One of the largest paid analytic tool only has around 3000 clients, so if you only have 3000 clients. How good is going to be its data? in terms of representing behavior, so please, check your sample bias or sample size before you get confidence with the data and spend money in digital marketing. I won't name it its name.
While biggest is the sample size or sample bias, the more it is the approximation to the real in terms of representing behavior of your customer in your website.
Every analytic tool has different source of data collection. So, don't compare data across different analytic tools.
In summary, I go for free analytic tool. Google Analytics.
Ciceron
Showing posts with label data. Show all posts
Showing posts with label data. Show all posts
Wednesday, February 18, 2015
Thursday, July 24, 2014
Direct Traffic Definition.
THE TKG TEAM
SEO WEB DEVELOPMENT GLOSSARY
direct Traffic
Web definitions:
- The number of visitors who directly accessed your site. Direct visits can be the result of bookmarks, browser home page, or manually typing in your domain URL. In other words, these visitors did not click on a search engine result, PPC ad, or link to access your site.http://www.tkg.com/seo-web-development-glossary
- SOCIAL MEDIA STRATEGY: BECAUSE CHECKING 50 TIMES A DAY FACEBOOK ISN'T STRATEGY.
Monday, July 21, 2014
Three Amazing Web Data Analyses Techniques For Analysis Ninjas.
Occam's Razor
by Avinash Kaushik
Day
in and day out we stare at standard tables and rows and convert them
into smaller or scarier tables and rows and through analysis we try and
move the really heavy beast called the "organization" into action.
It is hard.
This blog post has three ideas I've learned from other smart people, ideas that help surprise the "organization" with something non-normal and get it to take action. Each idea is wonderfully simple, and yet in its own sweet way makes us, Analysis Ninjas, think harder and deliver insights better.
Let's go.
Compute Actual Cost Per Acquisition Post-Facto Including Micro-Conversions.
I know that is confusing. Stay with me.
This idea, 100% of it, comes via my friend David Hughes. [He passed away recently and I miss his friendship, and our collaborations, tremendously.] From this post: Improve Search Marketing Conversion Rates through Email Registration. I'm going to redo the tables, just to make them fit the width of this blog.
David's idea is simple and genius.
Today when we measure our Cost Per Acquisition (CPA) for our campaigns (Search, Email, Affiliate, whatever), we just think of the macro-conversion and, perhaps worse, we think only of that session / visit.
Let's assume we are running www.macys.com and we got 1,000 Visitors to come to our site via a display advertising campaign. As dutiful Reporting Folks we will send this table out to reflect performance of that campaign.
$16.7 CPA might sound huge (or not depending on your margin), but on the surface it seems a lot. The flaw in this report of course is in assuming that all 1,000 visits were in play (wanted to convert / buy something). This is rarely the case.
I have repeatedly evangelized identifying all the jobs the site is trying to do (macro AND micro conversions) and then quantifying their economic value to the business. On the Macy's website of the 970 non-converting visitors, some might have signed up for a free account, some for email alerts or coupons, some opened a wedding registry etc.
If some of those 970 Visitors completed some micro-conversions, then shouldn't the CPA be on that basis rather than just the 30 orders above?
Simplifying the scenario a bit. if some of those 970 submitted an email address / signed up for price alerts and converted later then shouldn't the cost-per-acquisition include those future sales?
Say some of the Visitors did just that. What was the acquisition cost of each sign-up?
Nothing. Nice.
What will Macy's do next? Send the 100 folks the price alert they signed up for!
And what will come of it? Sales, of course.
This is a reasonable picture that will emerge.
So we got 30 orders from the original visits, and another 20 by re-targeting users via permission-based email.
What does the CPA of our original affiliate marketing campaign look like now?
A more respectable $10 compared to the original $16.7.
An immediate implication is that if at a CPA of $16.7 you were profitable, then you can communicate to your Senior Leaders that you were actually even more profitable since the final CPA is now only $10. And if you find yourself in a aggressive marketing siutation then you could even increase the bids on your display campaigns to get even more Visitors. Thanks to your clever micro-conversion and re-targeting strategy!
Lessons:
</sidebar>
Command Attention, Valuable Action, By Stating Raw Numbers.
This idea comes via Kaiser Fung, from this post: Further thoughts on the Facebook business model.
