This document discusses a framework for datafication of e-commerce prospecting. It considers using big data from sources like social media, website logs, and customer feedback to build a proprietary analytics architecture. The goals are to conduct big data discovery based on the business model and create methodologies to harness existing processes. Architectural considerations include data linking, text mining, and generating insights from structured and unstructured data. The framework would enable e-commerce decisions by providing an integrated customer lifetime value view and addressing data quality.

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Datafication of E-commerce Prospecting Framework
Discussion Document
Prepared by Aditya Madiraju

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Indicative Scope Questions to be considered..
Conduct Big Data Discovery (User-Case) based on the specificity of the business model
Create Information Harnessing methodologies based on the existing processes
Assist to build its Proprietary Big Data Analytics Architecture
Goal: Do justice to the phrase Big Data’s true name is
Datafication of everything
NB: Scope creep can occur due to the influences of 1 & 2

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Indicative Architectural Consideration
Social Media
Website Logs
Search Logs
Journal entries
Online Enquiries
Product feedbacks
Analytics Architecture

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Datafication of E-commerce Prospecting Framework
Solution Considerations
Prepared by Aditya Madiraju

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Solution Considerations – Big Data Capabilities on a Hype Cycle

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Solution Considerations – Data Stream
C-Sat Data
Agent Logs
CRM Data
Call Transcripts
Payment Data
Data Linking
& Cleaning
Text Mining
Framework
Derived
Attributes
Framework
Common Text
Representation
Indexed XML/
CSV files
Data
warehouse
Data Sources
Data Processing & Conversion
Stage
Data Storage Stage Analysis & Reporting Stage
Assisted Insight
generation
Decision Matrix
Reporting &
Automation
Social Signals
Digital Pathways
Enabling highest data quality and governance

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Solution Considerations – Structuring the Unstructured !!
Cruising Altitude (Fitness Value):
1. Sum of mutual information between cue & environment
2. Linear function of environment probabilities
Transition Altitude (Half-Life Value):
1. Qualitative Data Value =( Data Usefulness ) * ( Loss to
Competitive Advantage ) * ( Timeliness )
2. No. of days it takes for Qualitative Data Value to Half itself
Landing Altitude (Quality Value):
1. Completeness
2. Consistency & Integrity
Ground Level (Decision Value):
1. Quantitative Decision Matrix
2. Behavioral Decision Matrix

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Solution Considerations – E-commerce Decisions enablement

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Datafication of E-commerce Prospecting Framework
Solution Capacity

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Solution Capacity – Integrated LTV view

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 How long does value in information persist?
 How can we account for its value if its own utility, usefullness, quality, or
fitness falls?
 Does the value curve fall at the same rate as the fitness curve?
 How does information fitness change, and how is it changed, once
information assets are combined (think bills of material)
Solution Capacity – Information Excellence

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Source: Gartner (December 2014)
Solution Capacity - Big Data Discovery

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Solution Capacity - Addressing few myths out there