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

Democratization of tools

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

Store performance dashboard

Customer insights via Bing

Correlate demographic data with store performance