, and manpower deployment with follow-up.

In a remarkably similar way, this evolution to data technology is mirrored in the search marketing world. Once tethered to expansive excel spreadsheets, search marketing teams now have access to technology that allows for tracking global natural search trends in a previously inaccessible manner.

When Mayor Giuliani launched CompStat in the late 1990’s, one can imagine that there were Police Commanders who resisted the technology, insisting on doing things the old, gut-driven way. Others may have been skeptical about the kind of impact mere ‘information’ could have on their ability to influence their neighborhoods.

But with time, they came to realize that understanding the broad, trended view on a neighborhood-by-neighborhood basis, knowing where and when to deploy resources, modify tactics or try new things is vital to operational success in their neighborhoods, and indeed, leveraging technology to gain that view is the future of their industry. The alternative, perhaps, was to be left behind.

That’s their expectation. The reality is AdWords for even a moderate budget could really be a full-time, 40-hour-per-week job. There are many intricacies in an AdWords account, and it’s becoming more complicated by the day. Analytics plays a vital role in deciding how to spend your money effectively.

Buying a number one ad position in Google can bankrupt even the heftiest of budgets, and honestly, do you need that number one position to make more money?  Could number 4 make you more, or just as much, for a much reduced investment?  Once you determine the best position for your keywords, how do you make sure they stay there?

In this article, we’ll show you how to find the most profitable positions, and how to create automated rules in AdWords to ensure your keywords show ads in the most desirable places on the search results pages.

Now we can see in which positions our keywords become the most profitable – profitable – not necessarily the highest earners. I can spend $10,000 on a number one position for 30 days, and make $20,000. I made a 100% return on my investment. If I buy the same keyword into the number 4-6 positions, spend $5,000 for 30-days, and receive that same $20,000 in revenue – I just increased my ROI to 300%.

Here’s how to see the best position for the investment in Analytics.

First select “Advertising” from the sidebar menu.  Then choose “AdWords” and “Keyword Positions” from that sub-menu.

From this report view, you can see how many AdWords visits your keywords received, and the Site Usage for each of those keywords. This can help you determine which positions and keywords provide the lowest bounce rate, longest time on site, etc.

If you don’t track revenue with your AdWords account, this is still extremely important information to have.

If you do track revenue, you can take this report one step further. You can see how much revenue you generated per keyword, by position. This is really important when evaluating the necessity of a number one ranking.

First choose “Ecommerce” just below the Explorer tab above the graph:

Then choose “Revenue” from the dropdowns. You can do one or both here; it is good to see position performance for the keywords that make the most money and for the keywords that get the most visits.

In the example above, you can see that the keyword “Pink Widgets” made the most money in the 8^th position in the sidebar. This is odd, but it’s not unusual. You’ll see results like this all the time.

Here’s another example where position 3 at the top brought in 5x the revenue that position 1, 2, or 4.

So what do we learn? We now know that position one, for these keywords, is not necessarily the top converting position. The formula you use to determine ROI for any given position is:

I can now adjust my CPC bid for this keyword and use an automated rule to increase or reduce my budget to keep me in a particular position. Previously, we used the “Position Preference” feature, but that was retired last year…so we now create rules to handle positioning of keywords.

To create an automated rule, choose the keyword(s) in your AdWords account and pick “Automate” and then “Change max. CPC bids when” from the drop down menu:

From here, you set the position and bid where you think they need to be.  You’ll need to set a rule to raise the bid when the “Avg. position worse than” is triggered, and a separate rule for “Avg. position better than.”

This is definitely something that needs to be monitored and tweaked until it’s working perfectly. I strongly recommend setting a max bid for each click so the rule cannot keep escalating the bid without a limit.  I also think the Frequency setting will take a bit of work.

You can set 2 or 3 rules and apply them to multiple keywords at once, or pick them from the list of existing rules, so once your rules are set up – adding them across the account becomes much easier.

The “Automated rules” section of AdWords will list all of your rules and let you tweak them, and see when rules ran on particular keywords or ads. Be sure you read and understand how rules work before you start. I recommend the Automated Rules best practices document from Google.

TIP: Take a look at your “Matched Search Queries” report and compare it to your Keyword Report in analytics.  Avinash Kaushik has a great article about how Matched Search Queries are truth, wherein the Keyword Report is “lies.”  This can also help you fine-tune your AdWords bidding process, as well as understand what phrases are actually bringing traffic.

Once you’ve set up your rules, be sure to annotate the change in your analytics account so you can watch and see if the ROI from your AdWords account improves over time.

If things aren’t going well, tweak your bid or your position until they improve. This technique will definitely take time, but it can offer huge returns when done correctly. This is just one more reason your AdWords account should not be an afterthought in your entire online marketing strategy.

Campaign management tools and analytics provide fundamentally different information, and while directionally similar in many cases, choosing the wrong data source can lead to inconsistent perception of success metrics between the executive level and those tasked with execution.

