Sequential Segmentation in Adobe Discover

The segment builder for Adobe Discover had some great features added during last week’s release. To celebrate, I thought I would put out a short video explaining the new layout and sequential segmentation. I was planning on a video that would be just a few minutes in length but it turned into a half hour mini-training! I split it up into the three videos below so you can bite off a piece at a time. Hopefully these basic examples give you a good start. I will likely add more examples depending on the response to these videos and the questions I get. If you want more comprehensive training feel free to contact us to take advantage of our full Discover, SiteCatalyst, ReportBuilder, and Testing training courses. If you are attending our ACCELERATE conference in September you can also take a Discover class or one of our other great classes.

Part 1 – Intro to the New Discover Segment Builder

Part 2 – Sequential Segmentation

Part 3 – Sequential Segmentation with Time Intervals

 

Lastly, here is the example data used in the videos for your reference:

Published on May 31, 2013 under Uncategorized

How to Built a Cohort Analysis in Adobe ReportBuilder

As a follow-up to Adam’s Cohort Analysis post for SiteCatalyst I wanted to provide an example of how you can easily translate a standard output from Adobe ReportBuilder into the cohort view. I have seen some other posts on how to create a cohort analysis in Adobe ReportBuilder but they all seem to require a lot more work than you should have to put into a dashboard if you use a few more Excel tricks. The following dashboard shows you how you could create a cohort view without having to create a gazillion segments or a bunch of different ReportBuilder requests in the same workbook. Keep in mind you may still have to do some of that extra work if you aren’t implemented correctly but hopefully you have implemented in such a way that doing important analysis like this is easy for you.

What This Report Gives You

I think the coolest thing about this example, and the real value that the data provides, is that you can see the average attrition for each cohort over time. The cohort table below gives you the revenue attrition for each cohort for every month that cohort has been alive. However, I like to end it all with a simple output that is easy to understand. So you’ll notice that I stuck an Average Attrition column at the end which gives a single number representing the cohort’s performance over time. You can see in this example that the Feb-2012 cohort has had the most attrition (click for a larger view).

Once you have identified a bad or good cohort you can then investigate what kind of promotions or programs may have been in place for that group. Those may all contribute to the poor repeat business.

How to Make This Report

Before starting, keep in mind that there is a lot of date recognition going on in this example using custom American dates. The way Excel recognizes dates varies by local so you may have to adjust your classifications to work better for your region if it gives you trouble.

First, insert your ReportBuilder request. In Step 1 of ReportBuilder pick the Original Purchase Month classification and ensure that the time range encompasses all the data you want to look at.

On step 2 add the Month dimension from the “Dimensions” tab and include Revenue from the metrics tab. Insert the request into cell A5 of the worksheet. Notice that I also adjusted the report to include the“Top 1-10000” values. This is much more than I need but shouldn’t hurt if you have your date ranges correct.

With the ReportBuilder request inserted in the workbook and if you are using the same sort of data as shown in the example then that may be all you need to do. Continue reading, though, if you want to learn about the rest of the formulas.

  1. Start creating the table by setting up your start date in cell G6. This formula looks at all the dates under Original Purchase Month and takes the minimum date (the oldest date). This will establish the starting point of our table which will update automatically as you pull in different dates. Note that this is an array function which you have to press control+shift+enter to input. I’m using an array function here to evaluate every date individually otherwise the MIN function doesn’t work. If you are using a more standard date format for your classification you might not need the DATEVALUE in the array function.
  2. In cell G7 I use this formula to increment the month up for each row as it is copied downward.
  3. In cell H6 is where the real magic happens. This is another array function (remember to use control+shift+return) and it will match the Original Purchase Month on the same row with the Month that is X number of months ahead. X is determined by taking the column number that the cell is in and subtracting the column number at the beginning of the table. This is a good trick for making an auto-incrementor right in the formula. It will count up the months as you drag the formula over. The thing that really makes this an array function is the two MATCH criteria we have since we need to look for the right Original Purchase Month and Month.
  4. I hate doing manual work so I dragged the formula from cell H6 across the whole table. Then, to account for any cells that generate an error (because there is no data for that month) I applied conditional formatting to make the “#N/A” a super light gray so you know it is there but it isn’t in the way.
  5. The last part is the easiest part. You now make a similar table below (cell H22), calculate the change from month to month (see cell I22), stick an average on the end (column T), and apply some quick conditional formatting. As you apply the formatting be sure to apply separately to the body of the table and the averages since those are really different sets of data to evaluate.

