8 Steps to Optimize

the Retail Store with Behavior Analytics

Ronny Max @SiliconWaves

Retail Analytics

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Supply Analytics

• Merchandising • Logistics

Path to Purchase

• Marketing • Ecommerce • Behavior Analytics

for the Bricks-and-Mortar Stores

Customer Analytics

• Purchase Patterns from Point-of-Sale

Retail Analytics Moves to the Frontline

– RSR Research January 2014

Guidelines for Analytics What do we want to know? What can we know? What we do once we know?

Behavior Analytics is a framework of metrics that measure, monitor and predict

the Activities of Customers and Employees

in the Bricks-and-Mortar Store

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Behavior Analytics

Customer Service Model

Demand Analytics

Sales Conversion

Service Intensity

Service Productivity

In-Store Analytics

Queue Management

Predictive Scheduling

8 Steps to

Optimize the

Retail Store with

Behavior Analytics

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Bricks-and-Mortar Store

Return on Investment With Behavior Analytics

Retailers can Plan better in the Long Term

with Demand and Store Layout Analytics, and

Optimize the Immediate Term (4-8 weeks) with targeting Demand and adapting the Schedule,

and Manage in Real-Time

with In-Store Analytics, Queue Management and Predictive Scheduling

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1. CUSTOMER SERVICE MODEL Measure, Monitor and Predict Customer and Staff Activities

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Privacy: Tracking Me!

My Digital Identity • Email: What’s My Name? • Contacts: Who I Know? • Calendar: Who I meet? • Camera: What I see? • Media: What do I think? • Web: What do I own? • Location: Where I am? • Device ID: Connecting ID

Me to the physical Me

Me, in the Real World • My Name • My Buying History • My Path to Purchase • My Current Location

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Most optimization models for the physical store focus on

group behaviors, and do NOT require the identify of the

customer

Measure, Monitor, Predict!

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Entering (Passing) • Visitors • Occupancy

Browsing (Standing) • Zone Area • Service Area

Exiting (Moving) • Queues • Frontline

Customer Service Models

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Convert 45% of Visitors to

Buyers (Transactions)

Greet 95% of Customers in less than 60

seconds

Checkout 90% of Customers in less than 3

Minutes

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Service Level Measurement Store B

95%

Store C 87%

Store D 88%

Store E 91%

Store A 86%

Service Level Measurement is the local store’s

Success Rate To comply with the

Customer Service Model

2. DEMAND ANALYTICS What is the Sales Opportunity?

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Benchmarks for Demand

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Economic Environment

Marketing Campaigns

Local Traffic

Visitors to Store

Sales Opportunity is a Person Entering

the Store

Monitor Demand in Context

Demand = Footfall Traffic

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How Many People Entered

My Store?

What is the Ratio of My Visitors to People

Passing-By?

How Many People Are In

The Mall?

Marketing Effectiveness

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• What’s the impact of a campaign on demand?

• What’s the value of a Visitor per Campaign?

• What’s the Customer Value for the Store?

Campaign Analytics

• What’s the impact of an Event in the Location, to drive demand?

• What’s the Ratio of Entering to Passing-By?

• What’s the impact of Proximity Alerts?

Location Analytics

• What’s the impact of in-store marketing, such as digital displays, stands, and layout, on sales?

• What’s the impact of a single change, i.e. new merchandise, on sales?

In Store Analytics

The success of a marketing campaign can be defined by changes in demand behaviors

NO ID NEEDED

Comparing Stores

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Define Objectives

Define Criteria

Categorize by Traffic

Categorize by Demographics

Categorize by Store Type

Define Period of Time

Adapt for Seasonality

Exclude Outlier Events

Judge a Store Against itself

3. SALES CONVERSION How many browsers converted to buyers?

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Sales Conversion

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How many Visitors converted to

Buyers?

Sales Conversion is an Actionable Metric when monitored in

Context with Demand and Scheduling, per

Period of Time

Sales Conversion

In-Store Activities

Actual Demand

Sales Swing

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Sales Conversion

Arrivals vs. Exiting

Define Transactions

Define Period of Time

Behavior Anomalies

Basket vs. Transaction

Retail Reset*

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Omniverse Sales

Conversion

Website Visitors

Website Buyers

Store Visitors

Store Buyers

Sales Conversion = Buyers / Visitors

The Omni-Channel Transformation

Structured vs. Unstructured

Data

* RSR Research

4. SERVICE INTENSITY How many salespeople we need to schedule?

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Service Intensity

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Service Intensity Is the Ratio

of Staff to Customers

Service Intensity is a Holistic Metric defined by Customers, Associates, and Period of Time

Average Service Intensity

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As workforce systems proliferate and

forecasting moves away from spreadsheets into sophisticated systems, this opens the door for

innovation!

Calculating Service Intensity in time segments that reflect the stay time in

the store, paints a more authentic picture of how the store operates

Optimized Service Intensity

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Standard Deviation Targeted Metrics

Optimization is the most

challenging aspect of working with Service Intensity

Service Time Sales Conversion

Marginal Value of Salesperson

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Sales

Staff

Rachel

+ Mike

+ Abby

+ Jane

Adding 1 salesperson increases revenue but each sales segment is less

than the previous slice

5. SERVICE PRODUCTIVITY What is the value of a salesperson?

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The Probability that Linda’s Sales per Hour is $350

is 87%

If We Know ….

