0.5 trillion market untapped, retail,

1. Assortment Planning in
BRICS+MINT
PRESENTER:
SANDEEP SHARMA

2. Assortment Planning in
BRICS+MINTECONOMIC SIZE OF BRICS+MINT MARKET = 21 + 5 = 26 TRILLION
DOLLAR.
AVERAGE GROWTH RATE : 7%.
MAKING IT LARGEST AND FASTEST GROWING MARKET FOR PRODUCTS.
SURPLUS MONEY MAKE THEM TOURISTS, GLOBETROTTER,
INTERNATIONAL UNIVERSITY STUDENTS, WORKING PROFESSIONAL AT
GLOBAL WORKPLACES, IMMIGRATION PROPERTY OWNERS AT GLOBAL
REAL ESTATE PROPERTIES.
WHAT DOES IT MEAN?
WE ARE TALKING ABOUT IMMIGRATION FROM BRICS+MINT.
DEMOGRAPHY IS CHANGING EVERYWHERE. OUT OF THE CHANGE
WHAT IS CHANGING FASTEST AT ALL PLACES OF BUYING AND SELLING
ITS BRICS+MINT (PRESENCE IN EVERY COUNTRY/CITY/STATE).

3. What is Driving Change?CALCULATIONS US MARKET:
WORLD STUDENT FROM BRICS+MINT: 30% (1.1/4 MILLION)
PROFESSIONAL FROM BRICS+MINT: 0.5 MILLION OVER 10 YRS = 20 MILLION.
6%-10% IMMIGRANT + 6%-10% SAME TOURISTS +STUDENT 1-2% =
13-22% CUSTOMERS FROM BM.
BUSINESS OWNER FROM BRICS+MINT: PARTNER IN SUPPLY CHAIN
TOURIST BRICS+MINT: 40%
IMMIGRATION : BRICS+MINT: 65%
AMAZING FACTS US TOURIST SPENDING:
47% COMES FROM BRICS+MINT = 66/140 BILLION DOLLARS
55% REMITTANCE GOES TO BM. 246/440 BILLION.
33% OF WORLD GDP 26/77 TRILLION.
HOW MUCH CAPTURED BY RETAIL MARKET.

4. 50% ethnicity represented by BM

5. Opportunity Calculation: US
BM Market Size 0.5 Trillion
CALCULATIONS:
12-20% ~ 15% OF 3000 BILLION= 450 BILLION DOLLARS.
BM REPRESENT 450 BILLION DOLLAR IN US ALONE
TOURISTS +STUDENT +IMMIGRANT +BUSINESS = FROM BM COUNTRIES.
ALMOST 0.5 TRILLION DOLLAR ASSORTMENT CAN FROM BM.
MORE GROSS MARGIN RETURN ON INVENTORY INVESTMENT
GMROI
FOOD: INDIAN SPICES, CHINESE NOODLES, MEXICAN FOOD,CLOTHES
CAN THESE GET GLOBAL AUDIENCE?
TRADE-OFF BETWEEN VARIETY
HOW TO PLAN CITY WISE BM MARKET INVENTORY?
BIG DATA(SOCIAL DATA FEEDS WITH GEOTAGGING CAN HELP PLAN
VARIETY)

6. Targeting BM Sales: What needs
change
 Product Mix (earn loyalty), product range (alternatives).
 Loyality Application needs to gather BM data from social Media.
 Ways of gathering data (if BM data not captured sales missed)
 Data representing BM markings (new data structure,fields in db).
 BI, Datawarhouse, analytics Has to reflect that trends over Mapping
API. Scroll over market size, percentage size, growth yoy flag over
map
 Inventory data has to match social data optimized assortment. With
data science measure checks like lift (to validate rules).
 Web Recommedation systems: Content filtering + collaborative
filtering needs to optimized to BM data.

8. Understand Change Implication
Factors of
Change
What implications on Systems/processes? Example
What is
Changing?
Inventory Identification/Type/sets from BM
How ? Process need to fulfill above (Process
Identification/Flow/Type)
Where ? Distribution/supply chain Identified (Type/network etc.)
Who ? Responsibility Assignment: Organization structure for BM
sales/merchandising/sourcing
When ? Operational Intervals of Supply chain/ Timing BM
market changes in locale
Why ? Already know 0.5 Trillion Opportunity

9. Merchandising: Supply chain
Changes
 Retail in BRICS + MINT is not so advanced so supply chain
and logistics (shipment, order information, inventory
tracking).
 Manufacturing to places of scale and cost arbitrage like
BM countries.
Implies for Cost effective sourcing from BM countries due
to economies of scale and cheap labor cost.
 Economics dictate BM 0.5 trillion market assortment
needs of food/cloth/etc met only by sourcing from BM
countries.

10. Understand BM Through data
Science
Antecedent
(Input we control
Consequent
(prediction)
A 0
A 0
A 1
A 0
B 1
B 0
B 1
•Rule 1: A implies 0
•Rule 2: B implies 1
support for Rule 1 is 3/7
support for Rule 2 is 2/7
confidence for Rule 1 is 3/4 because
confidence for Rule 2 is 2/3 because
•The lift for Rule 1 is (3/4)/(4/7) ≈
•The lift for Rule 2 is (2/3)/(3/7) = 2/3 * 7/3 = 14/9 ≈
Validate BM Assortment rules by Lift measure for Each Store with data driven decisions
supp(A0) = P(A ^ 0) = P(A) P(0/A)= P(0) P(A/0)
conf(A0) = P(0|A) , conf(B1) = P(1|B)
Lift(A  0) = P(0|A)/P(0) = P(A ^ 0) / P(A) P(0)
Lift of 1  Antecedent and Cons independent, Rule 1 high confidence low lift preferred

11. Digital Strategy for BM for 5P, 5C
 Marketing Mix 5P have to tackled to align with changing BM presence.
 5P (Product ,Promotion ,Place ,Price , people)
 Product (assortment from BM) targeted by market surveys/social feeds
of region.
 Promotion: Targeted online advertisement to BM prospects.
 Online Place Strategy: have .retail TLD (permanent place away from
others), localization based on segment of BM population near store.
 Price: Sourcing from cheap manufacturing hub having economy of
scale keep merchandising cost down. Assortment Can easily compete
on prices to local store due to supply chain efficiencies.
 People: Engage People on BM specific blog on Sears Website.
Personalize web site using Machine learning categorized reasons.

12. Recommendation Engine Changes
 Content based Filtering tweaked for BM.
Bayesian Classifiers, cluster analysis, decision trees, and artificial neural
networks.
estimate the probability that the user is going to like the item.
 collaborative filtering: Unknown-Unknown
Predicting users like based on similarity to other users k-nearest
neighbour (k-NN) approach and the Pearson Correlation.
Choose BM option box in Assortment Type Get relevant feedback
More intuitive: Choose machine learning to get you either BM or non
BM specific products.