importance of latin american statistical expertise in our

A NIELSEN EXPERIENCE

IMPORTANCE OF LATIN AMERICAN STATISTICAL EXPERTISE IN OUR GLOBAL SOCIETY

VIJOY GOPALAKRISHNAN

THE NIELSEN COMPANY

PRESENTATION OVERVIEW

• Retail Measurement Service Overview

• Statistical method vision and goal

• Areas of contribution from our Latam team

• Specific examples in the areas of:– Universe Estimation

– Efficient sampling

– Global Standards


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RMS Business Overview Product Strategy

• Measure what consumers buy using Retailer Data (Scanning, Audit, Warehouse, etc.)

Value to the Market: Consumer Analytics , Store Optimization, Promotion Strategy

• Consumer Analytics:

– Measure of Dollars and Share within Regional, Account and Retailer Trade Area Markets

• Store Optimization

– Measure of Sales Distribution to analyze Market Penetration

– Supported by All Commodity Value (ACV) data

• Promotion Strategy

– Measure of Baseline Dollars and Incremental Dollars by Causal Conditions

– Supported by Flyer and In-store data

What it doesn’t cover:

• Behavioral data (product usage and ownership)


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Nielsen marketsLatam methodologies and leadership have large reach!


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Statistical Methods Vision

To be the world leader in measurement design innovation and thought leadership in order to

accurately reflect the marketplace for our clients

Fit for use Confidence Trust Fact based Impartial


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Measure of our goalBeating the “efficient frontier”

Costs

Quality

OCR - Surveys

Relative Facts

Dual Sampling

Stock Estimation

Digital Maps

Dual Sample RES

Local Market Hybrid


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Latam Measurement Science team

Areas of global contribution

• Leadership

• Technical expertise

• Standards

• Product Management

• Methodology Evangelism

• Exporting Talent


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Some Examples

In the areas of:

• Universe Estimation – RES Sample Optimization

• Efficient sampling – Dual Sampling

• Global Standards


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Problem Statement

Data collections costs millions annually to

conduct the Retail Establishment Survey

(RES) across 80+ countries.

Many questions on the RES

questionnaires are not required for critical

business purposes such as universe

estimation, sample selection, or revenue

generation. Other questions need not be

administered in all stores to meet service

standards for clients.

The execution of the RES is costly and

questions that are not required of all

stores should be removed or asked of in

a sub-sample of stores.

How to arrive at an objective sub-sampling rate?

Goal Statement

RES Sample Optimization

Attain efficiencies in RES globally withoutaffecting client satisfaction through:

• Questionnaire Revision: Elimination ofquestions that are not used or are notcritical to business objectives.Standardization of questionnaires intolong and short forms.

• RES Sub-Sampling Selection: Establisha holistic approach in calculating sub-sample rate, evaluating impact onstatistical quality and cost functionlevel


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Terms/Notations

• N = The size of universe

• n = The size of unreduced sample(RES sample designed using internalstandards )

• fn = RES sampling fraction

• k = The size of reduced RES sample(data collection with ‘long’questionnaire)

• R = The ratio of the reduced sampleto the unreduced sample (k/n)

fn = n/N R= k/n

NNnn

kk

NN

Goal = Find RVIJOY GOPALAKRISHNAN

THE NIELSEN COMPANY

-30

-25

-20

-15

-10

-5

0

95% 90% 85% 80% 75% 70% 65% 60% 55% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5%

Ratio of Reduced Sample (R)

Slo

pe

Benchmark = The Point of Slope Direction Change

• For values exceeding benchmark =>(smaller change in sample produces higher precision loss)

Recommendation: No more than 30% relative increase in variance!This is the area where one percent reduction in the sample is resulting in less than one

percent RSE increase.


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Why not fixed 50% Ratio of Reduced Sample?

50%-50% ratio produces relative

increase in SE exceeding 40% for

sample fraction of 5%


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Some Examples

In the areas of:





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Dual Sampling

• In Nielsen’s Retail Measurement Service, we estimate the sales of a given brand X out of a sample “n” when the total sales of the store is known (ratio estimation).

• Assumption is that high correlation indicates that larger stores sell relatively more of brand X than smaller stores.

• Hence, a gain in sampling efficiency results

• Of course this still requires us to collect item sales and total sales information in n to compute the ratio and apply to the universe – an expensive enterprise

• Is there a better mouse trap?


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Dual Sampling

• There is!

• In emerging markets such as Latam, distribution of an item i.e. whether the store sells an item is also a powerful predictor of item sales

• And it is less time consuming to collect item distribution in a store than collecting sales information

• There are cons: variance increases and to minimize variance we increase sample

• Overall, a good balance between efficiency and quality. Particularly when auxiliary variable has low correlation to item sales such as the case with low distributed items


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RMS Dual Sample Methodology

P&D Sample

Reduce Manual Volumetric Audits by 33%...and improve Reporting Granularity with Quarterly Price & Distribution Studies

2/3

1/3

Current

Audit

Sample

Proposed

Dual

Sample

Reduce by 1/3rd

Price & Distribution D-Sample• 4 to 1 Replacement Ratio• Quarterly Frequency • Rolling Sample

