Digital Insights

Monitoring your KPIs performance with Adobe Workspace Functions

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Today I am going to show you how to use Adobe’s Calculated Metric functions in order to set up a dashboard and monitor when your critical metrics & KPI’s perform outside normal/predicted behaviour.

Currently Adobe Sitecatalyst gives a feature called “anomaly detection”, know to most Google analytic users as “Intelligent alerts”.

Anomaly Detection in Adobe Sitecatalyst and why you don’t really need it

Anomaly detection will spot unpredicted behavior – whether this is positive or negative – based on previous data you hold (you can read more about this here). They way it does this is by calculating the daily total for the selected metric and compare it with the training period using each of the following algorithms:

  • Holt Winters Multiplicative (Triple Exponential Smoothing)
  • Holt Winters Additive (Triple Exponential Smoothing)
  • Holts Trend Corrected (Double Exponential Smoothing)

Each algorithm is applied to determine the algorithm with the smallest Sum of Squared Errors (SSE). The Mean Absolute Percent Error (MAPE) and the current Standard Error are then calculated to make sure that the model is statistically valid.

Theoretically speaking, Triple Exponential Smoothing is ideal for including “seasonality” trends (unlike Double exponential), however Anomaly Detection biggest weakness is it’s 90 day window.

90 days is pretty much insignificant for retail web sites where seasonality and trends plays a big role in terms of performance, hopefully they will add a YoY feature in future releases but in case they don;t that is ok because as long as you have the data available you can do it yourself as shown below.

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Google Analytics Implementation Guide for Beginners

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We discussed on a previous post the different Google Analytic Code configurations you can do in order to set the correct tracking on your web site.

Today I am to show how to plan your Google Analytics Implementation setup by providing a guide with detailed task lists, linking at the best sources/blogs I have seen on the internet. Those sources, hopefully, will take you through the process whether you plan to use Universal or an Enterprise (Google 360) Solution.

Please bear in mind that this was useful to me a couple of years ago when I worked on an acquisition focused web site and it is NOT a good guide for e-commerce web sites.

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How to measure your YouTube video length performance

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Quick post today about YouTube video analytics and in particular how to measure your Youtube video length’s performance with scatter plot chart .

This information will help you make decisions around your video content length creation as well as help you decide at which point you should position your call to actions.

Note that the analysis below makes more sense to long length videos rather small 30 seconds advertorials. The samples below were taken from 30+ minute webinars that were promoted via YouTube advertising.

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Find Information Conversion Rate – The low operational costs metric that matters

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This metric (find information conversion rate) probably won’t mean much to most people, however it is not uncommon on retail or customer focused web sites. 

Call centers can cost a lot of money for tasks that a user can complete online and a lot of retail and customer service companies are now working on digital products and optimisation roadmaps in an attempt to digitalise self-service customer tasks and reduce costs.

What is Find Information Conversion Rate?

 The big book of key performance indicators by eric peterson from Neo Consulting is describing in great detail what this metric is about, but if you can’t bother reading the whole chapter, I summarize below a couple of examples of what the metric is and how it should be used.

A lot of web sites hold critical information that can lower operational costs by preventing site visitors from placing a phone calls such as “when is my order going to be delivered“?, “I placed the wrong order online, how can I change it“, etc, since this kind of information can be found online.

HELP microsites, FAQ or other INFO type pages, exist for that reason, however with minimum understanding of digital behaviours this can lead to poor results.

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Adobe Marketing Cloud Pulse Chrome Extension

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Back in 2010, Adobe introduced Digital Pulse Debugger, a JS snippet that you could add to your Bookmark and act as your Sitecatalyst, Test & Target, AdLens and Audiomanager debugger. Effectively, an image requests that shows you via a basic interface all the variable and success events that fire once your page loads amongst other useful information such as Report Suite ID, Visitor ID etc.

Pulse became a must have extension, however it lacked the ability to show you a sequence of requests (you could do that with Charles), onclick events and variables that fire without a page load as well as possible errors with your variable settings within the admin console. For that you have to go via the network option in the development console or use 3rd party tools such as Observe point and Charles.

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How to set up an optimisation test with Google Optimize (BETA)

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Another quick post this week to show you how to set up an optimisation test with what seems to be Optimizely’s new competitor, Google Optimize (Part of Google Analytics 360 Suite).

The product is still in BETA, so not sure how many more additions they plan to make; however, at a first glance it seems that Google is ready to start getting some market share from the optimisation world.

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Calculated Metric: Bounce Adjusted Unique Visits

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I was reading the other day the very interesting article published by Adam Greco about Creating Conversion Funnels via Segmentation for Adobe Sitecatalyst where he basically breaks down visits into four different segments, depending which section of the site a user visited during a session. One of the things that a couple of colleagues debated was the first segment called “Awareness”.

According to Adam;s post, we include in the awareness segment people who have come to your website, but never seen a product, attempted to download a trial of it or purchased it. They simply express an interest because they saw an ad, heard something or read an article about it.

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Exit Rate % Metric Explained like never before

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The majority of the time landing page analysis is the primary focus of any web analyst when carrying out a web site review. However, little attention is given to visitor departure pages, also known as the Exit Rate.

This lack of focus is somehow understandable, especially on heavy acquisition web sites, where CMOs and Heads of Digital spend heavy budgets on biddable media and want to know which channel performs best when it comes to their KPIs. Consequently, the focus goes to landing page optimization metrics, in order to analyse visitors. It’s important that you don’t, however, ignore your visitors and what they do next.

Most of the time this happens because unlike the Bounce Rate, there isn’t a set rule for the Exit Rate. A visitor could end the visit at any moment without a meaningful reason, thus it is harder to explain. I believe that an exit rate analysis can give you a lot of important answers as to why particular visitor segments are exiting your site prematurely.

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Path Analysis with Google Analytics & Excel

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In this post I will show you how to use the standard CSV report from Google Analytics in order to do path analysis and identify how users navigate through your web site and which particular journey results in a successful conversion. A lot of people and digital marketers pay attention to landing page performance, relevant behavioral and conversion metrics (conversion rate, bounce rate, etc). However, in many cases the bounce rate is low on the landing page, but high on the next page that catches the user’s attention.

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Google Analytics Code Configuration, A Complete Guide

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I decided to write a post about the Google Analytics code, primarily for people new to the web analytics world. Even if this a very old topic that has been exhausted by various renowned bloggers, I realised that a lot of people still struggle to understand the basic differences between Google Analytics tracking code versions.

Hopefully in this post I will clarify the main differences and modifications that you can do to your Google Analytics code, when to use each version, and what you’ll get in return. I will also cover how to do the same modification with Google Tag Manager. If you do not know what Google Tag Manager is, Google recently released a series of videos that will help you understand the basics.

I will not go through the old school Urchin.js code, and dive directly to the codes you are most likely to see in various web sites today.

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