In a blog post with thoughts about a graph from WebTrends, that shows click-through rates (CTRs) and cost per clicks (CPCs) on Facebook. Kaiser made this simple insight:
And of course that's clicks, not conversions.
It caused my eyes to open wide.
That is astonishingly low.
Somehow when someone tells you "Facebook's ads CTR is 0.01%" you don't quite get it. I mean, it does not feel pathetically minuscule, as it should.
I have championed the contextual use of raw numbers to deliver insights, especially when using Averages, Percentages and Ratios. [See: Actively Avoid Insights: 4 Useful KPI Measurement Techniques]
Yet the 0.01% number did not make the impact on me it should have. And that is exactly the problem when you present conversion rates (also pathetically low on every single website on the planet) and other such metrics.
So make sure you show raw numbers.
The first number might not get your management team to take any action; it just does not evoke any feeling.
The second set of numbers might get someone to scream: WTH!
They might ask:
Lessons:
Either way, remember that your job is to divert people from becoming lulled into a false sense of everything's okay. Scare them into paying attention and asking you tough questions.
Face Reality By Computing "Convert-able Audience" & "Real Conversion Rates."
This idea comes via Thomas Baekdal, from this post: Converting Traffic to Subscribers.
In it, Thomas postulates that even if you have 1,000,000 Absolute Unique Visitors to the website, that does not mean that your possibly "convert-able" audience is a million.
Some people will visit once and never again. That was not an audience that would have converted, ever. For example, the link above is to Baekdal Plus. I pay $49 per year to access that premium content because it is so good. Many of you may not want to pay for content on the web. So for Thomas, not all the Visitors from the above link are actually in play for conversion. [Though I wish they were.]
So it is imprudent to count those folks; better to only count returning Visitors.
Then, some content attracts traffic, other content actually is "valuable and will convert people into subscribers." Thomas's guidance is to only count the latter in the in play for conversion bucket.
Now you can calculate the "convert-able" audience. In Thomas's example here's how his picture looks:
(1,000,000 less the 63% one-time Visitors) less the 20% valuable traffic = 74,000.
Possible convert-able audience = 74,000.
Real audience you even have a remote chance of converting: 74k.
So small, right? After starting with a million.
I rarely see Web Analysts doing this simple exercise and educating their Senior Leadership of this harsh truth. We assume every single person who will visit www.tesco.com is there to convert. Every single person who visits www.etsy.com is there to buy. Our conversion rate calculation, Orders/Visits (bad version) or Orders/Visitors (better version), reflects that, sub optimal, mental model.
We show our leaders that we suck more than we actually do by computing conversion on the basis of All Visits (bad version) or All Visitors (better version).
If Thomas has 3,700 conversions in a month, we would normally report that as 0.37% conversion rate. [(3700/1000000)*100]
Of course, the reality is that the conversion rate was 5%. [(3700/74000)*100]
Not that 5% is orgasmically higher. But it is more reflective of the truth than 0.37%.
You would take one set of actions with 5% and a completely different set with 0.37%.
Compute your "convert-able audience." Please.
Use whatever common-sense approaches you can find.
In a post written in Nov 2006, I presented a similar thought (though in a different context than Thomas). [See: Excellent Analytics Tip #8: Measure the Real Conversion Rate & "Opportunity Pie"]
My graphics were a lot less sexy in comparison to Thomas's.
The idea was to get you to identify your "Real Conversion Rate", by identifying your "Opportunity Pie."
My recommendations were:
Throw out everyone who bounced, just for now, and also if you use log files (ohh those were the days!), then throw out "visits" by robots / junk. That gives you a rough idea of your "Opportunity Pie" (convert-able audience).
Or
If you have a qualitative survey deployed (with the three greatest survey questions ever), then throw out the percentage of Visitors who do not state their Primary Purpose as visiting your website to "buy" or "research products and services" (I generously assume we can convince the latter bucket to buy through amazing marketing on the site). So now you know just the people for sure in play and possibly in play.
This second path will also give you a great rough idea of your "Opportunity Pie" (connect-able audience).
My recommendations were different from the ones Thomas is using. But both reach for the same goal: To get you to understand that not every single Visitor will convert, and you should know, even roughly, how many are in play / convert-able.
Perhaps you'll come up with your own rules. You might throw out everyone who was there to check Order Status. Or those that logged into their account to update settings. Or those that only visited the /blog/ directory. Or the Social Media of course they will never every buy but eat our bandwidth daily digging diggers!
