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High Level Explanation of Marketing Analytics Ecosystems and examples
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Marketing & CRM OptimizationUsing Big Data & Predictive Analytics
Bob SamuelsTechConnectr
@techconnectr Graphic Source: http://www.truecloud.com/Solutions and TechConnectr.com
BI Dashboards & KPIs
Vertical Market Applications
Predictive Analytics
OSS
BI Platform / Reporting
OSS
Visualizations
Unstructured / Search
Indexing / MetadataSearch
NLP
Hadoop Analytics
Hadoop Dev Platforms / Automation
HDFS
General ‘Big Data’ & Analytics EcosystemsAP
PLIC
ATIO
NS
TOO
LSDA
TA M
ANAG
EMEN
T
STRUCTURED UNSTRUCTURED
Transactional DB
High Performance Analytical DB
NewSQL
Enhancement
Distributed
NoSQL
Graph DocumentKey Value /
Column
Enterprise Apps
Internet Apps
Social Media Web Content Mobile Devices Camera / DVR Sensors / RFID Logfiles
Hadoop aaS
HDFS Alternatives
DBaa
S
HANAGraphDB
Filesystem
EMR
Text / Sentiment Analysis
Data as a Service
Data Warehouses
vFabric L
Drill
Vertical Market Applications
Impala
Messaging Optimization Data Integration / CEPOSS
IMDG
Redshift
Based on Source: Perella Weinberg Partners
AI
CRM & Digital Marketing Ecosystems
Bob SamuelsThe TechConnectr – www.techconnectr.com
Cell: 408-206-5858Strategic * Marketing Analytics * Client & Partner Door Opening * Demand Generation & Nurturing * Financial ROI Optimization
Digital Marketing & eCommerce Analysis and Optimization Applications: Real-Time-Bidding eMail Recommendation Engine Search Demand Side Platforms CRM Loyalty Programs Display Web Analytics Games Customer Experience Mobile / Location SEO VideoTargeting / Personalization Community / Social Marketing Automation Yield Optimization Re-TargetingData Management Platform Sharing Tools Integrated Marketing Management Feedback / Surveys
DATACorporate 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
Domain ApplicationsCustomer Personalization
Digital Mktg / eCommerce
Healthcare / Bioscience
Insurance / Risk Mgmt
Investment Management
Telecom / Utilities
IT & Operations
Manufacturing / Logistics
Oil & Gas Exploration
Government & Defense
BI, Analytics & VisualizationBusiness Intelligence
Dashboards / KPIs
Data Discovery
Descriptive Analytics
Statistical Packages
Predictive Analytics
Machine Learning
Prescriptive Analytics
Decision Management
Graphs / Visualization
Platforms / ToolsHardware & 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
Multi-channel two-way messaging
Website Mobile site Mobile app
CRM / ERP POS Call Center / IVR
Email Display Social
DATA LAYER
Onsite
Online
Offline
CustomerHistory &
Profile
Credits to Ensighten for graphics
Data & Data ManagementPredictive AnalyticsMulti-Channel Campaign Execution
Predictive Analytics - Increasing Value of Data
PrescriptivePredictiveBiz IntelligenceData Mining
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
Analytics by Segment ClusterResponsiveness by channel and message
A B C D E F
Marketing Segment
Marketing OptimizationWhat 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
Thank YouBob Samuels – TechConnectr.com