Big data and marketing is becoming an important tool for companies. The document discusses how big data can be used for personalization, listening to customers, and responding to better serve their needs. It outlines the key steps in the process from data collection and analysis to insights and actions. Various big data tools and techniques are mentioned to understand customer behavior and trends in order to tailor marketing and customer experiences. The importance of data visualization to tell the story of patterns and create useful insights for businesses is also highlighted.

2. Edward Chenard
Big Data and Marketing
How Big Data is Becoming a Marketing
Tool
STAV Data

3. According to Gartner 85% of
Fortune 500’s are not doing it.
According to Accenture, of those
who are doing it, 75% are failing.
Few can describe it and even
fewer know how to do it.
What is Big Data?

4. 1. Big Data Collection
(HDFS)
2. Big Data Processing
(Hadoop)
1. Data Mining at Scale
(Hive)
Breaking down the IT of Big Data

5. Big Data Tools
Words you May Hear
BlinkDB
CassandraHive
Python
Pig
Stinger
HadoopGiraph
Spark
GraphX
MLbase
You don’t need to be an expert in these tools, but knowing
how they are used goes a long way
Impala

7. Image
Unstructured
Semi
Structured
Structured
• Click Streams
• Social Streams
• RSS feeds
• XML Documents
• Spreadsheets
• Relational
Databases
Data ecosystem, what is it, how to understand it.
Unstructured data is the goldmine,
it is growing while structured data
is shrinking. But to make big data
work for you, you need to structure
of the unstructured

8. Image
Structured
Unstructured
First understand what kind of data
you have to work with.
How to Make Data
a Marketing Tool

9. How we Personalize Big Data and Marketing in Use
Combine the strengths of Google and Facebook’s methods with psychograph
techniques.
Listen, Adapt, Respond
Services co-created with customers and are interpedently with wider service
networks.
Benefits
People will log in more
Higher conversion and AOV
Better emotional bond between company and customer
Psychograph
Self
Facebook Self
Google Self
Clash between Today and
Future
Aspirational You
Present You
1-1

10. Sentiment
Expressed as
positive, neutral, or
negative, the
prevailing attitude
towards and entity
Behavior
These signals
identify persistent
trends or patterns in
behavior over time
Event/Alert
A discrete signal
generated when
certain threshold
conditions are met
Clusters
Signals based on an
entity’s cohort
characteristics
Correlation
Measures the
correlation of
entities against their
prescribed attributes
over time
Rate of Change
(Slow or Fast)
Quality
(Predictive or Descriptive)
Sensitivity
(Sensitive or Insensitive)
Frequency
(High or Low)
All signal types have certain qualities that describe how quickly signals can be generated
(frequency), how often the signals vary (rate of change), whether they are forward
looking (quality), and how responsive they are to stimulus (sensitivity)
Signals have attributes depending on their representation in time or frequency
domain can also be categorized into multiple classes
Signal Types

11. Timing/ Recency
Measure the
freshness of the
data and of the
insight
Source
Measure
sources’
strength:
originality,
importance,
quality, quantity,
influence
Content
Derive the
sentiment and
meaning from
tracking tools to
syntactic and
semantics
analysis
Context
Create symbol
language to
describe
environments in
which the data
resides
Clickstreams
Social
Articles
Blogs
Tweets
For each dimension, develop meta-data,
ontology, statistical measures, and models
High quality signals are necessary to distill the relationship among all the of the Entities across all
records (including their time dimension) involving those Entities to turn Big Data into Small Data
and capture underlying patterns to create useful inputs to be processed by a machine learning
algorithm.
Finding Signals in Unstructured Data

12. Behavioral
Patterns
1 to 1
Marketing
Product/Service
Compatibility
Market Trends Social
How the Data Becomes Customer Experiences
Crowd based user
actions drive
recommendations
Personalized
email
marketing
Recommendations
based on products
Use machine
learning
algorithms to
predict trends
Small world
network
communication
Algorithms analyze data
Data Capture Points, Experience Delivery Points, Metrics
Data Capture Ecosystem

