Showing posts with label analitica. Show all posts
Showing posts with label analitica. Show all posts

Thursday, July 24, 2014

3 Advanced Web Analytics Visitor Segments: Non-Flirts, Social, Long Tail

Occam's Razor
by Avinash Kaushik


3 Advanced Web Analytics Visitor Segments: Non-Flirts, Social, Long Tail

SunshineThe last blog post shared custom analytics reports that you can use to find amazing insights faster, enabling you to create a focused, truly data driven organization.
In this blog post I want to continue the let's help make your day-to-day life better path. I'll share three advanced segments that I personally find to be of value in the process of moving from data to actionable insights. I hope you'll download and use these segments, but more than that I hope you'll learn how to create delightful analytics segments with the options you have at your disposal.
I am an unabashed segmentation fan: Web Analytics Segmentation: Do Or Die!
Without segmentation our analysis is focused on unrecognizable blobs of traffic. Total Visits. Average Page Views Per Visitor. Overall Conversion Rate. Yada, yada, yada. Boring. Useless. Life wasting.
With segmentation we focus on groups of people and we focus behavior that has logical connections (everyone who used a particular keyword, group that came via Twitter, people who viewed a TV ad, visitors who saw more than 4 pages on our site etc., etc). That helps us understand data & performance better. It helps us get data-gasms, improve ROI for our web efforts and get our bosses promoted.
How can you not love that?
Below are examples of segments that help us make a lot more sense of all the data we have and the insights that await us. You'll be able to download these segments and import them into your Google Analytics accounts and start using them right away!
Additionally, as I often do, you'll learn lots about the types of delicious analyses you can do with these segments. For good measure there is also a tutorial on regular expressions at the end (no good analyst can live without regex!).
If you use Adobe's Site Catalyst or CoreMetrics or Yahoo! Web Analytics or WebTrends or. . . you'll have enough detail below to create a segment in 5 minutes in those tools as well. Trust me, it takes just 5 minutes and, like with Google Analytics, you won't need to update your JavaScript tags or have to do extra work with IT or buy other expensive versions of their products just to do segmentation.
Let's go. . . three awesome analytics data segments. . .
#1: Non-Flirts, Potential Lovers
Did I get your attention? :)
We all obsess with our bounced traffic because it seems nutty that the person you spent so much time and love attracting to your website bounced! They did not click to see another page. They did not hit play on the video on the landing page. They did not click on a link on your landing page to your corporate site. They just left.
Here's how that segment looks:
web analytics segment bounced traffic
It is tempting to analyze these people. Where did they come from? What campaigns? What landing pages? Etc., etc.
You can find value, but to grow the business it is not prudent to focus on analyzing just the people who flirt with us.
Why not first analyze people who do engage with us?
At this point people switch to analyzing all the non-bounce traffic. This is how that segment looks (bottom right):
web analytics segment non bounced traffic.png
[The above is a standard segment in GA, just look under Default Segments.]
Better. Ignore the flirts. Focus on everyone else.
Unfortunately that is still a "blob." It includes anyone who just had two "hits" in their visit (hits is a technical term for a page view, event, custom variable, etc., etc., more than one hit = non bounce visit).
I want us to be a lot more deliberate.
Look at the Depth of Visit report (standard report in GA in the Visitors section). It shows the distribution of the pages people see on your site (not the "silly" metric, average page views per session).
The distribution will show you the "tipping point," the point at which a core group of people decide to stick with your site after overcoming their initial "fears" (and your perhaps sub optimal pages!).
Segment that.
To use a metaphor. . . look for people who made it with you to a third date. For many sites, but not all, that's people who have seen three pages. It might take 14 pages to buy, but if they stay to three they are giving you a chance. They might read 8 stories on your non-ecommerce content site, but you note that people who see three engage for a longer time.
Here's that segment:
page depth engagement analytics data segment
So simple right?
These 7,610 Visits were ripe with promise. Some people ultimately ended up buying, others just gave you a chance and decided not to consummate.
Rather than focusing on the bounce traffic ("flirts") it is much much more interesting and valuable to initially focus on people who give you a chance.
Where did they come from?  segmentation engaged traffic sources
12.11% from Organic Search via Google. Enough? Not enough?
More questions for you to answer. . . .
What pages did they enter on? What campaigns have a higher percentage of these people? What countries? What keywords? What is the delta between content they consume on your site compared to everyone else?
Look at the row with % of Total. . .
nonflirttrafficcontentconsumption
Helps you find what they are interested in, right?
More questions to answer. . .
Do they all happen to use the comparison chart first? Do they all absolutely read the Sports section? What's so unique about them?
This an astoundingly simple segment to create. Yet analyzing visitor behavior for this segment helps you identify, and perhaps do more of the things you already know are working.
Do this first.
Here's how you can get this sweet and simple segment:
  1. Log into Google Analytics.
  2. Come back here.
  3. Now click on this link: Non-flirt Potential Lovers Segment. It will open in GA.
  4. Click on the Create Segment button to save it in your account.
If you want to share this report with others (say via Twitter / email) you can use this url: http://goo.gl/SuwKp
Have fun.
#2: Social Media, Baby!
Social media is all the rage. Suddenly Marketers have discovered that convincing people to buy their products/services or read their content or apply to university takes just two things:
    A. 140 character missives sent frequently during the day extolling the glories of the company / newspaper / university
    B. Creating a Facebook page, and then proceeding with the glory extolling
So easy. </ever so slight sarcasm>  : )
Our job is to hold the feet of these adventurous people to the warm accountability fire, right?
[Remember everything below is only if you use Twitter, Facebook et al for pimping. If you are participating in those media in the manner in which you are supposed to, conversation and adding value rather than pimping, then I encourage you to read my Social Media Analytics post to learn what the best metrics and tools are.]
The challenge in measuring social media impact on your business is two-fold.
    1. Most content gets consumed in applications (think tweetdeck, my beloved twicca, mobile etc). They don't send referrers allowing us to tie to the source with our analytics tool (any tool, GA or Omniture or CoreMetrics).
    2. Splitting out activity that we caused vs. activity that was caused by others.
