Tastes, Trends, Touch Points - Understanding

Shoppers Through Machine Learning

ShiSh Shridhar (@5h15h)Director, Retail Analytics

Microsoft Corp

Su Doyle (@sudoyle)RFID Applications Director

Checkpoint Systems

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

The Opportunity

+

SENSORS ARE PROLIFERATINGEXPECTATIONS ARE

INCREASING

THE RETAILER WITH

ACTIONABLE DATA WINS!

• In the Supply Chain

• In Stores

• For Consumer Apps

• Shoppers expect more

convenience and selection

than ever

• Faster Omni-Channel

Fulfillment

• Better Assortment Planning

• Increased On-Shelf

Availability

• Browse vs. Buy Insights

• Faster Design to Delivery

Store Pickup

Sensor Evolution in Retail

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Manufacturing Retail StoresDC

Supply Chain & ShopperJourney:

PROCESSAUTOMATION:

RESPONSIVERETAIL:

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Mobile & OnlineSocial Media

Search

S U P P L Y C H A I N J O U R N E Y

S H O P P E R J O U R N E Y

!Sensors Process Information Alert

! !! !

Sensors & The Shopper Journey

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8:0

0A

M

Friend shares photo

w/ link on Social

Media

“…Jenny,

I think these

are ur shoes

4 the big

event!”

9:1

5A

M

Jenny searches a local store online to see

if they have the shoes in her size – they

do! Jenny Reserves the Shoes to Try On In

Store after 4pm that Day.

• RFID-Enabled Inventory

Mgmt.

Search in Store

10

:45

AM

Store Associate Receives

Jenny’s Request, Locates

Shoes and Sets them Aside. The

shoes are a new style, so the store

associate receives an alert on her

phone to put a pair

on display.• RFID Pick – Pack

–Reserve

• RFID Display

Compliance

5:2

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M

Jenny Checks In at the

Store with her Loyalty

App. Her favorite jeans

are on sale -- and her

size

is in stock!

• Beacons

integrated with

Loyalty Apps

• RFID-Enabled

Inventory Mgmt.

Recommendations

Jenny tries on the jeans with the new

shoes and gets recommendations

on other items (in stock

at the store) to go

with the outfit.

• RFID Magic Mirror

• RFID-Enabled

Loyalty Apps

• RFID-Enabled

Inventory Mgmt.

Jenny purchases the shoes, jeans,

a new top and a bag to go with the outfit.

RFID at the point of

sale – mobile or checkout counter makes

processing the sale much faster and

automatically decrements inventory and

connects Jenny’s purchase to

her browsing & buying behavior.

• RFID-Enabled

POS

The retailer is able to connect the dots between

what Jenny browsed and purchased across

channels and what sorts of promotional offers she

responded to.

Building personas like Jenny helps retailers predict

which products, services and merchandise locations

shoppers are most likely to respond to.

• Sensor-based Analytics

• Machine Learning

40%Off

Sensor Business Cases

SUPPLY CHAIN

EFFICIENCY

DEMAND-DRIVEN

SUPPLY NETWORKSHELF AVAILABILITY

RFID, RTLS, GPS, PLCs

Process Automation &

Exception Handling

Dynamic Assortment

Planning

Distributed Order

Management

Omni-Channel

Fulfillment

Demand-driven

Replenishment &

Assortment Planning

Productivity Tools for

Sales Associates

STORE OPERATIONS &

CUSTOMER-FACING

LOGISTICS

Omni-Channel

Fulfillment

Automated Pick / Pack

& Ship from Store

Task Management

Store-to-Store

Transfers

SHOPPER INSIGHTS

Platform for responsive retail,

continuous improvement

Browse v. Buy

Heat Maps

Platform for Predictive Analysis

!

#105

Order Fulfillment Time & Accuracy

by Store

#101

#251 #479

91% 84%

99%79%

Shipment A7849 is

Incorrect! Route to

Rework Area

How quickly and

accurately can we fulfill

store orders &

respond to demand?

How do we ensure

replenishment

products are in stock

and on the shelf?

How do we leverage the

world of sensors,

customer-facing & in

the supply chain?

How can we

automate customer

facing logistics??

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Sensors Informing an Operations Dashboard

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Imagine if…

Imagine a seamless, personalized experience for

your customers, in stores and online. Imagine

understanding your customer’s needs and

supplying the right products at the right time.

Imagine sales associates spending more time with

customers, providing personalized assistance and

incentives, and increasing sales.

