Leaving Money on the Table in Performance Marketing

There's an Analytics opportunity that’s leaving money on the table - Las Vegas style.

I grew up in Brookfield, a small town in Illinois where there was one candy shop. When I was five my Dad walked me there, and the clerk filled up a paper bag with jelly beans. I proudly walked back home, holding that bag with two hands to guard my treasure, and when I got home, there were only a few left in the bag. Some tiny hole let out one jelly bean at a time as we walked. Being five, it was the end of all good things.

There’s an unspoken huge money drain that happens when marketing campaigns aren’t visible to analytics, and nobody talks about it. It’s embarrassing, it’s messy, and it’s one of the simplest money making fixes for a business. It all has to do with keeping your jelly beans once you bought them.

In this case, the jelly beans represent performance marketing, and the bag with the hole is cruddy campaign URL tagging.

Paid traffic is a huge investment. Here are three ways that businesses consistently lose big money, and where their analytics are completely wrong.

1. Campaign URLs are not tagged at all
2. Tagging standards are not enforced, inconsistent, or wrong
3. Traffic partners mess up your campaign tags

What are campaign URL’s and Tagging?

Campaign URLs are the links that lead people who click on the paid internet ads you bought on Google, Bing, or other places,  or the social media links you included in a Tweet or Facebook post. A user clicks on one of these, and they go to your site.

There are things you put on those Campaign URLs called “parameters” which tell your analytics system how to understand that this bit of traffic is purposeful, such as being a visit from your “New Shoes for Valentines” campaign, vs just a generic visitor coming from some unknown wherever.

Campaign URL tags are the sets of parameters all taken together, and put onto the end of your URL, which is then provided to the paid traffic partner (e.g. Google), or shoved your Tweet. This whole URL is called a Landing Page URL, or Landing Page.

When Campaign URL tagging is correct, life is awesome. Your analytics data tells you wonderful things about the mega amounts of people coming from your Google ad and want to buy “Shoes for Valentines”.

When tagging is cruddy, life sucks and your analytics tells you half or less of the story or virtually none. Bad tagging is kindly referred to as an Attribution Problem.

Don't’ have bad tagging, lose your jelly beans and have an Attribution problem.

Some companies I've worked with (no names) pay in excess of $200 million / year on paid marketing, and call it Performance Marketing. When your analytics can’t track the keyword you bought then it’s pretty tough to know if making money. On average, I've seen this problem affect up to 10% of a company’s traffic when they think they are tagging successfully = a $40 mil/yr problem. Even in Vegas, that buys you High Roller status, except in this case, you’re losing.

Performance marketing is supposed to refer to good performance.

You’re leaving money on the table when there isn't good Campaign URL tagging, and in my experience, it usually goes undetected for months and that’s completely fixable.

For all the money spent with Google, Bing, Adconion, or whatever company where we buy marketing traffic, none of them care how you tag your URLs. It’s all good as long as we pay them. If you use a cool ad management platform like DoubleClick for Search or Kenshoo, you have a great platform to track your stuff. But, once again, nobody is at home and watching your house. Ad platforms and ad or bidding management software doesn't do anything to take care of ALL of your Campaign URLs, especially for traffic not bought from Google or Bing.

There is nobody watching: no one cares about your URLs except you. Why? Because it’s your problem.

4 Ways to Fix the jelly bean bag:

1. Create a set of Campaign URL tagging best practices and stick to it.

Google Analytics and Site Catalyst have tagging standards for URL parameters.  Your company may use both, so create a matrix in Excel to standardize what goes where and for what partner. That sounds kinky, but it isn't.

2. Assign someone as the owner of your tagging scheme.

This person is the Czar with absolute authority to tell anyone they’re tagging is kaput, whether for a single silly Tweet,or for the owner of the $500 million dollar performance marketing budget. This person also debugs problems with tagging, and makes sure new marketing people and campaigns do tagging according to plan.

3. Assign another someone to work with traffic partners, and get them to “fix it”

Traffic partners like Google and Bing don’t listen to this noise because they aren't causing the problems. The smaller companies are the culprits. MANY times I've seen them just refuse to do the tagging that I've laid down. That’s just not good poker... for them. Assign someone (maybe the Campaign Czar above) to remind these partners that you’re paying them for a service, and to please do what you told them to do. They usually will. Many times, it’s just a set of honest mistakes, or bad coding that they’ll gladly fix. They want to make money too.