My recommendation is, again, two-fold.
First, if you tweet / update / tumble links back to yourself then please for the sake of all that is holy in the world add campaign tracking parameters.
Here's the link I tweet:
http://goo.gl/myisj
It points here:
http://www.kaushik.net/avinash/2010/12/best-downloadable-custom-web-analytics-reports.html?utm_source=social-media&utm_medium=twitterfb&utm_campaign=aktw
See the campaign (utm_) tracking parameters? Trackability, sweet, trackability!! Mobile, apps, html5 pages, bring it on. All visits tracked!
[If you use Site Catalyst or WebTrends or Yahoo! Web Analytics your campaign tracking parameters won't look like the above. Check with your vendor and tag appropriately.]
Second, split your social media value analysis into two different segments. Activity caused by you and activity on your site by all social media visits.
self driven social media traffic segment
You drag over Source and input the Value you are using to tag your SM links, in my case the utm_source is imaginatively titled social-media.
This tracking mechanism (campaign tag) is used both on Twitter and Facebook links. I can, and often do, split out Twitter and Facebook separately by using a different value in the utm_medium value. I can further segment them separately if I want. For now I want to analyze them at a higher level together.
I did pimping. I got 2,486 Visits. So what?
Easy question to answer, go to your outcomes report and apply your newly minted segment:
social media conversion rates
Pretty darn pathetic, right?
Only one of the above goals is connected to a "hard" conversion (leads generated, Goal 2). The rest are "engagement" and videos played and other such goals.
Still pathetic, right?
Do you know how awesome, or not, social media efforts directly initiated by you are? It's not that hard. Go figure it out.
Oh and yes, you don't have to stop here. You can apply this segment to your amazing Page Efficiency Report, to your Visitor Loyalty and Recency reports, to your. . . well any report you have. That allows you to measure a broader view of the success of your social media efforts, rather than my effort to instantly put your feet in the fire! :)
Here's how you can get this social media segment:
  1. Log into Google Analytics.
  2. Come back here.
  3. Now click on this link: My Social Media Traffic Segment. It will open in GA.
  4. Click on the Create Segment button to save it in your account.
If you want to share this report with others (say via Twitter / email) you can use this url: http://goo.gl/fvuXP
Time to create our second, more expansive, social media segment.
As I had mentioned above, this time around we'll look at the social media to our website(s) from our efforts as well as that of all our friends / BFFs / haters.
Before you create this segment you should go checkout your All Traffic Sources report and see how your web analytics tool is capturing various channels people show up from. Based on that review of my site, here's the segment:
analytics segment all social media
A quick explanation.
Values for twitter, facebook, sphinn, stumbleupon are there for people who use web based versions of these social media websites. I can add delicious, digg etc., etc., if I want to. They are simply not that important a source of traffic for me. See why the review of the All Traffic Sources report recommended above was important?
[Some people will obsess and create a ginormous catch-all segment. But remember, you don't need to understand data from the last 10 visitors to make smart decisions.]
The value for "social-media" is there to capture the social media campaigns tagged by me. See our first social media segment above. You'll use your own tracking values.
Value for awe.sm is because for a while I was using awe.sm to auto-tag all my links. There are some latent visits which should get flushed out of the system in the near future (as I have standardized on www.goo.gl and www.bit.ly).
That's *my* All Social Media Segment.
If you are thinking: "Good lord that is messy!"
Welcome to the world of social media tracking. It is messy-ever changing-and you should know that you are going to babysit this constantly. Sorry.  [Also see comment above about needing the last 10 visitors: you don't!]
But after you create the segment, awesomeness follows. . .  analysis!
Step one: Answer: "So What?"
social media conversion ratesall sources
Better, but honestly still pretty pathetic. Remember the goals are a mix of hard and soft conversions (see above)!
By now I am never surprised when I see the above result for Social Media efforts of most outcomes-driven pimping efforts via those channels.
Perhaps you are an exception. Now you know how to measure it!
As mentioned above Analysis Ninjas won't stop at just Outcomes analysis and will dig deeper to see if there is any value that this traffic is adding to our company. My personal favorite place to start is Visitor Loyalty analysis.
Ok so these people are not delivering any hard or soft conversions. Does their loyalty profile look any different?
Here, check it out (standard report in Google Analytics and other tools):
visitor loyalty analysis social media traffic
Hmm. . . a very different profile from other visitors to the site.
Other traffic to the site has much less loyalty than social media traffic. See the delta between 60.98% and 44.35% in the first two rows? Also see the much better, sweeter, distribution for Visitors who visit from 9-14 times through 26-50 times.
For this content website there is value in the social media efforts in that they are delivering an audience that tends to then be much more loyal than all other traffic that ends on their website.
Provable value! From social media!! I know!!! :)
A couple more ideas for our Ninjas to dig deeper, and types of analysis they could do to determine other types of value.
It is trivial to measure the base metrics for your website for your Social Media segment. Visits, Pages/Visit, Average Time on Site, % New Visits, Bounce Rates, Conversion Rates. . . . and so on and so forth. . .
visits pages per visit avg time on site percent new visits bounce rates
You can quickly see at an aggregate level, or a detailed level, if your social media are delivering on the promise outlined by your $150,000 Social Media Consultant.
Here's another bit of analysis that can be useful for certain types of websites.
Say you have a real estate website, or you are responsible for craigslist.com. Both sites are primarily internal site search driven. People come to the site, search, find what they want, do business.
Take your newly beloved Social Media segment and apply it to your delightfully sweet pre-configured Internal Site Search reports. [Left nav -> Content -> Site Search]
Here's what you'll see. . .
internal site search analysis for social media traffic
You'll be able to analyze if people who come to your site from your Social Media campaigns engage with your site more or less (Total Unique Searches per Visitor). Do they exit from the internal site search results faster or slower (% Search Exits)? Do they have a harder time or an easier time finding the right result (Results Pageviews/Search and % Search Refinements)? And other such analysis.
You don't have to just report clicks and visits from social media. In our real estate website we got to the root of what's a deeper engagement (searching) and we got down to measuring real value (or lack thereof).
