How Companies can Select Winning Ideas and Forecast Sales Before Launching New Products

How Companies can Select Winning Ideas and Forecast Sales Before Launching New Products

Presented to:

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Copyright © 2011 The Nielsen Company. Confidential and proprietary.

What is innovation? What does it mean to you? Can you think of any examples?

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New product forecasting

• Simulated Test Marketing (STM) Overview

• STMs Explained: Inside the “black box”

•What really drives new product success?

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What is Nielsen BASES? What do we do?

Nielsen BASES Objective is optimizing our clients’ high potential initiatives, and minimizing the risk of launching failures.

Nielsen BASES Mission is to help our clients grow through successful innovation on their brands.

Nielsen BASES Philosophy is building strong and lasting relationships with our clients.

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is part of the family

As part of

BASES Has Developed

Unparalleled Access

To Insights, Experts,

our Clients Succeed

The Nielsen Company,

And Data, To Help

through Successful

Innovation

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Here is a snapshot of some of the clients that we work with:

Simulated Test Marketing Overview

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Typical new product development process for consumer packaged goods

Market DefinitionMarket Definition

Idea Generation and ScreeningIdea Generation and Screening

Concept Testing / OptimizationConcept Testing / Optimization

Product Development and Evaluation

Product Development and Evaluation

Commercialization and LaunchCommercialization and Launch

Prove Business CaseProve Business Case

Cost of Failure:• Year 1 Advertising / Promotion: $5-

30MM• Manufacturing Costs: $5MM+• Opportunity Costs: good products

not launched• Brand Equity: negative halo of failed

product (consumer and trade)• Job Security

Product Forecasting Methods:•Best Guess•Secondary Data Comparables•Qualitative (focus groups)•Live Test Markets•Simulated Test Marketing

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70s: STMs:•Concept Evaluation/ In-Home Product Test•Statistical Sampling•Many advantages...

A history of product testing from the US

1930 1940 1950 1960 1970 1980

70s: Controlled Store Testing:•Product Stocked at Controlled Store•Smaller Markets than TM… Less Expensive•Custom ads delivered to homes

40s: Rise of National Brands:•Launching Expensive•Success Unpredictable

50s: Test Markets:•“Little America” •Representative Cities -> “Will it play in Peoria?”

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Advantages and caveats for STMs

• Improved accuracy• Identify product weaknesses to fix• Identify source of volume• Weed out losers before test market• Shorter “reading” time• Enhance concept / product security• Perform quick competitive forecast• Reduced product requirement• Eliminate final packaging need

• Marketing elements assumed:– distribution levels and builds– media spending - timing and

execution• Major competitive or economic

changes prior to launch impact forecast• Deviation of tested versus

launched concept / product impact estimates

STMs Applied: Consumer Data

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Consumers contactedConsumers contacted

Our methodology ensures data consistency and reliability

CONCEPT(Pre-Trial)CONCEPT(Pre-Trial)

AFTER-USE(Post-Trial)AFTER-USE(Post-Trial)

Exposure to Concept Stimulus

Exposure to Concept Stimulus Evaluation of ConceptEvaluation of Concept

Consumers Re-Contacted after

usage period

Consumers Re-Contacted after

usage periodEvaluation of ProductEvaluation of ProductEligible Consumers

Placed with ProductEligible Consumers Placed with Product

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A new product with strong consumer interest pre-trial will drive significantly higher sales

270

852

0

100

200

300

400

500

600

700

800

900

Weak concept Strong concept

+315%

BASES Food Example – A comparison of the best and the worse initiative in BASES Database

Vol

ume

pote

ntia

l

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Example of pre-use consumer measures

(Total Consumers) (250)

Purchase Intent (%)

Definitely would buy 59

Probably would buy 26

Definitely or probably would buy 85

Might/might not buy 8

Probably would not buy 1

Definitely would not buy 6

Mean Liking Rating (6-point scale) 4.5

Mean Value Rating (5-point scale) 3.0

Mean Uniqueness Rating (5-point scale) 3.7

(Favorable Consumers) (212)

Mean Claimed Units at Trial Purchase 1.8

Mean Claimed Annual Purchase Frequency 23.1

AdeS Chocolate

(Total Consumers) (250)

Purchase Intent (%)

Definitely would buy 59

Probably would buy 26

Definitely or probably would buy 85

Might/might not buy 8

Probably would not buy 1

Definitely would not buy 6

Mean Liking Rating (6-point scale) 4.5

Mean Value Rating (5-point scale) 3.0

Mean Uniqueness Rating (5-point scale) 3.7

(Favorable Consumers) (212)

Mean Claimed Units at Trial Purchase 1.8

Mean Claimed Annual Purchase Frequency 23.1

Is this a good concept?

Without some benchmark, it is almost

impossible to know if the scores are good or not.

BASES has developed extensive databases that provide a robust context for evaluating

initiatives.

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BASES’ cornerstone assumption

• Strong correlation between consumers’ claimed future purchase behaviour and actual purchasing.