Final Thoughts

This was an example around monthly time ranges. Keep in mind that you could do week or other granularities. Just make sure you have a classification in place that matched that granularity.

Another thing I would only do for this example is include the final table on the same sheet as the source data. For a real dashboard I would move the data and intermediate steps to a different tab and just show the final report on the first tab.

We’ll, there you have it…a workbook that easily translates a typical ReportBuilder output into a cohort table. Enjoy!

Published on March 15, 2013 under Uncategorized

Badgeville Integration with SiteCatalyst – The New Engagement Score

Engagement scoring has been around in the web analytics industry for a long time. The idea behind this kind of score is to give visitors points based on actions they perform that are positive for the business. If a visitor does something important (like viewing certain content) then you give a few points. If a visitor does something very important (like contributing content) you might give them a lot of points. In the end, the goal is to come up with a final number that summarizes just how valuable that visitor has interacted with your site. The most valuable visitors should then be examined and compared with visitors at lower tiers to better understand how the groups differ and how you might encourage visitors to move to a higher score which provides more value to your business.

The rising trend of gamification introduces an interesting twist on engagement scoring. Gamification is the way that you make your site, intranet, or portal more engaging by using elements borrowed from games such as points, achievements, and missions. These intrinsic rewards encourage deep engagement without increasing the cost of the campaign. Previously, engagement scoring was a passive indicator that was often arbitrarily assigned. Now, however, it is a score that visitors to your site see and are interested in improving. As marketers, we can now not only observe engagement but actually be involved with the engagement as it is unfolding.

Introducing the Badgeville to SiteCatalyst Integration

To measure this new type of engagement Web Analytics Demystified and Badgeville have teamed up to bring you a new integration between Badgeville and SiteCatalyst. This is a Genesis integration that will help you bring your Badgeville gamification data directly into SiteCatalyst. You will then have the ability to see your Badgeville data in the context of your larger SiteCatalyst dataset. Additionally, you can use the powerful features of SiteCatalyst and other Adobe Marketing Cloud tools to analyze the data and even export the augmented data to other systems.

This is the first version of the integration which gives you a solid foundation in understanding the engagement of your visitors or “players” and how they are interacting with your gamified site. The integration currently offers the following reports:

  • Badgeville Player ID: The unique ID for each player. The web behavior associated with these IDs can be tied to your Badgeville and CRM information to create a rich and personalized dataset.
  • Badgeville Total Points: The lifetime value in points for a player. This report will give you the total sum of points a player has earned in its lifetime which is helpful in understanding the level of experience a visitor has had with your site.
  • Badgeville Total Point Groupings: This is similar to Total Points but combines many unique point levels into larger groupings which analysts will find easier to analyze and compare.
  • Badgeville Incremental Points: This is the number of marginal points awarded to the player from page to page as they interact with the site and play the game. This is a great indicator of the current level of activity and what is happening now on your site.
  • Badgeville Behaviors: As visitors perform actions on your site this will tell you just what it is they are doing (view a video, post a comment, read an article, etc) and how many times they are doing it.

We have created quite a bit of information around these reports which are in the Badgeville Integration Guide (see the Getting Started section below) and how you can use the data. One of the examples that you might find interesting is looking at the Badgeville Total Point Groupings and include other data points that may or may not be part of your game design. Here is an example of how players are combined into general point groupings for analysis. Below we see that the group of 1200-1299 points is amazingly active with the 4th highest number of Video Views. This is apparently a group that has had a history of being very engaged with the site and is still consuming a lot of video content.

All of the other groups at the top of this report are much “younger” groups as far as their total point value. Somehow, though, the 1200-1299 group is behaving very differently and you wouldn’t have even known that was happening before. You can now dig deeper to understand just why that group is so special and if you might be able to adjust your campaign to help improve the performance of the other groups.

On a negative note, we see that the video views per visit for the 400-499 group is lower than what we see in the other groups (red arrow). With this information you might be able to modify your game design by introducing a level or status to help bump that group to a higher Video Views/Visit number.