As long as they pass the common-sense filter, go for it. You'll be earning your Analysis Ninja chops, and delivering something extremely valuable to your management team (even if they perceive it to be a cold bucket of water on their faces, the first time).
Lessons:
Preen proud as a peacock; this is your moment of greatness. Tell them why having thoughtful micro-conversions is so important on the site. Tell them you are going to compute the micro-conversion rate for the non convert-able audience. Tell them that with some of the non convert-able audience you'll hence establish a longer term relationship: with some you'll just hope to create delight and make them your recommenders, and with others still you'll do re-targeting and use David's method (all the way up top of this post) to reduce cost-per-acquisition.
All really great business outcomes.
In a nutshell. the goal is not to abandon a majority of your traffic. The goal is not to just ignore all the bouncers (fix that, tips here: Six Tips For Improving High Bounce / Low Conversion Web Pages). The goal is not to be depressing. The goal is to face reality, give it a hug and then figure out how to kick things up several notches.
Are you Ninja enough to accept that challenge?
Of course you are. You read this blog! : )
Know that I'm rooting for you.
Okay, now it's your turn.
Does your company do re-targeting to captured email addresses? If not, why not? If yes, then do you compute real CPA? Have you computed your "convert-able audience?" Is it 100% of your website Visitors? When was the last time you used raw numbers to shove a dose of reality in front of your Senior Leaders? Are there other techniques you've used that worked?
Please share your Analysis Ninja tips with the rest of us Ninjas-in-training using the comment box below.
Thanks.
by Avinash Kaushik
Three Amazing Web Data Analyses Techniques For Analysis Ninjas
Day
in and day out we stare at standard tables and rows and convert them
into smaller or scarier tables and rows and through analysis we try and
move the really heavy beast called the "organization" into action. It is hard.
This blog post has three ideas I've learned from other smart people, ideas that help surprise the "organization" with something non-normal and get it to take action. Each idea is wonderfully simple, and yet in its own sweet way makes us, Analysis Ninjas, think harder and deliver insights better.
Let's go.
Compute Actual Cost Per Acquisition Post-Facto Including Micro-Conversions.
I know that is confusing. Stay with me.
This idea, 100% of it, comes via my friend David Hughes. [He passed away recently and I miss his friendship, and our collaborations, tremendously.] From this post: Improve Search Marketing Conversion Rates through Email Registration. I'm going to redo the tables, just to make them fit the width of this blog.
David's idea is simple and genius.
Today when we measure our Cost Per Acquisition (CPA) for our campaigns (Search, Email, Affiliate, whatever), we just think of the macro-conversion and, perhaps worse, we think only of that session / visit.
Let's assume we are running www.macys.com and we got 1,000 Visitors to come to our site via a display advertising campaign. As dutiful Reporting Folks we will send this table out to reflect performance of that campaign.
$16.7 CPA might sound huge (or not depending on your margin), but on the surface it seems a lot. The flaw in this report of course is in assuming that all 1,000 visits were in play (wanted to convert / buy something). This is rarely the case.
I have repeatedly evangelized identifying all the jobs the site is trying to do (macro AND micro conversions) and then quantifying their economic value to the business. On the Macy's website of the 970 non-converting visitors, some might have signed up for a free account, some for email alerts or coupons, some opened a wedding registry etc.
If some of those 970 Visitors completed some micro-conversions, then shouldn't the CPA be on that basis rather than just the 30 orders above?
Simplifying the scenario a bit. if some of those 970 submitted an email address / signed up for price alerts and converted later then shouldn't the cost-per-acquisition include those future sales?
Say some of the Visitors did just that. What was the acquisition cost of each sign-up?
Nothing. Nice.
What will Macy's do next? Send the 100 folks the price alert they signed up for!
And what will come of it? Sales, of course.
This is a reasonable picture that will emerge.
So we got 30 orders from the original visits, and another 20 by re-targeting users via permission-based email.
What does the CPA of our original affiliate marketing campaign look like now?
A more respectable $10 compared to the original $16.7.
An immediate implication is that if at a CPA of $16.7 you were profitable, then you can communicate to your Senior Leaders that you were actually even more profitable since the final CPA is now only $10. And if you find yourself in a aggressive marketing siutation then you could even increase the bids on your display campaigns to get even more Visitors. Thanks to your clever micro-conversion and re-targeting strategy!
