Creating Business Value - Use Cases in CPG/Retail

Business Value Consul t ing for a PREDICTIVE and AGILE Enterpr ise

STRATEGY + ANALYTICS + TECHNOLOGY

ENABLING BIG DATA TRANSFORMATIONS FOR CONTINUOUS ADVANTAGE ™

rightedge ™

rightedge.com

‹#› © COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION

Rightedge™ Confidential & Intellectual Property

Material cannot be reproduced or distributed in any

form without express written permission.


C r e a t i n g B u s i n e s s Va l u e - U S E C A S E S I N

C P G / F M C G / R e t a i l / E - C o m m e r c e


AGENDA ①  Industry Big Data Challenges (30 mins)

②  4 Use Cases (40 mins)

•  CPG

•  FMCG

•  Retail

•  E-Commerce

③  Use Case Takeaways (15 mins)

④  Closing Thoughts (10 mins)

⑤  Q & A (25 mins)


5 © Copyright 2013 Pivotal. All rights reserved.

‹#› © COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED.

5

Industry Big Data Challenges


Which industries are creating Data?


Big Data in Consumer Context

Chart based on IDC and UC Berkeley Data Growth Estimates, Adapted from Source: IDC & CosmoBC.com: http://techblog.cosmobc.com/2011/08/26/data-storage-infographic/

Petabyte

PC Internet Time Mobile Mainframe

Terabyte

Data Volume

Exabyte

Zettabyte

Machine

2011

Transactions

M 2 M

Interactions

Consumers

Patterns/Trends Behaviors Activities Internet of Things

Mobile Apps

U G C Social Networks

Sales of Goods & Services

People

Machines

Markets


Which Verticals Are Impacted?

May 2011

B2C Sectors 1.  Generate/Have/Need lots

of “customer” & “machine” data

2.  Need to use it to compete, grow & profit while reducing cost to serve

3.  Demand/consumption estimates are crucial due to high volume low margin plays and resource optimization

4.  Margins under pressure due to Consumerization -Consumer has more Info to make a choice than what companies know

Utilities is another sector to consider


Velocity

Variety

Volume

Ability to Make Sense of Data in Real-Time To Take IMMEDIATE Action Big Data Analytics For B2C Companies

Billions of Signals/Events

Terabytes to Petabytes to Exabytes

Structured, Semi-Structured, Unstructured

Business Value

Actionable Insights Leading To Superior

Outcomes

Variance Sparse, Missing, Partials, Inaccurate

Market Share Revenue Margins

Growth Rate …


Enabling Context Driven Decision-Making What B2C Companies Need NOW?

1

2

3

Predictive analytics

Real-time analytics

Investigative analytics

Predict What is going to happen so I can plan ahead or pre-empt

Know What is Happening Now so I can respond or adjust ASAP

Analyze What & Why it Happened so I can learn, refine, experiment




11

Use Cases In CPG, FMCG, Retail, E-Commerce


Key Elements of Business Strategy

Complex & Dynamic

Interplay of

CUSTOMERS

MARKETS

PRODUCTS

In Exchanging VALUE

Industry Sector

economics Products

Customers Markets


Key Elements of Business Strategy

Profitable Customer =

WTP – CTS

WTP – Willingness To Pay CTS – Cost To Serve

VALUE EXCHANGE

Industry Sector

economics Products

Customers Markets


Consumer Packaged Goods, Fast Moving Consumer Goods

Type of Good consumed every day by average

consumer ü  Replenished frequently ü  Perishable ü  Price Sensitive ü  Highly competitive ü  High market saturation ü  Low switching costs


CPG/FMCG Key Success Factors

Type of Good consumed every day by average

consumer ü  Replenished frequently ü  Perishable ü  Price Sensitive ü  Highly competitive ü  High market saturation ü  Low switching costs

①  Product Innovation (R & D)

②  Brand Marketing

③  Flexible Manufacturing

④  Strong Distribution Network

⑤  Pricing Prowess

⑥  Advertising & Promotions


CPG/FMCG Challenges

Big Data Drivers - Social, Mobile, Internet Usage

①  High Volume, Low Margin Business – Dog Fight for Market Share & Profitability

②  Brand Stickiness in Consumer Path to Purchase

③  Keeping track of Individual (& Group) Consumer Lifestyle & Behavior Shifts

④  Reaching the Consumer at the right time (purchase cycle) with the right message

⑤  Responding to Consumer & Market Signals As Soon As Possible


CPG/FMCG Key Use Cases

①  Consumer Path to Purchase

②  Consumer Awareness of Brand & Loyalty

③  Consumer Income Levels & Shifts

④  Consumer Spending Patterns/Trends

⑤  Consumer Choices & Availability at POS


WTP – CTS


VALUE EXCHANGE


Retail/E-Commerce


Retail/E-Commerce

Goods Sold in Stores, Online Only or Both

ü  Product Selection ü  Price Ranges ü  Visitor Experience ü  Location & Access ü  Highly competitive ü  Store Overhead ü  Low switching costs


Retail/E-Commerce Key Success Factors

①  Product Assortment

②  Store Location & Experience

③  Online & In-Store Marketing

④  Strong Merchandising

⑤  Pricing Prowess

⑥  Advertising & Promotions

Goods Sold in Stores, Online Only or Both

ü  Product Selection ü  Price Ranges ü  Visitor Experience ü  Location & Access ü  Highly competitive ü  Store Overhead ü  Low switching costs


Retail/E-Commerce Challenges

Big Data Drivers - Social, Mobile, Internet Usage

①  High Volume, Low Margin Business – Dog Fight for Customer Share of Wallet

②  Comparison Shopping (Price, Assortment)

③  Store “Show rooming effect”

④  Inventory Level Management – Avoid Understock or Overstock

⑤  Consumer Shopping Experience (Online, In-Store, Both)


Retail/E-Commerce Key Use Cases

①  Consumer Path to Purchase

②  Shopping Cart Abandonment

③  SKU Level Demand Forecasting

④  Pricing Optimization

⑤  Consumer Product Switching at POS

⑥  Consumer Shopping Experience


WTP – CTS


VALUE EXCHANGE




24

Retail Markdown Optimization


Markdown Optimization Project

•  Department Store, $17 Billion Revenue

•  1000+ Stores, 100,000 SKUs at each store

•  Last 10 years experienced a gradual decline in gross-margin

•  especially on permanently marked down merchandise.