How to Get Started

If you are a SiteCatalyst and Badgeville customer you can get started with this integration right away. To enable the integration and to access all of the documentation log into SiteCatalyst and navigate to Genesis:

Once you are in Genesis, select Add Integration on the left. Then switch over to the Labs section of Genesis by modifying the dropdown as shown below. Labs is where Genesis places integrations that don’t have any professional services built in. This allows the integration to be completely free if you choose to rely solely on the documentation. If you would like expert assistance, you can contact me (kevin@webanalyticsdemystified.com).

Once you see the Badgeville icon you can drag the integration over to the Adobe Marketing Cloud on the right to start the configuration process. You will see a popup with instructions that contains links to access the integration documentation. This documentation thoroughly outlines all steps and technical considerations you will want to keep in mind with this integration. Additionally, the document provides many examples to get you started in analyzing the data.

Keep in mind that, as with any Genesis integration, you should thoroughly QA before committing anything to production.

Final Thoughts

I hope that you enjoy this integration and are able to quickly implement it on your site. There is documentation available to help you if you decide to do the integration on your own; however, if you would like assistance putting the integration in place please contact me (kevin@webanalyticsdemystified.com). I created the integration so you might find me useful in helping you set it up. We have consulting packages available should you need assistance.

There were other attributes and metrics that we considered including in this integration that will be saved for the next version. As you work with the integration please provide feedback to the Badgeville support team. This will then help to mold future enhancements to the integration.

Published on March 6, 2013 under Uncategorized

Are JavaScript-Based Trackers Still Relevant?

This is a question that we have had many clients ask recently. You can imagine, with the heavy usage of JavaScript in web analytics, the thought of a decreased acceptance of JavaScript would be a terrible thing. The reason this question keeps popping up is due to the following SiteCatalyst report which gives the breakdown of visitors that have JavaScript enabled or disabled. You can see in this example that the percentage of visitors that come with JavaScript disable is about 17%. You can find this report under Visitor Profile>Technology>JavaScript.

Well, not to worry! Your JavaScript implementation isn’t worthless even if you have a high amount listed here as disabled. This is just a reporting oddity. For some reason SiteCatalyst is counting all mobile visits as not accepting JavaScript. Obviously that is not correct. If you segment out mobile visits you can get a more accurate view for non-mobile devices. Here is an example of a segment you can use to get to non mobile.

With the segment applied you should see the percentage disabled drop to about 1% or less. Take comfort in knowing that non-mobile devices still love JavaScript.

But what about mobile devices? How are we going to tell what the acceptance rate is like in case we need to take a different implementation approach for mobile? Well, until this report changes, I would suggest looking at the devices in your mobile reports and compare them to the JavaScript information in DeivceAtlas. DeviceAtlas has a Device Data repository and they allow you to search for a particular device that you might be interested in. Once you look up the device you can check out the JavaScript section of the report for details on what is accepted. Here you can see that the iPhone 5 does accept JavaScript.

Now keep in mind that JavaScript settings are really an aspect of the phone’s browser and not the actual phone. A user can always modify their individual settings but this Device Atlas information gives you an idea of what the defaults are.

So, in the end, don’t worry about the JavaScript report in SiteCatalyst. JavaScript isn’t always the right thing to use with an implementation but in general it is still a very valid approach.

Published on February 21, 2013 under Uncategorized

New Calculated Metrics in Adobe Discover

You have always been able to use segments and calculated metrics in Adobe Discover but now you can include segments WITHIN your calculated metrics! This greatly increases the flexibility of your metrics and will enable you to do more comparison work within Discover which historically has been very difficult.

As we walk through this feature let’s use an example. Assume that you are interested in understanding the mobile vs non-mobile breakdown of your campaigns. Previously you could segment to get the same data but now we can build out metrics that make this easier and help to differentiate mobile from everything else. This is useful since, by default, there is only one mobile-specific metric in Discover–mobile views.

