This document discusses how retailers can use data and analytics to better understand customers and enhance the customer experience. It provides examples of how retailers like Pier 1 Imports, Ziosk, and JJ Food Service are using Azure Machine Learning and predictive analytics to personalize recommendations, predict customer purchases and orders, and improve inventory management. The key benefits highlighted are delivering a more personalized customer experience, gaining better customer insights, and helping customers find what they need more easily.

2. Listening to the voice of the customer
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What Really Happened
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3. Retail has a multitude of
devices that generate
petabytes of potential
insights
Monitoring and mining
social media data enables
retailers to enhance
customer insights
Open data sources and
internal sources enable
retailers to better
understand customers
Democratization of data

5. 
Democratization of tools

6. Business users access results from anywhere, on any device
Delivering advanced analytics
• HDInsight
• SQL Server VM
• SQL DB
• Blobs and tables
Devices Applications Dashboards
Data Microsoft Azure Machine Learning
Storage space
Integrated development
environment for
Machine Learning
ML
Studio
Business problem Business valueModeling Deployment
• Desktop files
• Excel spreadsheets
• Other data
files on PC
Cloud
Local
Data to model to web services in minutes
http://studio.azurem
l.net
Web
Clients
API
Model is now a web
service
Monetize this API

7. •Assortment
•Inventory
•Out of stock/overstock
•Price optimization
Demand Analytics
•Online recommendations
•Call center
•Assisted selling
Recommendation
•Customer segmentation
•Cognitive intelligenceChurn Analytics
•Targeted marketing
•Media mix modelling
•Channel mix marketing
•Search engine marketing
Marketing Analytics
•Employee theft
•Video analytics
•Web transaction analytics
Fraud Analytics

9. We are especially pleased that our analysts can focus on the results and not
worry about the complex algorithms behind thescenes
Andrew Laudato
Pier 1 Imports
Objectives
• Give customers a better
experience and selection
• Understand what
customers are looking for
based on online search
Tactics
Combine online and in-
store transactional and
behavioral data to
predict what products
customers would be
most likely to purchase
next
Results
• Customers have more personalized
choices
• Targeted campaigns
• Better inventory forecasts
Delight customers with the right offers
Use technology to determine what customer would purchase next

11. We are using Azure to make our UX smarter and truer to its purpose: enhancing the guest
experience.
Kevin Mowry
Chief Software Architect
Ziosk
Objectives
• Give guests a
personalized experience
• Understand what
customers are looking for
based on user
engagement data
Tactics
Deliver mobile
experience at every table
and use profile and
engagement data to
personalize experience
Results
• Personalized experience for users
• Better and real-time customer
insights
Personalizing the guest experience
Use technology to personalize guest preferences

12. With Azure Machine Learning, the wow factor is huge. Customers are amazed that we can
predict so accurately what they need.
Mushtaque Ahmed
COO
JJ Food Service
Objectives
• Make recommendations
to customers based on
demand patterns
• Improve ordering process
by predicting what
customers would order
Tactics
Use predictive analytics
to determine what
customers would need
based on patterns. Use
recommendations online
as well as in call centers
Results
• Quicker and easier ordering
process for customers
• Better inventory management
Predicting what customers will order
Use technology to streamline the ordering process

14. Customers
pursuing their
‘data dividend’
$1.6Tdata dividend available to
businesses that embrace data
over the next four years
Speed
More
people
New
analytics
Diverse
data
How?
Data Source: Microsoft and IDC, April 2014

15. “The era of ambient intelligence has begun, and we are
delivering a platform that allows companies of any size to
create a data culture and ensure insights reach every
individual in every organization.” Satya Nadella – SQL Server 2014 Launch, 4/15/2014

17. Store
performance
dashboard

18. Customer
insights via
Bing

19. Correlate
demographic
data with
store
performance