Lessons:
- 1. It is important to think in terms of micro-conversions, beyond
your main objective. For the 98% of people who won't convert on your
site, do you have a way of engaging them again in the future?
2. It is critical to have a robust re-targeting strategy (as in our case above). Hopefully it will be intelligent, relevant to the customers and non-torturous.
3. If you do #1 and #2 then be a dear and ensure you compute the "final CPA" of your original campaign (search or email or affiliate or social or whatever).
4. You can't do the above analysis inside Google Analytics (or even Site Catalyst or the base versions of WebTrends or CoreMetrics). You'll use Excel or a simple database (or possibly the data warehouse versions of Omniture, CoreMetrics, WebTrends).
</sidebar>
Command Attention, Valuable Action, By Stating Raw Numbers.
This idea comes via Kaiser Fung, from this post: Further thoughts on the Facebook business model.
In a blog post with thoughts about a graph from WebTrends, that shows click-through rates (CTRs) and cost per clicks (CPCs) on Facebook. Kaiser made this simple insight:
"What does a 0.01% CTR mean? Yes, that's 100 clicks per 1 million ads shown to Facebook users."Let me restate that astonishing number. If your ad shows 1,000,000 times, you get 100 clicks!
And of course that's clicks, not conversions.
It caused my eyes to open wide.
That is astonishingly low.
Somehow when someone tells you "Facebook's ads CTR is 0.01%" you don't quite get it. I mean, it does not feel pathetically minuscule, as it should.
I have championed the contextual use of raw numbers to deliver insights, especially when using Averages, Percentages and Ratios. [See: Actively Avoid Insights: 4 Useful KPI Measurement Techniques]
Yet the 0.01% number did not make the impact on me it should have. And that is exactly the problem when you present conversion rates (also pathetically low on every single website on the planet) and other such metrics.
So make sure you show raw numbers.
The first number might not get your management team to take any action; it just does not evoke any feeling.
The second set of numbers might get someone to scream: WTH!
They might ask:
- 1. Are we showing the wrong ads on Facebook?
2. Are we using any intelligent ad targeting strategy or just randomly showing ads?
3. If we double our budget to 2,000,000 impressions is there even relevant inventory (desired demographic / users) on Facebook for us?
4. Would it be worth it?
5. Why do we suck so much? Is it us? Is it Facebook?
Lessons:
- 1. Big numbers are not your enemy, especially if they lead to tiny numbers.
2. Resist the urge to turn everything into an average, percentage, ratio or a compound metric.
3. At least one person will tell you that 1,000,000 impressions is great branding. Tell them that branding is measurable and you can prove them right or wrong so you don't have to take their word for it. [See: Brand Measurement: Analytics & Metrics for Branding Campaigns]
Either way, remember that your job is to divert people from becoming lulled into a false sense of everything's okay. Scare them into paying attention and asking you tough questions.
Face Reality By Computing "Convert-able Audience" & "Real Conversion Rates."
This idea comes via Thomas Baekdal, from this post: Converting Traffic to Subscribers.
In it, Thomas postulates that even if you have 1,000,000 Absolute Unique Visitors to the website, that does not mean that your possibly "convert-able" audience is a million.
Some people will visit once and never again. That was not an audience that would have converted, ever. For example, the link above is to Baekdal Plus. I pay $49 per year to access that premium content because it is so good. Many of you may not want to pay for content on the web. So for Thomas, not all the Visitors from the above link are actually in play for conversion. [Though I wish they were.]
So it is imprudent to count those folks; better to only count returning Visitors.
Then, some content attracts traffic, other content actually is "valuable and will convert people into subscribers." Thomas's guidance is to only count the latter in the in play for conversion bucket.
Now you can calculate the "convert-able" audience. In Thomas's example here's how his picture looks:
(1,000,000 less the 63% one-time Visitors) less the 20% valuable traffic = 74,000.
Possible convert-able audience = 74,000.
Real audience you even have a remote chance of converting: 74k.
So small, right? After starting with a million.
I rarely see Web Analysts doing this simple exercise and educating their Senior Leadership of this harsh truth. We assume every single person who will visit www.tesco.com is there to convert. Every single person who visits www.etsy.com is there to buy. Our conversion rate calculation, Orders/Visits (bad version) or Orders/Visitors (better version), reflects that, sub optimal, mental model.
We show our leaders that we suck more than we actually do by computing conversion on the basis of All Visits (bad version) or All Visitors (better version).