To start, access the new metric builder by going to the Metrics pane on the left-hand side, select the options icon, and then select “Calculated Metric Builder”:

You will then see the Metric Builder which allows you to drag metrics and operators over to the formula field. Below is how you would build a simple Order Conversion metric:

Adding Segments to Your Discover Metrics

Now we can make it really fun by adding segments to the mix. The segments are hiding behind the metric tab on the top left. For our mobile example, let’s say that we want to build a metric that gives us the percentage of visits that were from a mobile device. To do this you would drag over and divide two visits metrics, apply a “Visits from Mobile Devices” segment to the numerator (as shown in the screenshot below), and adjust your metric name and formatting as needed:

After you save this metric you can then include it in your campaigns report to see the percentage of the campaign that came from mobile. You can also sort by this metric to see what campaign has the highest percentage of mobile usage.

Include Calculated Metrics in Other Calculated Metrics

After you start building your calculated metrics you may want to include an existing calculation in another metric. The new builder lets you do that as well. Once you create a metric, as we did with our “% Visits from Mobile” metric it will appear in your metric list with a small chart-looking icon next to it. We will build on this to get the percentage of traffic NOT from mobile. We do this by entering a number field of “1” (red arrow in screenshot below) and then subtract the previously-created “% Visits from Mobile” metric as shown here.

Other metrics you could build for our mobile report may include:

Mobile Conversion 

Non Mobile Conversion (you have to make the Non Mobile segment first)

Tablet Visits as a percent of all Mobile Devices (you have to make the tablet segment first)

Return Mobile Visits as a percent of all Mobile Devices (you have to make the return mobile segment first)

You can go on an on but hopefully that gives you an idea of what you could do.

Comparisons using Metrics

If you think about comparison, they are just an extension of the new formulas that we can now make. All you have to do is create a metric that compares the data points you are interested in. To make this easier, Discover lets you select two columns that are already in your report and you can right click on the column header to select some of the quick calculation options. I wish it had an (A-B)/B option in the list but for now we will use an A/B Percent comparison to quickly see the percentage change between our Mobile and Non-Mobile Conversion metrics. Here is where you select the option:

This will then give you a new column with the comparison as shown here:

That makes for an easy comparison. If you would like to tweak the comparison you can right click on the column header and select edit. I would then modify the comparison as follows to get an (A-B)/B comparison instead of just A/B.

Be careful to keep track of what is in your comparison and use meaningful names since the metric doesn’t dynamically reference the columns that it was built from. If you were to switch out one of the original metrics the comparison would not automatically update. That would be a cool feature, though.

Final Thoughts

While this functionality has been in tools like Adobe Insight for a long time I am happy to see it available in Discover. It provides much more flexibility in creating metrics and comparisons. I had a client once in the theme park business that liked to segment their orders by the many different checkout types they had. They could use this to create specific metrics for each type without having to burn up a lot of events. Hopefully this makes its way into SiteCatalyst.

Published on October 30, 2012 under Uncategorized

Sorting with Formulas for Bounce Rate – Excel Tip

During my career I have developed a ton of Excel tricks that enabled me to mold data just the way I like it. It all began when I took an investment banking class and if you didn’t know enough hotkeys to get by without a mouse then you were shunned. During the years I was at Omniture/Adobe I was able to develop a reputation as being “Mr. Excel” which is a pretty high bar among a group of hundreds of consultants that use worksheets regularly. Users in general aren’t very good at Excel and many people don’t know all the creative things that are possible. With that in mind, this will be the beginning of many tips that help you use Excel better with web analytics so that you can spend less time gathering data and more time using data.

Automatic Sorting with Formulas

To start, let’s talk about sorting. Excel has built in ways to sort data using filter and sorting tools but they all require human interaction to make it happen. Through formulas, you can create sorting that is automatic, macro-free, and more user friendly. A great use case for this is bounce rate. In SiteCatalyst, if you were to look at the pages with the highest bounce rate you will most-likely be given some pages that have a 100% bounce rate. What a find! You now know of a bunch of pages that need to be fixed. Not so! If you look at the visits to those pages, chances are that just one person actually saw the page and bounced. Those pages probably are not worth your time fixing.

You can do quite a bit to calculate a weighted metric that takes into account volume and the rate. Another simple solution is to use a tool like ReportBuilder to automatically pull in the X most popular pages by visits and apply the formulas below to resort the data. When the report is delivered to the user, the formulas will automatically run and the user wont have to do a thing. This way you know which pages that have the worst bounce rate AND are still getting significant traffic.