Understanding the difference between analytics and ad server data may be the only way to avoid making decisions that can potentially hurt your business in the long run.

Before getting too far into this, there are two unique types of reporting systems, analytics and campaign management tools, with a fundamental difference – conversion attribution.

An analytics system leverages day of conversion attribution; that is conversions / events that happen on a given day are associated with that day regardless of when the click occurred [which led to that conversion].

On the flip side, a campaign management system uses day of click attribution – conversions / events are associated with the day the click occurred which led to a conversion regardless of when the conversion occurred.

Note: some campaign management / analytics systems offer both types of attribution. These tools are versatile but cost money.

The Differences Among Reporting Systems

Different reporting systems each have their purpose. Campaign management systems (e.g. AdWords and AdCenter) offered by ad servers to manage campaigns may be dressed up with fancy graphs and charts to look like analytics systems; but if you look beyond the bells and whistles, they are still just management systems.

With a bevvy of reporting systems available, it’s no surprise that choosing which system to trust for internal reporting is a difficult task. Why not use the ad serving tools to determine performance?

In order to answer this question, we need to evaluate pros and cons of each attribution model. There is a place for both day of click and day of conversion attribution because they each provide unique, different insights in to the performance of your marketing efforts.

Day of click attribution is useful for managing the value of a click (bid management). In order to understand the value of a click, you need to know the number of conversions / events driven by common clicks (e.g. clicks driven by a given keyword, query, or demographic target).

Moreover, in order to understand hours of the day which drive more valuable clicks (for use in processes such as day parting) you need to be able to associate the time of the click with the conversion. What time a user converts is not relevant to the value of a click because we’re only concerned with whether or not a user executed a conversion after the click.

While marketers manage their campaigns, the finance team is measuring the financial flux of the business – in other words, the flow of money in and out of the company.

When reporting on what happened in a given time frame, a business operates on an accrual basis – money spent and revenue received in a given week is associated with that week – hence day of conversion attribution is useful for reporting. The CEO / CFO needs to know how much the company made and spent for a given date range. Without day of conversion attribution, this would not be possible.

The issue with day of click attribution is that when used for reporting purposes it can lead to well intended yet short-sighted decisions. Imagine a situation where you’re analyzing recent performance (let’s define ‘recent performance’ as clicks in a live cookie window; a widely accepted cookie window is 30 days).

If you aren’t looking back over the full cookie window, then you are most likely underreporting performance: clicks made a few weeks ago may have driven conversions that occurred yesterday. In the day of click attribution model, they would be associated with two weeks ago and would not be visible in your report for yesterday.

Compensating for the ongoing historical attribution requires a complete cookie cycle view of performance data sets as the accrual of conversions occurs. For this reason, we prepare daily rolling reports (a day over day snapshot of performance) for all performance monitoring to understand trends and expected performance on a single day’s events.

The window of data analyzed should account for the vast majority of conversions as observed in the average time for click to conversion, if not the entire observed cookie window up to a reasonable period of 14 – 30 days.

To understand relative performance trends, consider the case where we observe 66% of converting events on the same day as the click and the remaining 33% of spread out over the subsequent 29 days of the cookie window.

If your goal is a $5 CPA, a one day CPA of $7.50 is acceptable ($5 / 66%) and we would expect the CPA for that one day to make it’s way down to $5 over the course of the 30 day window (when using day of click conversion attribution).

If you forced yourself to achieve a $5 CPA on one-day performance then you will end up with a 30 day CPA of $3.33 ($5 * 0.66%) meaning that you left opportunity on the table.

Or breaking it down by day:

Another major advantage in analytics systems is de-duplication across channels. Online marketing is not a single channel ecosystem but we have to attribute conversions somewhere. Furthermore, the waters get even muddier when considering display attribution rather than search because advertisers will not only get click-based conversions but also view-based conversions via display.

Analytics will attribute a conversion to the last click-based referrer and as a result, a view based conversion will never be attributed to the display channel because, by definition, the user never clicks on the ad.

In order to accurately determine the viability of view based conversions, advertisers need to match up transactions between the ad server and analytics to see if the display channel was the only paid channel to reach the user or if there were other channels involved later in the funnel.

As analysts and marketers it’s our job to manage up, educating executives on the nuances of data latency and why one system is right for determining the value of a marketing channel while another is right for monitoring performance.

It’s rare that the C-suite wants to get into the weeds of campaign management; rather they want to provide accurate target metrics for their team(s) to execute on. Education about the difference in data within analytics versus management tools will only help the C-suite better understand and set those goals with more accuracy. Proper education should make it very clear why analytics is the right tool for reporting.

However, a high click-through rate does not assure a high conversion rate. The two can even have an inverse relationship: an ad copy geared towards curiosity clicks will result in fewer conversions, percentage-wise, than an ad copy geared towards qualified clicks.

