High Level Explanation of Marketing Analytics Ecosystems and examples

1. Marketing & CRM Optimization
Using Big Data & Predictive Analytics
Bob Samuels
TechConnectr
@techconnectr
Graphic Source: http://www.truecloud.com/Solutions and TechConnectr.com

2. Vertical Market Applications
Predictive Analytics
OSS
BI Platform / Reporting
OSS
Visualizations
Unstructured / Search
Indexing / Metadata
Search
NLP
Hadoop Analytics
Hadoop Dev Platforms / Automation
HDFS
General ‘Big Data’ & Analytics Ecosystems
APPLICATIONSTOOLSDATAMANAGEMENT
STRUCTURED UNSTRUCTURED
Transactional
DB
High Performance
Analytical DB
NewSQL
Enhancement
Distributed
NoSQL
Graph Document
Key Value /
Column
Enterprise
Apps
Internet
Apps
Social Media Web Content Mobile Devices Camera / DVR Sensors / RFID Logfiles
Hadoop
aaS
HDFS Alternatives
DBaaS
HANA
GraphDB
Filesystem
EMR
Text / Sentiment Analysis
Data as a Service
Data
Warehouses
vFabric L
Drill
Vertical Market Applications
Impala
Messaging Optimization Data Integration / CEP
OSS
IMDG
Redshift
Based on Source: Perella Weinberg Partners
AI

3. CRM & Digital Marketing Ecosystems

4. Bob Samuels
The TechConnectr – www.techconnectr.com
Cell: 408-206-5858
Strategic * Marketing Analytics * Client & Partner Door Opening * Demand Generation & Nurturing * Financial ROI Optimization
Real-Time-Bidding eMail Recommendation Engine Search Demand Side Platforms CRM Loyalty Programs
Display Web Analytics Games Customer Experience Mobile / Location SEO Video
Targeting / Personalization Community / Social Marketing Automation Yield Optimization Re-Targeting
Data Management Platform Sharing Tools Integrated Marketing Management Feedback / Surveys
Corporate Structured Data
Structured / Unstructured
Content Management
Data as a Service
Web Content / Search
Social Media
Images / Video
Mobile / Location
Sensors / RFID / Satellite
Machine / Log Files
Customer Personalization
Digital Mktg / eCommerce
Healthcare / Bioscience
Insurance / Risk Mgmt
Investment Management
Telecom / Utilities
IT & Operations
Manufacturing / Logistics
Oil & Gas Exploration
Government & Defense
Business Intelligence
Dashboards / KPIs
Data Discovery
Descriptive Analytics
Statistical Packages
Predictive Analytics
Machine Learning
Prescriptive Analytics
Decision Management
Graphs / Visualization
Hardware & Infrastructure
Natural Language Processing
ETL / ELT
Data Integration
Data Governance
Marshalling
MapReduce
Databases
Hadoop / In-Memory
Distributed File Systems
CRM & Digital Marketing Applications
DATA SOURCES DATA PROCESSING DATA ANALYTICS APPLICATIONS

5. Multi-channel two-way messaging
 Website
 Mobile site
 Mobile app
 CRM / ERP
 POS
 Call Center / IVR
 Email
 Display
 Social
DATA
LAYER
Onsite
Online
Offline
Customer
History &
Profile
Credits to Ensighten for graphics
Data & Data Management
Predictive Analytics
Multi-Channel Campaign Execution

6. Predictive Analytics
- Increasing Value of Data
PrescriptivePredictiveBiz IntelligenceData Mining

7. Predictive Analytics Examples
(both Clusters & Individuals)
• Customer Metrics, Advanced Clustering, & Predictive Analytics
Models
• Detect Changes of Behavior; Sources; Trends – quantity, quality
– Risks & Opportunities
• Group Clustering - Buying Pattern; look at DNA – look-alikes
• Clustering models for Products, Brands, Behavior
• (based on what they buy, when.. i.e. moms vs. athletes vs. healthcare)
• Info helps target & individualize future marketing efforts
– Predicting what is going to happen
– i.e. likelihood of visit to store vs. web
– Correlations and Causality
• Bought this – what next?
• Look at ‘similar’ people, neighbors

8. Analytics by Segment Cluster
Responsiveness by channel and message
A B C D E F
Marketing Segment

9. Marketing Optimization
What it takes to succeed
• Relevance – timely personalized messaging – to
individuals and clusters of individuals
– Flip Model.. Customer Centric vs Campaign Centric
• Multi-channel – input from all customer touch-
points and right-channel messaging
• Prescriptive Analytics – identify important data
and patterns & algorithms - and sift out the noise
• Interfaces – to/from customer touch-points web,
POS, CRM, ERP, ESP, external data
• Attribution – figure out what is working, and why
• Ease of Use – self-service, support, ‘less’ data

10. Thank You
Bob Samuels – TechConnectr.com