Click here for an example workbook on sort with formulas and below are step-by-step instructions:

Simple Sort

After you have downloaded the workbook above follow these steps which walk through the example:

  1. (Column A:C) Insert your data into the workbook sorting on your popularity metric (Visits in this case)
  2. (Column G) Use the LARGE function to determine which bounce rate is the highest based on the Nth value. To calculate N I use the ROW function to get the current row number and minus the first row number. This is a good tip for creating an automatic counter so that N increases by 1 with each row.
  3. (Column E) Use a combination of INDEX and MATCH to get the page name for the sorted bounce rate numbers. This works like VLOOKUP but allows you more flexibility if your lookup values aren’t on the left of your lookup table.
  4. (Column F) Now that we have the page name we can just use VLOOKUP to get the rest of the metrics from the original report.

Advanced Sort

Keep in mind that the previous example works if all of your sort values are unique. In the example worksheet I have also included an advanced example where pages have duplicate bounce rate values. Not to worry! we can solve this with a few more steps:

  1. Do the same thing you did for steps 1 & 2 of the simple sort
  2. (Column M) Create an instance count for each value of your sort metric. Note how the beginning of the range is anchored but the end is relative. This formula lets us know how many duplicates of any given number there are as we move down the list. This count, along with the bounce rate value, creates a unique key that we can line everything up by.
  3. (Column O) This is the tricky part! It is very much like what we did for step 3 of the simple sort but it uses an array function which allows us to use the bounce values AND the instance count for the lookup. To enter this function don’t just press Enter! You need to press Control + Shift + Enter. This lets Excel know that you want to use the formula as an array function.
  4. (Column P) Use a VLOOKUP based on the page name to pull in the rest of your metrics.

Now you should have a beautifully resorted report. Hide the original report on some other worksheet where it is out of the way and just present the new report to the user.

Final Thoughts

This example was centered around bounce rate but it has many applications. For example, you may want to see which of your most-popular pages has the highest revenue participation per visit. Sorting is such a foundational aspect of using data that you will be able to apply this tip in many scenarios.

Let Me Know What You Think

I have been thinking about developing a class for Adobe ReportBuilder that would not only teach you the neat things you can do with that tool but would go beyond ReportBuilder to show you how to super-charge your workbook with Excel techniques that make the data much more useful. Let me know if you would be interested in such a class (kevin @ webanalyticsdemystified.com)

Published on October 24, 2012 under Excel Tips, Uncategorized

Kevin Willeitner: The Latest Partner at Demystified

I am wildly excited by the opportunity to join the Demystified team. I look forward to contributing my own expertise to the deep knowledge of Eric, John, Adam, and Brian to provide even greater value to our clients. I work with clients to evolve their web analytics program and to build their digital solutions through system integrations. I enjoy measurement evaluations, solution design, implementation management, data quality evaluations, basic and advanced user trainings, testing, analysis feedback sessions, all things Excel, tool development, and executive presentations and communications.

Previously I worked at Adobe (through the Omniture acquisition) as Principal Consultant for Digital Analytics and Optimization. I had the pleasure of working with a lot of great people and technology. I certainly did not leave Adobe due to any level of dissatisfaction. I truly had a dream job at Adobe. Then Web Analytics Demystified came along and I saw it as a wonderful opportunity to advance my career and to continue doing what I love–helping companies use data and systems to provide impactful business results. It is almost as if I am now in a dream within a dream (Inception anyone?). I would like to give special thanks to the managers I worked with at Adobe along the way including Matt Belkin,Cameron Barnes, Josh Dahmer, Dave Kirschner, and James Hodges for the great opportunities they provided to me. Also a thanks to my many friends on the Adobe Consulting Services team that I worked with for many years.

Of the many successes I had at Adobe, the most…unique…was to win the 2011 Halloween costume contest. If you are at all familiar with the way that Adobe does Halloween in Utah then you know that there is an amazing amount of competition for this prize. I mostly mention this because it is funny, but I also think it is indicative of the creativity and quality of work I provide.

On a personal note, I am a husband to a beautiful wife and father to three beautiful little girls. I live in Utah.  I enjoy outdoors activities such as rock climbing, canyoneering, backpacking, and snowboarding. I’m also trying to get better at surfing but that has been difficult to do given my land-locked state. I volunteer as an Assistant Scout Master to help boys in the neighborhood get cool merit badges and build character.

