embrace analytics to dramatically improve retail performance

Embrace AnalyticsDramatically Improve Retail Performance

with special guest:

Welcome!

Todd HarrisDirector of Marketing

@ Celect

Vivek FariasCTO, Co-founder

@ Celect

Paul SchottmillerPartner, Retail and

Consumer Goods @ KSA

© 2016 Celect, Inc. All Rights Reserved.

An Intro to Celect

• Who we are‒ Predictive analytics (SaaS) platform focused

on the retail sector‒ Based in Boston, MA‒ Venture-backed, award winning technology

out of MIT

• Awards & Recognitions‒ MIT Computer Science and Artificial

Intelligence (CSAIL) Top 50 Greatest Innovations


Kurt Salmon’s Analytics Services help clients infuse analytics to transform business processes with actionable insights that

solve hard problems and unlock new opportunities across the consumer goods value chain.

Unparalleled insights delivered to retail and consumer companies with a heritage of thought leadership

Expertise across the value chain, from growth strategy to execution

Global consumer and retail industry practice with consultants across three continents

Over 80 years of delivering performance enhancing strategies

Specialized.

Integrated.

Proven.

Global.

An Intro to Kurt Salmon


Emerging tech + analytics

• Volume, Velocity, Variety

• Connectivity - Moore + Metcalf + Cloud

• AI

• Machine Learning

The opportunity …

• Precision

• Personalization

• Prediction

• Automation

Challenge is the same

• Right product

• Right place

• Right time

• Right price

A Transformed Competitive Landscape

Gartner predicts that by 2020, more than 75 percent of organizations will deploy advanced analytics as part of a platform or analytics application to improve business

decision-making. Companies are accelerating the shift in focus of their investments from measurement to analysis, forecasting and optimization.


Operational and Financial Performance

Forecasting

Consumer Insights

Merchandising, Planning, and Buying

Product Design & Development Sourcing Distribution & Logistics Channel Operations

Marketing/Promotion

Physical Flow

Data Driven Decision Making –5 Key Areas

Create/Buy More WinnersSample Less

Shorten Leadtimes

Sell MoreMarkdown LessIncrease Loyalty

Next Generation Performance

Reduce CostsAutomate Decisions

Increase Operating Margin

Allocation, Replenishment, Flow

Precision, Prediction, Personalization, Automation


Tough Problems, New Opportunities

› Store replenishment

› Speed of delivery vs. shipping cost

› Fulfillment leveraging all channels

› Localization

› Winners vs. losers› Optimal store

assortment

Marketing & Consumer Insights

Product Development & Merchandising

Sourcing &Supply Chain

Channels

› Customer’s lifetime value

› Customer’s profitability

› Optimize marketing / media spend

› Potential out of stocks

› Traffic by channel and location

› Personalized interactions


Effectiveness

Revenue: 7-12% increaseGross Margin : 6-8% increaseTime to Market: 30-40% decrease

Efficiency

SG&A: 5-15% decrease

How Big is the Opportunity?

Operating Margin: 25-40% increase

Precision, Prediction, Personalization Automation


Innovating Requires a Business Value Focus

• Business value driven vs. technology driven• Test and learn approach

‒ Speed wins‒ Failure IS an option‒ Tinkering is good‒ This is not ERP

• Data quality is critical• Cross organizational sponsorship is required

Shiny Objects


Predictive AnalyticsUsing Context & Choice to Understand True Demand


The Power of Context …


Reveals Answers


Context is Everywhere


Context highlights answers …

… and context is everywhere in your data.


A Choice Model tells you what a customer would prefer to buy when

given the choice.

Today, you only know what a customer bought

Understand True Demand

• We sold out of a particular product in Store 28 à What was the right allocation?

• We have never sold a particular product in Store 58 à How well would it sell there?

• We are looking to add a new brand in to a set of stores à What impact will that have on store sales?

What to place in Store 113 vs Store 127?


Example: Traditional Store Clustering


Cluster 4

Cluster 1 Cluster 2 Cluster 3

Cluster 5

What Is The Operational Value of This?

Clustering and Assortments via Choice Modeling

• Build representative customer choice models for every store• Cluster stores based on choice

‒ Limited number of localized assortments = Easier Ops‒ Easy to interpret from an operational perspective

• Optimize assortments to a cluster


Celect gives you insight into true demand.

Assortment Recommendations


Celect Optimization Platform


Thank you!

Visit www.celect.com for more!

Subscribe to our blogblog.celect.com

Twitter:@celect@KSRetail


embrace analytics to dramatically improve retail performance

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