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Pages Tagged with 'Adobe Analytics'

Injecting Historical Data In Adobe Analytics

Data collection anomalies happen and they can take a variety of shapes—from not collecting any data, through collecting spotty data, to over-counting data. Data analytics solutions downstream of such problems have developed various damage control measures. Adobe's Virtual Report Suites unlock valuable capabilities for "cleaning up" processed data, but in many cases there is no data to clean up! In this post we'll address the specific issue of lapses in data collection and present three different techniques for backfilling historical data in Adobe Analytics.

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eVars & Props — Adobe’s Favourites

Every Adobe Analytics resource has definitely come across both the terms : eVars and Props , at least once in their lifetime. If they haven’t — they aren’t an Adobe resource. Prop,eVar,events are Custom variables that Adobe Analytics provides, in order to perform an effective tracking.

/learn/articles/2019/01/evars-props-adobes-favourites-fcd9ebefefa.html
Turning Shopping Cart Abandonment into A Golden Marketing Opportunity

Trigger Your Re-Marketing Now with Adobe I/O"Shopping cart abandonment-when shoppers put items in their online shopping carts, but then leave before completing the purchaseis the bane of the online retail industry."Cooper SmithIts a million dollar question with millions of dollars at stake: how do you turn the instant loss of shopping cart abandonment into long-term gain?If youre a customer of Adobe Analytics and Adobe Campaign, you can now use Adobe I/O Events to build an event-driven, lightweight solution that allows you to quickly re-target and re-market your customers who abandoned cart.

/learn/articles/2017/11/turning-shopping-cart-abandonment-into-a-golden-marketing-opportunity.html
Visitor Retention Analysis with Adobe Analytics - Part 3 - Churned Users

In part 1 of this article, I have set the product analytics context for accounting for user growth and I have shown you how to define the new user(t) metric.In part 2, I have provided you with the approach to calculate repeat user(t), retained users(t) and resurrected users(t).In part 3, I'll show you the process to define the churned users(t+1) metric.

/learn/articles/2017/11/visitor-retention-analysis-adobe-analytics-part-3-users-burrafato.html
Attribution Theory: The Two Best Models for Algorithmic Marketing Attribution – Implemented in Apache Spark and R

In my last post, I illustrated methods for implementing rules-based multi-touch attribution models (such as first touch, last touch, linear, half-life time decay, and U-shaped) using Adobe Analytics Data Feeds, Apache Spark, and R. These models are indeed useful and appealing for analyzing the contribution any marketing channel has to overall conversions. However, they all share a common problem: they’re heuristic – meaning at the end of the day, a human being is still left guessing at marketing performance without any real data to back those guesses.

/learn/articles/2017/11/attribution-theory-the-two-best-models-for-algorithmic-marketing-attribution-implemented-in-apache-spark-and-r.html
Workspace project without numbers

a few days ago i published a blog article in german as a response to the "no number" challenge from jexnerW4D. i translated the whole text since i think it is useful for all of the adobe community. feel free to add comments what you think about the ideas below.   „No Number“ challenge – vision and reality The improvements in Adobe Analytics and especially in Analytics Workspace have been great during the past month. While taking the first steps in Workspace projects it has been common that it was just a mix up of different tables. So basically it was just the same as you can see in the normal reports, but all together on one (or more) screens. By the time more and more features where implemented which allowed to do custom visualization and custom calculation. The improvements with calculated metrics, segments and date ranges have been a great...

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Visitor Retention Analysis with Adobe Analytics - Part 2 - Repeat Users

In part 1 of this article, I have set the product analytics context for accounting for user growth and I have shown you how to define the new user(t) metric.In part 2, I will present you with the approach to calculate repeat user(t), retained users(t) and resurrected users(t).

/learn/articles/2017/10/visitor-retention-analysis-adobe-analytics-piermarco-burrafato.html
Edit description for tables in Analysis Workspace

I just have to write a post about this small tip that I noticed on Adobe’s forum: https://forums.adobe.com/ideas/10106 Thanks to Jen!(Btw, if you haven’t voted that idea… what are you waiting for!? Hit the “vote up” button!)In Workspace, you can add “text” panel to your project and write detailed descriptions for different tables and visualizations. Have used a lot this “text” panel. However, this way text is always on a different “module” and sometimes it might be difficult to align these text panels to the data panels etc. Thanks to Jen, I realized that you can edit freeform table’s description and this way you get same kind of text panel injected directly to the same freeform table. How did I have missed this feature?

/learn/articles/2018/03/edit-description-for-tables-in-analysis-workspace.html
Using RSiteCatalyst With Microsoft PowerBI Desktop

With pretty regular frequency I get emails asking if RSiteCatalyst can be used with Microsoft Power BI. While admittedly I’m not a frequent user of the Windows operating system (nor dashboarding tools like Tableau or Power BI), I am pleased to report that it is fact possible to call the Adobe Analytics API with Power BI via RSiteCatalyst!Step 1: Call Adobe Analytics API Using Get Data MenuThe majority of getting RSiteCatalyst to work within Power BI desktop is getting the R script correct. From the Get Data menu, choose the More... menu option to bring up all of the data import tools that Power BI defines:

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Python for Adobe Analytics audit and regression testing

With Python, it’s possible to build automation scripts that would allow you to do quick regression testing of your Adobe Analytics tags. It’s an excellent option when you don’t have access to enterprise data quality auditing tools (like ObservePoint etc.) and the task is not practical for manual testing. How does it work –The script will start with a single base url. Through the base url, it would launch all (or specified) links present on the page and record the tag variables fired on those specific links. The script framework that I am sharing below can be customized as per the testing needs. It’s especially helpful if you have made changes in limited number of pages and want to test –

/learn/articles/2018/03/python-adobe-analytics-audit-regression-testing-abhinav-sharma.html