If you need help with your digital solutions feel free to reach out to me by email (kevin AT webanalyticsdemystified.com) and you can follow me on Twitter (@willeitner). I look forward to working with all of you in the digital marketing community.

Published on September 16, 2012 under Uncategorized

 


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SiteCatalyst Unannounced Features
Adam Greco, Senior Partner

Lately, Adobe has been sneaking in some cool new features into the SiteCatalyst product and doing it without much fanfare. While I am sure these are buried somewhere in release notes, I thought I'd call out two of them that I really like, so you know that they are there.

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Hello. I'm a Radical Analytics Pragmatist
Tim Wilson, Partner

I was reading a post last week by one of the Big Names in web analytics…and it royally pissed me off. I started to comment and then thought, "Why pick a fight?" We've had more than enough of those for our little industry over the past few years. So I let it go.

Except I didn't let it go.

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Competitor Pricing Analysis
Adam Greco, Senior Partner

One of my newest clients is in a highly competitive business in which they sell similar products as other retailers. These days, many online retailers have a hunch that they are being "Amazon-ed," which they define as visitors finding products on their website and then going to see if they can get it cheaper/faster on Amazon.com. This client was attempting to use time spent on page as a way to tell if/when visitors were leaving their site to go price shopping.

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How to Deliver Better Recommendations: Forecast the Impact!
Michele Kiss, Partner

One of the most valuable ways to be sure your recommendations are heard is to forecast the impact of your proposal. Consider what is more likely to be heard: "I think we should do X ..." vs "I think we should do X, and with a 2% increase in conversion, that would drive a $1MM increase in revenue ..."

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ACCELERATE 2014 "Advanced Analytics Education" Classes Posted
Eric T. Peterson, Senior Partner

I am delighted to share the news that our 2014 "Advanced Analytics Education" classes have been posted and are available for registration. We expanded our offering this year and will be offering four concurrent analytics and optimization training sessions from all of the Web Analytics Demystified Partners and Senior Partners on September 16th and 17th at the Cobb Galaria in Atlanta, Georgia.

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Product Cart Addition Sequence
Adam Greco, Senior Partner

In working with a client recently, an interesting question arose around cart additions. This client wanted to know the order in which visitors were adding products to the shopping cart. Which products tended to be added first, second third, etc.? They also wanted to know which products were added after a specific product was added to the cart (i.e. if a visitor adds product A, what is the next product they tend to add?). Finally, they wondered which cart add product combinations most often lead to orders.

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7 Tips For Delivering Better Analytics Recommendations
Michele Kiss, Partner

As an analyst, your value is not just in the data you deliver, but in the insight and recommendations you can provide. But what is an analyst to do when those recommendations seem to fall on deaf ears?

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Overcoming The Analyst Curse: DON'T Show Your Math!
Michele Kiss, Partner

If I could give one piece of advice to an aspiring analyst, it would be this: Stop showing your "math". A tendency towards "TMI deliverables" is common, especially in newer analysts. However, while analysts typically do this in an attempt to demonstrate credibility ("See? I used all the right data and methods!") they do so at the expense of actually being heard.

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Making Tables of Numbers Comprehensible
Tim Wilson, Partner

I'm always amazed (read: dismayed) when I see the results of an analysis presented with a key set of the results delivered as a raw table of numbers. It is impossible to instantly comprehend a data table that has more than 3 or 4 rows and 3 or 4 columns. And, "instant comprehension" should be the goal of any presentation of information - it's the hook that gets your audience's brain wrapped around the material and ready to ponder it more deeply.

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Automating the Cleanup of Facebook Insights Exports
Tim Wilson, Partner

This post (the download, really - it's not much of a post) is about dealing with exports from Facebook Insights. If that's not something you do, skip it. Go back to Facebook and watch some cat videos. If you are in a situation where you get data about your Facebook page by exporting .csv or .xls files from the Facebook Insights web interface, then you probably sometimes think you need a 52" monitor to manage the horizontal scrolling.