Keeping An Eye On CTR Helps Maintain Conversion Volume

Focusing on conversion rate optimization and neglecting CTR will keep you from scaling up your paid search program. You need to maintain a high traffic volume to maximize the number of conversion opportunities. If you don’t do so, you’ll end up with great conversion rates but low conversion volume overall.

A better way to go about it is to maximize the conversion rate within a certain CTR range, thereby keeping a good rank at a decent cost per click and maintaining traffic volume.

Even excluding the effect of CTR on Quality Score (and thus CPC), it is important to find the right balance between an appealing ad copy which generates lots of clicks, and a more conversion-oriented ad copy which generates fewer clicks at a higher conversion rate. The Quality Score factor makes it even more important to optimize the CTR as it helps mitigate the average cost per click and lowers the cost per acquisition (CPA) as a result.

The best performing ad copies are rarely those with the highest CTR or the highest conversion rates, but typically somewhere in the middle for both metrics. They are appealing without being deceptive or too generic. Best-performing ad copies usually have a strong CTR and an ok conversion rate – as opposed to an ok CTR and a strong conversion rate – because of the Quality Score factor and its consequences on CPC.

Beyond CTR & Conversion Rate: Maximizing Profit Margin

In the following chart (inspired from actual data) ad #1 has the lowest CTR and the highest conversion rate, while ad #10 has the highest CTR and the lowest conversion rate. Again, these metrics do not always have an inverse relationship, but it is the case very often.

If you are still unconvinced, you can give it a try for yourself with two ads in rotation – the first one clearly geared towards curiosity clicks, the second one clearly geared towards qualified clicks – and this logic should be verified.To get back to our sample data below, it is worth noting that neither ads #1 nor #10 are the best-performing ones overall.

If you look at the conversion volume, ad #6 is doing the best without having the strongest CTR, nor the strongest conversion rate.

If you look at the CPA, ad #8 has the lowest CPA. Ultimately, ad #6 performs best with respect to the effective profit margin.

In a nutshell, one should keep an eye on the CTR when testing ad copies in order to maintain decent traffic volume and maximize overall profit margin. The right balance might take time to find, but the important thing to keep in mind is that the best ads in terms of CTR or conversion rate are most likely not the best-performing ads out there. There is a middle ground where profit margin can be maximized.

Also, while Quality Score is only available at the keyword level (and unfortunately not at the ad level) it does make sense to monitor this metric when testing multiple ad copies geared toward qualified clicks rather than curiosity clicks.

Google engineers designed their paid search algorithm in such a way that the inverse relationship between CTR and CPC forces search marketers into coming up with ads which don’t necessarily generate the most revenue per visit, but the most clicks. It is mostly about relevance to the end users, Google says – where relevance is defined based on clicks, whatever their engagement level is.

There are certain things which simply cannot be done on a mobile device – but the gap is quickly closing and tablets are at the center of attention as consumers are embracing mobile tablet PCs as their primary computing devices.

With “more normal” sized screens, tablets don’t have the same issues with site formatting that high-end mobile devices have long suffered from. As a result, we would expect users to have an easier time engaging with a website via a tablet.

In terms of market share, tablet PCs are being gobbled up by consumers (shipments are up 42% Q/Q and 210% Y/Y) so it’s about time you and your business take a serious look at how tablet users are engaging with your site and develop a strategy for reaching tablet users with your marketing program.

Fortunately, recent targeting modifications in AdWords allow marketers to isolate tablet traffic and even target ads by operating system. It’s only a matter of time for other marketing channels to step up their game and provide the same level of targeting. (Hello Facebook – maybe this is why they’re bringing ads to mobile operating systems…)

With the ability to parse data comes an opportunity for a test. Tagging URLs by device (I usually append the device to the campaign name and populate it into the utm_campaign parameter in GA) makes for a clean data set to determine the differences in user behaviors by device.

Comparing Desktop, Mobile & Tablet Performance

Below, I’ve normalized some key performance indicators for mobile and tablets relative to desktop. The terms being analyzed are generic category terms for an online-only service provider.

It’s worth noting that the company has a mobile and desktop version of the site but does not have a tablet specific site or app. Sniffer scripts are used to automatically send mobile users to the mobile site. Tablet users are sent to the desktop version of the site.

Albeit simple, the data set is quite revealing.

The most significant variance by device comes from impression volume. There just isn’t as much volume on tablets relative to mobile searches and more significantly desktop searches.

Even though relative click-through rates for mobile are higher than tablet users, tablet users show more intent to engage at a deeper level convert ata significantly higher rate than both mobile and desktop. Even more interesting is that the average order value for tablet users more closely resembles desktop than mobile search.

This is probably the most insightful metric on the table as it identifies the inherently unique, and impulsive, nature of mobile search behavior relative to desktop and tablet. Maybe mobile behaviors are different due to browser security concerns and the fear of credit card security (maybe forthcoming technologies like Google Wallet will fundamentally change conversion rates on mobile devices).