13. The Data, Insights, Action Gap
The Data Insights Gap
Data to insights can often fall short
for a number of issues
- Difficulties in defining areas of
focus for external data
- Only gradual adoption of
exception analytics and
automated opportunity seeking
- Example (P&G / Verix Systems)
- Opportunity seeking business
alerts
- Value share alerts
- Out of stock alerts
- New Launch alerts
The Insights Action Gap
Processes and systems designed
prior to big data thinking
Examples:
- CRM
- Pricing: Buy now in-store pricing
- Supply chain and logistics
- Prevalence of operational ,
internal metrics
- Complex new concepts:
“Intents”

14. Image Activity Based Thinking

15. Human Motion Graphs
Human motion graphs help understand movement of
customers and helps to predicts timing of marketing
activities

16. Image
Tracking How People Respond

17. Image
Data Discoverers
Data Discoverers are setting the trend in what will be
common place in just a few short years.
More people will want to use their data and the
consumerization of data and technology will continue.
As this trend goes, only organization that learn to merge
the various disciplines of strategy, analytics and IT, will be
successful
Data as a Lifestyle

18. Real-Time Firehose
Services
Apps
Multimedia
Places
Internet of Things
Our Data Sources are Changing

19. Search On-sites Sensors Re-marketing
Customer
Feedback
Signals Hub
Social
Personalization Products
Customer
Service
Digital
Marketing
In-store
Creating Customer Signal Hubs

20. Where we are Going
How we organize our data is getting more customized and
real-time for real bottom line improvements
0%
5%
10%
15%
20%
25%
Vendors Hadoop Customized Customized
Realtime
Big Data Technology Evolution
Personalization Technology
Evolution

21. How to Take Advantage of Data

22. data
visualization
strategy /
review
technology
implementation
analytics
“The STAV Cycle”
“gaining insight and telling stories with data” © 2014 STAV Data
www.stavdata.com

23. Phase Typical
Issues
Recommended
Approach
Strategy / Review
Define goals / outcomes / expectations in the
form of business benefit / customer benefit;
form hypotheses / build business case;
Evaluate whether expectations for the
current cycle were met; identify
opportunities for improvement; set
expectations for the next cycle
Ignored / under-emphasized Increase emphasis
Establish formal methodology
Build capability
Technology Implementation
Identify the tools needed to accomplish the
business goal; define the technical path for
accomplishing the business goal; establish
development schedule
Over-emphasized
Initiated too early
Inadequate skill set
Decrease emphasis
Employ proof-of-concept
Use external services
Build skill set gradually / incrementally
Analysis
Analyze the data collected by the IT
implementation – find the gems; a function
for data scientists or traditional BI – not an IT
function; data science = 80% data analysis /
data cleaning, 20% algorithm creation
Descriptive orientation (business
intelligence)
Dis-integration of business
intelligence / data science
Organized in IT function / focus
on algorithm creation
Adopt predictive orientation (data science)
Integrate business intelligence / data science
Organize in business function / focus on data
analysis and data cleaning
Data Visualization
Tell the story of the patterns in the data; a
function for designers – not data scientists;
critical to making the analysis useful from a
business perspective
Located in IT function /
performed by data scientists
Focus on methodology vs.
results
Locate in business function – branding or UX
Assign to designers
“Making the Impossible Possible”
“Big Data is good for solving
impossible problems;
it just makes simple problems more
complex”
The STAV Cycle will increase the probability of of success
for any organization. Implementation of the cycle includes
many more details; it needs to be adjusted to each
organization and the goals of each project; but the basic
framework doesn’t change. If you use this framework, your
big data project will be successful.
© 2014 STAV Data
www.stavdata.com

24. internal /
external
(medium investment /
medium scope)
internal
(large investment /
broad scope)
external
(small investment /
narrow scope)
© 2014 STAV
Data
www.stavdat
a.com
Maturity Model /
Product Development
Life Cycle

25. Vision &
Goals
Governance
Execution
Clearly articulated vision for marketing and data
use, precisely defined goals with how to measure.
Defined scope of the product.
Market strategy, customer segmentation,
prioritization, org focus, measurement and
incentive systems
Production process, flexibility at scale, efficiency,
relationship management, benchmarking, metrics,
initiatives
How work gets structured

