Impact and effectiveness of business performance analytics on the retail industry E-commerce

2. Introduction
Research Problem Identification, Framing and Methodology
Case study Company ,Data and Tools Used
Data Analytics: Case Study – 2 Scenarios
• Business Performance Analytics(BPA)
• Operational Analytics
Results and Conclusion
Further Research

3. Statistical & Quantitative Analysis
DATA IT Tools BUSINESS DECISIONS
Reporting & Predictive Modeling
Analysis (BI) Vs Analytics(BA)
Improved PMS(Performance Management System)
Business Performance Analytics(BPA) or (PMA)
KPI’s Strategic GoalsAnalyse,Report & Monitor

5. • Will there be an effective framework to
quantify the benefits of business data
analytics for effective performance
management for effective management
decisions?
“What is the Impact & effectiveness of business
data analytics on retail industry e-commerce?”
• How the key performance drivers can be identified in a
business scenario using analytical tools and technologies and
using a theoretical framework to connect it to the performance
management system?
• How “big” data analytics have transformed the traditional
business analytics from the art of mere data analysis to
predictive analytics?

6. Startup company based in Basel , Switzerland
http://www.cloudfeet.net/
Consultancy for B2B - IT/Supply Chain/Retail Sector
Current E-Commerce Value Chain
Manufacturer=>Supplier=>Distributor=>Retailer=>Customer
New Disrupted Value Chain
Manufacturer/Supplier=>Cloudfeet.net=>Retailer/Customer
Detailed Business Model is proprietary

7. Value Chain :Retailer  E –
commerce Website  Customer
Simon’s 4 levers of
control(systems) : Belief ,
Boundary, Diagnostic Control , ???

8. Simon’s 4th levers of control(systems) :Interactive Control

9. DATA COLLECTION
•Collected using Google
Analytics (embedded in the
site)
•Eg:Clickstream Data, Search
Key words
Static Dynamic
•Social,Demographic and
Geographical data
•Eg:Census Data
Strategic Goals (PMS) : Increase Revenue,
Brand awareness,
Customer engagement

10. Analysing the data Collected using Google Analytics(GA)
GA dashboard gives the infrastructure to identify cause-
effect relationships of captured data & existing PMS
DATA LINKING
DATA CONTROL
Identifying crucial levers of control or KPI’s based on step 2
Using a data mining tool to identify the “contextual
patterns”
E.g.: Hadoop cluster running with Hbase

11. These KPI’s are measured,analysed and reported to
Management
Objective : To Increase Revenue
•Conversion Rate
•Cart Abandonment Rate
Communicate
BUSINESS DECISIONS

12. Add to
Cart
Billing
Page
Payme
nt
Page
Review
Order
Page
3,940(30%)
Proceeded to
Billing Page
2,758(70%)
Proceeded to
Payment Page
2,482(90%)
Proceeded to
Review Order
Page
1,371(91%)
Proceeded to
Sales
Confirmation
Total Active Users =13,135
SAMPLE Sales Funnel Data:
Adapted from Google Analytics
Dashboard(Goal Funnel) for Cart
Abandonment Rate
70 % loss
Strategy to
reduce this???

13.  Business Scenario:Increase Basket Size Cross sell / up sell
 Product Recommender Engine (PRE)
iPAD 2 Black from $399
More
$$$ Lightning to 30-pin Adopter
$ 29.00
Bose Soundlink Mini Bluetooth Speaker
$ 199.95
Apple TV $ 199.95

14.  Similarity calc. (Correlation) for Neareast Neighbour formation
eg: Users 2 & 9 similar to User 8
 Identified Item 1 and Item 9 are not yet purchased by User 8
 Predict by taking weighted sum

15.  Performance Analytics
Eg:Identify KPI’s with GA
 Operational Analytics
Eg: Product Prediction
with PRE
BA Value Additions:
•New Effective PMA framework
•Customer “Behaviour Prediction” with Contextual Mapping
PMS Vs PMA : How?  Why? What?(will happen)
Further Research
ROI of PMA
PMA success story as Benchmark

16. •Bibilioteche Unibo
•Thesis “Impact and effectiveness of business performance
analytics on the retail industry”
•Google Scholar