This document discusses using business data analytics to improve performance management in a retail e-commerce company. It describes collecting data through Google Analytics and identifying key performance indicators like conversion rate and cart abandonment rate. It then shows how operational analytics like a product recommender engine can help increase basket size by predicting complementary products. The conclusion is that business analytics can enhance performance management by enabling behavioral prediction and establishing an effective framework to quantify benefits and connect analytics to strategic goals. Further research opportunities are identified around measuring return on investment of performance analytics and identifying benchmark case studies.

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