With such a massive discrepancy in average order value, it’s worth considering making a unique, cheaper, offering for mobile visitors to your site in an attempt to get them to stick around, engage, and convert.

Look At User Engagement By Device In Your Analytics

Diving deeper into the post-click analytics, we can better observe what happens between the click and conversion via on site metrics such as pageviews per visit and bounce rate:

With respect to PPV and Bounce Rate, once again, mobile shows poor performance relative to desktops with tablets in the middle, skewing closer to desktop behavior. Mobile users are less likely to stick on a site, moving faster and bouncing immediately more often than not, whereas users are more willing to engage with and seek out interesting content on a larger format device.

However, Time on Site shows some very interesting behavior – when a mobile or tablet user is engaged with a site, they spend far more time going through it.

There are many possible explanations for the dramatic variance in time on site, but I believe it comes down to when users are engaging on a given device. Tablets and mobile devices are more commonly associated with recreation while a desktop / laptop is more associated with work.

Personally, I use my computer for work, tablet for play, and when I’m commuting I use my phone. During a commute or while I’m sitting at home on the couch, I’m far more likely to spend a few extra minutes fumbling through a site.

If I’m surfing during the day, I’m usually pretty efficient with my time and less likely to spend too much time on a given site (except Search Engine Land, of course); and while I’m not a massive sample size, I don’t think it’s a far cry from the norm.

Conclusion

The numbers indicate that tablets do bridge the gap between desktop and mobile behavior patterns. Similar to mobile, tablet users search for a purpose and are more likely to click through an ad.

But unlike mobile, tablet users are not necessarily mobile and are more likely to engage with a site after clicking, displaying stronger engagement metrics and overall conversion behavior closer to desktop users.

Depending on your site’s method of monetization, the value of a click on a tablet may outweigh the value of a click form any other device.

Why is this important? I give one explanation of this as a measure of “Are my eggs all in one basket?” and “What happens if they are?”

For example, the client above has 66.57% of their traffic from search engines. This is a combination of organic and paid traffic. Of that 66.57%, when we drill further into that segment of data, we see that 84.5% of that is from Google alone with a breakdown of 45% CPC and 55% Organic Search.

We’re looking at around 36,000 visitors alone from Google for March 2012. What would happen if you didn’t receive 55% of those visitors? What would happen if an algorithm change or a shady SEO company (we all know there are many out there) caused your organic rankings to plummet? What would you do?

The flip side?

I’ve seen Google completely shut down a PPC account for a perceived violation of some sort. Yes – some account suspensions are legitimate, but sometimes Google gets it wrong and a perceived violation is not really what it looks like to them.  Instead of making a phone call, they just stop showing your ads. What would you do?

Having a healthy division of traffic sources is the key to weathering a storm like I described in the scenario above. In my opinion, relying on more than 50% of your traffic from any one source without a contingency plan is a bad idea. (We’ll talk more about the contingency plan in a moment.)

Many clients come to me wanting to improve organic rankings so they can reduce spend on CPC placements. I get it – PPC is expensive – and it’s kind of like crack – you get a hit (a visitor), you might get high (a sale) – and then it’s gone.

That being said, there is so much more to paid advertising than just keywords and ads in search results. That’s an entirely different topic though.

As search professionals, we tend to talk of things in terms of Google and “Not Google” – with a side of snark and sarcasm for the “Not Google” category. These “red-headed step-children” of the search engine world don’t drive the volumes of traffic that we’re used to seeing in the big “G” but they are definitely worth paying attention to.

If you are banned, filtered, or suspended in Google – you need to look elsewhere to supplement traffic. Here is where the contingency plan comes in and having your AdWords and Analytics data handy helps.

What Do I Do If My Google AdWords Account Gets Banned?

Create an occasional export of your AdWords account and save it in a safe place. If your AdWords account gets suspended, you have the data available to quickly import into Bing AdCenter and create some quick PPC campaigns with the money Google won’t take from you while your account is suspended. You can use this to supplement the traffic you’re not receiving from Google AdWords.

I’d love to say you just call a phone number from the AdWords team (*see Postscript at end of article) and get the account turned back on after a conversation; but that’s not how it works if you don’t spend a significant amount of money per month in Google’s eyes. If you’re spending less than somewhere around $10,000 a month, you’re relegated to the “contact-us form” and days and even weeks to try to get back what you lost. 

What Do I Do If My Organic Rankings Plummet?

Just like losing AdWords traffic, losing organic traffic can have a contingency. There are a few things you can do to mitigate the loss of traffic while you work on getting it back.

First, you should have a healthy presence in Bing. Should you change all the optimization on your site so you rank more keywords in Bing than Google? No, you shouldn’t. Should you make sure you’re getting a decent portion of traffic from Bing? Yes, you should.