Ready to do some real social media ROI analysis?
Here's how you can get the all social media traffic segment:
  1. Log into Google Analytics.
  2. Come back here.
  3. Now click on this link: All Social Media Visits Segment. It will open in GA.
  4. Click on the Create Segment button to save it in your account.
If you want to share this report with others (say via Twitter / email) you can use this url: http://goo.gl/YnJON
Good luck!
#3: Search Queries With Multiple Keywords [3, 4, 5, 10, 20]
On this blog and in my keynotes I have bemoaned the obsession Marketers have with brand keywords and their sub optimal strategy of optimizing for keywords, rather than key phrases.
I am a search long tail lover. It is the way to happiness (and finding relevant users!). Hence our first segment focuses on helping you understand the balance between keywords and key phrases in the queries used by Visitors from search engines.
It is not actually a "segment," it is more like using advanced segmentation as a reporting engine in a way you can only do in Google Analytics!
My strategy is simple. Use a regular expression to get GA to segment search queries into various "words this query contains" buckets. Here's what it looks like:
searchlongtailwordssegment
"Magical" part: ^\s*[^\s]+(\s+[^\s]+){2}\s*$
Not that magical actually, just a humble regular expression. It is looking for the number of words in a query (in this case queries visitors typed into Google or Bing or Baidu that contained three words). The second regex counts visits with four word search queries.
[A quick note of thanks to Nick Mihailovski for helping me come up with the perfect regular expression. I was using ^\w*\s\w*\s\w*$. It was good but would not have caught some variations and it would not work for queries in non-English character languages.]
Ok back to using advanced segmentation as a long tail search report.
The final segment I have created, using the method above, has more "or" conditions that contain buckets for counting search queries with 3, 4, 5, 10, 20 and 20+ words. You can of course create any buckets you like; these were ones I find initially interesting.
When you click the Test Segment button (top right) you get this gratifying view (cropped to a small size):
search query words used distribution
Delightful right? It really is.
You get such an immediate sense of the long tail in a way that is hard otherwise in the mass of queries from search engines.
521 Visits from people who typed more than 10 words into Google/Bing! There were 36 visits by people using 20 words in their search query! And 237 people typed in more than 20+ words as their search query!
OMG!
Is your search engine optimization and paid search strategy accommodating for this type of behavior? You still bidding on a word or two?
While the above is not even your complete search universe view, it is a very simple and straightforward way to appreciate how long your search tail is.
And notice you did not even look at a report. You could do all of the above in the advanced segmentation view!
You likely want other buckets than 3, 4, 5, 10 , 20. No problem. Just download the segment below and make the appropriate changes and bam!
Here's how you can get this search long tail segmentation reporting:
  1. Log into Google Analytics.
  2. Come back here.
  3. Now click on this link: Search Query Length Segment. It will open in GA.
  4. Click on the Create Segment button to save it in your account.
If you want to share this report with others (say via Twitter / email) you can use this url: http://goo.gl/v3KbM
Being the Ninja that you are I am sure your thirst of knowledge is not satiated.
Now you are probably wondering how the bounce rate looks for one segment of the long tail traffic (lower usually) or how the conversion rate looks (higher usually) or how many pages do they see (more engagement usually) etc., etc.
The above segment won't help you with that. But all you have to do is create the segment you want.
For example here's the segment for people who see four words exactly:
segmentforfourwordsinthequery
Save the segment, here it is: Visits via Search Queries containing 4 words.
Now apply it to your favorite search report and hello sweet, sweet delicious data!
performancedataforfourwordsinthequery
You know the search queries, you know how many people came and you know their performance ("engagement" or conversions or downloads or leads etc., etc).
Furthermore, you can also segment this data by Paid Search and Organic Search, or Google vs. Bing and start to do very focused analysis that should fundamentally improve your search marketing program.
You can also take another slice at segmenting your search head, mid, and tail. For example you can easily create a segment for Visitors who came to your site via search queries that had more than four words in the query.
Here's that segment: Visits via Search Queries with more than 4 words.
Now go apply it to your search engine or organic search or paid search or goals reports and do really valuable analysis that will earn you the eternal love and adoration of your peers and superiors!
[SIDEBAR]
In case you wanted to do something more sophisticated beyond what's outlined above here are a quick set of instructions, and a tutorial on using regex.
If you want to create a segment for search queries that contain just one word use this regular expression in your advanced segment:
^\s*[^\s]+\s*$
If you want Visits with two words in Google search queries use this:
^\s*[^\s]+\s+[^\s]+\s*$
or
^\s*[^\s]+(\s+[^\s]+){1}\s*$
If you want to identify Visits by people who use three words in their search queries:
^\s*[^\s]+(\s+[^\s]+){2}\s*$
Now you can keep adding to the number in parenthesis and do a happy dance.
Some more cute things.
If you want to query for more than x words, say more than three words use this:
^\s*[^\s]+(\s+[^\s]+){2,}\s*$
Did you see the comma after the number two above? Good.
If you want to identify all search queries where visitors to your site typed 2 or 3 words into the search engine, use this regular expression:
^\s*[^\s]+(\s+[^\s]+){1,2}\s*$
Fun eh?
So what the heck are all those characters in these regular expressions doing? Glad you asked.  Let's consider the regular expression we used to identify 2 word search queries.
The expression is (identified above): ^\s*[^\s]+\s+[^\s]+\s*$
Here's an explanation (as best as I can express in lay terms). . .
^          start at the beginning of the line
\s*        match zero or more white space characters
[^\s]+   match at least one or more non-white space character
\s+       match at least one or more white space character
[^\s]+   match at least one or more non-white space character
\s*        match zero or more white space characters
$          end of string
I hope all this "magic" makes a lot more sense.
[/SIDEBAR]
Isn't advanced segmentation cool? And to think you did all this with your standard javascript tag, all on the fly (including historical data analysis) and without having to buy extensive expensive add-ons!
Ok it's your turn now.
What are your absolutely dearest advanced segments? What's the coolest thing you have done with the advanced segmentation capability in your web analytics tool? Care to share some of your favorites? Perhaps a downloadable link?
It would be incredible to have your wisdom help all of us. Please participate.
Thanks.

