Quru data sheet Comp..

36 Pages·2015·1.33 MB·English

Digital business with wisdom
Comparison of the Google and Adobe Analytics Stacks
Author: Steve Jackson
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Adobe and Google Analytics compared
Table of Contents
TABLE OF CONTENTS 2
INTRODUCTION 3
ABOUT THE AUTHOR 4
METHODOLOGY & CREDITS 5
THE SCORING SCALE 6
THE ADOBE CASE 7
THE GAP CASE 9
NO WEIGHTING 11
IMPLEMENTATION 13
DATA COLLECTION 15
RAW DATA MANAGEMENT 16
SAMPLING 18
DASHBOARDS AND CUSTOMISATION 20
SEGMENTATION 22
REPORTING ITEMS (OUT OF THE BOX) 24
IMPORTING ADVERTISING AND OTHER DATA 26
APPLICATION PROGRAMMING INTERFACES (APIS) 28
FREE OR PAID INTEGRATIONS 29
SERVICE 31
PRICING 32
GENERAL SUMMARY 33
WHY ADOBE? 34
WHY GOOGLE? 35
HOW ABOUT YOU? 36

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Adobe and Google Analytics compared

INTRODUCTION

The reason behind this paper is to compare and contrast the two biggest
analytics and marketing stacks on the market today: Google and Adobe.

The idea is that looking at the differences between the two sets of
technology will allow us to better understand what drives business decisions
in favouring one over the other, whether they can be used in conjunction
with one another and look at it from a buyer’s perspective.

When we talk about GA we mean the latest version of Universal Google
Analytics (also known as UA).
When we talk about GAP we’re discussing Google Analytics Premium
(Universal Analytics but paid).
When we talk about AA we’re discussing Adobe Analytics suite version 15
including data warehouse and its visualisation tools but without all the paid
integrations.

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Adobe and Google Analytics compared
About the Author

Quru are ideally positioned to comment having used both stacks over the
years for a number of their customers and the principal author of this work
was their CEO at the time of writing Steve Jackson.

Steve is an analyst and entrepreneur having founded 3 companies since
1997. The first was wound down when he left the UK for Finland in 2002.
The second was acquired by Satama interactive PLC in 2006 and he serves as
CEO for Quru which he co-founded in 2009.

He has written 2 books, The Cult of Analytics1 (2nd edition released
December 2015) and The Sucking Manifesto2. He’s also been a regular
speaker at events like eMetrics, Search Engine Strategies, Internet Marketing
conference and Divia in Finland.

He was awarded an honour of recognition by the Digital Analytics
Association in 2009 for his work in developing analytics in the Nordic
markets where he served as a Chairman for the region.

In his spare time he can often be found biking, boxing, fishing, sampling
good malt whiskeys or pulling his hair out watching SAFC.

1 Cult of Analytics - 2nd edition

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Adobe and Google Analytics compared
Methodology & Credits

We have looked at dozens of different opinions from analysts who have
compiled their own blog posts, product sheets and scorecards. We’ve talked
to Google and consulted their GAP versus GA comparisons to come up with
many of the variables. We’ve also looked at previous studies we’ve done for
customers where we’ve developed weighted scorecards across many of the
product variables you see here. We’ve sparred with a variety of subject
experts for both Adobe and Google none of whom had less than 5 years
experience with both tools.

Quru itself has experience amounting to over 100 years internally in GA,
GAP and Adobe.

We’ve had full access to all the tools so were able to go in and check
assumptions where necessary.

In particular I’d like to credit Halee Kotara3 for her help with some of the
nuances of Adobe Analytics versus Google Analytics. Halee works on a
variety of different cases every day using Adobe Analytics and also has
strong experience using Google Analytics.

When developing a scoring methodology, scoring a set of features 0 to 5
where ‘0’ is bad and ‘5’ is good is never enough. If 1/4 of your reporting
requirement is the ability to measure video streaming then a higher value
should be placed on a tools ability to measure video streaming. You need to
weigh the video streaming variable higher than say reporting page views.

For the purpose of this study we’ve developed 2 cases weighted in favour
of Adobe and GAP respectively and then an unweighted score where we
describe all of the differences as clearly as we can. The scores fluctuate
wildly depending on what your business needs are so it is very important you
understand what’s important to you before selecting a tool. We have not
created a case for GA on its own as there is no comparison between Google
Analytics and Adobe Analytics in terms of functionality. It is a case of you
get what you pay for. However GAP and Adobe Analytics are now very close
in terms of what they offer.