Audit V-Sample• Manual Audits reduced by 1/3rd

• Monthly Frequency • Stock & Purchases (derived sales)

Dual Sample Design• 1/3 of stores are V-Sample• 2/3 of stores are D-Sample


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Trend Analysis - beverages

Share Volume Sales

46

.1

46

.9

46

.2

46

.0

45

.8

46

.0

45

.8

45

.5

10

.0

9.6

9.3 9.49.9

9.6

9.5

9.4

9.0

8.7 8.9

8.79.5

9.4 9.7

9.6

5.3

5.3

5.4

5.5

5.5

5.5 5.6

5.7

Mar'12 Abr'12 May'12 Jun'12

COCA-COLA REG COCA-COLA DUAL RED COLA REG RED COLA DUAL

JARRITOS REG JARRITOS DUAL PEPSI COLA REG PEPSI COLA DUAL

Volume

Sales

Trend

Dual Sampling 1,403,676 1,513,010 1,509,454 1,572,699

Regular DB 1,391,106 1,468,847 1,506,775 1,562,883

Difference 0.9% 3.0% 0.2% 0.6%

MAR12 APR12 MAY12 JUN12

City A

Volume

Sales

Trend

Dual Sampling 63,282 69,933 69,492 71,530

Regular DB 63,172 69,945 71,695 72,695

Difference 0.2% 0.0% -3.1% -1.6%

MAR12 APR12 MAY12 JUN12

City B

Share Volume Sales

62

.2

61

.6

62

.4

62

.3

61

.5

60

.9

62

.2

61

.0

15

.2

15

.2

14

.9

14

.6

14

.5

14

.7

14

.4

14

.6

4.0

4.1

4.2

3.7

5.1

5.2

5.3

5.1

2.2

2.0

1.7

1.7

1.8

1.8

1.5

1.5

Mar'12 Abr'12 May'12 Jun'12

SHARE VOLUME SALES

COCA-COLA REG COCA-COLA DUAL VICTORIA REG VICTORIA DUAL

JARRITOS REG JARRITOS DUAL FANTA REG FANTA DUAL

• Sales trends are very well approximated for both markets

• Sales volume difference is small in both markets

• Volume share differences are less than 1 point for both markets

Audit vs. Dual Audit vs. Dual


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Some examples

In the areas of:




RMS data accuracyThree types of factors drive accuracy of RMS Estimates

External factorsSample Design Biases

Characteristics of audit sample that impact accuracy of estimates:• Stratification• Sample size and allocation• Sample selection schemes• Sample representativity•Projection method•Designed precision levels•Granularity of data

Internal factors that systematically deviate estimates from population characteristic (sales)•Under/Over estimated universe•Data pick-up•Frequency of data collection•Biased / non-representative

sample•Projection method•Estimation of non-coop chains

Market and environmental factors impacting estimates (bias and variability)•Undocumented purchases• Lack of cooperation of key

retailers•Rate of retail universe change• Importance of Modern Trade• Importance of non-covered

channels•Economic stability•Social / Political stability

Yhat+σYhat

YY

Yhat+σYhat

What happens with changes?Conceptually, the total error of changes (sales, share) should be function of:

Changes in external factorsChange in variablity from

Sample DesignChanges in Biases

ΔσV(Yhat,t+n / Yhat,t)

Δββt+n / βt

Δδδt+n / δt

Total error = sqrt[(Δβ)2+(Δδ)2(Δσ)2]


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How to measure total error in sales/share changes?

Intuitively, the effect of changes in biases and changes in external factors are already

reflected in our estimates

Therefore, by modeling the RSE of changes in sales and/or share of market over a large

sample of countries, product categories and brands, we could obtain an interval for the

total error (change tolerance)VIJOY GOPALAKRISHNAN

THE NIELSEN COMPANY

Nielsen Stability Index

Market Segmentation Model

Macro Factors• Social/Political• Economic• Rate of Urbanization

Retail Structure• Traditional Trade• Concentration• Documented Purchases• Universe Structure• Non-Cooperator Importance

Country X – Level 1• Unstable macro environment: presence of social, political,

or economic volatility, higher rates of urbanization, andsignificant undocumented purchase data.

• Dynamic retail structure : high levels of traditional trade,rapidly evolving universe structure, and significant retailnon-cooperation.

Bahrain

Romania

Vietnam

Canada

Pakistan

Mexico

Russia

China

India

Brazil

Retail Structure

Soci

al P

olit

ical

Eco

no

mic

Egypt

Greece

USA

4 3

2 1

Not all market measurement is created equal

Environmental factors strongly influence the confidence of data interpretation


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Watch Builder Standards

Measure Compliance

Improvement Plans

CTQ WB Standards

Deployment

• By 4 Tiers (by countries)

• Easy to follow

• Practical to implement

• Robust structure

• Strong Change Management

• Robust compliance metrics

• Visible to clients & leadership

• Identify any gaps

• Design solutions

• Implement & measure


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RMS - Standards Dashboard

Overall compliance Compliance by key process

Not based on real data - for illustration purposes only

Each column lists attributes of a country

Not based on real data - for illustration purposes only


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THANKS!Q&A

importance of latin american statistical expertise in our

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