The document provides an overview of digital analytics and Google Analytics. It discusses why analytics is useful for solving problems like Wanamaker's dilemma of not knowing which advertising channels are effective. It explains how analytics works by collecting anonymous data on user behavior and interactions with a website and turning that raw data into useful reports and metrics. Key aspects of Google Analytics that are covered include its data model of users, sessions, and interactions. The document also discusses segmentation, internal vs external analytics, and how analytics can be used for reporting, optimization, and strategic decision making.

1. Information Technology Program
Aalto University, 2015
Dr. Joni Salminen
joolsa@utu.fi, tel. +358 44 06 36 468
DIGITAL ANALYTICS
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2. WHY LEARN ANALYTICS?
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3. Wanamaker’s dilemma (ca. 1901)
“Half the money I spend on advertising is wasted;
the trouble is I don’t know which half.”
• The marketer uses several channels for advertising.
• He knows advertising increases sales.
• But: which channel and how much?
• If we cannot measure the results, it’s harder to improve
(i.e. kill bad channels and scale up good ones).
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4. Voilà! Wanamaker dilemma solved
(let’s go home…)
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Channel Sales

5. Marketer’s intuition
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The more experienced a
marketer is, the better he thinks
he knows things beforehand
 However, even an
experienced professional can be
wrong.
With experience, the speed for
evaluating different alternatives
increases. Simultaneously the
ability to think beyond them
decreases.
 Never forget the fallacy of
marketer’s intuition…

6. Analytics overcomes marketer’s intuition
“After analyzing the online buying behavior of over
600,000 consumers across numerous e-commerce
sites, I learned that surprisingly 75 percent of
shopping cart abandoners would actually return to
the site they abandoned within a 28-day period. This
defies conventional wisdom: we polled online
marketers and 81 percent believed that the majority
of abandoners never return.” (SeeWhy, 2013)
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I’m a marketer.
I’m always
right!

7. (The issue with cart abandonment.)
• I did a small survey to my students in 2012
• It was discovered that shopping cart abandonment is
natural behavior in which users test or window-shop.
• If abandonment is natural behavior, does it make
sense to do conversion optimization?
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8. Problems analytics can help solve
• Wanamaker’s dilemma
• Marketer’s intuition
• Attribution problem
• The leaking bucket of customer acquisition
• (doesn’t really solve them, but makes us aware of
them: awareness → solution)
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9. Changes in the marketing landscape
a. projects (campaigns) → process (platforms)
b. one campaign → hundreds or thousands of
campaigns
• leads to…
– need for continuous optimization (instead of attention-
grabbing tricks)
– rules/automatization (to help handle the complexity)
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10. Finally, don’t forget…
There are good
opportunities in the job
market for people who
know analytics!
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11. ABOUT THE COURSE
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12. Who am I?
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Joni Salminen
PhD, marketing
• Bachelor’s thesis 2007: Search-engine
marketing on the Internet
• Master’s thesis 2009: Online advertising
exchange
• Dissertation 2014: Strategic problems of
early-stage platforms on the Internet
Experience:
• Teaching digital marketing at the Turku
School of Economics (2012 →)
• Marketing manager (ElämysLahjat.fi)
(2011 →)
• Hobbies: floorball & swimming.