Business Goal

Client wanted to get more precise with their markdown strategy

1.  Develop optimal depth and timing of markdowns based on the store-level

inventory and anticipated future demand. (Forecasting Model)

2.  Reduce frequency of markdowns as this has significant impact on store

labor. (Optimization Model)


Modeling Challenges

Forecasting Model Challenges •  Short-life cycles for fashion products •  High volume of data at the store level •  High levels of promotional activities Optimization Model Challenges •  Store level inventory •  Price elasticity •  Baseline forecast, while accounting for numerous business constraints.


Data Pooling

Typically data is too noisy and sales are insufficient at the lowest level (PC9/Store/Week)

Pooling data to higher level provides better estimates of model parameters (seasonality, trend, marketing effects)

Sales Price


Business Value Delivered

Expected to generate $90 Million annually in margin improvements

through more precise clearance markdowns


FOODS BACKGROUND •  One of the fastest-growing & innovative retailers based out of Britain (with over 800 stores in UK & £2.5

Billion in Sales) serving Frozen (Foods/Grocery/Snacks/Produce) & Chilled Items •  I.F. own legacy forecasting system is considered to be basic and ill-suited to promotions (% OFF, BOGO, BAGB, Display, Coupons, Flyers, TV Commercials/Media etc.) •  A more accurate forecast would enable inventory reduction while maintaining adequate service levels •  Some additional sales uplift might also be achieved in specific categories.

Next steps •  Assess the economic value of better forecasting using our technology for Iceland Foods at SKU/STORE/WEEK level




31

E-Commerce Pricing Optimization


ACME Computers: A Pricing Tool for Used Parts

Cost / Margin

Demand / Supply

Technology Life Cycle

Competition

Inventory / Elasticity

§  Who are selling a part? §  At what price? §  New or refurbished? §  In stock or backorder? §  What else do they sell?

How do we get the above data? §  Website scrubbing §  APIs

o  Google Shopping API o  Semantics3 API o  Indix Price API o  Invisible Hand API

Wants a model that will automatically adjust prices for Newport’s used computer parts


Iden

tify

Peer

Gro

up

ACME Computers: A Pricing Tool for Used Parts

HDD and more … Adapter Battery CPU Cable Memory System Board

and more …

Experiment to vary price over time

Determine elasticity

Adjust prices based on inventory and

elasticity

Base Price Weighted Price

Distribution

Categorize Parts




34

Takeaways


Big Data

Technologist Lens

Business Value

Analyst Lens

Decision Maker Lens

Big Data Views

KPIs

•  Real-time •  Interactive •  Batch

•  Predictive •  Descriptive •  Prescriptive

•  Revenue •  Margin •  Market Share

KVBI™

Models

Queries F.A.I.T.H

F.A.I.T.H


Tied Together by F.A.I.T.H™ Methodology

F

A

I

T

H

Framing the business problem, formulating biz case, strategizing on scenarios

Analysis & Modeling of the business problem with KVBI™, Relevant Data

Insights Extraction, Interpretation and Validation

Timely Action & Visual Reporting (using Technology)

Harvesting Yield & KPI Monitoring for Closed Loop Feedback


Get F.A.I.T.H™ Certified

Strategy + Analytics + Technology = Business Value

F A I T H

CONSISTENT. ITERATIVE. REPEATABLE. CLOSED-LOOP.

Create, Grow, Build Data-Driven Decision-Making Mindset


Big Data

Technologist Lens

Business Value

Analyst Lens

Decision Maker Lens

Bootcamps Value

KPIs

•  Real-time •  Interactive •  Batch

•  Predictive •  Descriptive •  Prescriptive

•  Revenue •  Margin •  Market Share

KVBI™

Models

Queries F.A.I.T.H

Bootcamp #1: Intro to Data-Driven Decision-Making

Bootcamp #2: Intro to Business Analytics

Bootcamp #3: Intro to Big Data Technologies

Develop Business Sense

Develop Technology Sense

Develop Analytical Sense




39

Closing Thoughts


Key Decision Areas (B2C Companies)

①  Consumer Behavior – Path-To-Purchase, Loyalty (repeat purchase)

②  Dynamic Segmentation (Micro – Operational, Macro - Strategic)

③  Prediction and Recommendation (Relevant, Timely Offers/Advt.)

④  Constant Experimentation with Various/Variant Offerings

⑤  Cross-sell, Up-Sell Opportunity Realization


Keys To Superior Outcomes (B2C Companies)

①  Fragmented & Highly Variant Path-To-Purchase Data

②  Data Sharing is Crucial - Manufacturers, Distributors, Retailers

③  Real-time (Micro) Segmentation & Targeting is becoming necessary

④  Buying & User Experience - key drivers for Brands & Retail mindshare

⑤  Demand Forecasting & Modeling becoming more important


Applying advanced analytics in consumer companies

http://bit.ly/1b8dlKS


Thank You!

Balu Rajagopal [email protected]

Questions ? Comments ?

Please Email Me.




44

Q & A

Creating Business Value - Use Cases in CPG/Retail

Business

Transcript of Creating Business Value - Use Cases in CPG/Retail