3 Halee Kotara - Consultant

http://www.kotaraindustries.com/
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Adobe and Google Analytics compared
The Scoring Scale

The scoring scale is measured on our opinion of what we believe is a
reasonable score based on level of effort required to do the job and whether
the tool would require a paid integration or not.

0 = No ability. Is currently not supported by the tool at all or via
integrations.
1 = High complexity/level of effort and costs more than standard., i.e.
not out of the boxand requires paid consultation.
2 = Costs more than standard with a medium or low effort.
3 = High level of effort required and/or standard. 3rd best in category.
4 = Medium level of effort and/or standard. 2nd best in category.
5 = Low level of effort and/or standard. Best in category or equally good.

High level of effort might require a minimum of 2 weeks man-hours of
work to complete, cost €10K+ for a paid consultation to a 3rd party or
require specialist skills (such as strategic consultation or a high level
specialist in implementation/tagging).

Medium level of effort may take more than more than 1 day but less than
2 weeks of man-hours to complete, cost between €4 and 10K for a paid
consultation to a 3rd party or require somewhat specialist skills (JavaScript
experts).

Low level of effort takes less than 7.5 hours to complete or works
immediately with minimal set-up requirements. If you have to pay a 3rd
party it’s typically less than €4K as to an average 3rd party specialist
company this kind of work is routine.

Best in category or equally good means that the tool is either the best fit
for the job or equal to any other tool across that score.

2nd or 3rd best in category means that the tool comes a close second or
3rd from the tools in the study.

Standard means that the tool has the feature as part of the feature set
without paying anything extra.

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Adobe and Google Analytics compared
The Adobe Case

In this first case we’re weighing it more toward the strengths of Adobe as
we see it.

1) Dashboards & Customisation 15%
2) Segmentation 15%
3) Pricing 10%
4) Raw data management 10%
5) Sampling 10%
6) Reporting items (out of the box) 10%
7) Importing advertising and other data 5%
8) Free integrations or paid 5%
9) APIs 5%
10) Service 5%
11) Implementation 5%
12) Data collection 5%

This is weighted towards developing a strong reporting interface, across
aggregate and raw data sets with strong segmentation capability. This
requires the data should be unsampled and there should be good tools to
measure raw data down to the session level. Pricing is always a
consideration so it also weighs in at 10%.

It’s also assuming that you have good internal resources that know how to
get the most from analytics tools, therefore data collection,
implementation, supporting service, connections to other tools and such like
are less important than being able to manipulate data, analyse and report
effectively across an enterprise.

The scoring when weighted for business objectives that we’re slightly
favorable towards Adobe were as follows where the white result in the table
with the green background shows Adobe as the strongest solution.

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Adobe and Google Analytics compared
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Adobe and Google Analytics compared
The GAP Case

In this second case we’re weighing it more toward the strengths of GAP
as we see it.

1) Service 15%
2) Importing advertising and other data 15%
3) Pricing 10%
4) Segmentation 10%
5) Sampling 10%
6) Data collection 10%
7) Dashboards and customisation 5%
8) Free integrations or paid 5%
9) APIs 5%
10) Reporting items out of the box 5%
11) Implementation 5%
12) Raw data management 5%

This is weighted towards services and understanding how online
marketing works. Most companies today use Google AdWords as part of their
advertising strategy and there is no better tool than Google Analytics
Premium to measure Google advertising with a low level of effort. It also
weighs data collection and sampling quite highly as one of GAP’s strengths is
the massive scale that even their lowest tier starts collecting and reporting
on data (1bn server calls per month). The value for money is also extremely
high when you consider that services are included from the GAP resellers
across the globe that are heavily vetted by Google prior to being accepted
by the program.

It also assumes that measuring raw data, implementation, APIs, and
customisation of the interface are less important factors. Raw data
processing is certainly possible in GAP but it requires higher levels of effort
than AA without knowledge of for instance SQL programming.

The scoring when weighted for business objectives that were slightly
favorable towards GAP were as follows where the white result in the table
with the green background shows GAP as the strongest solution.

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Adobe and Google Analytics compared
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