Use Your Analytics Data To Recover Quickly

Export the keywords you were ranking for organically from your analytics dashboard, export the PPC keywords you’re already ranking for, remove duplicate keywords and create AdWords campaigns with what’s left over.

You will have to supplement your AdWords accounts with more money while you file your re-inclusion request, after you’ve fixed or explained whatever got you banned. Yes it stinks, but can you live without the traffic and sales you’re losing while you wait?

I’d also beef up PPCs in Bing AdCenter until your organic rankings come back. Yes – you’ll have to spend money – but if you can’t afford to completely lose the sales, the investment in PPC will come back.

The same thing can happen with a reinclusion request if your site is filtered in the organic rankings. You fix the perceived violation, and then submit a request to get rankings back, and then you wait.

I see traffic and revenue for my lodging clients from Bing, AOL and Ask on a regular basis. Enough that we at least need to tip our hat, say thank you, and take a look at things from the perspective of those engines every once in awhile.

My point here is to have a plan, or at least the outline of a plan should something happen. We follow the rules nearly to the letter at my agency, and we’ve had PPC accounts suspended because they thought we had malware on a site (we never did – and never found any evidence of it happening.)

You, too, can follow the rules and still have unforeseen issues crop up. Having a plan and using your analytics to solve it can save you a pile of loss in the long run.

Postscript:  After this article was initially published, a Google spokesperson reached out to provide this clarification on advertiser support:

Any advertiser can call our free phone support line (866-2-GOOGLE) at any time with any questions. Advertisers can also request a review or appeal of an ads policy issue by calling that number, and we review advertiser appeals very closely. More information can be found here.

Editor’s note: Advertisers using the above help line should be aware that the review process may not result in immediate action, and followup time could range anywhere from a few hours to a few weeks or more; advertisers may want to take note of the author’s original recommendations due to this potential delay.

Given more time to reflect, I want to offer up what I hope will be a constructive line of thinking around SPYW and the appearance of social results in the SERPs (snarky title of this article aside).

Google Introduces Us To Contextual Marketing

When Google first introduced us to the concept of contextual marketing with Gmail’s ads on the side of our email, there was a fair amount of noise made about this newly intrusive way of marketing.

At the time, the catch for Google was this: Knowing that a business model must emerge from the considerable investment in building a multi-gigabyte-per-user email service, how do you market contextually relevant ads to a Gmail user without telling them you have ‘read’ their mail?

The answer is that you don’t. Marketing contextually necessarily involves telling the user you have ‘eyeballed their stuff’.

By now, several years later, I suspect that most online users are not entirely comfortable with being marketed to in a manner that broadcasts that someone is leafing through their mail or social activity (even if it is just an algorithm), but they have made the calculation that the tradeoff in exchange for the convenience of what is still an excellent search engine, and for a mail service that revolutionized mail in searchable form with lots of free storage is a decent one.

The calculation has been so widespread, in fact, that a recent (Feb 2012) Conductor survey of 750 online users showed more than six out of ten (64%) online users use Gmail as their primary method of accessing email (57% via Gmail.com and 7% use Gmail via a mail program).

Contextual But Not Intrusive

Part of the ‘calculation’ may very well have been made because the ads are mostly not intrusive. In keeping with the standards Google themselves established on Google.com – isolating sponsored results outside of my online space at a time when competitors were mixing them in with organic results – Gmail ads are off to the side:

Others, such as Facebook, recognized users’ acceptance of Google’s nonintrusive advertisement tactics and followed suit:

Put another way, the ‘intrusions’ into personal space have been ‘(mostly) out of sight, (mostly) out of mind’ (and therefore have been tolerated by users).

With SPYW, The Google-User Contractual Relationship Changes

With that in mind, perhaps some of the hullabaloo around the SPYW results in the SERPS is because it crosses the unspoken ‘(mostly) out of sight (mostly) out of mind’ threshold that has been implicit in the user-to-online marketer relationship.

That is, the overzealous mixing in of Google+, Picasa, etc. results leads to a shock factor — one I’d argue extends beyond the initial shock of an interface change–in seeing information from my private photo albums, social networks etc. in what has traditionally been a ‘sacred space in the search results.

Mentally, it has crossed the ‘(mostly) out of sight (mostly) out of mind’ contractual threshold that I have forged with Google through years of precedent.

A Lesson For Google From The Brick & Mortar World

To drive this point home, we turn to a recent lesson on consumer marketing from the brick and mortar world. In February, the New York Times ran a lengthy story titled How Companies Learn Your Secrets.

The article was, in large part, an expose on how Target mines consumer purchase behavior to derive a shopper’s life milestones (e.g. a pregnancy) in order to carefully market to them.