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Past Performance of a Salesperson

Probability of Current Performance Within Service Intensity Parameters

Probability of Current Performance Outside Service Intensity Parameters

F

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Probability of Future Performance

Per Salesperson

We can calculate the…

How do you view your frontline staff, as Liabilities and Costs , or Revenue Generators?

Scheduling Combinations

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Traffic

Employee Training

Employee Preference

Skills

Service Productivity Provides a Measurable Method to Optimize Who Should Be Working,

When, to Maximize Sales

Schedule to Demand

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Service Intensity • Identify the number

of employees, per location, per period of time

Service Productivity • Identify the productive rate

of a salespeople, in context to the store’s environment

Optimization • Schedule to sales, skills

and training and the employees preferences, while complying with corporate objectives and priorities

We forecast individual performance with Service Productivity

We measure the store’s ability to adapt to actual traffic with Service Intensity

6. IN-STORE ANALYTICS Where, when, and for how long customers stayed?

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Even by 2025, bricks- and-mortar stores should still account for approximately 85 % of U.S. retail sales.

McKinsey & Company

What is In-Store Analytics?

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Real-Time Marketing

Store Operations

Long Term Planning

Wi-Fi

BLE

NFC

Video

RFID

In-Store Technologies for tracking activities

In Store Analytics is measuring, monitoring and predicting the activities of customers, specifically where they linger (location), when and for how long (time)

Tracking Technologies

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Facial Recognition Heat Maps RFID / NFC

Bluetooth Beacons Device Tracking Video Analytics

Qualifying (Your) Data

Accuracy challenges in In-Store Analytics: • Capture of Data: Accuracy of Technology • Quality of Data: Accuracy of the Sample • Quality of Context: Accuracy of transforming

the data into contextual information • Quality of Knowledge: Accuracy of a model

for Actionable Metrics July 2014 Ronny Max @SiliconWaves 33

The nature of the technology affects the nature of the data, its accuracy, and what retailers can do

Store Analytics

Due to data challenges of in-store conversion, proceed with caution!

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Measure & Monitor • Product Location

Effectiveness • Monitor Relationships

between Display, Price and Sales

• Impact of Employees on Basket & Sales

• CPG Sales Conversion

Store Ops

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Customer Not Present

Lingering Customers

Carts Tracking

Energy Analytics

Out of Stock

Product Compliance

Tailgating Employees

• Energy • Loss Prevention • Inventory Management

When Location = Engagement

• Customer Flow vs. Occupancy • Stay (Dwell) Time in Location • Proximity Promotions • Internet Access • The Super Fan

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Identifying Customers in the store: require opt-in, active features, and clear, and relevant, benefits!

Personalized Marketing!

Price vs. Value Per Customer

7. QUEUE MANAGEMENT How many customers are waiting in line, and for how long?

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Waiting Time

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We serve 90% of customers in less than 3 Minutes

Wait Time is a Key Performance Indicator for Customer Service

HATE 2WAIT

Frontline Management

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Queue Management and Predictive Scheduling Solution for Supermarkets, Big Box Stores, and Airports

How Many Stations Should be Active?

Queue Flow

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Regardless of how many counters are open, measure the exit speed, in seconds, from the queue, as a data metric for customer service

Queue Flow Solution is ideal for - • Waiting is the beginning of the customer experience, i.e. (in-store) Quick Service Restaurants and Retail Banking • Long Queues where cost is a big factor, such as airports and hotels

8. PREDICTIVE SCHEDULING Where to position employees when they matter most?

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Predictive Scheduling

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Predictive Scheduling is the Real-Time Management of Employees to Actual Demand

71% of Retailers say the amount of store workload has increased over the last year. Source: Retail Horror Stories and Why Workforce Management Matters, Reflexis, October 2013

Today’s Tasks - Administration

Fulfillment Checkout Service?

Real-Time Deployment

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Technology and Analytics and Deployment must work in harmony for Predictive Scheduling to be effective

Employees On-Site

Actual Demand

Time to Deploy

Timed Activities

Service Model

Alerts & Triggers

Optimized Customer Service

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With effective predictive scheduling, the level of service stays consistent, regardless of the number of customers

Playbook

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Define

Select

Adapt Manage

Optimize

Define Customer Service Models

Select Technology and Vendors

Adapt to Customer Service Models

Manage Stores in Real-Time

Optimize based on Feedback and Sales

Behavior Analytics defines

Actionable and Sustainable Customer Service Models

in order to Optimize Store Performance

and Maximize Sales

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… until the retail industry improves the KPIs associated with in-store technology, ambivalence and doubt will continue to reign.

- RSR Research What’s In Store for Stores, June 2014

Ronny Max is the author of Behavior Analytics in Retail (October 2013), and founder of Silicon Waves, a consultancy specializing in people counting, queue management, and in-store analytics Our mission is to nurture, train, and educate a community of Behavior Analytics Professionals

Ronny Max @SiliconWaves 47 July 2014

This presentation can be redistributed, in commercial and non-commercial form, as long as it is passed along unchanged and in whole, with credit to Ronny Max

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