•Consumers, however, tend to overstate their intended purchase behavior (albeit with great consistency). The level of overstatement varies by country, by culture, and by key demographics.

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Germany UK France Russia Spain Italy

Can you guess which countries have high overstatement?

Purchase intent claims by country








Source: The Nielsen Company

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For example, consumers overstate their transaction size, but it correlates to actual behaviour

Trial Units

Co

nce

pt

Cla

imed

Un

its

(Fav

)

1:1 Line1:1 Line


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Similarly, their claimed frequency of purchase lines up with actual purchase cycle

After-Use Claimed Frequency

Pu

rch

ase

Cyc

le

Low High

Sho

rtLo

ng


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Quick Quiz

When can you reasonably forecast a really new product’s sales?1. When you can accurately predict its market share2. When you can predict share and market growth3. It is often not possible to predict sales using market share4. This looks like a trick question and I’m not answering

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Quick Quiz

When can you reasonably forecast any new product’s sales?

3. It is often not possible to predict sales using market share alone4. This looks like a trick question and I’m not answering

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Forecasting really new products: Two questions

1) Can consumers make reliable judgments about their future purchase behaviour for “really new” products with no competitive set or frame of reference?

2) What unique problems do “really new products” pose for pre-market sales forecasting?

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Why is purchase cycle important innew product forecasting?

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Purchase cycle drives repeat rate (and repeats per repeater as well)

High

Low

Rep

eat

Rat

e (P

anel

)

Short LongPurchase Cycle (Panel)


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Answer to Question #1

Can consumers make reliable judgments about their future purchase behaviour for “really new” products with no competitive set or frame of reference?

Yes, because consumers’ claims regarding “really new products” are no more overstated than their claims for common, everyday new products.

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What unique problems do “really new products” pose for pre-market sales forecasting?

A) Calculating market share alone may not work

– Share of what?– What competitive shelf-set?

B) Using “comparables” for estimates also can cause problems– Category comparables may be misleading or worse.– What if there is no category to pull a purchase cycle from?

Answer to Question #2

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Validations indicate our methodology works

10

20

30

40

50

60

70

80

90

100

91% of Cases

87% of Cases

Within 20% of Actual Sales

All Initiatives

Unique Initiatives


STMs Explained: Inside the “Black Box”

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Volume is calculated by combining together consumer responses with planned marketing

Remove consumer bias factors

What consumers actually do

Distribution

Adjust for OverstatementAdjust for Overstatement

Awareness

Consumer ClaimsConsumer Claims

Interested UniverseInterested Universe

Promotion/in-store activity

Volume EstimateVolume Estimate

What consumers say they will do

Adjust for what marketers do to influence consumers

Total Addressable

Market

Measure Consume

r Perceptio

n

Impact of Marketing Support

Volume Forecast

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Looking at consumer claims alone will be misleading

Determine Consumer InterestDetermine Consumer Interest

+ Adjustment for Overstatement + Adjustment for Overstatement

= Interested Universe = Interested Universe

% of consumers becoming aware% of consumers becoming aware

% of consumers find the product where they shop

% of consumers find the product where they shop

+ other activities (e.g. promotions)+ other activities (e.g. promotions)

Trial RateTrial Rate

50% Purchase Intent

BASES Model

Marketing Plan +

BASES Model

20% Interested Universe

5% Trial Rate

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Volume is calculated by adding together trial and repeat

Trial Volume

Trial Volume

RepeatVolumeRepeatVolume

+

=

TotalVolume

TotalVolume

55 million

10%

1.1

6.1 million

Households

Trial Rate

Number of Packages / Purchase

Trial Volume

Example

5.5 million

40%

1.2

3.0

7.9 million

Triers

Repeat Rate

Number of Packages / Purchase

Repeats / Repeater

Repeat Volume

14 million

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The relationship between trial and volume is almost linear

R2 = 0.84

Year I Trial Rate

Un

it V

olu

me P

er

100 H

ou

seh

old

s

Low High

High

Low


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Quick Quiz

True or False?1. Awareness alone strongly predicts trial

True2. Advertising strongly predicts awareness

True3. Internet advertising generates high awareness

???

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Low High

Hig

hL

ow

Awareness is critical for new products’ success

R2 = 0.56

Maximum % Awareness

Yea

r I

Tria

l R

ate


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Tracked Awareness

Year I Trial Rate

Total Unit Volume

Example: Four Products (Same Category)

Brand A

20%

4.5%

9.3MM

Brand B

44%

9.0%

17.4MM

Brand C

48%

8.0%

17.5MM

Brand D

78%

14.3%

31.4MM

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Product category affects awareness generationA

war

enes

s

GRPs

Food Personal Care Health Care


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Media drives awareness

GRPs

To

tal A

war

enes

sL

ow

Hig

h

Low High

Higher Impact GRPs (higher recall)

Lower Impact GRPs(lower recall)

Even without any advertising there will be some awareness, from distribution.

Even without any advertising there will be some awareness, from distribution.


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Innovative ideas are more likely to be remembered

Commercial Related Recall

• Related recall scores from copy testing show an advantage for innovative products.