It describes how, with the help of a statistician, Target:

• Determined that few ‘life events’ leave an opening to changing shopping habits like a pregnancy
• Figured out the purchase indicators that signal a woman is pregnant (among other indicators, many increase their purchases of unscented lotion in the second trimester).
• Using that knowledge, and knowing pregnancy is a rare opening to condition a woman to turn to Target for all her shopping needs, and with the ability to send each customer a unique ad booklet, they began sending baby product advertisements to the women in their database they determined were pregnant.
• Discovered many women did not react well.

From the article:

“…With the pregnancy products, though, we learned that some women react badly,” the executive said.

Next, they began mixing in other products around the baby ads:

Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We’d put an ad for a lawn mower next to diapers. We’d put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance.

And we found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.”

While the data guy in me is impressed that Target could not only figure out that a consumer’s buying habits are susceptible to influence when pregnant, but also the actual buying triggers that indicate a woman is pregnant, the consumer in me is more than a little freaked out about everything they know about my family (you see, with four small children, a large percentage of my paycheck goes to Target every month).

Judging by the reaction to the article on Twitter at the time it was published, I am not alone—the reaction seemed to be universally ‘freaked out’ by what a major retailer knows about us. It’s pretty impossible to read the article and not have that reaction.

Difficult though it might be, I want to set aside for a minute some of the very legitimate concerns about modern marketing practices that emerge from the NY Times article, and focus on a specific piece of information about consumer behavior that emerged from what we learned about Target’s marketing tactics, with the goal of returning to Google and SPYW.

In being so ‘in your face’ in sending pregnant women ads that clearly told them what they knew about them, Target crossed – no – they leaped over the ‘(mostly) out of sight, (mostly) out of mind’ threshold.

While I am not suggesting Marketers be devious in their Marketing, and there is real value to a consumer in receiving targeted advertisements that are relevant to them personally (no pun intended), but the Target experiments strongly suggest consumers have an upper limit on what they will tolerate and that marketers must adhere to the ‘(mostly) out of sight, (mostly) out of mind’ threshold.

Let’s Take It Slow

This is the lesson for Google. Logic dictates that there is value somewhere in melding social results with search. But right now Google is the awkward teenager who keeps stepping on the prom queen’s toes at the dance and have violated the ‘(mostly) out of sight, (mostly) out of mind’ threshold.

They need to find some suaveness and be less in-your-face about what they are trying to do by clearly showing users the value they will derive from the introduction of social and other personal results in the SERPs, and gradually conditioning users to accept this fusion of information.

So Google: Ditch the ‘awkward teenager trampling the prom queen’s toes in your eagerness to make headway with her’ persona and channel some of the Rico Suave in you. Mix the social results in the SERPs just a bit at first. Let’s have a bit of social in the SERPs foreplay before you attempt to score.

Right now, my client load consists of about 19 different domains that all have their own analytics installs. Each week that’s a lot of information to check, verify, monitor, and create an action plan from. Without my dashboards, and most importantly, my consistently formatted dashboards, I would spend hours finding information – now I have it all at the tip of my fingers.

Formatting dashboards across multiple installations used to be a pain. I’d have to open one client’s dashboard, then add widgets to another client’s dashboard one by one and arrange them in the order I prefer to see them. This was definitely a tedious and time consuming task.

Then I saw something magical in my dashboard one day – a simple little link. I knew I had just gained yet another chunk of time with which I can spend to make my clients’ money, as opposed to looking for data. I’m not sure how long this link has been there, so many changes have been happening in Google Analytics lately, who knows when it first appeared. Now that I see it, I just had to share, because it’s something that is going to make your life so much easier, I promise.

Before we talk about the magic, let’s talk about KPIs. What should you be looking at on a daily or weekly basis?

To be honest, that really depends on what is important to you. As I’ve shared before, all of my clients are in hospitality, and 99% of them are lodging hospitality of some sort, so the KPIs I monitor on a daily or weekly basis are similar for the most part.

Here is a list of the KPIs I look at and the order in which I have them set up in my analytics dashboard.

• Revenue
• Bounce Rate
• Referrers
• Transactions
• Visits
• Organic Visits
• Paid Keywords
• Landing Page
• Unique Events (this is a click on a call to action – generally a “Book Now” type action)

Because I stay organized, and I keep every widget in the same spot, for every client’s dashboard, I can efficiently find information. An unexpected client call doesn’t leave me with uncomfortable silences while I log in and dig through links to find data; I have it all at the tip of my fingers.

I promised you magic, so here it is. Now within your Google Analytics dashboard, you can click “Share Dashboard” – and magically you’re provided with a link you can paste into a browser while logged into another account, and it will create your dashboard with the new client’s data pulled in.

Here’s How It Works

Have your dashboard set up the way you’d like it in one account. At the top of the “Home” page click on “Share Dashboard.”

You’ll be given a link – copy it somewhere safe for the next few minutes.

Now, login to your next client’s Google Analtyics account. (See the quick tip below for details on how to make this easier.)