Google Analytics Tips: 10 Data Analysis Strategies That Pay Off Big!

Occam's Razor
by Avinash Kaushik

Google Analytics Tips: 10 Data Analysis Strategies That Pay Off Big!

manymany In the coming year, based on current announcements, Google Analytics is set to go through an almost unprecedented amount of evolution. My postulation is that by this time next year the tool will be almost unrecognizable. [My favorite is Visitor Analytics, and visitor level segmentation that will be pervasive throughout the product. This is insanely cool.]
But it turns out Google Analytics, just like SiteCatalyst, WebTrends, and other web analytics tools, already has plenty of pretty valuable deeply insightful stuff in it. Yet so few people have mastered what's already there. Sometimes I wonder if we should actually be all that excited about the insanely cool stuff if the sanely cool stuff remains unmastered.
As we hopefully look forward to an exciting year, let's take a moment to address the latter challenge. Allow me to help you with your resolution of mastering the sanely cool stuff!
One way to do it is for me to just tell you what my top ten Google Analytics reports are that you could familiarize yourself with. The problem is that you'll know where to go, but not what to look for when you get there.
Each selection by me of a top ten (standard!) report in Google Analytics below includes a small brain dump of quick insights, Google Analytics tips if you will, I seek when I'm looking at that report. The stories and examples will hopefully help you intelligently approach your own data in these reports and quickly find insights you can action / share with your management team.
[Sidebar for people who want to be BIG winners]
Before you log into Google Analytics it is really really helpful to get context about the company/client's business.
I realize that you are pressed for time and you might not want to do it. But in case you want to win big rather than just win, I encourage to read the six tips outlined in this post: The Biggest Mistake Web Analysts Make… And How To Avoid It!
I guarantee that if you invest this time, you'll find 5x better insights when you log into Google Analytics or Adobe SiteCatalyst. If you don't invest this upfront, fun, time you'll hurt my feelings but I'll understand, you don't want to win big. :)
[/Sidebar for people who want to be BIG winners]
Below are the top ten standard reports in Google Analytics that you should know well, especially if you are only a part-time user of Google Analytics.
If you are an Analyst, of any tool, check out the Bonus tips included to kick your efforts up a notch or two.
Everything here's simple. You don't have to be a particularly deep expert to find value in this training.
1. Sources Overview report.
Start with the pie. It helps you understand how reliant the company/client is on Search (too much is actually not good). What other sources are big for them? If you don't see other sources (campaigns – email, social, display) are not tagged. A very bad thing.
acquisition portfolio balance
Like everything in life, you want a balanced portfolio (left).
Then go to Traffic Source > Sources > Campaigns to get a feel for how many display, social, email, other campaigns the company might be running. What's their performance? Very good context.
Search is always big for everyone. So you want to drill down into the Traffic Source > Sources > Search > Overview to understand the macro balance between Organic and Paid (this, by default, will only show AdWords though it can show Bing, Yandex etc).
It is hard to get overall search keyword performance in GA, so grab this quick custom report All Search Performance and apply the standard advanced segments to it (Non-paid Search Traffic, Paid Search Traffic). Tons and tons of insights here. Better organic keywords, performance for same words between organic and paid, goal value comparisons, so much more. Go crazy.
While you look at three reports, you quickly end up with a robust understanding of *all* the things the company is doing and a detailed understanding of paid and organic search performance.
Bonus: Download the All Traffic Source End to End report for best, in depth, analysis. [Make sure you are logged into GA, then click on the link, save the report to your account.]
2. Landing Pages report.
Zero companies will win without great landing pages. You stink there, bye, bye large amounts of money. Great landing pages equals more customers enticed to engage plus higher conversions plus higher (AdWords) quality score.
Start by looking at the top 20 landing pages. Content > Site Content > Landing Pages.
Identify ones with high bounce rates. What is wrong with them? Visit them. Missing calls to action broken links, not enough content, content unrelated to the ads, something else? Low hanging fruit. Fix it.
Learn to apply the top traffic segments (see #1 above) to this report. Find high bounce rates for one segment (Paid Search) and look at other segments (Display) where pages have low bounce rates. Learn from the winners, apply to the losers.
Bonus: Smart people look at the Page Value delivered by each landing page and not just bounce rates. Sadly it not easy to find. No worries, I've got your back. Download this custom report: Landing Pages Analysis .
landing page analysis custom report
For each page now you know how often it is a landing page (Entrances/Pageviews), how much it stinks (Bounce Rate), how much money it is making you (Page Value). Ignore your home page or any cart or checkout pages that might show up. Look at all others.
Why do some pages only make 97 cents and others make you almost four dollars? Prioritize using a mix of bounce and page value, analyze details using referring keywords and referring urls (drilldowns are already built into above custom report!).
3. Goals Report.
Macro + Micro Conversions. Macro + Micro Conversions. Macro + Micro Conversions. Macro + Micro Conversions. Macro + Micro Conversions. Macro + Micro Conversions.
Got it? Macro + Micro Conversions!
The difference between companies that win and the companies that will lose is simply this: Economic Value.
So look at the standard goals report. Conversions > Goals > Overview. This report shows all the goals converting, in addition to the ecommerce order now conversions.
goals conversions report