If Thomas has 3,700 conversions in a month, we would normally report that as 0.37% conversion rate. [(3700/1000000)*100]
Of course, the reality is that the conversion rate was 5%. [(3700/74000)*100]
Not that 5% is orgasmically higher. But it is more reflective of the truth than 0.37%.
You would take one set of actions with 5% and a completely different set with 0.37%.
Compute your "convert-able audience." Please.
Use whatever common-sense approaches you can find.
In a post written in Nov 2006, I presented a similar thought (though in a different context than Thomas). [See: Excellent Analytics Tip #8: Measure the Real Conversion Rate & "Opportunity Pie"]
My graphics were a lot less sexy in comparison to Thomas's.
The idea was to get you to identify your "Real Conversion Rate", by identifying your "Opportunity Pie."
My recommendations were:
Throw out everyone who bounced, just for now, and also if you use log files (ohh those were the days!), then throw out "visits" by robots / junk. That gives you a rough idea of your "Opportunity Pie" (convert-able audience).
Or
If you have a qualitative survey deployed (with the three greatest survey questions ever), then throw out the percentage of Visitors who do not state their Primary Purpose as visiting your website to "buy" or "research products and services" (I generously assume we can convince the latter bucket to buy through amazing marketing on the site). So now you know just the people for sure in play and possibly in play.
This second path will also give you a great rough idea of your "Opportunity Pie" (connect-able audience).
My recommendations were different from the ones Thomas is using. But both reach for the same goal: To get you to understand that not every single Visitor will convert, and you should know, even roughly, how many are in play / convert-able.
Perhaps you'll come up with your own rules. You might throw out everyone who was there to check Order Status. Or those that logged into their account to update settings. Or those that only visited the /blog/ directory. Or the Social Media of course they will never every buy but eat our bandwidth daily digging diggers!
As long as they pass the common-sense filter, go for it. You'll be earning your Analysis Ninja chops, and delivering something extremely valuable to your management team (even if they perceive it to be a cold bucket of water on their faces, the first time).
Lessons:
- 1. Don't scam your Senior Management by lulling them into believing every Visitor is convert-able.
2. Ignore the standard Conversion Rate definition in Google Analytics, Omniture, WebTrends, CoreMetrics, whatever else you are using. Focus on People. (Unless your business model is that everyone must convert, and does convert, on every Visit.)
3. You might get resistance when you first present the "real conversion rate" or "convert-able audience" metrics. Worry not. Charge forward. Good will come.
Preen proud as a peacock; this is your moment of greatness. Tell them why having thoughtful micro-conversions is so important on the site. Tell them you are going to compute the micro-conversion rate for the non convert-able audience. Tell them that with some of the non convert-able audience you'll hence establish a longer term relationship: with some you'll just hope to create delight and make them your recommenders, and with others still you'll do re-targeting and use David's method (all the way up top of this post) to reduce cost-per-acquisition.
All really great business outcomes.
In a nutshell. the goal is not to abandon a majority of your traffic. The goal is not to just ignore all the bouncers (fix that, tips here: Six Tips For Improving High Bounce / Low Conversion Web Pages). The goal is not to be depressing. The goal is to face reality, give it a hug and then figure out how to kick things up several notches.
Are you Ninja enough to accept that challenge?
Of course you are. You read this blog! : )
Know that I'm rooting for you.
Okay, now it's your turn.
Does your company do re-targeting to captured email addresses? If not, why not? If yes, then do you compute real CPA? Have you computed your "convert-able audience?" Is it 100% of your website Visitors? When was the last time you used raw numbers to shove a dose of reality in front of your Senior Leaders? Are there other techniques you've used that worked?
Please share your Analysis Ninja tips with the rest of us Ninjas-in-training using the comment box below.
Thanks.
Saturday, April 05, 2014
The Data in Google Analytics should be match with the Data or Blogger Statistics.
The Data in Google Analytics should be match with the Data or Blogger Statistics:
For Geo Location Audience is very different from the Public Data of the same month interval, and is a marked difference. This advice I extend to all other segmentations :
Example:
In my case, my blog: Digital Marketing & A Bit of All,www.alexrojasriva.blogspot.com appears as follows :
March 7, 2014 - April 5, 2014 .
