Author: Atlantic BT

How To Model Google Analytics SSL Data

Google’s decision to remove traffic information about Google Account holders who are logged in when they visit your website using Secure Socket Layer SSL protection in the name of “privacy” can cause data comparison pain as well as a heart attack or two. When you see “not listed” in your analytics (starting from about October 2011), the “not listed” number represents traffic whose details are now hidden.  To create an approximation of your actual numbers to compare to baselines, follow these few simple steps.

Step 1: Determine the SSL Traffic as % of Total Visits

SSL varies by time, so be sure to limit the range to whatever time constraints you are using. Here is a recent spreadsheet in which I was working by months:

In January, Atlanticbt.com had 4,925 visits: a 58% growth over January 2011’s 3,117. January 2012 had 1,577 “hidden” or Not Listed visits, for 32% of the total.

February had 33% Not Listed, and June had 40%. The Not Listed SSL number has been going up as more and more traffic logs in to Google and then surfs the web. Be sure to be careful about using time consistently in your Not Listed SLL calculation. Don’t model on a single month alone if you are working by months.  If you aren’t worried about being exact, you could use a longer time frame, say, six months, then average the % of Not Listed as a % of the total, and apply that number across the board. I personally don’t mind doing the work by month when I’m already working that way and there is a big difference between 32% Not Listed in January and 40% in June.

Step 2: Determine an Adjusted 2012 Number

If a keyword or page had 509 visits in January 2012, we know that isn’t the “true” number due to the 32% “not listed” traffic generated by people signed into their Google accounts and thus hidden from our view.

We need to adjust the 2012 number to add back the hidden Not Listed. Once we have the “Not Listed” % of total (32%), it is easy to create an adjusted number:

509 X 1.32% = 162.983

509 + 162.983 = 671.983 (672 rounded)

Step 3: Compare to 2011 baseline

Once we have an adjusted 2012 number, compare the adjusted number of 672 to 2011’s actual. Let’s say it was 670.

You can then see that your adjusted number is +2 your 2011 baseline and not a steep drop of -161 as it first appeared (509 non-adjusted vs. 670). The non-adjusted number can cause stress and pain, the adjusted number may help calm the nerves.

SSL Modeling for Heart Attack Avoidance

Google’s change will eventually be moot, as SSL “Not Listed” will be in your current and comparison set. Once SSL is in both sets, then you have a modeled system that can determine how SSL should be categorized easily (just use the non-SSL numbers to create % by action). Until Not Listed is in this year’s and last year’s numbers, use the simple steps outlined above to model an adjusted number BEFORE you show your boss. No need for both of you to have heart attacks (lol).

Note From A Friend
My Friend Phil Buckley Tweeted (@1918) that he thought some would read this post and model too much data in this fashion and then find the point of uncertainty when “Not Listed” reaches beyond 50%, as it may soon. I reminded Phil of Taleb’s book The Black Swan.

My Black Swan take away is that everything is a guess. Some guesses are more informed than others. If you are comfortable with a spreadsheet and statistics, you could model your entire site from 10% to 20% of actual data. When I managed a large ecommerce site, I could FEEL if something was wacky long before the source of my dissonance showed up in the data. Is that HEALTHY? Not at all (lol).

I’ve also blogged about the relative nature of what we as Internet marketers really KNOW. What we KNOW for sure is what is happening NOW. Until GA’s real time moves further out of beta, we look at the past and hope to understand our website’s NOW and its immediate future. There is another way of saying this: We guess.
– Marty

Google Adwords, Social Signals, Pay Per Click

Researching keywords, I noticed a funny thing. Google seemed willing to sell us more ads when we blogged, once social signals were involved. I started wandering around deep inside of Adwords. I wanted to answer a simple question.

Are social signals influencing PPC? It was a fascinating journey.

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Once Google created a floating search engine, in which you see a different search result set on the same keyword(s) as me, even if we search at the same time on the same term, they created infinite ad inventory.

Once your SEO result set is different than mine, Google can mash, spin and snip inventory to meet any demand, segment or call. Keywords become an open air bazaar with Google deciding who sees and can buy what. Google’s float creates a new financial instrument that may be the most powerful currency printing press in the world.