Are there at least six micro conversions identified? Yes? Good. Does each goal have values identified? Yes? Magnificent. The company you are analyzing is ready to rock the web!
If the answer to either question is no, at best the company will scratch out a living on the web. More likely their competitors are going to slap them around.
What are the high micro conversions you need to start focusing on (G6, G7, G2, G1 above)? Do you understand how elements of your paid, owned, earned inbound marketing efforts drive each of these? How do these goals tile to your macro conversion, G3? Does the CEO understand the complete value of digital ($233,810 above)?
Bonus: Ecommerce is sexy, so don't forget to look at that. Specifically focus on what products are being sold. Go to Conversions > Ecommerce > Product Performance. (For this to work the ecommerce tag has to be implemented right. If it is not you have bigger problems.)
What are the top selling products, what's the average quantity? How about when you apply segments for your top traffic sources? What is Search really good at selling? What about Social? What about Display? What about in Florida vs. New York? Understand, have a smarter CEO conversation.
4. Conversion Funnels Report.
Fastest. Way. To. Make. Money.
The conversion path is three or four pages. What's your abandonment rate? Why is it a criminal 65%? Is there a better way to make money than to take it from people who have started the checkout process and want to give you money?
This post is about standard GA reports, but the standard cart/checkout funnel visualization in GA is value deficient. So as your standard report use Paditrack. For the same number of button presses you'll get 25x more value than Google Analytics.
paditrack funnel visualization
Where do most people drop off? How can you have a minimum number of text fields? Is it possible to not have garish banner ads in the checkout process? When do people enter coupons? Is the error checking when the person submits the page or is it (awesomer) in-line when the person moves from one field to the next?
Bonus: Apply top traffic sources segments to the above report. Or just apply the top paid search referring keyword to the funnel report…
paditrack segmented funnels
Do you see differences in abandonment rates? Why? What is causing a particular keyword, email campaign, display ad, offer, to convert higher or lower? What lessons can be applied to all other visitors? Go fix!
5. MCF Assisted Conversions Report.
Multi-channel attribution was the flavor of the month for every month in 2012. It will be the same in 2013. And just as in 2012 magic pills will be scarce, FUD will be plentiful, and vendors will promise the moon. You, I guarantee it, will be just as confused. :)
But get to know the assisted conversions report. It is fairly straightforward.
If *all* your campaigns *always* include campaign tracking parameters, this report is really good at answering this critical question: Is channel x more likely to be at the end of the conversion process or drive traffic that might convert later via a different channel? It is extremely valuable to know the answer.
Conversions > Multi-Channel Funnels > Assisted Conversions.
multi channel funnels assisted conversions
In the above case I was astonished that while our email was primarily a direct response "here's a coupon to convert" marketing, it actually drove more conversions via other channels (!).
Impact? 1. We were not giving email enough credit. 2. Were we sending emails to people we had seen recently on our site? 3. If email assists, can we understand its order in the conversion process and which channel it most assists? (Yes. Go to Top Conversion Paths reportand search for Email.)
Even if you never get into the mess of attribution modeling and all that other craziness, you are much smarter by just analyzing the data, and implications, from at this report.
Bonus: You will want to know what to do about attribution modeling craziness. :) Read answers to questions one, two and three here: Attribution Modeling, Org Culture, Deeper Analysis. After that if you can't resist the itch, go play with the, now free to everyone, Attribution Modeling Tool in GA. Read the three answers first, please.
6. Mobile Devices Report.
Mobile is all the rage. You can't walk into any about digital or not about digital at all meeting without a solid grasp of where the company is when it comes to mobile.
This is a standard report in GA, but I've pressed a few buttons to make it smarter. You'll find the report in Audience > Mobile > Devices. On top of the graph click on Select A Metric and choose Goal Conversion Rate. Now you know the Visits and the Conversions. Smart.
Then on top of the table click on the Pivot icon (see mouse below). Then from Pivot By choose Source.
mobile devices pivot report
First, you quickly learn what the main big mobile consumption platforms are. Second, equally quickly, you know the main sources of traffic via mobile are. [If you remember from our first report above, direct was #3 in overall and social was #4, but on mobile direct is #1 and social is #3. Did you realize your acquisition was distinct on mobile? Does your mobile marketing reflect that?]
As you look at the "scorecard" (just under the graph) you can look at the little numbers in gray and understand overall mobile performance compared to site performance. Very handy.
Bonus: Download a super awesome all-encompassing mobile custom report: Complete Mobile Performance Report. It has unique built in drill-downs, customized metrics that give you the ability to deeply analyze mobile data by devices, search behavior and content content consumption (click on each tab). You will never need another standard mobile report!
7. In-Page Analytics Report.
Traffic Sources > Content > In-Page Analytics.
There is no simpler way to understand how consumers are behaving on a company's website then to just look at their clicks. In-Page does that really well. Just look at the link, look at the corresponding number.
in page analytics google analytics
On the home page it is so easy now to see which product categories people really care about (Calico Critters! Put them on sale! Buy all the keywords! Run email campaigns! :). You can also easily see that zero people have clicked on the ScooterX Skateboard (time to remove it), at least some care about Mini-Motos but what people really care about is the Marble Run (pimp away!).
