Information Technology Program

Aalto University, 2015

Dr. Joni Salminen

joolsa@utu.fi, tel. +358 44 06 36 468

DIGITAL ANALYTICS

1

WHY LEARN ANALYTICS?

2

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).

3

Voilà! Wanamaker dilemma solved

(let’s go home…)

4

Channel Sales

Marketer’s intuition

5

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…

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)

6

I’m a marketer.

I’m always

right!

(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?

7

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)

8

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)

9

Finally, don’t forget…

There are good

opportunities in the job

market for people who

know analytics!

10

ABOUT THE COURSE

11

Who am I?

12

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.

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.

13

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!

14

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 :)

15

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!

16

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.

17

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.

18

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.)

19

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.

20

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.

21

BASICS OF ANALYTICS

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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)

23

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?

24

How does analytics work? (Mullins, 2011)

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• users

• sessions

• hits (interactions)

Website

Javascript code

Server

Processing

• dimensions

(qualitative)

• metrics

(numeric)

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.

26

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.)

27

How e-commerce tracking works

(Cutroni, 2013)

28

Example of tracking script (Cutroni, 2013)

29

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.

30

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.

31

Aggregation problem

• All data looks the same when looking from far

enough!

• The solution:

• segmentation

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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!)

33

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.

34

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

35

Google Analytics shows what happens

after the click, these show what happens

prior to it (data is in the platforms).

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)

36

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

37

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!

38

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, 39

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).

40

GOOGLE ANALYTICS

41

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

42

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

43

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.