Blogger Google Analytics
Statistics -Vision General-Public Audience-Geo-Location
________________________________________________________________
America America
Included Canada Not Included Canada
Not Included Venezuela Included Venezuela
Not Included Ecuador Included Ecuador
Not Included Peru Included Peru
Not Included Bolivia Included Bolivia
Europa Europa
Not included France Included France
Not Included Italy Included Italy
Not Included Romania Included Romania
Not Included Greece Included Greece
Not Included Turkey Included Turkey
Africa Africa
Not Included Senegal Included Senegal
Not Included Equatorial Included Guinea Equatorial Guinea
Not Included Zambia Included Zambia
Asia Asia
Not Included Thailand Included Thailand
Not included Malaysia Included Malaysia
Not Included Indonesia Included Indonesia
Not Included Philippines Included Philippines
Not Included Japan Included Japan
Middle East Middle East
Not Included Israel Included Israel
Not Included Iraq Included Iraq
Not Included United Arab Emirates Included United Arab Emirates
For Geo Location Audience is very different from the Public Data of the same month interval, and is a marked difference. This advice I extend to all other segmentations :
Example:
In my case, my blog: Digital Marketing & A Bit of All,www.alexrojasriva.blogspot.com appears as follows :
March 7, 2014 - April 5, 2014 .
Blogger Google Analytics
Statistics -Vision General-Public Audience-Geo-Location
________________________________________________________________
America America
Included Canada Not Included Canada
Not Included Venezuela Included Venezuela
Not Included Ecuador Included Ecuador
Not Included Peru Included Peru
Not Included Bolivia Included Bolivia
Europa Europa
Not included France Included France
Not Included Italy Included Italy
Not Included Romania Included Romania
Not Included Greece Included Greece
Not Included Turkey Included Turkey
Africa Africa
Not Included Senegal Included Senegal
Not Included Equatorial Included Guinea Equatorial Guinea
Not Included Zambia Included Zambia
Asia Asia
Not Included Thailand Included Thailand
Not included Malaysia Included Malaysia
Not Included Indonesia Included Indonesia
Not Included Philippines Included Philippines
Not Included Japan Included Japan
Middle East Middle East
Not Included Israel Included Israel
Not Included Iraq Included Iraq
Not Included United Arab Emirates Included United Arab Emirates
La data de Google Analytics debe ser unificada con la data de Blogger.
La Data de Google Analytics debe ser unificada con la Data o Estadisticas de Blogger:
En el caso de la Audiencia Geo-localizada es muy diferente de la Data de Publico del mismo intervalo de mes, y se ve una diferencia muy marcada. Este consejo lo extiendo a todos las demas segmentaciones:
Ejemplo:
En mi caso, mi blog: Digital Marketing & A Bit of All,
www.alexrojasriva.blogspot.com aparece de la siguiente manera:
Marzo 7, 2014 - Abril 5, 2014.
Blogger Google Analytics
Estadisticas-Vision General-Publico Audiencia-Geo-Localizacion
____________________________________________________________________
America America
Incluye Canada No Incluye Canada
No incluye Venezuela Incluye Venezuela
No Incluye Ecuador Incluye Ecuador
No Incluye Peru Incluye Peru
No Incluye Bolivia Incluye Bolivia
Europa Europa
No Incluye Francia Incluye Francia
No Incluye Italia Incluye Italia
No Incluye Rumania Incluye Rumania
No Incluye Grecia Incluye Grecia
No Incluye Turquia Incluye Turquia
Africa Africa
No Incluye Senegal Incluye Senegal
No Incluye Guinea Ecuatorial Incluye Guinea Ecuatorial
No Incluye Zambia Incluye Zambia
Asia Asia
No Incluye Tailandia Incluye Tailandia
No Incluye Malaysia Incluye Malaysia
No Incluye Indonesia Incluye Indonesia
No Incluye Filipinas Incluye Filipinas
No Incluye Japon Incluye japon
Medio Oriente Medio Oriente
No Incluye Israel Incluye Israel
No Incluye Irak Incluye Irak
No Incluye Emiratos Arabes Unidos Incluye Emiratos Arabes Unidos
En el caso de la Audiencia Geo-localizada es muy diferente de la Data de Publico del mismo intervalo de mes, y se ve una diferencia muy marcada. Este consejo lo extiendo a todos las demas segmentaciones:
Ejemplo:
En mi caso, mi blog: Digital Marketing & A Bit of All,
www.alexrojasriva.blogspot.com aparece de la siguiente manera:
Marzo 7, 2014 - Abril 5, 2014.
