Challenge in
deploying BI Solutions
Alok Dashora, IT Strategy and Consulting
Thank You
for hosting us today
Information Excellence2012 July/August Session
BI & ANALYTICS JOURNEYFROM GROUNDS UP
IN A MEDIA BUSINESS
Alok DashoraInformation Excellence , 4th July,2012
Disclaimer
• This presentation is based on my personal observations being a part of business and will have no relevance to business actuals.
• The contents are intended for technological knowledge exchange and not for business strategy development.
• Presentation does not contain any copyrighted information.
• Any similarities will be merely consequential.
•• Initial StageInitial Stage•• Initial StageInitial Stage1
•• Expansion StageExpansion Stage•• Expansion StageExpansion Stage2
•• Efficiency StageEfficiency Stage•• Efficiency StageEfficiency Stage3
Business Phases
•• DataData•• DataData1•• ReportsReports•• ReportsReports2•• InformationInformation3•• KnowledgeKnowledge4 4
•• InsightInsight•• InsightInsight55
•• ActionAction•• ActionAction66
Data to Insights Cycle
Initial Stage
Business Dynamics
• Establishment • Brand• Technology• Timelines /Events• Finances• Refinements
Data Life Cycle
• Data Quality• System Scalability• Technology Superiority• Information Security• Reliability • Timelines of Business Events
Expansion Stage
Busin
ess V
olum
e Pr
ojec
tion
Initial Stage
Efficiency Stage
Business Dynamics
Growth Stage
Business Scenario
• Business Volumes Increasing• Scalability Testing• Business Models Evolution• Performance Enhancement
Technology Journey• Report Requirements Emerge• Sales Performance Reports• Installation Performance Reports• Call Center Performance Reports• Isolated Time Delayed Data • Business Models Evolution• Multi System syncronization
Dawn of quench for information• Need of integrated picture
across the systems, business functions
• Life Cycle Views of Customer, Inventory, Fund Flows
• Trend Analysis from Multiple touch points
• Integration of heterogeneous reports
• Lead time reduction
Incr
emen
tal
ETL
Customer Decision Mart and Analytical Data Foundation
Solution enabling Data to Action Marketing Lifecycle with integrated Solution Suite
Propensity Models
Deactivationbehavior
Subscriber spends
Package characteristics
Third PartyData Feeds
Subscriber Demographics
CallCenterRecords
Active Services
Subscriber Single View
Subscriber self-care
Master Tables
Summary & RollUps
Marketing Variables
LTD Values and Scores
Bandings
Subscriber Events
Adhoc, Train of Thought Analysis
DashboardsNRC, MMR, ARPU,
Score CardingChurn, Behaviour
Customer Base
Transactions
Cal l records
VariablesBanding
Virtual fields
Exception Dashboards
ADS
Data
Foun
datio
nDa
ta Fo
unda
tion
Campaigns
Data Quality and Process Audit
Iteratively Enriched Marketing Decision Mart
Subscriber
dealer
packages
Recharges
Add Ons
Promos & Campaigns)
CRM Data
Billing
Cust
omer
Dec
isio
n M
art
Vend
or
ACE
ADS
Data Flow across Modeling Environments
KXEN
SAS
Derived Variables
Modeling Variables
Modeling Variables
Cam
paig
n Ex
ecut
ion
&
Trac
king
Data Evolution• Volume Increment• Quality Expectations Mounting• Confidentiality Needs• Emergence of Custodians• Cost / Budget Pressures• Human Skills Needed
Data Foundation
Journey
Subscriber Demographics
Various Components of
Subscriber Spend
Call Center
Records
Service Request Records
Vintage of the
Subscriber
Activity / non Activity History
Subscriber Usage History
Package Type Characteristics
CRM
Billing
3P Service Providers
Data Sources and Dimensions
Snap-shot Recency Normalized
Recharging / Channel adoption
•How many times has subscriber recharged on web?
•When was the last time subscriber recharged?•Was the subscriber early adapter of web recharge?
•What is the latency of recharging?•What is the subscriber affinity towards one recharge mediums?
Product Purchase behavior
•What is current base pack?•How many Add-on packs?
•What was the product purchase behavior in first 90 days?•Were there package drops before subscriber churned?
•What is the average days subscriber uses a package?•What is the maximum no of add-on packs subscriber has used
Transforming Data to Predictive Variables
Snap-shot Recency Normalised
Demographic / Affinity
• Which class of city does subscriber come from?•Is the subscriber package different from the region (South)?
• How soon did subscriber register on portal?•Has there been relocation before churning?
•What is the average spent on Our Company?
Churn behavior•How many times do subscriber deactivates?
• Is the deactivations a recent phenomena?•Is the subscribers new to deactivation
• What is the national average of subscriber deactivations?•Ratio of Active day to total vintage?
