Creating Business Value - Use Cases in CPG/Retail

Business Value Consulting for a PREDICTIVE and AGILE Enterprise
STRATEGY + ANALYTICS + TECHNOLOGY
ENABLING BIG DATA TRANSFORMATIONS FOR CONTINUOUS ADVANTAGE
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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
MobileMainframe
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
CustomersMarkets

Key Elements of Business Strategy
Profitable Customer
=
WTP – CTS
WTP – Willingness To Pay
CTS – Cost To Serve
VALUE EXCHANGE
Industry Sector
economics
Products
CustomersMarkets

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  High market saturation
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
Profitable Customer
=
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  Store Overhead

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  Store Overhead

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
Profitable Customer
=
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

IdentifyPeerGroupACME 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
balu@rightedge.com
Questions ? Comments ?
Please Email Me.

44
Q & A

Creating Business Value - Use Cases in CPG/Retail

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