Blogger Google Analytics
Estadisticas-Vision General-Publico Audiencia-Geo-Localizacion
____________________________________________________________________
America America
Incluye Canada No Incluye Canada
No incluye Venezuela Incluye Venezuela
No Incluye Ecuador Incluye Ecuador
No Incluye Peru Incluye Peru
No Incluye Bolivia Incluye Bolivia
Europa Europa
No Incluye Francia Incluye Francia
No Incluye Italia Incluye Italia
No Incluye Rumania Incluye Rumania
No Incluye Grecia Incluye Grecia
No Incluye Turquia Incluye Turquia
Africa Africa
No Incluye Senegal Incluye Senegal
No Incluye Guinea Ecuatorial Incluye Guinea Ecuatorial
No Incluye Zambia Incluye Zambia
Asia Asia
No Incluye Tailandia Incluye Tailandia
No Incluye Malaysia Incluye Malaysia
No Incluye Indonesia Incluye Indonesia
No Incluye Filipinas Incluye Filipinas
No Incluye Japon Incluye japon
Medio Oriente Medio Oriente
No Incluye Israel Incluye Israel
No Incluye Irak Incluye Irak
No Incluye Emiratos Arabes Unidos Incluye Emiratos Arabes Unidos
Sunday, February 09, 2014
Conversion optimization, design and copywriting tips.
The daily egg
Conversion optimization, design and copywriting tips
January’s Best: Favorites in Analytics, Conversion, and Copywriting
by The first month of the year has come and gone. Yikes!
Is anybody else wishing that time would just slow down so we could fully embrace life’s wonderful moments? Pretty soon, the holidays are going to happen all over again.
You deserve a break to reflect on all the great things you accomplished this month, so bring out your iPad and take a breather with these 12 amazing blog posts.
Here are, in this marketer’s opinion, January’s best conversion, analytics, and copywriting articles (it’s been an awesome month edition).
Conversion Optimization
1. 7 Tips for Building a Better Web FunnelPost by: WiderFunnel’s Chris Goward via Clarity
Conversion optimization requires constant testing and iteration. And the best way to get started is to, well, start! This blog post walks readers through a marketing framework that is powerful for any business, whether you’re part of a startup or enterprise team.
2. 14 Conversion Rate Optimization (CRO) Terms You Need to Know
Post by: Cate Seago via Optimizely
Jargon can be frustrating and confusing. If you find yourself wondering ‘what the heck it all means,’ don’t worry. You’re not alone. This blog post will give you the crash course you need to learn the basics of CRO.
3. 5 Mistakes Brands Make Selling Direct-to-Consumer
Post by: Linda Bustos via GetElastic
Brands and manufacturers strive to build close relationships with their customers. When executed correctly, this sales channel can evolve into a powerful revenue stream. This post walks through best practices as well as mistakes to avoid.
4. Five Landing Page Videos that Will Make You Jealous
Post by: Me (Shameless Self Plug) via Unbounce
Online videos are the internet’s gift to the world—and are invaluable to your brand’s conversion optimization strategy. If you’re wondering where to get started, look no further than this roundup for inspiration.
Analytics
5. The Future of Mobile Analytics: What Does the Crock Pot Say?Post by: Curtis Silver via the Adknowledge blog
“The landscape of mobile advertising and mobile analytics is changing at a quickened pace. Is your business armed with the knowledge and intuition to keep up?” If your answer is yes, it isn’t good enough. You need to be 10 steps ahead.
6. 12 Signs to Identify a Data Driven Culture
Post by: Avinash Kaushnik via Ocaam’s Razor
Numbers are only half of the analytics equation. The other half? It’s your people. This blog post walks you through the nuts and bolts of building a data-driven culture.
7. Understanding the Limits of Google Analytics
Post by: Caleb Whitemore via Analytics Pros
Google Analytics is the more powerful web analytics tool on the planet. What can’t you do? More than you may realize. This blog post explains why.
8. How to Implement Google Analytics Events in Google Tag Manager
Post by: Eric Fettman via KISSmetrics
Google Tag Manager is designed to minimize your dependency on web developers. This video makes the process as easy as possible.
Copywriting
9. 5 Ways to Offer Awesome Content Without Pulling Your Hair OutPost by: Alexis Grant via Clarity
Does the thought of writing another blog post make you want to hit your head against your keyboard? Don’t let frustration win. Read this blog post instead.
