Secrets to Remarketing with Google Analytics Lists

Slideshare: Secrets to Remarketing with Google Analytics Lists

Secrets to Google Organic Keyword End

Google finally closed the door on the ability to track ANY organic keyword searches coming from Google. 

See article link here: http://searchenginewatch.com/article/2296351/Goodbye-Keyword-Data-Google-Moves-Entirely-to-Secure-Search

Google has transitioned to HTTPS instead of HTTP which means it’s using secure protocol to send traffic from any “free” organic search terms to a site.

What Happened: 

This was a process that started late in 2011, when Google removed about 20 – 30% of the inbound organic keyword visibility from any analytics source. It was speculative that they would take away the rest, but it’s been completed as of today’s announcement. Google has moved to a Paid-only model regarding search traffic visibility by incorporating organic search impression data into AdWords, and eliminating it from external tools (analytics). Article here. http://adwords.blogspot.com/2013/08/analyze-and-optimize-your-search.html

Impact: Optimizing organic (SEO) terms becomes more complicated, but doable. Google organic keyword will now show up as “(not provided)” in all analytics products – including Google Analytics. Google organic keywords are hidden, but Bing isn’t – and neither is Yahoo, Ask, or other smaller search engines. Google Analytics, and the Ginzametrics tool, as well as Comscore, and others will no longer show Organic keywords with high accuracy.

Work arounds: SEO (organic inbound search) keyword/competitive optimization, and SEM paid to free optimization will need to rely more heavily upon Bing, and other search engine sources in Analytics, rather than Google. We can report on traffic sources that are NOT Google for organic (free) search traffic. Even better,  AdWords reporting now includes Google Organic keywords. This means we still have visibility, but ONLY inside of AdWords.

What is not impacted: Any Paid search. It’s immune: if it follows our tagging rules. If tagging has not be properly implemented based upon the standards I’ve created, then it’s going to be hidden from Analytics, and other tools that attempt to decipher it. In Google Analytics, or any analytics, we can still see the amount of organic keywords coming in, but no longer any of their keyword names – we can do a count of (not provided) from Google/Organic source. This at least gives us a gross number for Organic metrics, but not specific organic keyword performance. As stated, AdWords Organic keyword reporting will provide our needs.

What to expect: Bing, and other search engines may provide some commentary, or even other product offerings to try to take advantage of the small window of opportunity this provides in the organic market to rebrand and gain market share in Paid traffic. Long term: seems like at least a small opening for other search engines.

Big Data: Key Metrics for Optimizing SEM Contextual Ads

Search engines like Google and Bing don't provide reporting that really shows you what you need to fully optimize ads and make adjustments. 

This is especially true for attributing Revenue to their cost reports.
Google and Bing both have APIs that allow your scripts to grab cost and placement fields.

Placements are the site URLs onto which Google or Bing will post your Contextual buys to in order to get "in clicks" for your web property. Instead of a keyword, you track a placement URL.

Google provides two types of placements:

1. Automatic -- it chooses the placement URLs that it thinks match your ad, chosen terms, and context -- thus the term "contextual"
2. Managed -- YOU choose the exact placement URLs that YOU think match your needs

Missing are the Key Metrics for Optimizing Contextual Ads:

1. Revenue
2. Margin (revenue - cost)
3. Margin % (cost / revenue)

It's no easy task to associate revenue to cost. A good back end system is needed.

Requirements:

• API grabs of Paid Traffic Google / Bing placement data --> into a file, every day for previous day's data.
• Similar grabs of Revenue associated with your website's revenue partners: like Google Adsense, Yahoo Sponsor Links, Commision Junction afflicate revenue. 
• Store this revenue into a back end database or file system 
• Create a method to associate the incoming traffic from your Google or Bing paid campaigns to the outgoing "clicks" on each of the above Ad Revenue partners: requires a custom click ID and traffic tag.
• Associate the  Paid Traffic from Google / Bing, to the Ad Revenue fields. 

The finished product is a report that roughly has the following fields:

• Placement URL
• Placement Type (Managed / Automatic)
• Impressions
• Clicks
• Cost
• Revenue
• Margin
• Margin %
• CTR
• RPI (revenue per impression)
• RPC (revenue per click)
• Campaign Name
• Adgroup Name

Key Metrics to optimize Placement Optimizations:

• Margin%
• Revenue
• Cost
• CTR
• RPI 

Conclusion:Takes sweat equity, but worth the effort: NO Bid Management system does it.

Big Data for Marketing: Retail Store Example

Big Data

Cost and Size scales up and down based upon need

Big Data grew from a need for speed and size. Now, it's everywhere - running Netflix, iTunes, Ebay, Amazon, our banks, schools. We can get information on customers from re-marketing and affiliate companies like Dotomi, Commission Junction and more. 

It's easier to identify businesses that aren't using Big Data than to list the ones who are. The "Taco's Ensenada" restaurant in Duarte, CA where I frequent for fish tacos is probably not using Big Data, except to blast music from a Mexican Pandora channel. Even my tacos are Big Data influenced.

Big Data is everywhere but its influence and how it benefits our services is not very well understood by consumers, or even by experts in the information industry. 

How different is Big Data? -- A retail clothing store example:

Old System: Relational / Queuing Systems:

• You're standing in line at H&M and your arms are full of clothes it took an hour to find. There are three lines, and about 30 people waiting. But, there are only 2 cashiers.
• Everyone waits one at a time for one of the cashiers to free up. The cashier's availability is governed by how quickly they can check out the individual transactions for each person. 
• One guy in line has 10 shirts and a return -- he's the line hog and everyone is going to have to wait for him. Not his fault.
• A teenage girl is in line to buy one bracelet and will wait the same amount of time everyone else does. Not her fault.
• In this model each person waits in a queue to check out. Each is treated as a separate transaction. The time it takes you to check out once you're at the register is determined by  the number of clothes you have, returns, sale items, or your credit card company -- these are sub-transactions. 
• Checkout Time p/p = Multiply time per sub-transaction by clothes, returns, sales items, credit or cash, and number of people, then divide by 2

New Big Data clothing Store: 

• Imagine you walk up to the counter at H&M ready to buy 10 items, and there are no cashiers. Then instantly, 10 cashiers pop up -- one for every item.
• Then, you are physically multiplied by the number of your items and you stand in front of each cashier.
• All of the transactions are done at once. Each takes no time. 
• At the end, you're reassembled into one person, you have your receipt, and you leave the store.
• Imagine this happens for every single customer who will ever buy things.
• Checkout Time p/p = time to do one transaction, divided by endless computers.

That's Big Data -- cost & performance scale up & down for high & low demand

Big Data: scales up or down in cost and size and performance based upon needs.

Big Data: performance is great at large and small data storage sizes

Big Data: is cost effective because it is designed to scale to needs

Big Data: powers nearly every large online business.