1

MarketSim ModelingKey elements

2

Steps in a MarketSim projectItem Detail Timeframe Notes

Kick-off & Business

question refinement

Understand the category

Set data gathering scheduling1 to 2 days

ROMI Training First session ½ dayTo the wider marketing

team

Data gathering See separate sheet 1 to 3 wksDepends on Market

Research sophistication

Data set validationInitial data validation, check for

completeness2 to 3 days

Model development and

calibrationDepends on completeness

4 wks to 5

wks

Depends on category

complexity

Preliminary results

presentation

With key decision-maker to make sure

results make sense, deepen business

question understanding

1 day

Includes:

ROMI, Brand elasticity,

price elasticity,

distribution elasticity

Develop

recommendations; answer

business questions

Run simulations to perform

optimization, run sensitivity analyses1 to n wks

Depends on business

question complexity

ROMI TrainingSecond session combined with results

delivery½ day

To the wider marketing

team

Begin 4wkly cycle Develop data update timing & planning ½ day

Data sets Scanner/Transaction data – Nielsen/IRI

Price (promoted/unpromoted) by brand by SKU by channel)

Distr. (weighted distribution) by brand by SKU by channel

Display/Feature (weighted distribution) by brand by SKU by channel

Digital/social

Own - Adobe, Google analytics/Facebook Insights and others

Competitive – SocialBakers

Brand Imagery – monthly, quarterly, 6-monthly, annually, (whatever is available. If none available, field a study at begin and end of project)

Brand association scores

Initial awareness and persuasion; Prior use

Usage and Attitudes for general category knowledge and dynamics

Mass Media – From agency

PR – if available for major events

BTL (Trade) – From sales team for costs of consumer/trade promotions

Product attribute (functional) breakdown

Generated with help from client for functional attributes. This can also be read from IRI product classifications

Product profit margins by SKU – only needed if optimizing price or if ROI is desired

Internal data sources

MarketSim timeframes

Strategic marketing decision support

4

Initial conditions

• Awareness

• Penetration

• Etc.

Initial agent learning

• (typically 6mo.)

Calibration

• (typically 24mo.)

Validation

• (typically

6mo.)

4wkly update process

Simulation/projection

• (12 or 24mo.)

Validation is always the last 6 months. As opposed to statistical

modeling where the validation can be from any time period.

Because ABM has a history, it needs to be the last period.Today

3yrs

ago

MarketSim tactical marketing

decision support (4-weekly cycle) Tactical marketing decision support – rolling analysis & optimization

5

Actual data Projected data sets

Model re-validation

Projected results

We recommend beginning

with a 4weekly cycle, then

possibly moving to a bi-

weekly or weekly cycle later

on.

6

Model & Simulation

Veracity

6

Typical model veracity

At 4-weekly level comparing real sales to simulated sales

5% MAPE at the brand level

10% MAPE at the SKU level

Better for larger SKUs

Worse for smaller SKUs

Agent population v MAPE Error

For a single brand (Unit sales), represents confidence

level of simulation agent population

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

25k 50k 100k 200k

% D

iffe

rence

Population Size

MAPE Error v. Population Size

9

Case Study

MarketSim™ Strategic InsightsFMCG Case Study: Food

9

1.5 1.3

1.0

4.8

2.4

1.8

-

1.0

2.0

3.0

4.0

5.0

BTL Digital Billboards Print TV-Promo TV-Thematic

mROMI

mROMI Factors – PREMIUM 2010mROMI - For every dollar spend on media, this is the incremental profit margin generated. It is done using marginal increases (of 10%) in the advertising (except Digital and Billboards)

Marketing mix recommendation: Reduce Billboards by 50%. Increase Print by 100%.Focus on Promo. Cut BTL by 25%. No change to Digital.

10

Breakeven

0

0.5

1

1.5

2

2.5This brand is recommended by doctors

This brand has a taste that my childlikes

This brand gives the best affordablenutrition

This brand makes me proud to buy itfor my children

This brand helps me to be a goodmother

This brand helps my child progress inlife

This brand helps my child to becomemore intelligent

This brand helps my child to be bettereveryday

This brands knows all there is to knowabout nutrition

This brand brings my child excellentnutrition

This is a brand I can trust

This brand helps my child get the mostout of life

This brand offers me good value formy money

This brand helps my childs immunity

PREMIUM SEGMENT

Premium Segment: Consumer preferences top attributes‘Brand recommended by doctors’ is top attribute. Taste is second. 2 of 3 targeted attributes

are in top preferred attributes of top 14

Recommend messaging theme: ‘This brand helps me to be a good mother’ and ‘This brand helps my child progress in life’

attributes

11

• Product centric• Child centric• Mom centric

Bran

d D

This brand helps me to be a

good mother

This brand helps my child

progress in life

12

(60.0)

(30.0)

-

30.0

60.0

-30% -20% -10% 0% 10% 20% 30%

Price (rel.) elasticity -PREMIUM(M USD)

GUM

IF

(800,000)

(600,000)

(400,000)

(200,000)

-

200,000

400,000

600,000

800,000

-30% -20% -10% 0% 10% 20% 30%

Price (rel.) elasticity -PREMIUM

(vol – 1,000Kgs)

GUM

IF

Relative price elasticity - Mainstream

12

Decreasing price versus the category by 5% delivers 14.9M US. This price change is a relative price change to the rest of the category.

Price elasticity of all SKUs in Growing Up Milk (GUM) and Infant Formula (IF) in +/-10% range are linear. For both IF and GUM, lowering price leads to more profit. Especially strong for GUM. Care must be taken not to invoke a price war, if the overall price is changed in the category.

We recommend further simulations to optimize pricing between variants

-5%

13

3.4 4.0 1.0

17.8 13.6

4.6

-

5.0

10.0

15.0

20.0

25.0

Total contr margin 2009 Total contr margin 2010 Total contr margin 2011Q1

Incr. contribution margindue to 10% increase in wghtd distr perc. (M USD)

GUM Mainstream

IFT Mainstream

Scenario analysis: Wghtd distr %

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In 2010 and 2011 the base wghtd distr percentage is less than 2009

21.2

17.6

5.6

Increasing distribution by 10% delivers 17.6M US. We recommend a distribution increase of at least 10% above existing values for all SKUs. (some with lower current distribution will be higher, others with current higher distribution won’t increase as much) The cost will be the investment in trade marketing plus internal sales costs. (10% of Trade 3.9M USD + 10% of BTL 6.2M USD + ??? Sales)

D/L distribution is declining. It has impact on advertising effectivenessTBD: What is the cost of 10% increased weighted distribution percentage? Is it more than the trade marketing expense in 2010 of 38.5M US? Increasing the wghtd distribution is a medium to long term effort

90.8

21.7

17.3

14.4

13.1

10.9 1.5

(5.2)2010 Margin Increase

Distribution10%

IncreasePrint 100%

DecreasePricing 5%

DecreaseBTL 20%ExistingStores

IncreasePromo TV by

33%

CutBillboards

100%

DecreaseThematic TV

by 0.08

73.7M US

Summary recommendations

14

Initial results through uni-dimensional simulation indicate opportunity

for 73.7M US in incremental margin (6-cities)

Assumes no net increase in marketing budget

5.

2

15

15

• Drive long term strategic

change in marketing decision-

making methods

• Provide training to over 2,000

senior marketers, globally

• Build consumer centric

software (MarketSim family of

products)

North America

Russia

Malaysia

Hong Kong

Vietnam

Kenya

Strategic decision-making support Global experience

Sri Lanka

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Japan

Morocco

Indonesia

Tanzania

+1 404-816-4344, Atlanta, USA

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