Go to their profile and simply paste the URL into the browser. You’ll be asked which profile you want to add the dashboard to:

Once you choose your profile from the drop down menu, give you dashboard its name – you may want to set up a different dashboard for your client, they might like to see different data than you do. Click “Create Dashboard”.

Your dashboard will appear, in the order you want it, with the new profile’s data populated for your convenience.

Quick Tip for Beginners who review multiple accounts:

Set up a Google account and make it an administrator for all of your client’s analytics accounts. Do not create new profiles with your own Google account, you want the client to own their own data, so make sure you’re in their account when you set up new profiles and installations. By adding yourself to their account with administrative access, you can access every Google Analytics profile with one username and password.

If you and your client decide to go separate ways, they own their data, and can disable your access. If a new client comes on board, they can add you easily enough to their existing installation. Personally, I think it is 100% shady when an agency says they own your GA data because it’s in their dashboard and “can’t” give you the account. Yes, I’ve seen it happen more than once.

Helpful tips like those I’ve given you above are invaluable to making your work life more efficient and less tedious. If your day depends upon pockets of time, this technique will give you more of those pockets and less headaches.

As always, I’m very interested in any time-saving analytics tips you might have – please share them in the comments below – I know I’m not the only one looking for more time in my already hectic day!

While I’m not a huge fan of sampling data when not necessary, larger data sets put a significant load on servers and sampling becomes a necessary evil when trying to deliver quick high on high volume data sets. As a result, I’m a fan of how the GA team has integrated data sampling into reporting.

On the custom reporting tab there is a new button resembling a checkerboard. Below the button is the sample size.

To adjust the sample size, click on the checkerboard button to populate a sliding scale going from “Faster Processing” on the left to “Higher Precision” on the right.

Faster processing uses a smaller sample size, delivering results more quickly. Higher precision uses a larger sample size for more accurate reporting.

As with any data sampling process, the smaller the sample size the greater the margin of error due to the assumption that the sub-set of data reflects the trends of the aggregate data set.

This is a significant change in how we can read into data sets as 1) it gives analysts a mechanism for more real time insights as the data aggregation takes several hours before being made available in the interface; and 2) the integrity of data can be put into question due to statistically insignificant sample sizes.

When Does Sampling Occur?

As noted in the “Learn More” link under the sliding scale, sampling automatically occurs “By default (until you use the slider to change your sampling preference)…when report data exceeds 250,000 visits. However, you can use the slider to increase this threshold to as high as 500,000 visits…Sampling in Multi-Channel Funnel reports automatically occurs when the data includes more than 1 million conversion paths, regardless of your sampling preference setting.”

How Will This Affect My Data?

In short, you will lose clarity and there is the potential for misleading insight if the sample size is too small. If you have an account and run reports with more than 500,000 visits, your data sets will be truncated and assumptions made be made.

Is this cause for alarm? Yes and no. So long as Google delivers statistically significant data samples, then there is no cause for alarm. However, this feature is new and it’s unclear how statistically significant the data samples are. If you require no sampling you’ll need to run reports with less than 500,000 visits.

As noted above, one exception to the 500,000 visit limit is in multi-channel funnel reporting. Multi-channel funnels will be sampled once the number of paths exceed 1,000,000.

To be frank, 1,000,000 conversion paths is a lot of conversions and there aren’t that many companies out there who are pulling in more than 1,000,000 conversions in a relevant time frame (given seasonality; if you are one of these companies, I suggest the paid version of Google Analytics or another solution to mitigate the issue).

As a result, I don’t expect the relevancy of multi-channel conversion funnel reporting to be impacted by data sampling.

If anyone has already analyzed the effects of data sampling on the relevance of their reporting please feel free to comment below. I will be following up once I have enough relevant data to share.

Then, along came the Internet, and with it a new kind of marketing. Marketing online brought with it ‘cause-and-effect marketing’ and a greater ability to measure the effectiveness of marketing effort.

New metrics such as click-through rates, bounce rates and page views made its way into our lexicon and marketing gradually became a discipline rooted in data, with Excel the primary tool of choice.

The Era Of Big Data Arrives

As the ways for online users to interact online grew, methods of marketing to online users grew with it. Marketers could soon choose from a broad spectrum of ways to spend their marketing dollar ranging from SEO, to PPC, to display, to social and email marketing.

Along with it came a torrent of digital data – multiple data sets from disparate sources and ushered in an era of ‘big data’ — large sets of data that, when mined, could reveal insight about online marketing efforts. This includes data such as search rankings, site visits, SERPs and click-data.  In the SEO realm alone at Conductor, for example, we collect tens of terabytes of search data for enterprise search marketers every month.

How Prevalent Is This ‘Big Data’ Thing?

Big data is not a trend that has been limited to online marketing. As I sat down to write this piece, I came across several recently published articles on the trend in the New York Times and Read Write Web.