I hear you. Clicks are ok but you only care about money. No worries. Change the metric on top of the page to Goal Values and bam! What you now see is the distribution of which link is making you how much money. Sweetness.
This report is your easiest way into Web Analytics.
Bonus: Open your top landing pages in this report and then apply the Advanced Segment (button on top of the report) for your big traffic sources to see how differently your visitors click. Then at least for your top most landing pages, consider creating a custom one for each of the main traffic source.
Bonus 2: GA now allows for enhanced link attribution in this report. That is very cool because if you have a link in the header, a link in the side bar and a link in the main body all pointing to the same product page, Analytics will show you exactly how many people click on each of those links. You can then eliminate the big promo in the side bar because you now have data which shows that zero people click on it (because it looks like a banner ad!).
8. Location Report.
People have weird conceptions of where their traffic comes from. Sure they can sprout the number of tweets or top search keywords, but rarely do they have a robust understanding of the geographical distribution of their audience.
Illuminate yourself by going to Audience > Demographics > Location. Then on top of the graph change the metric from Visits to Goal Conversion Rate.
geographic conversion rate distribution
The default view (Visits) will always underline your bias. For me it is always USA #1 (hurray!). But USA is only 40% of my traffic. And when I look at Conversion Rates there are a whole bunch of countries that are way better than USA (#47!). There are 14 countries with Conversion Rates 2x of USA (OMG!).
That changes things, right? Changes campaign targeting, changes content development, changes social strategies, changes product mix, changes keywords for search engine optimization.
You can run this type of analysis at a State and a City level as well, the results are always eye opening / preconceived notions busting.
Bonus: Every GA report shows clicks you actually get, there is only one that shows you clicks you could possibly have gotten. Traffic Sources > Search Engine Optimization > Geographical Summary.
It shows, by country, where you currently show up on Google properties (Impressions) and the number of clicks you get. It took me 110k impressions to get 10k clicks in the UK and 60k impressions to get 10k clicks in Germany. Time to dial up SEO awesomeness in the UK!
9. Site Search Terms Report.
Another hugely underutilized resource is the intent your visitors are actually expressing on your site by typing into your site search engine (best way to stink is not to have one).
Content > Site Search > Search Terms. Admire the default view for a second, but quickly switch to Goal Set 1 (or Ecommerce if you are one of the aiming to hit a low bar with no Goals defined). You'll get this view…
internal site search goal value report
Do you know what are the top things people are looking for that they can't find on first glance? Above. Do you know how many of those top expressed wishes then lead to a zero (!) percent conversion rate? Above. Do you know how much money you make off each search term/expressed intent? Above.
Now would you not want all the top things people look for to have a $2.39 per search goal value rather than 0.12 or 0.63? Of course. You have work to do.
Bonus: This might be stretching it a bit but 100% of your internal site search terms should probably be on your SEO keyword list and likely a part of your Paid Search campaigns. If people are coming to your site and looking for stuff (and you have it) then there is no better signal to grow your keyword list.
In my case that is 20,217 keywords I can quickly add to my Bing/Baidu/Yandex search campaigns and start measuring performance. My additions will be geo targeted by which keywords on my site were searched for from each country!
10. E2E Paid Search Report.
I tried really hard to keep this to just standard reports, but I had to squeeze in one "standard" custom report. It comes from my recent post Google Analytics Custom Reports: Paid Search Campaigns Analysis .
The report shows the end to end view of your search campaign performance.
end to end paid search analtyics report
Any Analyst worth their salt will spent a lot of time trying to understand what is happening on the site in conjunction with trying to understand what happening inside AdWords! This report does that very effectively. Above it merges data from AdWords with your site performance data (how cute is it that you can see cost per click and revenue per click right next to each other!).
Additionally it has pre-built drilldowns (below) that allow you to navigate this performance in context of your AdWords account structure.
paid search analytics dimensions filters
Identify which campaigns are actually delivering value. Identify if you can optimize your AdGroups to deliver higher performance (impressions, clicks). Identify what your Match Type decisions are doing to your performance (Broad, Phrase, Exact, what's up?).
There is a lot more you can do in terms of AdWords Analytics, most of your starting points sit in the above report. Hence it is my standard AdWords report, even if it is a custom report. Download: E2E Paid Search Report.
That's it. Ten standard reports that high insights in plain sight. And a bonus five custom reports to allow you to truly bring out your inner Analysis Ninja!
If you are able to master the standard set, you'll be above average when it comes to understanding site performance. Better still, you'll be able to identify a robust set of actions that will please the toughest CEO and over a period of time earn you a glory and a higher salary.
Now that's something worthwhile to shoot for in 2013!
As always, it is your turn now.
Are these standard reports a part of your current Analysis Ninja arsenal? Do you have a favorite standard report that is not listed above? If yes, what is amazing about it? If you use these reports already, are these the types of insights you seek? Are there other hidden insights gems that I might have overlooked above? Got a omg this is my secret weapon custom report you want to share with us?
Please share your insights, questions, favorite reports, and feedback via comments below.
Thanks, and good luck!