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The Recent Forrester Wave on Web Analytics ... is Wrong
Eric T. Peterson, Senior Partner

Having worked as an industry analyst back in the day I still find myself interested in what the analyst community has to say about web analytics, especially when it comes to vendor evaluation. The evaluations are interesting because of the sheer amount of work that goes into them in an attempt to distill entire companies down into simple infographics, tables, and single paragraph summaries.

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Funnel Visualizations That Make Sense
Tim Wilson, Partner

Funnels, as a concept, make some sense (although someone once made a good argument that they make no sense, since, when the concept is applied by marketers, the funnel is really more a "very, very leaky funnel," which would be a worthless funnel - real-world funnels get all of a liquid from a wide opening through a smaller spout; but, let's not quibble).

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Reenergizing Your Web Analytics Program & Implementation
Adam Greco, Senior Partner

Those of you who have read my blog posts (and book) over the years, know that I have lots of opinions when it comes to web analytics, web analytics implementations and especially those using Adobe Analytics. Whenever possible, I try to impart lessons I have learned during my web analytics career so you can improve things at your organization.

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Registration for ACCELERATE 2014 is now open
Eric T. Peterson, Senior Partner

I am excited to announce that registration for ACCELERATE 2014 on September 18th in Atlanta, Georgia is now open. You can learn more about the event and our unique "Ten Tips in Twenty Minutes" format on our ACCELERATE mini-site, and we plan to have registration open for our Advanced Analytics Education pre-ACCELERATE training sessions in the coming weeks.

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Current Order Value
Adam Greco, Senior Partner

I recently had a client pose an interesting question related to their shopping cart. They wanted to know the distribution of money its visitors were bringing with them to each step of the shopping cart funnel.

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A Guide to Segment Sharing in Adobe Analytics
Tim Wilson, Partner

Over the past year, I've run into situations multiple times where I wanted an Adobe Analytics segment to be available in multiple Adobe Analytics platforms. It turns out…that's not as easy as it sounds. I actually went multiple rounds with Client Care once trying to get it figured out. And, I've found "the answer" on more than one occasion, only to later realize that that answer was a bit misguided.

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Currencies & Exchange Rates
Adam Greco, Senior Partner

If your web analytics work covers websites or apps that span different countries, there are some important aspects of Adobe SiteCatalyst (Analytics) that you must know. In this post, I will share some of the things I have learned over the years related to currencies and exchange rates in SiteCatalyst.

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Linking Authenticated Visitors Across Devices
Adam Greco, Senior Partner

In the last few years, people have become accustomed to using multiple digital devices simultaneously. While watching the recent winter Olympics, consumers might be on the Olympics website, while also using native mobile or tablet apps. As a result, some of my clients have asked me whether it is possible to link visits and paths across these devices so they can see cross-device paths and other behaviors.

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The 80/20 Rule for Analytics Teams
Eric T. Peterson, Senior Partner

I had the pleasure last week of visiting with one of Web Analytics Demystified's longest-standing and, at least from a digital analytical perspective, most successful clients. The team has grown tremendously over the years in terms of size and, more importantly, stature within the broader multi-channel business and has become one of the most productive and mature digital analytics groups that I personally am aware of across the industry.

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Ten Things You Should ALWAYS Do (or Not Do) in Excel
Tim Wilson, Partner

Last week I was surprised by the Twitter conversation a fairly innocuous vent-via-Twitter tweet started, with several people noting that they had no idea you could simple turn off the gridlines.

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Omni Man (and Team Demystified) Needs You!
Adam Greco, Senior Partner

As someone in the web analytics field, you probably hear how lucky you are due to the fact that there are always web analytics jobs available. When the rest of the country is looking for work and you get daily calls from recruiters, it isn't a bad position to be in! At Web Analytics Demystified, we have more than doubled in the past year and still cannot keep up with the demand, so I am reaching out to you ...

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A Useful Framework for Social Media "Engagements"
Tim Wilson, Partner

Whether you have a single toe dipped in the waters of social media analytics or are fully submerged and drowning, you've almost certainly grappled with "engagement." This post isn't going to answer the question "Is engagement ROI?" ...

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It's not about "Big Data", it's about the "RIGHT data"
Michele Kiss, Partner

Unless you've been living under a rock, you have heard (and perhaps grown tired) of the buzzword "big data." But in attempts to chase the "next shiny thing", companies may focus too much on "big data" rather than the "right data."

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