Information Technology Program

Aalto University, 2015

Dr. Joni Salminen

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

DIGITAL ANALYTICS

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WRAP-UP WHAT DID WE LEARN?

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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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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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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 (lying with data)

• …it’ll still be fun :)

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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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Closing thoughts follow…

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What’s the difference between data and

information?

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It’s a rocky road from data to changes in the

world…

Data → Information → Insight → Action

At every step, there are obstacles to bring you down!

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Show me the mone…data!

fact > data > assumption > opinion

"it's like this“ (high certitude)

"it seems like this“

"i think it's like this“

"it's like this" (high certitude)

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What’s the best data?

behavioral data > survey data > interview data >

guesses

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What are the questions different data

answers to?

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What are the questions different data

answers to?

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Don’t forget qualitative data!

In usability studies, they often refer to ”task completion”.

This is measured in % of people able to perform a given

task (e.g., find and buy a specific product on an

ecommerce site). If the user fails to accomplish the task,

usability researchers ask why. We can adopt the same

logic in analytics:

– Why did customers come to your site?

– Were they able to complete their intended task?

– Why they were (or not) able to complete their task?

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The curse of ”Excel marketing managers”

“I agree completely that direct marketing attitudes and

skills have met online metrics, and the relationship is not a

healthy one for advertising. Measuring direct response to

ads undervalues advertising by a wide margin, because it’s

ADVERTISING. It’s meant to have an indirect influence

on perceptions and preferences, not trigger a

transaction. Ultimately, the Google and Facebook metrics

engines play into these attitudes by allowing ROI and other

calculations to be applied to advertising that is presumed

to be much more transactional in nature. But there’s

nothing like a beautiful print or banner ad to subtly shape

perceptions and preferences.”

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”Data is just one part of decision-making”

(Mosseri, 2010)

• data-driven: data tells us what to do

• data-informed: data helps us finding out what to do,

but we apply our strategy

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”Data alone is worhless” (Hamel, 2014)

“You need context to turn data into knowledge, and it

needs to be actionable data to be considered insight.

Tools can make you dumb. Limiting your ability to ask

“why?” and think outside the box.

Explaining the context and process— leading to

insight—often has more value than the result itself.”

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Analysis strategies

1. Ask questions or formulate hypotheses (deductive)

2. Find anomalies (inductive)

In both cases, you can visualize the data to find

answers.

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Just like research.

There are two type of metrics…

a. relative

b. absolute

• both are needed!

• relative will give you a comparable view on other

campaigns

• absolute will give view on relative (!) importance

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Explain it! (xkcd.com)

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You have some observations on difference

between A and B. When will you use

percentage and when the actual value?

• actual number = when there are few observations

• percentage = when there are a lot of observations

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Data can exist in two forms…

a. cross-sectional, a ”snapshot” of the situation

b. longitudinal, a ”trend”, i.e. development of the

chosen metrics in time

…so, be careful that you’re not only measuring metrics

in one point of time, but actually thinking about the

development of performance in a given period.

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Interpreting metrics: use tooltips!

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There are hundreds of metrics – the best way to learn

them is inside platforms! Just by browsing Google

Analytics + Facebook you learn 99,9% of the relevant

metrics. (Just give it a go!)

One step at a time (Kaushik, 2013)

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At the highest ladder (the most difficult to

measure!)

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(Kaushik, 2013)

Predicting CLV: Signal or noise?

The issue with predicting customer lifetime value is that

there may be no theoretical reason to assume that a

channel, age, or any known segmentation would

effectively predict the differences in spending patterns -

people are that much unique. If this holds, we can only

identify the most profitable customers ex post, which is

of course useful also: let's make sure they remain loyal.

#analytics

(in other words, no use in CPA calculation, but use in

retention-focused actions.)

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However, you can do a breakdown analysis…

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What are the common characteristics of

these people? (e.g., source, campaign,

demographics (age, gender, location)

ROI or CPA - which would you use to

measure success of online advertising?

Everyone talks about ROI, but the fatal flaw is that ROI

doesn't measure profitability. CPA is much better when

you know your average margin, because then you can

know whether your campaigns are profitable or not

(instead of knowing whether they are "effective").

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Always know what

is being measured.

Don’t take crap

(metrics) from

anyone!