In one section of the New York Times article, titled Welcome to the Age of Big Data, the author describes how the online industry is leveraging big data and that it is a topic on the minds of many, including economists gathered in Davos:

The new megarich of Silicon Valley, first at Google and now Facebook, are masters at harnessing the data of the Web — online searches, posts and messages — with Internet advertising. At the World Economic Forum last month in Davos, Switzerland, Big Data was a marquee topic. A report by the forum, “Big Data, Big Impact,” declared data a new class of economic asset, like currency or gold.

A check of Google Trends shows ‘big data’ is a term whose searched volume has skyrocketed in 2011, going into 2012. Even a cursory check of the news articles that accompany the Google Trends graph show a recent commitment by some of the world’s largest companies to big data.

Assuming we may not all be able to develop our Excel skills to the point we can cash in on the riches available to big data gurus in silicon valley, what are implications of the big data trend for the online marketer?

The New Dual-View Of The Modern-Day Online Marketer

The arrival of the era of big data signals to the truly successful online marketer that they must now view the online landscape in their niche from two distinct perspectives:

• The Channel View:

The channel view is one in which the Marketer asks, “What is and is not working in each channel?” This includes the use of tactics such as A/B testing and content optimization, and makes use of technology such as web analytics and PPC bid management software. It encompasses metrics such as bounce rates, click-through curves and conversion rates in order to discern the levers that require adjustment.

Until now, this is the sole view Marketers have taken of their online marketing efforts. In addition to enhancing the cross-channel view, big-data also enables the marketer to go deeper in the channel view. For example, whereas SEO used to be solely about search rankings, now it is about rankings, conversions, traffic etc.

• The Cross-Channel View:

The cross-channel view is one that spans the length and breadth of the online marketer’s channels. It began with attribution, but the era of big data that is now upon us means marketers have the opportunity to take advantage of large data sets from each channel and leverage them for deeper, actionable insight — provided they are looking at their channels through those eyes and have acquired the technology to do so.

How Has Big Data Arrived In The SEO World?

From our vantage point at Conductor, the move to the era of big data has been catalyzed by several distinct occurrences:

• Move to Thousands of Keywords: The old days of SEO involved tracking your top fifty keywords. Today, enterprise marketers are tracking up to thousands of keywords as the online landscape becomes increasingly competitive, marketers advance down the maturity spectrum and they work to continuously expand their zone of coverage in search.

• Growing Digital Assets: A recent Conductor study showed universal search results are now present in 8 out of 10 high-volume searches. The prevalence of digital media assets (e.g. images, video, maps, shopping, PPC) in the SERPs require marketers to get innovative about their search strategy.

• Multiple Search Engines: Early days of SEO involved periodically tracking your rank on Google.  Today, marketers want to expand not just to Yahoo and Bing, but also to the dozens of search engines around the world as enterprise marketers expand their view to a global search presence.

All the above factors combined mean there are significant opportunities for an  increase in both the breadth and volume of data available to search professionals.

‘Big Data’ Marketing View Brings Us Far Closer To Actual User Behavior

We’ve established that far larger volumes of data are available to the marketer. Next, we’ll illustrate the benefits of leveraging data cross-channel, but before we do, it’s important to note that another reason for marketers to transition to a holistic ‘big data’ view of their marketing channels is because consumers do not operate in channel silos, so neither should the marketer.

Today, online users encounter multiple digital channels together in one place in the search engine results page. They do not experience the channels in a ‘silo’, they experience them as a single fluid experience and that is the way marketers must begin to think about their online marketing.

Examples Of Cross-Channel Big Data

Given the growth in available big data across online marketing channels and the need to look at our marketing channels from the same perspective as the ‘non-silo’-d user, what are some ways marketers are integrating data across marketing channels, and what benefits are they taking away from these integrations?

• SEO and PPC: Modern enterprise SEO systems now integrate with enterprise level PPC systems, enabling the marketer to view paid search data alongside natural search data and make paid search bid decisions with natural search data, providing a holistic view of the SERP.

• Social and SEO: The marketer can now see social data together with natural search data on the page level in the enterprise SEO platform. This enables the marketer to gain a more complete view of how earned media is driving traffic both on an individual page level, and in a holistic sense to their web properties.

Strategy To Leverage Cross-Functional Data Increasingly Critical For Marketers

The growth in the volume and breadth of data across online marketing channels combined with the maturation of marketing technology to leverage that data cross-functionally means the era of big data is officially upon us.

Now, Marketers must begin to give due attention to plugging the available data into their marketing strategies. For some, this begins with starting to think about their marketing channels in a more holistic fashion. For others, it means taking action on a cross-channel line of thinking already in place by going after the tools and technology that will enable them to make the transition into marketers leveraging cross-channel data.

Either way, the ‘big-data’ writing is on the wall: the new marketer is part CIO, and if you do not have a strategy in your organization to leverage cross-channel data you may soon find yourself with yesterday’s view of your online marketing efforts.