Transforming Data to Predictive Variables
Efficiency Stage
Business View
• Oligopoly• Market Aggression • Innovate of Perish• Cost Pressures• Multiple pursuits to
same human and technology resources
Technology Eco System
• Emergence of Cloud• SaaS, PaaS, Iaas evoltion• Market Heading towards
specialist on demand • Replication Technologies• Columnar DB
alternatives emergence
Knowledge Need
• Customer Profiling• Campaign Efficiency • Churn Prediction• Inventory Models• Capacity Optimization
Saas For BI
Key Criteria• Speed to market, agility• Lack of internal expertise• Fluctuations in requirements• Disparate Set of Metadata within enterprise• Predictive Modelling
Experience
Upsides• Infrastructure and technology issues
streamlined within weeks – connectivity, instance, extraction
• Started with customer analytics and headed to predictive modelling
• Integration from and to multiple sources, Call centers, CRM System etc
• End Result – ARPU above industry avg.
Challenges
• Information Security and Data Access• Integration with heterogeneous systems• Scalability to enterprise levels• Risk Mitigations• Arbitration between multiple solution providers• Fault tolerance and Reliability• Technology Evolution
Direction
Yes
Yes Yes
No
Small Large
Application Size
Real Time
Take your time
Summary
• Define your challenges– Technological as well as business
• Take Ecosystem and Technology Paradigm in Mind
• Mastery is not achieved overnight• Journey and a pursuit for excellence is more
important than goal attainment.
QUESTIONS?
Moving to Predictive Analytics
32
Problem Definition
Validated?
Hypothesis creation
Dimension & Data Model
Data exploration
Modeling process – step by step
No
Approach
Model Deployment
Model evolution
Regeneration of Model definition
Yes Trend Analysis
Model Building & Validation
Business problem / Business Opportunity
This is where a lot of Business inputs come in from Our Company team
33
Problem Definition
Validated?
Data exploration
Modeling process – step by step
No
Approach
Model Deployment
Model evolution
Regeneration of Model definition
Yes Trend Analysis
Model Building & Validation
Model usage recommendations are provided and model is ready for roll-out
Hypothesis creation
Dimension & Data Model
Moving to Campaign management
Actionable Analytics
“ Let me find a group of people to talk about it.”
“ I have an offer …”
offer
Current Campaign Management
“ Let me find the best offer to fit this person ’s need. ”
offeroffer
offeroffer
“ Let me find the best offer to fit this person ’s need. ”
offeroffer
offeroffer
offeroffer
offeroffer
“I have a person with a change in behaviour that suggests a
need…”
Subscriber Campaign Management
Campaign Management
Let me find the best offer to fit this person ’s need.
offeroffer
offeroffer
“ offer ’ ”
offeroffer
offeroffer
offeroffer
offeroffer
“I have a person with a change in behaviour that suggests a need…”
Subscriber Campaign Management
Campaign Approach
• How do I find the “Right Offer” for the “Right Subscriber”?• How do I differentiate the subscribers based on their current status
with Our Company?• What is the order of campaign events for each of the opportunity
with subscriber?
Campaign Framework
Target Identification
Develop Rule Engine
Track & Measure Refine
Approach 1: Rule based
Approach 2:Behavioral Classification
Approach 2: Clustering
Develop the Rule engine which will define the campaign structure for each of the target segment
Track & measure the campaign effectiveness and conversion on Test & Control approach; Identify the factors effecting response uplift
Develop a process to refine the target selection & rule engine based on campaign history
Our Company
Model Data Base Creation
Cluster Profiles
Outlier Treatment
Missing Value Treatment
Multicollinearity Treatment using Factor Analysis
Variables Standardization
Cluster Solution Development & Validation
Variables across Demographic, Transaction, & Call and Service specific parameters taken into consideration
Data & Methodology
Advocate
Nomads
Overlay Segments ActionBuild Model ~
Targeting
Selection Universe
Marketing Objective
Acquire New Customers
Develop Existing Customer Relationship
Retain Customer Relationship
Revenue Growth
Customer Strategy
Gather Data
Usage/Payment Behavior
Calling Behavior
Newbies
Platinums
Offer/ Treatment
Attrition Model
RevenueRevenueComponent Component
ModelsModels
Build Model
Dynamic Pricing
Differentiated Service at call centre
Differentiated offers for each segment
Up-sell
Different creatives by segment
Reduce targeting of non profi table segments
Test different channels for communication
Reactive and Proactive Retention
Bargainers
Switch oners
Campaign Approach
Community Focused
Volunteer Driven
Knowledge Share
Accelerated Learning
Collective Excellence
Distilled Knowledge
Shared, Non Conflicting Goals
Validation / Brainstorm platform
Mentor, Guide, Coach
Satisfied, Empowered Professional
Richer Industry and Academia
About Information Excellence Group
Progress Information Excellence
Towards an Enriched Profession, Business and Society
About Information Excellence Group
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