4. Build or Buy a Campaign Management system that does your tracking for you.

Super important. Since there are over a hundred paid traffic partners out there in the US alone not counting dynamic duo of Google and Bing, there are a super huge amount of potential campaign URL tags. It’s a lot of debugging to do for hundreds of thousands of URLs and can make people lose their minds. Look for a Campaign Management solution that will allow you to create and store your URLs for ANY traffic partner, error check them, and store your stuff historically, as well as upload them to your hundred partners. There is only one I know of, and it’s under development by experienced people who worked their way through and conquered this big money drain problem.

* Watch for an update from me on a new solution for this problem in coming weeks.

Don’t drop your jelly beans on the ground; get a better bag.

This stuff is difficult only because it’s very exacting, but it’s not very technical. Think of it like running a tiny library, but each book makes you money if it gets returned. Don’t leave money on the poker table. Vegas always makes money, and Analytics was invented there by Ballys.

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.

Making the Home Screen Actually Cool in Google Analytics

"Lost time is never found again" -- Benjamin Franklin

I've personally lost too much time wandering around the home accounts screen in Google Analytics, trying and failing to quickly find the one single profile I need to access amidst a sea of more than 100. It ain't pretty... or, it wasn't until now. 

Let's face it: the Google Analytics home page is busy, and painful to navigate when you’re under the gun looking for a single profile. 

This is symptomatic of having Admin rights, which give you access to every profile, and make all of these profile names visible on “home” and “all accounts” when you are hunting for your target. It's a mess, and starts to resemble unstructured LISP code (don't ask).

Usually, the home page looks phugly like this: (forgive the white-outs)

I shortened it up here for a nice picture. I have access to a lot of GA profiles it's a time waster. It's also embarrassing when I'm hunting through them in front of a client, and can't find their site...

The Fix: on the grey bar on the top of the accounts home page:

• Go through the list of profiles, and click the Star next to the ones you want to “Favorite”
• Select “show metrics”
• Select the “flat” Mode
• Click the “star” on Mode
• Select a date range

This is what things can look like when you follow the steps above. To me, this looks like a your closet AFTER someone who loves you cleaned it: (return to this view any time by hitting Home icon)

Analytics Goal Conversion Funnel Examples

"In marketing, a conversion occurs when a prospective customer takes the marketer's intended action" -- Wikipedia

The purpose of funnels is to demonstrate user behaviors that lead to conversions, or lead customers away from them.

• A Conversion funnel  is a means of corralling customers through a channel you design by making their choices of NOT going through each of your options unpleasant or impossible.

• Conversion funnels are used to test the paths that we lay out as either Beneficial, or Destructive. 

• A Beneficial funnel demonstrates the happy path through the UX in our website. 

• A Destructive path is one that takes users away from true conversion, and shows where they are exiting our funnel.

•  Funnels should be short and sweet for the desired conversion: e.g. new user signup, purchase, existing versus newly signed up users/customers, customers coming from other websites to yours (by keyword search, display ad, or by referral traffic).

• Funnels display the way you WANT customers to complete a conversion, or how they actually ARE completing or not completing conversions. 

• Google Analytics provides an easy way to create "funnels" that is calls "goals" (current version). 

• Goals consist of beginning and end urls (or uri's actually within your site), and sub-uri's in between the beginning and ends of the funnel.

• When the goal/funnel is created, it provides not just a graphical report, but a fully functional test of the actual user flow that is indicated by the funnel

• Funnels can have values, and usually these are attributed to the eCommerce value of the conversion goal, or a fixed value that the user assigns. 

• Warning: once you create a "goal" in Google Analytics, it's there for life. They can be renamed and edited, but never deleted. If they are edited, the previous conversions are NOT removed, so reporting becomes "interesting" unless the date ranges requested consider the date of last edit.

• GA introduced Multi-Channel Funnels last year for the standard free edition. They allow multiple attribution point to be tracked and are a great improvement over single track goal funnels.

• GA Premium comes with an Attribution Marketing engine which acts like a turbo-charged version of marketing funnels, allowing companies to track inbound attribution away from the website or mobile app. This powerful feature requires the implementation of goal funnels, events, and it's suggested that eCommerce tracking is implemented.

Each step in a funnel needs to translate to a URL or to a Choice within a page, that leads to another page or pages of options.

Below are examples of Goal / Conversion Funnels from several sources. I included them to offer graphical examples from a wide variety of disciplines to give all of us many types of example graphics to use in presentations. 

Graphical examples of conversion funnels:

1. One  

2. Two

3. Three

4. Four

           

5. Five

 

6. Six -- a Google Analytics Funnel