13. Course description
The course combines theory and practice to educate
students about Web analytics. Students will learn how to
choose the right metrics for a given business, how to
interpret and report them, and how to apply analytics in
business decisions. The key topics include audience,
acquisition, behavior and conversion. Students will also
learn about attribution models and multichannel tracking.
The main platform of the course is Google Analytics.
As a part of the course, students will submit a practical
report. The report is based on an audit of a case
company's use of Web analytics and their actual
performance. It will help students understand how
analytics is used in companies and how to report
performance based on select KPI's and data.
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14. Program (1st week)
• Monday: Introduction & Basics of analytics
• Tuesday: Google Analytics (hands-on stuff)
• Wednesday: Metrics time
• Thursday: Dashboards, data problems, etc.
• It’ll be fun!
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15. Program: 2nd & 3rd week
• Optimization
• A/B testing / multivariate testing
• Cohort analysis
• Visualization
• Universal analytics & multichannel
• The real ”Big Data”
• Algorithm-based marketing automatization
• Data philosophy
• …it’ll still be fun :)
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16. Course philosophy
• Hands-on: bring laptops
• Practically useful, theoretically insightful
• Always ask! If you don’t, you’re missing out a useful
opportunity to learn!
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17. Material & grading
• Material:
– Lecture slides (will be shared in the Facebook group)
– Book: Lean Analytics by Alistair Kroll (find e.g. in
Amazon)
• Grading:
– Scale 0–5
– Team assignment will decide grading. Criteria: quality
of analyses, presentation, usefulness of given
recommendations. The more effort and the insights
discovered from the data, the better.
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18. You will learn to…
• choose relevant KPIs and metrics for a business
• manage data scientists and analytics projects
• make and report a website audit
• use dashboards to make better sense of data
• basic use of the best tools: Google Analytics,
Tableau, R
• …and, hopefully, how to make better business
decisions (and/or recommendations) based on data.
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19. Team assignment
• Your task is to do an analytics audit for a company of
your choosing (or the one given by Joni).
– In your report, you will answer select questions.
– (Answering some of them requires creating custom
reports – don’t worry, we’ll look at those together.)
– Then make a final presentation for the class room and
voilà! we can all go home.
• (Detailed instructions arrive by the end of the week.)
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20. Exercises in Tableau (week 2)
• Tableau is one of the most used business tools for
analyzing and visualizing ”big data”. It requires less
learning than R to get started.
• We will go through using Tableau in the class, then
you will use it to make analyses and visualisations for
your GA audit report.
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21. Exercises in R (week 3)
• R is programming language that enables powerful
statistical analyses and visualizations with fairly
simple commands. It is free, open source and very
expandable (better than SPSS).
• In the class, we’ll go through a couple of basic
examples; then, you can continue learning R in
MOOCs Joni recommends you.
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22. BASICS OF ANALYTICS
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23. What is analytics?
“Digital Analytics is the analysis of qualitative and
quantitative data from your business and your
competition to drive a continual improvement of the
online experience that your customers and potential
customers have which translates to your desired
outcomes, both offline and online.” (Kaushik, 2010)
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24. What kind of questions can we answer with
the help of analytics?
• What’s the most profitable source of visitors?
• What products are people buying? How much is the
average order size?
• Where do users come from? How long do they stay
on the site?
• How do new visitors behave in comparison to old
ones?
• What content is the most/less viewed?
• What keywords people use to find our site?
• Where do people exit the site?
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25. How does analytics work? (Mullins, 2011)
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• users
• sessions
• hits (interactions)
Website
Javascript code
Server
Processing
• dimensions
(qualitative)
• metrics
(numeric)

26. What kind of data is stored?
• B2C: Information is usually collected anonymously
and presented as aggregates (individual users are
not identified).
• B2B: The exception is so-called people-based
tracking, which aims at tracking individuals. This is
generally applied in enterprise markets, where the
number of buyers is relatively small.
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27. Analytics and privacy
• Two points to defend analytics & marketers:
1. The data is most often anonymous & aggregate
2. Using data solves the matching problem between ads
and consumers (we will have ads anyway, so
whatever makes them more relevant for you is a good
thing)
• (The EU is full of idiots, by the way.)
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28. How e-commerce tracking works
(Cutroni, 2013)
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29. Example of tracking script (Cutroni, 2013)
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30. Scripts, scripts, scripts… Setting up the
analytics
• Here are some of the scripts I’ve installed:
– google analytics conversion script
– facebook conversion pixel
– facebook custom audience pixel
– google tag manager
– vwo script, etc.
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31. Raw data is processed an turned into
reports
• “Reports package all data collected into a
readable format so that the decision makers
can study it and draw conclusions.” (Digital
Marketing Training Institute, 2014)
• Reports can then be customized and
reshaped by the analyst (custom reports &
advanced segments), and also put into the
form of dashboards.
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32. Aggregation problem
• All data looks the same when looking from far
enough!
• The solution:
• segmentation
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33. Segmentation
• Segmentation isolates your data into sub-sets for a
deeper analysis, and thereby solves the aggregation
problem.
• You can segment the data by
– date and time
– user’s device
– marketing channels
– geographical location
– etc. (dozens of options!)
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34. Ways to set up an analytics infrastructure
a. In-house (tailored system)
b. Ready-made tools (e.g. Google Analytics,
KissMetrics)
• Each one has advantages and disadvantages; for
example, in-house systems give the most accurate
conversion data, but take time and money to build.
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35. There are two types of traffic (& hence,
analytics…)
Analytics of organic
traffic
Analytics of paid traffic
Google Webmaster Tools Google AdWords
Facebook Facebook Insights Facebook Ads Manager
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Google Analytics shows what happens
after the click, these show what happens
prior to it (data is in the platforms).