Imagine anticipating demand and effectively

scheduling staff. Imagine optimizing operations,

reducing waste, and enabling your employees to

make better decisions.

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What product or

service to

recommend to Joe ?

Will Sarah come

back with us next

month?

What targeted incentive to

retain your customers?

APIML STUDIO

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Sales for Northwest

Territory retailer

Problem: Seattle is

not selling to

forecast

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2

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When we drill

down to

Seattle, we

can see a

problem in

soft drinks

Click and see

further details of

Seattle sales

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Sales driver analysis

– build a model that explains what

drives sales

Sales delta analysis

– use the model to see problems

3. How can we fix sales?

– apply the model to fix the problems

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20

25.6% variations

explained

Internal transaction

and marketing data

include variables as:

- Stock Up

- Price Elasticity

- Radio Advertising

- TV Advertising

- SKU presence

Transaction

dataset in AML

experiment

12

3

21

Variations

explained improves

to near 50%

External weather,

demographic, and

competitor data

include variables as:

- Temperature

- Precipitation

- Household size

- Annual Income

- Competitor

Price Gap

Transaction

dataset in AML

experiment

External dataset

enters the model

in AML

experiment

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1

3

4

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IoT dataset enters

the model in AML

experiment

Variations

explained improves

to 89%

New IoT, research

and online activity

data include

variables as:

- Survey research

- Web traffic

- Social media

traffic

- Mobile traffic

- Store traffic

- Shelf traffic

Transaction

dataset in AML

experiment

External dataset

enters the model

in AML

experiment

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4

1

35

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Monthly ∆ by sales

driver

Let’s first zero in on

the sales impact of

price gaps, as they

are the biggest

problem

Competitor price

gap caused 7,598

less units sold than

previous month

Click one of the

controllable

variables to see what

would happen if we

take some actions

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3

4

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See the impact

on physical sales

if we reduce the

price gap by

different levels

See the impact

on profit if we

reduce the price

gap by different

levels. When it is

reduced by 15%,

we would be able

to achieve 4.5K

incremental

profit.

Select

competitor

price gap as it

is a

controllable

variable

It would be

recommended to

decrease the

competitor price

gap

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1

3

4

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See the impact on

physical sales if we

increase social

media

engagement by

different levels

See the impact on

profit if we

increase social

media

engagement by

different levels.

When it is

increased by 20%,

we would be able

to achieve 12.6K

incremental profit.

Select Social Media

Engagement as it is

a controllable

variable

It would be

recommended to

increase social

media

engagement

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1

3

4

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See the impact on

physical sales if we

increase

advertising by

different levels

See the impact on

profit if we

increase

advertising by

different levels.

When it is

increased by 20%,

we would be able

to achieve 7.1K

incremental profit.

Select Own Brand

Advertising as it is a

controllable variable

It would be

recommended to

increase our own

brand advertising

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1

3

4

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We can use the

SKU sales lift to

see where we have

”dog” SKUs that

decrease overall

sales, and were we

have high

potential “hidden

stars” to replace

them with

“Dog” SKUs with

negative sales lifts

can be replaced as

they decrease total

sales because of

cannibalization

“Hidden Star” SKUs with

high sales lift but currently

have low distribution

Each dot represents a

different SKU. The Y axis

placement of each dot

indicates the physical

volume increased by each

SKU in the store-weeks

where it is present2

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3

4

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Click PRD013

as it has the

lowest sales lift

We could achieve the

highest incremental

physical sales if replace

PRD013 with PRD014

See more

details about

PRD013

See what the

recommended

replacements are

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1

3

4

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Click PRD016 as

it has the

second lowest

sales lift

We could achieve the

highest incremental

physical sales if replace

PRD016 with PRD018

See more

details about

PRD016

See what the

recommended

replacements are

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1

3

4

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Click PRD015 as

it is another

“dog” SKU

We could achieve the

highest incremental

physical sales if replace

PRD015 with PRD021

See more

details about

PRD015

See what the

recommended

replacements are

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1

3

4

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See the overall impact

on physical sales if we

take the

recommended actions

The sales will

continue sliding

down if no actions

are taken

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3

Within the budget

constraints, select

the recommended

actions

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How Can You Make This Real

Learn Machine Learning http://bit.ly/1OwTYO6

Experiment with Machine Learning http://bit.ly/236rODf

What are some of the questions you wish you could answer about your operations/customer ?