digitl data

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LESSON 6:

“Insights from Digital Data”

3Important rules of

context ensureimpact in analysis

5Primary analysistechniques usedto analyze data

Key questions inyour plan direct

your analysis

Each techniqueoffers a unique viewon a key question

If sec

i



Documenting your plan at this stage

is critical to success

BusinessObjective

Key Question Data —> Source(s

GrowLoyalty

How has consumer interest inour brand trended over time?

Search Volume —> Goog

Customer Inquiries

CSR Database

What consumer group isour strongest advocate?

Consumer Groups Segmentation Stu

Twitter Volume —> Twit

Which marketing programshave grown advocacy?

Marketing Events —Company Intranet

Hashtag Volume —> T

Note: * Here “source” is used in a way synonymous with “tool”



There are five primary categories of marketing

data analysis

Source: Adapted from Jeffrey Leek, “Types of Data Science Questions”Note: A sixth analysis approach (“Mechanistic”) has been omitted

Predictive Descriptive

Inferential

Exploratory

Causal



There are five primary categories of marketing

data analysisUses data on some object to

predict the future value of others

Depends heavily on having theright data and data quality

Must remember that if X predicts Y, X does not cause Y

First kind of a

Commonly apGeneralizatiopossible with

Seeks to determine what happensto one variable when a change is

made to another

Usually seen as average effects

Usually the “gold standard”for data analysis

Seeks to discopatterns in the

Good for defini

Should not be u

generalizing or

Uses a smsomething

Commonly

Results depopulation


Causal Inferential

Exploratory




These categories offer graduated levels

of analysis depth


Causal Inferential

Exploratory




Q: “How has consumer interest in [X] trended

over time?”


Causal

Offer recommendations for

actions that will successfullydrive brand interest in the future

Determine whor are not inte

Identify when inbrand, when it

interesting patt

Explain what has been done inthe past that successfully drove

brand interest and otherinfluential brand attributes

Provide a a typical cinterestedlike and ho

Inferential

Exploratory




Different analyses require the collection of

different data


Causal

Media optimization modeling

Attribution modeling

Other response modeling (e.g.,customer & channel response)

Web traffic da

Web server preports

Web transact

Competitive i

Clickstream a

Outcomes anal

Other regressioperformance co

analyses

Experimentation and testing

Multivariate & A/B testing

Site optimization

Other campaign optimization(e.g., “Heavy up” media tests)

Usability s

Voice of c

Other surva populatiInferential

Exploratory




Three rules of context ensure impact

in your analysis

! Never produce an analysis that reports a metric all

by itself … period

! Use benchmarks (internal or external), goals, or even p

performance to give some kind of context

! Include insights — in words — to summarize performa

and recommend actions in every analysis

Source: Kaushik, “Five Rules for High Impact Web Analytics Dashboards” (2007)



”“

If there is not a 60 second story,then there is no story at all. You

even tell the story of Dostoyevsk

‘The Idiot’ in 60 seconds. Michael Fassnacht, President FCB Chicago (2006)



LESSON 6:

“Insights from Digital Data”

3Important rules of

context ensureimpact in analysis

5Primary analysistechniques usedto analyze data

Key questions inyour plan direct

your analysis

Each techniqueoffers a unique viewon a key question

If sec

i



Supplemental reading for this lesson

! Dialog Intelligence:http://marketinggeek.blogspot.com/2006/12/dialog-

intelligence.html

! Making Accountability Sexy:

http://marketinggeek.blogspot.com/2006/07/making-

accountability-sexy.html

! The Veil of Statistics:

http://marketinggeek.blogspot.com/2006/06/veil-of-

statistics.html

http://marketinggeek.blogspot.com/2006/06/veil-of-statistics.html



References

1.Je$rey Leek. “Types Of Data Science Questions”. Ret

from http://jtleek.com/modules/01_DataScientistToolb

03_01_typesOfQuestions/#1

2. Avinash Kaushik. 2007. “Five Rules for High Impact W

Analytics Dashboards”. Retrieved from http:// www.kaushik.net/avinash/five-rules-for-high-impact-w

analytics-dashboards/

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