Transcript of "Alok Dashora bi analytics journey information excellence"
Information Excellence 2012 July/August Session Alok Dashora, IT Strategy and Consulting Challenge in deploying BI Solutions Thank You for hosting us today
BI & ANALYTICS JOURNEY FROM GROUNDS UP IN A MEDIA BUSINESS Alok Dashora Information 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.
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
Customer Decision Mart and Analytical Data Foundation Solution enabling Data to Action Marketing Lifecycle with integrated Solution Suite Subscriber spends Adhoc, Train of Va ri ables Summary & Thought Analysis Ba nding Subscriber RollUps Demographics Vi rtual fi elds Dashboards NRC, MMR, ARPU, ETL Call Marketing Center Records Variables Exception Dashboards Data FoundationActive LTD Values ADSServices and Scores Incremental Propensity ModelsDeactivation Master Subscriberbehavior Tables Single ViewSubscriberself-care Score Carding Bandings Churn, BehaviourPackage Cus tomer Basecharacteristics Subscriber Tra ns actionsThird Party EventsData Feeds Ca l l records Campaigns
Data Flow across Modeling Environments Iteratively Enriched Marketing Decision Mart Vendor ADS ACESubscriber Deriveddealer Variablespackages Execution & Campaign Customer Modeling Decision TrackingRecharges KXEN MartAdd Ons VariablesPromos &Campaigns)CRM Data SAS ModelingBilling Variables Data Quality and Process Audit
Data Evolution • Volume Increment • Quality Expectations Mounting • Confidentiality Needs • Emergence of Custodians • Cost / Budget Pressures • Human Skills Needed
Data Sources and Dimensions Subscriber Demographics Various Package Type Components of Characteristics Subscriber Spend 3P Service CRM Providers Call Subscriber CenterUsage History Records Billing Activity / non Service Activity Request History Records Vintage of the Subscriber
Transforming Data to Predictive Variables Snap-shot Recency Normalized •What was the •What is the average product purchase days subscriber uses •What is current baseProduct Purchase behavior in first 90 a package? pack? days? •What is the behavior •How many Add-on •Were there package maximum no of add- packs? drops before on packs subscriber subscriber churned? has used •When was the last •What is the latency time subscriber of recharging? Recharging / •How many times has recharged? •What is theChannel adoption subscriber recharged •Was the subscriber subscriber affinity on web? early adapter of web towards one recharge? recharge mediums?
Transforming Data to Predictive Variables Snap-shot Recency Normalised • Is the deactivations • What is the national •How many times do a recent average of subscriber subscriberChurn behavior deactivates? phenomena? deactivations? •Is the subscribers •Ratio of Active day new to deactivation to total vintage? • Which class of city • How soon did does subscriber come subscriber register on •What is the averageDemographic / from? portal? spent on Our •Is the subscriber Affinity package different •Has there been Company? relocation before from the region churning? (South)?
Saas For BIKey Criteria• Speed to market, agility• Lack of internal expertise• Fluctuations in requirements• Disparate Set of Metadata within enterprise• Predictive Modelling
ExperienceUpsides• 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 Application Size Small Large Real Time Yes No Take your time Yes Yes
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.
Modeling process – step by step Model evolution This is where a lot Model of Business inputs Deployment com e in from Our Data Com pany team exploration Yes Approach Trend Validated? Analysis No Hypothesis Model Building creation Regeneration & Validation Dimension & of Model Data Model defi nition Business problem / Business Problem Definition Opportunity32
Modeling process – step by step Model Model usage evolution recommendations are provided and model is ready for roll-out Model Deployment Data exploration Yes Approach Trend Validated? Analysis No Hypothesis Model Building creation Regeneration & Validation Dimension & of Model Data Model defi nition Problem Definition33
Actionable Analytics Moving to Campaign management
Campaign Management Current Campaign Management “ Let me find a group of people to talk about it.” “ I have an offer …” offer Subscriber Campaign Management “I have a person with a change “ Let me find the best offer in behaviour that suggests a to fit this person ’s need. ” need…” offer offer offer offer
Campaign Approach Subscriber Campaign Management “I have a person with a change in “ Let me find the best offer behaviour that suggests a need…” to fit this person ’s need.” ’ offer offer offer offer • 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 Develop Rule Track & Refine Identification Engine MeasureApproach 1: Rule Develop the Rule Track & measure Develop a processbased engine which will the campaign to refine the define the effectiveness and target selection &Approach 2: campaign conversion on Test rule engine basedBehavioral structure for each & Control on campaignClassification of the target approach; history segment Identify theApproach 2: factors effectingClustering response uplift
Data & Methodology Variables across Demographic, Transaction, & Call and Service specific parameters taken into consideration Outlier Treatment Missing Value Treatment Our Company Cluster Multicollinearity Treatment using Factor Analysis Model Data Base Profiles Creation Variables Standardization Cluster Solution Development & Validation
Campaign Approach Marketing Build Model ~ Objective Targeting Overlay Segments Action Selection Universe Customer Strategy Offer/ Treatment Dyna mic Pri cing Gather Data Advocate Di fferentiated Service a t call centre Acquire New Usage/Payment Behavior Customers Nomads Di fferentiated offers for Calling Behavior ea ch s egment Develop Existing Up-s ell Customer Bargainers Relationship Build Model Di fferent creatives by s egment Switch oners Retain Customer Revenue Component Reduce targeting of non Relationship profi table segments Models Revenue Growth Newbies Tes t different channels for communication Attrition Model Platinums Rea ctive a nd Proactive Retention
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