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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
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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)
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Industry Big Data Challenges
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Which industries are creating Data?
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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
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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
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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
…
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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
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Use Cases In
CPG, FMCG, Retail, E-Commerce
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Key Elements of Business Strategy
Complex & Dynamic
Interplay of
CUSTOMERS
MARKETS
PRODUCTS
In Exchanging VALUE
Industry Sector
economics
Products
CustomersMarkets
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Key Elements of Business Strategy
Profitable Customer
=
WTP – CTS
WTP – Willingness To Pay
CTS – Cost To Serve
VALUE EXCHANGE
Industry Sector
economics
Products
CustomersMarkets
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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
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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
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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
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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
WTP – Willingness To Pay
CTS – Cost To Serve
VALUE EXCHANGE
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Retail/E-Commerce
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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
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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
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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)
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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
WTP – Willingness To Pay
CTS – Cost To Serve
VALUE EXCHANGE
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Retail Markdown Optimization
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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.
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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)
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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.
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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
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Business Value Delivered
Expected to generate $90 Million annually in margin improvements
through more precise clearance markdowns
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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
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E-Commerce Pricing Optimization
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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
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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
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Takeaways
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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
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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
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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
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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
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Closing Thoughts
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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
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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
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Applying advanced analytics in consumer companies
http://bit.ly/1b8dlKS
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Thank You!
Balu Rajagopal
balu@rightedge.com
Questions ? Comments ?
Please Email Me.
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Q & A