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How to Use Classifications With Adobe Analytics Data Feeds and R

October 8, 2017April 30, 2022 Trevor Paulsen

Follow @trevorwithdata Adobe Analytics Classifications is one of the most useful and popular features of Adobe Analytics, allowing you to upload meta-data to any eVar, prop, or campaign that you may be recording in Adobe Analytics. Classifications are useful when you need to do things like: Classify your marketing campaign tracking codes into their respective marketing […]

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Using Secondary Sort to Enhance Adobe Data Feed Processing in Hadoop

June 13, 2017April 30, 2022 Jared Stevens

In my last post, I described the basics for processing Adobe Analytics Click Stream Data Feeds using Hadoop. While the solutions outlined there will scale remarkably well, there is a more memory efficient way to do it. Having this flexibility is nice if you have lots of CPU cores available but not as much ram. […]

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Introduction to Processing Click Stream Data Feeds with Hadoop and Map/Reduce

June 6, 2017April 30, 2022 Jared Stevens

In an earlier post, Matt Moss showed how to process data feed data using an SQL database. This can be useful in a pinch when you have a smaller amount of data and need an answer quickly. What happens though when you now need to process the data at a large scale? For example, you […]

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Marketing Mix Model for All: Using R for MMM

June 1, 2017April 30, 2022 Jessica Langford 4 Comments

Understanding the ROI across all of your paid marketing channels is a top priority for senior-level executives across every industry and every geographical market.  Getting a clear sense of the ROI on each channel allows companies to answer really important questions.  For example: What will happen if I increase my Email spend by 20%? What […]

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Propensity Scoring in Adobe Analytics Using Data Feeds and R

May 13, 2017April 30, 2022 Trevor Paulsen

Follow @trevorwithdata When I was a kid, my favorite TV gameshow was “The Price Is Right” – it’s flashy, fun, and to this day I still love watching it – that is except for one thing: the ads.  I still find it obnoxious to be bombarded by annoying (and sometimes gross) TV ads about hemorrhoid […]

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Using Adobe Analytics Data Feeds and SQL for Basic Reporting

May 6, 2017April 30, 2022 Matt Moss 2 Comments

Three DB SQL’s walk into a NOSQL bar. A little while later… they all walked out, because they couldn’t find a TABLE… Joking aside, online marketers frequently use analytics tools like Adobe Analytics, but find that the granularity and accessibility of the data in the tool doesn’t meet their needs. A few examples: Loading Adobe Analytics […]

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Parsing Products and Events in ClickStream Data Feeds

May 1, 2017April 30, 2022 Jared Stevens

A lot of companies that I’ve worked with are initially confused when processing Adobe Analytics Data Feeds. The data comes out of Adobe Analytics in TSV format and you’d naturally expect that the data is ‘flat’ (meaning just rows and columns). Unfortunately, this isn’t the case. Columns like ‘post_product_list’ and ‘event_list’ are lists of data that […]

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Algorithmic Bot Filtering in Adobe Analytics Using R

April 24, 2017April 30, 2022 Trevor Paulsen

Follow @trevorwithdata Over the last few years, I’ve noticed a marked increase in the number of companies that are worried about their analytics data becoming contaminated with non-human traffic – and with good reason.  According to a fairly recent report from Imperva, websites that have more than 100k human visitors everyday should expect nearly one […]

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Dealing with Special Characters When Parsing Adobe Analytics Data Feeds

April 19, 2017April 19, 2017 Jared Stevens 2 Comments

Adobe Clickstream Data Feeds are the most granular way to view your analytics data. They effectively contain all the information that Adobe Analytics needs to build its reports. Having a good understanding of how to use these feeds will allow you to use Analytics data in ways that aren’t possible through LiveStream, the Web Services […]

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Importing Statistical Models from R into Adobe Analytics Using Customer Attributes

April 10, 2017April 30, 2022 Trevor Paulsen 2 Comments

Follow @trevorwithdata One of the most common problems I hear from data scientists is that it’s incredibly difficult to make a statistical model useful to an entire organization.  Oftentimes, a skilled data scientist will build an awesome model and do some amazing analysis, only to have it wind up in some Power Point presentation that […]

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