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Marketing Evolution, Data Base Marketing and Predictive Analytics
Feyzi Bagirov28 Oct 2013
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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
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Feyzi Bagirov
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Your marketing job is about to become obsolete.
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How does Traditional Marketing Work?
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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
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Optimization methods
“Shotgun” marketing strategy
Industry standards
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Optimization?Basic Advanced
Technological Changes that affected Marketing in the past 20 years
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0
5
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9
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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
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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)
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20
0
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(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
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The idea to win the Internet battle for the visitors, possible buyers and advertising revenue, was to aggregate the best content.
Early-mid1990s
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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
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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
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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
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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
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Demand Side Platforms (DSP) Supply Side Platforms (SSP) Online real-time ad exchanges
Advanced Optimization Platforms – Real Time Bidding
Basic Advanced
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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
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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
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Today's companies are processing 1000 times more data than they did just 5 years ago.
Going forward
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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
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DBM use – Primary ResearchMarketing Research Process
Secondary Research Primary Research
-Research data, collected by others
-Focus Groups-Oral Surveys-Paper Surveys-Online Surveys
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DBM use – Primary Research
Step 3 – Design & Prepare Research InstrumentsStep 4 – Sampling & Data Collection
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DBM use – Primary Research
Step 5 – Analyze Data
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◦ Response modeling for direct customers◦ Uplift modeling for direct customers◦ Customer retention with churn modeling◦ Churn Uplift Modeling
DBM Use - Optimization
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Use Case 1 – response modeling for direct marketing
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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
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Use Case 2- Uplift Modeling for Direct Marketing
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Use Case 2- Uplift Modeling for Direct Marketing
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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
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Use Case 3 – customer retention with churn modeling
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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
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Use Case 4 – Churn Uplift Modeling
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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
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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
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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
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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
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Your marketing job is about to become obsolete.
Conclusion We have no choice but to evolve.
We have no choice but to evolve.
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Questions?
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