Transcript New Data Sources

NEW DATA SOURCES
3 Cases
• Industry: Varying Data Sources
• Official Statistics: Surveys and Censuses
• Academe: Simulation-Based
OBJECTIVES
■
■
■
needed to understand factors
affecting volume performance
needed to quantify impact of
each factor
possible forecast of immediate
future performance
 volume decomposition
 quantified impact of each
factor
 effects of short/long term
activities (promo, media)
Classes of Data
Internal

Sales by Subcategories
Government
 economic indicators ( GNP, inflation, P-$, unemployment, etc)
 weather ( temperature, rainfall , humidity)
Competitive
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Retail Audit
Consumer confidence
Media TARP
Events/Promotions
APPROACH
Pre- analysis
Estimate of Base Volume
Volume Decomposition
Effect of Short term activities
Simulations
PRE-ANALYSIS
– assigned dummy variables for events (
month of occurrence)
Group of Indicators
 Economy
 Weather
applied data reduction technique to
address problem on short time series and
no. parameters > no. of observations
used principal components per group of
indicators to come up with composite
scores
 Humidity
 Rainfall
 Media
 Distribution
 Promotion
 Events
 Pricing
 Competitor Act.
Predictive Analytics
All factors affect volume but of varying degrees
Economy
Weather
Media
Distribution
Promotion
Events
Pricing
Competitor
Activities
SALES
Publication
Consider these…
■ Big Data
■ Public Use Files, Census and
Surveys
■ Data Mining
■ Understanding the
Households
■ Business Analytics
■ Consumer Behavior
■ CRM, Strategic Marketing,
Corporate Planning
■ Strategic Marketing,
Corporate Planning, PolicyMaking
OBJECTIVES
■ Characterize the Filipino HH
■ Describe spending behavior of HH
■ Characterize segments according to spending behavior
■ Business insights from consumer behavior
DATA SOURCES
■ Family Income and Expenditure Survey
■ Reference Period: 2003
■ Sample: 42K HH from 16M HH
■ Domain: Regions
CHARACTERISTICS OF HOUSEHOLDS
■ Household Type:
– Single Family:
– Extended:
■ HH Agri:
79%
20%
■ Employed Spouse:
36%
■ With Radio:
65%
■ With TV Set:
59%
■ With VTR:
33%
■ With Ref:
34%
■ With Phone:
■ With PC:
29%
4%
30%
■ Main Source of Income
– Wage Non-Agri:
38%
– Crop Prod:
16%
– Wage Agri:
8%
– Assist. Abroad:
7%
– Whole & Retail:
7%
CSD EXPENDITURE PATTERN
■ Average Household/Yr:
■ Per Capita/Yr:
■ At P5/day, consumption days/Yr:
P981
203
41
■ Average of 1 serving per week,
Total Expenditure on CSD/Yr: PhP 10.57 Billion
EXPENDITURE PATTERN
Estimated Annual Expenditures
■ CSD:
10.57B
■ Coffee:
7.86B
■ Juice:
5.42B
■ Cocoa:
5.24B
■ Beer:
4.88B
■ Liquor:
3.53B
■ B. Water:
2.74B
■ I Cream:
1.54B
■ Wine:
1.28B
■ F. Milk:
645M
■ Tea:
402M
 Meal at School:
8.07B
 Meal at Work:
29.94B
 Meal at Restaurant: 8.39B
 Snacks:
37.73B
 Recreation:
9.95B
 Home Food:
867B
 Total Food:
943B
Summary of Insights
■ Consumers: 1/3 Children
■ 3 of 4 HH Heads are Elementary/HS
■ 1/3 of HH Heads are Farmers
■ With Radio:65% With TV: 59%
– Importance of retail store involvement!
■ Bottom 30% dissavers: campaigns with
values=>social benefits
Summary of Insights
■ Upper 10% spend on CSD 9x of the Lower 10%
– Focus on the middle classes!
■ Highest expenditure to CSD than any beverage, water
only 1/3 of that, juice is 1/2
– Prospects of activation in water and juices
■ Complementing campaigns re: CSD, water, and juices.
Summary of Insights
■ Snacks: largest expenditures on food, ¼ is
expenditure on CSD
– Promote, Collaborate with snack items
■ Food outside home mostly at work
■ CSD 1 serving per week
– Promote availability at snack time in the
workplace
– Availability of multi-serve can increase
consumption
Summary of Insights
■ SEC: More than 1/3 C, almost half D
■ D biggest per capita, E negligible
■ NCR: per capita CSD declining over SEC
■ Highest per capita in NCR
■ AOMM, high in Ilocos, Davao
■ AOMM, low in Bicol, MIMAROPA
Summary of Insights
■ Activate in CALABARZON, they spend more on
Juices, least in CSD
■ DAVAO & NCR high CSD, Water, Tea Consumption
■ Maintenance of CSD in Central Visayas (big
market, big expenditures)
Monte Carlo Simulation
■ Stochastic methods to generate new configurations of a system
of interest – simulation of a phenomena
■ Monte Carlo: importance sampling or systems at equilibrium.
– Start: initial configuration of the system
■ can be data-based random variable generation
– Change the configuration
■ acceptance/rejection of changes
Monte Carlo Simulation
■ Given a data-generating mechanism
– Example: drawing colored balls from an urn, input-output
model, adaptive sampling, etc.
– model of the process you wish to understand
– produce new samples of simulated data, replicate current
data
– examine results of those samples
– may also amplify this procedure with additional
assumptions
Monte Carlo Simulation
■ Computer Simulation/Monte Carlo Models
– Not solved by mathematical analysis but are used for
numerical experimentation.
– Goal of Numerical Experimentation: Answer questions of
real world (What if-sensitivity analysis)
– Purpose of Sensitivity Analysis
■
Validation of the model
– Would the customers exhibit similar credit
behavior?
– Are their credit behavior similar?
Simulation
■ Big Data
Big Data
■ Machine
Learning/Modeling
Leading Indicators
■ Simulation
Validation
■ Estimation
■ Validation
Model-Based Estimation
Simulation Procedures (Resampling)
■ Construct a simulated universe
– composition similar to the universe whose behavior we wish to
describe and investigate.
■ Specify the procedure that produces a pseudo-sample
– simulates the real-life sample in which we are interested
– specify procedural rules by which the sample is drawn from the
simulated universe (purposive sampling)
■ Describe: if several simple events must be combined into a composite
event
■ Calculate the probability of interest
– estimate parameters
– test hypothesis
– Based on tabulation of outcomes of the resampling trials.
THANK YOU.