Saturday, April 07, 2012

POSTVIEW

postview o no postview, esa es la cuestión

Miércoles, enero 12th, 2011
Una de las preguntas más habituales en las canpañas online, es el retorno de la inversión que tiene remunerar el postview. En este post, voy a comentar mi opinión acerca de tener o no postview en un programa de afiliación. No creo que exista la receta mágica o la ecuación que nos pueda dar la respuesta. Lo que  pienso es que hay que ANALIZAR cada programa de forma INDIVIDUAL  y extraer las conclusiones de si nos interesa o no tener postview en cada caso.
No pretendo sentenciar, si el postview es bueno, malo o regular, Solo quiero lanzar al aire unos cuantos puntos sobre los que pensar y  analizar para que las personas que no estén muy familiarizadas con la gestión de un programa de afiliación o que se encuentren en la coyuntura de tener que lanzarlo, puedan valorar si les interesa mantener o incluir  el postview.
Probablemente, un programa de afiliación se  lanza con una buena dosis de recelo y bastante desconocimiento. Yo diría, que los anunciantes a priori, tenemos una postura que intenta ser conservadora y prudente a la vez que deseamos fervientemente maximizar las oportunidades que nos proporciona el marketing de afiliación. Por ejemplo, la cobertura que nos ofrece una red de afiliación es muy difícil conseguirla con acciones más tradicionales y además, en un pago por acción tenemos un  altísimo control de los costes de adquisición. Si el afiliado no consigue registros, ventas o el objetivo que le hayamos marcado, no cobra.
No es que a estas alturas no sepamos que el es marketing de afiliación, creo que hoy en día más o menos todos los profesionales vinculados al online tenemos una idea bastante clara acerca del tema, lo realmente difícil es conocer a los afiliados y su forma de trabajar. Con el paso del tiempo y muchas, muchas  pruebas  (con sus correspondientes errores en algunos casos) sabremos que afiliados son los que mejor rendimiento sacan a  nuestro programa y cuáles son los intervalos aceptables de comisión para cada modelo de trabajo en la red. No es lo mismo un afiliado dedicado a e-mail marketing que un afiliado que se dedica a comprar inventario invendido o que un afiliado que puja en buscadores. Cada uno tiene su especialidad y unos costes distintos asociados al canal por el que capta el tráfico y a con él a los potenciales clientes. Es absolutamente necesario conocer todos los segmentos o tipos de afiliados para ofrecerles una comisión adecuada y unas condiciones óptimas de acceso a nuestro programa.
La optimización de un programa de afiliación tiene dos fases principales, primero hay que estudiar a los afiliados y a continuación analizar los datos del programa. Volviendo al tema del post, necesitamos las dos patas para saber si nos resulta rentable tener remunerado el postview y en el caso de que la respuesta sea afirmativa, que comisión es rentable para ambas partes.
Antes de atacar a las métricas, me gustaría comentar algunos puntos que a veces se desconocen de los programas de afiliación y que afectan a la medición del postview.
- Las plataformas de afiliación disponen de tecnología para realizar el seguimiento de los registros o ventas distinguiendo si son transacciones directas, de postclick o de postview. La cookie de postview es distinta de la cookie de postclick.
- Dentro de la plataforma, suele haber normas establecidas para atribuir el mérito de la conversión a uno u otro impacto publicitario si han sido necesarios más de uno para llegar a ella. Por ejemplo, si el usuario que realiza una conversión (registro o venta) tiene una cookie de impresión de un afiliado y una cookie de click de otro, la conversión se atribuiría al afiliado que logró el click. Podría decrise que se considera la cookie de postclick  más importante que la de postview.
- Es posible asignar una comisión distinta (habitualmente menor) a las conversiones que proceden de postview.
Teniendo en cuenta estos puntos, las posibilidades son muchas. Desde la más sencilla, que sería  tener en las transacciones de postview con una comisión más baja que en las transacciones de postclick o las directas,  hasta la que probablemente sea la mas compleja, el “comission split” que consistiría en atribuir una parte de la conversión (y por lo tanto de la comisión)  a cada uno de los intervinientes en el proceso de conversión teniendo en cuenta la aportación que cada uno ha hecho a la misma.
Para analizar el rendimiento del postview en un programa, hay varios factores a tener en cuenta además del tipo de producto y tipo de accción que estemos comisionando.
Por ejemplo, para mi una métrica muy importante es el porcentaje de transacciones en sesión respecto al total de transacciones de postview. Si un progama de afiliación tiene un postview de 24 horas, pero un porcentaje alto de las transacciones atribuidas al postview se realizan en sesión (dentro de los siguientes 30 minutos a la impresión) podemos concluir casi con total seguridad que en estas conversiones si ha intervenido la impresión mostrada.
Otra métrica a monitorizar es el porcentaje de duplicidades en las conversiones de postview. Debemos saber cuántas conversiones “se apuntan” dos o más soportes en cualquier caso, pero en el postview es crucial. Podemos encontrarnos remunerando postview a una conversión que tiene uno o varios esfuerzos publicitarios posteriores. La duplicidad no se dará dentro de la misma plataforma porque ya hemos visto que existen reglas para atribuir el mérito de la conversión a uno u otro impacto. Pero no tenemos controlado que sucede en soportes distintos.
Una vez calculado este porcentaje, deberíamos intentar determinar si existe algún patrón con algún tipo de afiliado en concreto o incluso en el programa en general. En el caso de detectar alguna tendencia que indique aumento de duplicidades, debemos calcular si nos sigue resultando rentable pagar dos veces esa conversión o si por el contrario debemos crear un sistema de anulaciones para los registros/ventas que se encuentren en esta circunstancia.
La casuística es muy amplia y solo he intentado lanzar algunas ideas que permitan comenzar a investigar sobre el retorno de la inversión en el  postview.
Como conclusión: no debes fiarte de tu instinto si puedes medir, medir y medir para basar tus decisiones en datos.
Ana Soplón @ana_sopli
“Las conversaciones en red hacen posible el surgimiento de nuevas y poderosas formas de organización social y de intercambio de conocimientos.” Manifiesto Cluetrain
Ana  estudió Ingeniería Técnica Industrial en la Universidad de Zaragoza.  Siempre fascinada por el Marketing, realizó un post grado en esta materia a través de la UOC (Universitat Oberta de Catalunya)  y acaba de completar un máster en  Web Analytics a través de la UBC (University of British Columbia).
Tras formar parte de varios departamentos de Línea Directa Aseguradora, ha pasado la mayor parte de su trayectoria profesional en Marketing,  y sobre todo en la parte online. Actualmente trabaja como analista web y responsable de redes de afiliación. 

Cuadro de Mando o Dashboard.

Posts Tagged ‘analítica web’

Dashboards: del análisis al éxito

Lunes, enero 30th, 2012
 
Da igual lo bueno que sea el análisis que acabamos de hacer. Si no sabemos transmitirlo convenientemente, acabará en el cubo de la basura virtual y no le hará nadie caso, ni a nuestro informe, ni a nosotros.
El problema radica en que nos empeñamos en contar lo que hemos analizado hasta el último detalle y hacemos informes o powerpoints de un montón de hojas con un montón de datos, gráficos, indicaciones, flechas, etc… cuando lo que realmente necesita el receptor de ese informe es saber qué está pasando, qué puede pasar y qué puede hacer al respecto.

Empecemos por el principio: Necesitamos conocer los objetivos que tienen los receptores del análisis. Saber qué quieren y qué necesitan para hacer su trabajo. De esto dependerá la profundidad del análisis y sobre todo las KPIs o los indicadores que incluiremos en el informe final.

Una vez terminado el análisis y sabiendo exactamente qué es lo que produce un resultado final, las KPIs que están involucradas, iniciaremos la selección de éstas y el formato de presentación que se adapte mejor a lo que queremos transmitir. El resultado debe ser un dashboard o cuadro de mando o informe en el que las KPIs se complementen entre sí y nos lleven de la mano a tomar acciones.

