Marketing evolution, Database Markeing and Predicting Analytics

1

Marketing Evolution, Data Base Marketing and Predictive Analytics

Feyzi Bagirov28 Oct 2013

2

4,000, 000,000,000,000,000,000 bytes

“In 2013, the World will produce a 4 zetabytes (or 4 million petabytes) of new data”

(Gartner)

Exa Tera Giga MegaPeta KiloZeta

3

Feyzi Bagirov

4

Your marketing job is about to become obsolete.

5

How does Traditional Marketing Work?

6

Includes all the advertising methods that have been used in the recent past:◦ Business cards◦ Print ads in magazines or newspapers◦ Posters◦ Radio◦ Television commercials◦ Brochures and billboards

Traditional marketing using anything not digital to brand your product into minds of people.

Traditional Marketing

7

Optimization methods

“Shotgun” marketing strategy

Industry standards

8

Optimization?Basic Advanced

Technological Changes that affected Marketing in the past 20 years

20

0

5

19

9

9

19

8

9

Mosaic (first Web browser)

1993

Launch of e-mail services for home PCs (early 90s)

iPod&

iTunes (2001)

(2003)

(2005)

SL (2003)

(2000)

Rise of Blogs

(2003) (1995)

(2000)Pointcast

(early example of on demand

digital versions of

print publications

1996)

Web-based DIY travel arrangements

(mid 90s)

Web 1.0 Web 2.0 (Social Media)

Spreadsheet software

(mid 1980s)

Rise of PCs

(1998)

SEO

Era of Traditional Marketing

(2004)

Map

Red

uce

10

Technological Changes that affected Marketing in the past 20 years

Rise of Big Data

And Mobile Computing

4 Zb of Data in the World

(2013)

Development of Big Data

Infrastructure

Big Data Analytics platforms allows companies to collect

and analyze all data, structured and unstructured.

(2006) (2008)

Real Time Bidding (RTB)

(2009)(2005)

(2012)

(2007) (2009)

20

1

4

20

0

5

(2010)SmartPhone shipments

Passed Desktop PCs’

(2012)SmartPhone shipments

Passed Notebook and

Desktop shipments

Ebay, Wal Mart’s Corporate

Databases are at 5Pb

Today’s companies are processing 1000 times more data than they did just 5 years ago

(2006)

3G iPhone (2008)

Opened to everyone

13+ in 2006

11

The idea to win the Internet battle for the visitors, possible buyers and advertising revenue, was to aggregate the best content.

Early-mid1990s

12

But content was available for free (Rise of Blogs and social networks), what really mattered how to find the most relevant information.

Introduction of Goolge search algorithm in 1998 quickly shifted online advertisement revenues to search advertisement

Content is free, it’s all about the Search

13

In 2004, Google created MapReduce – an algorithm concept that would allow processing of Big Data.

Big Data – volume, variety and velocity. A Year later, a Yahoo engineer implemented

MapReduce in Java and called it Hadoop, after his daughter’s toy elephant.

Massively Parallel Processing (MPP)

Era of Big Data

14

By 2008, corporate database of companies like E-Bay and Wall Mart were 5 Pb, many others were close. Companies wanted to take advantage of the data they had.

Accrual of Corporate Data

15

In 2008, with introduction of iPhone, that created several new industries (and destroyed several existing ones), marketers got access not only to personal and professional, but also to a new dimensions of data (location). 

Shift from Brand-centric Marketing to a Consumer-centric Marketing

Rise of a Social Media and Mobile Computing

I am at the mall

16

Demand Side Platforms (DSP) Supply Side Platforms (SSP) Online real-time ad exchanges

Advanced Optimization Platforms – Real Time Bidding

Basic Advanced

17

Basic Advanced

Replaces: Google analytics that just shows descriptive statistics

The Heat Map Tool that shows why your visitors are leaving without buying/converting

Why are they leaving? Where do they get frustrated?

Advanced Optimization Platforms

18

Then Now

Intuitive decision-making, catchy jingles

Algorythms and data-driven decision making

Grand openings Optimization

Demographic segmentation Behavioral segmentation

Market share = attention Involvement = attention

Passivity Interactivity

Selective attention Fractured attention

Communication = monologue Communication = conversation

Reverence and earnestness Irreverence and irony

Authorities as influencers Peers as influencers

Consumers defined by brands Brands defined by consumers

Marketer for marketing jobs Engineer for marketing jobs

Shift of marketing jobs to engineers

19

Today's companies are processing 1000 times more data than they did  just 5 years ago. 

Going forward

20

Data based marketing is an approach of systematically analyzing and getting insights on how Customer base behaves over time

DBM is the analytics side of Customer focus, or putting Customer (and not the brand/product/service) at the core of everything

Uses of DBM:◦ Primary research◦ Optimization

Data Base Marketing

21

DBM use – Primary ResearchMarketing Research Process

Secondary Research Primary Research

-Research data, collected by others

-Focus Groups-Oral Surveys-Paper Surveys-Online Surveys

22

DBM use – Primary Research

Step 3 – Design & Prepare Research InstrumentsStep 4 – Sampling & Data Collection

23

DBM use – Primary Research

Step 5 – Analyze Data

24

◦ Response modeling for direct customers◦ Uplift modeling for direct customers◦ Customer retention with churn modeling◦ Churn Uplift Modeling

DBM Use - Optimization

25

Use Case 1 – response modeling for direct marketing

26

Use Case 1 – response modeling for direct marketing

Lifeline Screening: Response up 38%, cost down 20%, 62K more customers annually

PREMIER Bankcard: Direct mail response up 3-5%

Sun Microsystems: Doubled the number of leads per phone call

27

Use Case 2- Uplift Modeling for Direct Marketing

28

Use Case 2- Uplift Modeling for Direct Marketing

29

Use Case 2-uplift modeling for Direct Marketing

Leading financial institution: incremental conversion up 0.02% to 0.43%; Revenue per contact up by over 20 times

30

Use Case 3 – customer retention with churn modeling

31

Use Case 3 – customer retention with churn modeling

Reed Elsevier’s Caterer & Hotelkeeper: Reduced churn by 16%; Retention ROI up by 10%

PREMIER Bankcard: $8 million est. retained

Leading North American Telecom: Identified customers with a 600% increased risk of churn with social network analysis.

Optus (Australian telecom): Doubled churn model performance with social data

32

Use Case 4 – Churn Uplift Modeling

33

Use Case 4 – Churn Uplift Modeling

Telenor: Reduced churn 36%; Cost-of-contact down 40%; Campaign ROI up 11-fold

US Bank: Costs down 40%, lift up 2 times, and cross-sell ROI up 5 times

34

Only 20% of the data is structured and readily analyzable.

The other 80% is unstructured, including email, social networks feeds, videos, etc.

Lack of data/need to accrue

Possible Challenges

35

Need to start now not to be left outside Develop proper data strategy, data quality

controls and analytical talent now to be successful when the data analytics arrives to Azerbaijan in 3-5 years.

Proposed next steps

36

Primary Research Data Analytics◦ Online Survey Programming◦ Installation of the Open Source Analytic Tool (Rapid

Miner)◦ Introduction to statistical principles◦ Processing Primary Data with Analytical Tool

Advanced Data Analytics ◦ Response modeling for direct customers◦ Uplift modeling for direct customers◦ Customer retention with churn modeling◦ Churn Uplift Modeling

Proposed coursework

37

Your marketing job is about to become obsolete.

Conclusion We have no choice but to evolve.

We have no choice but to evolve.

38

Questions?

39