Joni’s criteria: actionable & useful

• There was a guy who was very proud of knowing all

the time how many users there are in his e-store.

Everywhere he went he’d always take up his mobile

phone and show the audience this and this many

visitors are on the store at that moment.

• …but, whereas it was nice bragging, I’ve since

wondered: who the hell cares? Knowing how many

visitors you have at a given point in time is not

particularly useful or actionable.

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I am guilty…

• ” Vanity metrics combined with an obsession with

checking stats is a deadly combination! Well, maybe

not deadly, but at least time-consuming and

unproductive.”

• (→ stats-checking addiction)

• “I'm guilty of being the guy who loves to look at

the Google Analytics charts but only rarely do I

ever DO something as a result.”

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“people upon whom this data is

regurgitated often do not posses skills to

understand the data, ability or access to

ask clarifying questions of the data or

key context to transform the data into

insights.” (Kaushik, 2014)

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Dashboard = KPIs + charts

• choose the right KPIs

• choose the right chart types

• (Remember, dashboards are great for reporting, but

you need deeper segmentation to optimize.)

• (Visualization, storytelling, context…)

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The ultimate problem of analytics

• Analytics does not create action, it only measures it.

• In reality, people act on intuition – this is how great

inventions in science and business are born.

• Analytics cannot explain the innovation process, it

can only measure its impact.

• The role of analytics is to be a part of continuous

evolution of stimuli-response, or a feedback loop,

in which the data on our actions is leveraged to

improve those actions in the future. (sounds

pretty impressive, right? ;)

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Data, creativity, and risk

creativity --> risk --> results

(no data) (data)

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If data driven,

creativity and

risk is minimized

and nothing

innovative is

created (data

slave syndrome)

If creativity

driven, results

are not

measured and

therefore cannot

be improved, OR

focusing on

vanity metrics

high degree of creativity

inherently involves high

degree of risk

You cannot be afraid

of lack of data up to

a degree it hinders

creativity, but you

cannot be ignorant

either.

Continue learning, and there will be jobs for

you…

• web analyst (Google Analytics, FB Insights, etc.)

• data scientist (R + stats)

• visualist (Tableau + R)

• analytics manager (wide basic knowledge, generalist)

• in all these professions, you can make a lot of money

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T skills (analytics)

You can specialize in one area, or have a general

knowledge on all of them:

– Data collection

– Analysis

– Visualization

– Reporting

– Presentation

• Pick yours!

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storytelling

”What if money was no object?”

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https://www.youtube.com/watch?v=khOaAHK7efc

How to carry on?

• understand you won’t learn skills in school

• understand you need to update your skills through

your whole life

• understand educating is an investment – make that

investment of time and money

• It’s up to YOU to learn – nobody else.

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Google Analytics: how to carry on

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Tableau: how to carry on

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R: How to carry on (my favorite course)

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Web scraping (data collection)

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Text mining and sentiment analysis with R

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Machine learning with R

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SQL (query databases)

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The future of education

So, education! Many university teachers are far

behind. I look at these MOOCs, and see some of them

are absolutely brilliant. Much better than my teaching

(and I don't consider myself being a bad teacher, at

least relative to other university teachers). If it was a

free market for education, many university teachers

would have zero chance in competing against MOOC

teachers. Young, energetic, inspiring, and most

importantly: they teach you the skills needed NOW,

not ten years ago. Bravo!

# Moocs # education # learning # futureisnow

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If you’re into books instead, here’s two

recommendations:

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…also, check Basecamp for my book!

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(+ Extra slides on Big data & Managing an

analytics team)

• Be sure to check them out on Basecamp.

• (+ CLV spreadsheets)

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…do this, and you’ll succeed.

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”Damn, that Joni boy is smart”

– Trump

Me & you??

1. Connect in LinkedIn: www.linkedin.com/in/jonisal

2. Check out the thesis ideas: https://goo.gl/qmXKnG

(you can always ask advice)

3. Do an internship at ElämysLahjat.fi & learn a bunch

of digital marketing stuff (joni@elamyslahjat.fi)

4. …oh yeah, join the Facebook group of digital

marketing students (in Finnish): https://www.facebook.com/groups/digimarkkinointi/

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I’ll always

remember you!

Good luck & see you around!

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