Debe quedar claro: dónde habría que seguir profundizando en caso de necesitar más detalles y sobre todo debe incluir unas conclusiones y recomendaciones. Lo más importante en este punto es que el analista ha de ser parte de la solución posible, nunca limitarse a señalar el problema. Lo ideal es que el resultado del análisis quepa en una única pantalla, ya que así concentramos toda la información en un formato que cualquiera ve sin necesidad de hacer scroll en su pantalla.

Si se trata únicamente de un dashboard que se realiza con cierta periodicidad, hay que dejar claro el “movimiento” de un periodo a otro, poner contexto en los datos para que el receptor sepa si el valor es el que debería tener o nos debería preocupar.

En el año 2008 creé un dashboard informativo de las KPIs más importantes en el caso del canal internet para la empresa para la que trabajaba en aquel momento:
A lo largo de estos años he conocido diversas variantes que otros analistas han creado adaptando el concepto de este dashboard a sus respectivas empresas. De eso se trata, de encontrar el dashboard perfecto para transmitir el conocimiento de un análisis en nuestro propio entorno: (dashboard copyright de Fernando Ortega y dashboard copyright de Raquel Madrigal)
Junto con la versión en inglés del libro de Avinash Kaushik Web Analytics: An Hour a Day, venía un DVD con un ejemplo de dashboard de la empresa americana Stratigent. Me llamó mucho la atención en su momento porque contenía mucha información en un formato limpio y muy claro.

Este año en el Emetrics en Nueva York tuve la suerte de poder asistir a un workshop de dashboards de la responsable de este dashboard, Jennifer Veesenmeyer. Siguen utilizando este tipo de dashboard adaptado a la necesidad de cada cliente, es un modelo que se puede adaptar a distintos negocios y que únicamente conociendo las KPIs importantes para tu negocio y con un poquito de maña en excel puedes tener en una sola pantalla todo lo que necesitas para tomar decisiones.
¿Qué es lo que hemos aprendido a lo largo de estos años como analistas web en cuestión de dashboards? Que tenemos siempre demasiados datos, que al final no se toman decisiones por puro desbordamiento. Que para ser un mejor analista web hay que pasar por ser capaz de seleccionar lo que realmente importa y saber lo básico que necesita un negocio saber sobre su web. A partir de ahí hay que averiguar qué se debe customizar dependiendo de los objetivos de cada negocio y cada situación en particular.

Sobre todo hacer hincapie no tanto en lo que ha pasado sino dejar entrever lo que podría pasar de no llevar a cabo cambios. Evolucionar del “qué ha pasado” hacia el “qué puede pasar”. Hacer uso de gráficos visuales donde se recoja el pasado, el presente y el futuro de forma que no haga falta explicar lo importante de tomar cartas en el asunto.

Por ejemplo, si solamente analizamos el qué ha pasado en 2011 en un gráfico, podemos pensar que el éxito a nivel de conversión se lo lleva todo España:
Sin embargo, lo que realmente ha pasado es que el mercado español es muy maduro y no hemos crecido nada, sin embargo llama la atención la subida de países como Rusia, de cara a tomar acciones durante este año. Este es el gráfico que realmente importa lo suficiente como para mostrarlo en un dashboard:
Lo ideal es que se genere un dashboard para cada uno de los que trabajan en internet, adaptado a sus necesidades, por ejemplo, el responsable de las Redes Sociales o el responsable de los Blogs corporativos deberían tener los suyos propios que les permita saber si la estrategia que siguen es la adecuada o no:
Si carecemos de tiempo o no tenemos el suficiente conocimiento como para lograr resultados vistosos en excel, no es excusa para no crear dashboards más rudimentarios pero igual de efectivos, ya que lo importante es el contenido y el valor que pueden aportar para tomar decisiones en la estrategia:

Por el contrario, si queremos avanzar en nuestro camino de transmisión de datos y llamar la atención con informes espectaculares, hay que aprender de infografía y combinar con acierto colores e imágenes: (dashboard copyright BankinterLabs)
Existen herramientas especializadas en hacer buenos dashboards y sobre todo en ir directamente contra las APIs de las distintas herramientas de medición y seleccionar los campos que necesitamos para monitorizarlos de manera automática. Son:
- Excellent Analytics:
- Nextanalytics:
Hay blogs que nos pueden ayudar a ir progresando en el arte de hacer buenos dashboards en excel.
El blog de Chandoo es un excelente recurso para sacarle el máximo partido:
Y ExcelCharts nos ayudará a crear dashboards efectivos con tablas y gráficos avanzados:

Hay que hacerlo bien, ¿por qué? Pues porque ser analista web es saber llegar a resultados que llamen a la acción… pero también saber comunicar los resultados y que se produzca dicha acción. ¿Para qué analizamos si luego no participamos en la toma de decisiones?
Artículos interesantes que te pueden servir de inspiración:
-       TEACHING ONLINE JOURNALISM
-       NY TIMES
-       EDUCAUSE

Y si te apetece leer libros sobre el tema a tener en cuenta (Stephen Few):
Show Me the Numbers: Designing Tables and Graphs to Enlighten
Information Dashboard Design: The Effective Visual Communication of Data
Now You See It: Simple Visualization Techniques for Quantitative Analysis

“Las conversaciones en red hacen posible el surgimiento de nuevas y poderosas formas de organización social y de intercambio de conocimientos.” Manifiesto Cluetrain
Gemma Muñoz es diplomada en informática y tiene un master en Web Analytics por la Universidad British Columbia.
Es Founder & Chief Analyst de la empresa El Arte de Medir . Tiene un blog sobre analítica web, ¿Dónde está Avinash cuando se le necesita? y es autora del libro “El Arte de Medir” publicado en 2011 como manual de analítica web.
Gemma Muñoz @sorprendida