10. The 29 Best Content Marketing Posts of All Time
Post by: Renee Warren via Onboardly
Is it cheating if I link to a blog post with 29 more content links? Be prepared for hours and hours of education.
11. Content Partnerships Fuel Startup Growth
Post by: Kate Gardiner via Contently
Distribution is the heart of content marketing. Startups can amplify their growth by teaming up with fellow content creators. This blog post explains how.
12. 16 Rules to Make Your Emails Rock
Post by: Scott Martin via CrazyEgg
It’s an understatement to say that email is a powerful marketing channel. These 16 rules will help your brand stand out from the endless amounts of spam that consumers face.
You pick #13. What was your favorite read this January?
Read other Crazy Egg posts by Ritika Puri.
Related Posts
About Ritika Puri
Ritika Puri is a San Francisco-based blogger who writes about trends in business, internet culture, and marketing. She’s inspired by the intersection between technology, entrepreneurship, and sociology. By day, she works for a large online media company, and after-hours, she runs her writing consulting business, UserGrasp.Friday, January 03, 2014
Overcoming the barriers to cloud with software-defined availability.
Overcoming the barriers to cloud with software-defined availability
Businesses today are increasingly being evaluated
on both their customer service levels and response times, as well as
what they sell. With people accessing cloud services from portable
devices that are connected to both the network and the services they
want, how can a business ensure its services are “always there” and
available to customers?
Availability is a critical element to conducting business, even if it’s not direct to the end user; think Twitter or Facebook. If either platform goes down it might be irritating to regular users who can’t get access, but it is catastrophic for the companies. Who can sell advertising when the space they’re selling isn’t being seen by anyone?
High profile IT outages in recent media reports have demonstrated the damage to financial, reputational and customer service goals that can be sustained following a period of downtime. Businesses need to capitalise on the options available to them to offer the benefits of cloud computing without the risks.
While security concerns have historically inhibited organisations from moving to the cloud, availability is fast becoming a bigger concern overshadowing the improved agility and economics that come with using a cloud infrastructure. To keep cloud applications up and running, they need to be designed to work around potential failures. For existing applications, that means rewriting code to make them “cloud-ready”.
But it doesn’t have to be this way. Software defined availability is what we’re calling a software layer that makes decisions about where an application should run. It provides the right level of availability at the right time, per workload, and helps companies take advantage of the elastic nature of cloud.
While not everyone is ready or will even consider switching to a cloud
environment, organisations that do need to make sure that their
availability solution provides the real uptime, speed and ease of
deployment to make this transition as smooth as possible. Doing this
with a solution that is software-defined will bypass these barriers.
Dave LeClair is senior director of strategy and product
management at Stratus Technologies, who are preparing for the
"always-on" world by pioneering a generation of high-availability
private clouds.
Stay tuned with ITProPortal in the coming weeks for more insights into the future of cloud with a video series by Dave LeClair.
Availability is a critical element to conducting business, even if it’s not direct to the end user; think Twitter or Facebook. If either platform goes down it might be irritating to regular users who can’t get access, but it is catastrophic for the companies. Who can sell advertising when the space they’re selling isn’t being seen by anyone?
High profile IT outages in recent media reports have demonstrated the damage to financial, reputational and customer service goals that can be sustained following a period of downtime. Businesses need to capitalise on the options available to them to offer the benefits of cloud computing without the risks.
While security concerns have historically inhibited organisations from moving to the cloud, availability is fast becoming a bigger concern overshadowing the improved agility and economics that come with using a cloud infrastructure. To keep cloud applications up and running, they need to be designed to work around potential failures. For existing applications, that means rewriting code to make them “cloud-ready”.
But it doesn’t have to be this way. Software defined availability is what we’re calling a software layer that makes decisions about where an application should run. It provides the right level of availability at the right time, per workload, and helps companies take advantage of the elastic nature of cloud.
Stay tuned with ITProPortal in the coming weeks for more insights into the future of cloud with a video series by Dave LeClair.
Read more: http://www.itproportal.com/2014/01/03/overcoming-the-barriers-to-cloud-with-software-defined-availability/#ixzz2pMVnA52S
Saturday, April 07, 2012
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
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:
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.
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:
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)
- Excellent Analytics:
El blog de Chandoo es un excelente recurso para sacarle el máximo partido:
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
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