36. Internal and external analytics
• Internal analytics = analyzing the data from own
website and properties such as social media pages in
order to improve the likelihood of desired business
results (e.g. Google Analytics)
• External analytics = analyzing competitors or the
market (cf. business intelligence, competitive
intelligence) (SimilarWeb, Google Trends)
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37. External analytics: an example
• Analyzing social media trends:
• ’Growth hacking’ and ’content marketing’ are
both popular topics in digital marketing. Which
one is more popular?
• Let’s see by using two tools:
a. Google Trends
b. Topsy
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38. Competitor analysis: example
• Turku School of Economics and Aalto Business
School are rivals in getting the best students.
• Which one has more traffic?
• Let’s run SimilarWeb, and find out!
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39. Hi Joni,
Thanks for reaching out! I hope you’re having a great Sunday so far :)
As for your questions:
Our measurements come from a combination of data scraping, powerful web crawlers and click-stream
data from our proprietary panel of tens of millions of users worldwide. The SimilarWeb panel is the
largest in the industry. It includes data from over 5,000 distinct sources, each representing various
demographic groupings and user characteristics.
Our panel users have given permission to collect some of their anonymous data such us browsing
patterns. We only extract aggregated information – no personal identifiable information is captured by us
in accordance with local and international privacy laws.
SimilarWeb’s crawler supplements the information collected by the panel and analyzes over 1 billion
pages a month, supplying input data for our sophisticated Similarity, Category and Content Analysis
engines.
Having the largest panel in the industry for web measurement guarantees our data to be exceedingly
accurate. However, websites with lower traffic means that our sample size is smaller and for these
websites, the level of accuracy may decrease.There is a rule of thumb that a site should have over 200K
hits per month to provide a higher level of accuracy. In that case we’ll have up to 15% margin of error,
meaning very high level of accuracy.
The reason why you may see “not enough data” when analyzing a domain on SimilarWeb is because
the engagement to this website is too low to allow us to get enough data to provide an accurate
estimation of the traffic to this website.
Hope the above helps. Please let me know if I can be of any further assistance.
All the best,
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40. The application of analytics
• analytics can be used for two things (Salenius, 2015):
1) reporting
2) optimizing
• …Joni would add: 3) strategic decision making (e.g.
budget allocation, attributing marketing performance)
• While analytics (data) is the requisite for optimization,
it’s also the pathway to automatization (more about
this later).
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41. GOOGLE ANALYTICS
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42. Why Google Analytics?
• “Google Analytics is a powerful tool which shows you
what is working on your website and what is not. It
helps you optimize your marketing efforts and
maximize the revenue.” (Promodo, 2013)
• It’s free!
• It’s used by maaaany companies
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43. Google Analytics data model (Google, 2014)
•User (visitor)—the client that visits the site,
such as the browser or mobile phone operated
by a person.
•Session (visit)—the period of time during
which the visitor is active on the site.
•Interaction (hit)—the individual activities that
send a GIF request (hit) to the Analytics
servers. These are typically characterized by a
pageview, but can include:
•a pageview
•an event (e.g. click on a movie
button)
•a transaction
•a social interaction
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In the basic data model used in Google Analytics, the user (visitor) interacts with your content over a period of time,
and the engagement with your site is broken down into a hierarchy.
•Each level in this model is defined as
follows:
Each of these three levels of interaction defines a specific scope of user engagement. This distinction is important in
Google Analytics because you may want to do analysis of your data at a particular scope. For example, you might
want to measure the number of sessions where users removed an item from their shopping cart. For this particular
case, you would be doing a session-level analysis that includes each session during which an item was removed from
a cart, even if the sessions are from the same user. On the other hand, you might want to measure the number of
unique users who removed items from their shopping cart at any time, regardless of session. For this example, you
would be doing a user-level analysis.