customer insights for retail
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Listening to the voice of the customer
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Sales Forecast in 1999
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What Really Happened
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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
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
•Assortment
•Inventory
•Out of stock/overstock
•Price optimization
Demand Analytics
•Online recommendations
•Call center
•Assisted sellingRecommendation
•Customer segmentation
•Cognitive intelligenceChurn Analytics
•Targeted marketing
•Media mix modelling
•Channel mix marketing
•Search engine marketing
Marketing Analytics
•Employee theft
•Video analytics
•Web transaction analyticsFraud Analytics
We are especially pleased that our analysts can focus on the results and not
worry about the complex algorithms behind the scenes
Andrew Laudato
Pier 1 Imports
Objectives• Give customers a better
experience and selection
• Understand what
customers are looking for
based on online search
TacticsCombine 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 offersUse technology to determine what customer would purchase next
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
TacticsDeliver 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 experienceUse technology to personalize guest preferences
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
TacticsUse 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 orderUse technology to streamline the ordering process
Customers pursuing their ‘data dividend’
$1.6T data 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
“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