26. Strategy
- Define
the goals
Social
Define how to
engage
IT
Assemble the
Technology
Analytics
Make sense
of the Data
Linguistics
Distributed Processing (Hadoop)
Algorithms Development
Cross team Customer Experience
Improvement
Data science is a discipline for making sense of unstructured as well as
numerous data sets at scale
Develop Your Team

27. Listen
•Listen to the data streams
Share
•Share the data with the rest of the organization
Engage
•Engage to the data to find the insights
Innovate
•Innovate new ideas from the insights gained from the data
Perform
•Perform insightful actions from the data to create better customer experiences
Always Remember: Data,
Insights, Actions

28. Print
Radio SEO and PPC
Social
Predictive
Marketing
Television
You Are
Here
Human History of Marketing
Image credit:
www.conducthq.com
Using Data for Marketing in the Future
Predictive Marketing

29. • Extreme machine learning
• Collaborative predictive analytics
• Scale-invariant intelligence
• Neural networks for machine perception
• Real-time interactive big data
visualization
• Graph all the things
• Large scale machine learning cookbooks
• Collecting massive data via crowd-
sourcing
“Without big data analytics, companies are blind and deaf, wandering out
onto the web like deer onto a freeway.”
Big Data: 2014

30. • Personalization everywhere
• Company and consumer collaboration in service design
• Predictive location based selling
• Digital Concierges
• Real time event networks
• Graph and signal hubs merge for better understanding of
ad placement
• Large scale channel disruptions
• Marketing becomes more analytical
Big Data visionaries pose existential threats
Predictive Marketing: 2016

31. What’s Next: Combining contextual and analytical approaches provide a more complete picture of how customers interact
with the firm
Both approaches privilege observation and
understanding what people actually do
and look for opportunities to fix, improve
and innovate.
Robin Beers, founder of Business is Human

32. Location
Analysis
Graph Analysis
App and Device
Analysis
Customer
Feedback
Personal Event
Networks
Social
Personalization Digital
Concierge
Real-time
Service
Better Ad
Performance
True Omni
Signal hubs will
become new centers
for data, helping to
create better
customer insights
Predictive
Analytics
Creating Customer Signal Hubs of the Future

33. Although IT can build the systems, it will still be left to analyst and marketers of all types
to create the actions needed to engage customers
How Predictive Marketing is Shaping Up

34. Web
PDS
Email
ECC
Personal Event Network
Appt
Scheduler
Add to
Calendar
Confirmation
Email
Add
Confirmation
and Appt to
PDS
Using the digital concierge system, we can
create easy to use appointment systems,
capturing the data and using it for future
personalization efforts
Appointment setting with a Digital Concierge

35. Image
Engaging millions at a time
Data Monetization
- Keep it
- Sell it
- Partner with it
- Share it
Marketing of a Mass
Personalized Scale

36. Processes are lined, linear chains of cause and
effect.
A service is different. Processes are designed to be
consistent, personalization services are not
consistent but individualized and co-created. The
differences are not superficial but fundamental.
Co-created value requires a relationship
Marketing of the Future: Process vs Service

37. Marketing as a service relies on the ability of an
organization to learn from customer’s responses
and to listen and adapt to those signals.
Causes of success are never revenue, costs, profits,
etc.., those are lagging indicators or effects.
What matters are the activities that generate the
profits, activities that create long or short term
value. You can measure that via personalization as
it is a leading indicator activity if done correctly.
Marketing is about Listening and Learning

38. An organization’s data is found in its computer systems, but a
company’s intelligence is found its biological and social systems
--- Valdis Krebs, researcher
Linking things changes things: social networks are good at
habit building. As behaviors are repeated, they form stronger
associations over time. You form strong bongs with people in
your life with whom you spend the most time, the same can be
said in a social interactive personalization model, customers
will form strong bonds with organizations they interact with the
most over a given period of time.
Small world networks: people banding together to achieve a
wide variety of shared objectives. These are the most powerful
types of social networks and the way to truly engage customers
is to beyond just social network sites and to get into the small
world networks as a valuable member of the network.
Marketing and Social

39. Start small, and remember, everyone else
is in the same boat
Online Resources
What You can do now

40. Thank you