This document discusses the need to rethink traditional marketing analytics approaches and leverage big data solutions. It notes that while many firms want to be data-driven, few are good at taking action on data. Traditional approaches have limitations in scaling and real-time processing across new data sources like mobile and apps. A big data approach allows for a 360-degree customer view, real-time campaign adjustments, accurate customer value scoring, and understanding customer behavior patterns. It presents architectures for ingesting diverse customer data, building customer profiles, modeling to gain insights, and optimizing marketing based on those insights.

1. Transforming Marketing
Analytics with Big Data
Solutions
RAJIV KUMAR
DATA SCIENTIST

2. Why to Rethink marketing analytics?
29%
of firms are
good at turning
data in to action
73%
of firms aspire to
be data- driven
Source: Forrester’s Global Business Technographics Data and Analytics Survey,2015

3. Why to Rethink marketing analytics?
• Big data means big opportunities

4. Why to Rethink marketing analytics? – Traditional Solutions
Campaigns
CRM/Profile
Location
Social
ClickStream
Data Marts
ETL/ Stored
Procedures
Data Warehouse
Segmentation and Churn
Analysis
BI Tools
Marketing Offers
Does not Model
easily in to
RDBMS schema
New data
sources-Mobile,
Apps, network
Logs?
Limited
processing
power
Scaling is
Expensive and
cumbersome.
Manual Work.
Few automated
system feeds
Based on
Sample and
Limited data
Limited
processing
power
Loss in
Fidelity

5. Why to Rethink marketing analytics?
…continued
• Gaining a competitive advantage requires operating in real-time across various customer
touch points.
• Big data analytics enables businesses to leverage data driven, insightful, 360 degree view of
customer to:
• Develop customer profiles based on characteristics of individuals and segments.
• Execute targeted marketing campaigns with real time adjustments to maximize performance.
• Generate Accurate customer life time value scores.
• Big data analytics allows business to analyze customer behavior in real time to:
• Identify customer behavior patterns for any anomalies in behavior.
• Fostering brand loyalty through clear understanding about customers.
• Real time tracking of customer journey in marketing funnel.

6. Why to Rethink marketing analytics? …continued
• Real time and Targeted cross
selling and upselling offers
Real time offer
management
• Real time Prediction of future
behavior of customers to act upon
before its too late
Real time
Prediction of
future behavior
• Understand customer interaction
through Omni channel touch
points to generate useful insights
• Which channels primarily maintain
brand awareness?
Brand
Interaction
Benchmarking
Existing
Marketing plans
Marketing
investments
Ratio
Purchase Intent
behavior
Increased ROI
• Reveal the ideal ratio of
marketing investments to
maximize sales.
• Mapping customer behavior
with purchase to target right
individual at right time
• Which channels are the primary
drivers of current sales?
• Which channels can deliver
incremental sales?

7. Journey to Advance Marketing Analytics
•Omni channel
Customer
interaction
•Purchase history
•Social media and
web activity
•Customer behavior
Integrate
and
Understand
•Customer affinity
•Conversion path
•Customer behavior
•Brand value
•Customer lifecycle
Analyze and
Discover
•Customer
segmentation
•Marketing channel
attribution
•Real time offer
management
•Business processes
•Decision making
Act and
Optimize

8. Journey to Advance Marketing Analytics-
Big Data Landscape

9. Hadoop Ecosystem
Journey to Advance Marketing Analytics-
High Level Path
Data
Ingestion
Customer
Profile
Statistical
Modeling
Structured Data
Unstructured
Data
Semi Structured
Data
Customer segmentation
Marketing channel attribution
Offer management
Informed business decisions
Improved Business Processes
Cost optimization
ROI

10. Sample Customer 360 degree profile
Who are
you?
Where are
you?
What have
you
purchased?
What
product
you prefer?
Who do
you know?
What can
you afford?
What is
your value
to
business?
How/why
have you
contacted
us?
Continue to
enrich
profile
Continue to
enrich
profile

11. Journey to Advance Marketing Analytics- Solution
Architecture
Customer Service Data
Demographic data
Customer interaction
Campaign Data
Sales Force Trouble Ticket data
Billing data
Orders data
Contact data
Contract Data
Product Quall Data
Survey
Social Media
Web Activity
Chat /email
interactions
Voice Call
Recordings
Textual
Correspondence
R
D
B
M
S
Semi Structured Data
Unstructured Data
HDFS
Data lake
Machine
Learning
Data
Ingestion
Data
Access
Data
Validation
Application layer
360 Degree
Customer profile
Real
Time/Stream
ing Data
Real
Time/Stream
ing Data

12. Reference Deployment Architecture
Ad hoc/on
Demand
Source
Streaming
Source
Batch source
Reference
Data
Spark Stream Processing
Data Pipe Line Long term Data
Warehouse
Advance
Analytics
Operational
Reporting
Data
Discovery
Business
intelligence
Machine
Learning(Spark ML)
Data Sources Data AccessData Processing, Storage & Analytics