Google can simply “print” more search result sets or feed existing sets to a larger number of buyers when they need to increase ad revenue. Both actions increase Google’s profit. Google’s cost is the data cutting. Cutting, segmenting and feeding data is something Google has down to an inexpensive science, so their costs are low.

The funny, ironic part of the story is how enamored everyone was with Facebook as Google constructed the world’s largest printing press in full view. Facebook has people, but no one has more explicit, near-conversion searches than Google. Translate “explicit, near conversion searches” as “ad inventory” or simply MONEY.

We may figure out how to convert visitors to buyers in Facebook, but Google’s brilliant response to Social Media’s threat – to create a floating and so infinite search result set – makes fighting Google difficult. If Facebook looks disruptive to the most valuable inventory of keywords in the world, Google tweaks two knobs and counters effortlessly without spending much in defense.

Facebook’s R&D budget will need to exceed Google’s by an order of magnitude, for a long time. Chances of Facebook winning are not good, absent a large disruptive force such as Mobile (and Facebook isn’t leading in mobile, far from it).

Implications of Social Signals in PPC

If Google’s PPC algorithm is willing to sell more inventory to buyers with social strength, a new dimension to social ROI is being discovered. The data is tantalizing and suggestive, but not definitive. I looked at the Atlantic BT website’s stats for two 11-day periods, wanting to find days when unique traffic was almost equal so that traffic could be ruled out as the cause of any difference. I used blog days vs. non-blog days as the marker for social signals. The results were stunning:

Google sold 66% more PPC ad inventory on blog vs. no blog days.

By controlling the two sets to keep unique visitors almost identical (no blog days had slightly more visitors), we can rule out visits and visitors as causing the difference. The largest difference between the two sets of days was social signals. Blog days received 57 direct Retweets via TweetMeme.

Past experience shows that TweetMeme only captures about 25% of total social signals. Topsy is a better judge of absolute numbers: Topsy is more accurate with Friends of Friends Retweets. Our model doesn’t require knowing ReTweets down to the follower level, though. It is enough to know that the blog days generated significantly MORE social signals than non-blog days.

The data is far from perfect, but if I were a betting man, and as an Internet marketer I have to be, I bet social signals are influencing the ad arbitrage Google creates every minute of every day. Your website will be allowed to buy more ads faster if you have social strength.

It takes getting used to this idea that you are granted the RIGHT to buy ads Google is willing to sell. It takes even more getting used to the idea that Google is judging your website on criteria so complex and dynamic it is hard to fix what may be wrong. Your Adwords quality score may be the tip of a much larger iceberg, a trailing, NOT a leading, indicator.

Social signals influencing PPC makes sense. Let’s say your website is the world authority on “widgets”. You have a PageRank of 8, and all kinds of inbound links claiming your website is the place to learn about widgets. When you want to buy the highly competitive “widget” keyword before the float, you had to get in line with everyone else and bid. Now Google can construct a highly relevant “widget” result set and feed it to their best “widget” searchers, rewarding your authority with better ads, faster.

Google makes money moving MORE searches to a relevant home faster, much more money than bouncing searchers around the web. The sooner a search produces relevance and lands, the fewer Google resources that searcher uses. Think of searches as costing and making money, and of course social signals are in PPC. Social signals are just one more means Google has to determine the most relevant place to send keyword traffic.

It might seem Google has a vested interest in keeping traffic clicking. Clicks do equal money, but clicks also cost money. Google must seesaw the cost of additional resource calls (clicks) against the ad arbitrage (what they can sell the click for). Money NOW always beats the possibility of money later.

Marty’s Social Signals in PPC advice:

• Embrace social media NOW.
• Apply Google’s post Panda and Penguin algorithm changes broadly (read Storytelling – Panda’s Secret SEO Implication).
• Assume if your tribe and the web’s mob doesn’t love your content, Google won’t either.
• Assume that having MORE Followers, Shares and LOVE is better than LESS.
• Tell great stories people want to share on social media.
• Create great events people want to share on social media.
• Develop great games people want to share on social media.

Still have social media naysayers in your company? Share this post. Social media naysayers may think PPC is the last thing they can just BUY. Afraid not, it appears PPC too is under the thumb of social signals.

– Marty