0

10

20

30

40

Me-Too Products Innovative NewProducts


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What is the value of memorable advertising?

GRPs

Recall

Persuasion

Awareness

Trial Rate

Sales Index

2,000

20%

9%

35%

8.5%

1.00

2,000

30%

9%

45%

11.2%

1.35

Me-Too Innovative


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Advertising timing has an impact on volume

Time in 4 Week Periods

TrialRate

0123456789

10

Early Flighting

Spread-out Flighting

5.3%

4.9%

0 1 2 3 4 5 6 7 8 9 10 11 12 13

40


Distribution has an even stronger impact on volume and its importance cannot be underestimated

R2 = 0.83

Distribution

Tri

al

Ra

te


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Median Year 1 distribution build - Russia vs. Europe

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12 13Year 1 periods

Ave

rage

wei

ghte

d di

strib

utio

n

RUSSIA*

France

UK

Italy

Spain

Germany

One of the challenges for launching new products in Russia is distribution


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VolumesInfluence on volumes

= 40%

Month 12

B

A

Month 12

Trial rate

Influence on trial

= 26%Fast: A

Slow : B

Dist Construction

Month 12

More time for trialMore time for repeat

Higher volume!

Distribution timing is also important

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Media plans typically used for new product launches might not work well in Russia ... Case Study: Coffee in 4 countries including Russia

Shifting the advertising start by five periods to align with distribution increases volume potential by ~10%

+ 9%+ 9%

100

109

Actual GRPs

Shifted GRPs

Vol

ume

Inde

x

Periods

Actual GRPs

(400)

(300)

(400)

(300)

Shifted GRPs

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12 13

•Russia •France •Uk •Poland


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1000

1378 1377

1109

888954

907

Original BASESForecast

Actual Distribution Actual GRPs Copy Test Results Delayed Launch ofTrial Pack

Actual Sampling Revising Pricing

Influence of actual in-market execution on performance of the initiative

-19%

-20% -5%

0%

+38%+7%

Comparison Between the Original BASES Forecast and Expected Performance based on the Launch Execution Plan


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Quick Quiz

True or False?

There is little correlation between the number of:triers who repeat a first time, and repeaters repeating a second time.

False -STMs would not be possible without this strong correlation

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1st vs. 2nd Repeat Rate

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80

1st Repeat Rate

2n

d R

ep

ea

t R

ate


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2nd vs. 3rd Repeat Rate

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

2nd Repeat Rate

3rd

Re

pe

at

Ra

te


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3rd vs. 4th Repeat Rate

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

3rd Repeat Rate

4th

Re

pe

at

Ra

te


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Nu

mb

er o

f H

ou

seh

old

s

Number of Repeat Purchases

Trial

First Repeat

Stabilization

1 2 3 4 5 6 7 8

Strong Product

Weak Product

Buyers can be ‘lost’ – if you have a bad product they will be lost more quickly

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Incremental trial is important to replace lost buyers• Marketing efforts influence a

brand's ability to grow in Year 2.• For brands that decline, ad

support is generally cut significantly versus Year 1 support.• Ideally, a new product should be

thought of as “new” for two years rather than one.

Year II to Year I Ad Spend Ratio

1.06

0.49

Up/ Stable Brands Declining Brands


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The accuracy of the BASES Model has been validated over 1,700 times, with the average forecast within 10% of actual sales

1700

971532

100

85

26

1,714


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Summary

• STMs work because of the predictable relationships between (among others):–Consumer claims and consumer actions–Advertising and awareness– Initial and subsequent repeat purchases

• STMs and other marketing models fail when:–Marketing inputs incorrect (probably too optimistic)–Category dynamics change after test but before launch–Products aren’t launched as tested

What Really Drives New Product Success?

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"Success" defined by distribution trends

This chart, based on actual in-market data, shows the average % distributionbuilds (and declines) for Successful, Marginal and Failed initiatives over three years

0

10

20

30

40

50

60

70

80

90

100

% A

CV

Dis

trib

uti

on

Success

Marginal

Failed

Year 1 Year 2 Year 3

Successful, Marginal, and Failed Products

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Activity: From all of BASES Key Measures, pick the

most predictive measures of in-market sustainability(and list them in order of importance)

1. ________________

2. ________________

3. ________________

Pre-Use Purchase Intent

Pre-Use Liking

Pre-Use Value

Pre-Use Uniqueness

Pre-Use Claimed Units

Pre-Use Frequency

Post-Use Purchase Intent

Post-Use Liking

Post-Use Value

Post-Use Uniqueness

Post-Use Claimed Units

Post-Use Frequency

Performance vs. Expectations

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Which is more likely to succeed?1. A good concept with an average product 2. An average concept with a good product.

Quick Quiz

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Quick Quiz

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How much does high uniqueness contribute to a concept’s success?1. A lot (more than 100%)2. Not much (less than 50%)

Quick Quiz

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How much does high uniqueness contribute to a concept’s success?1. A lot (more than 100%)2. Not much (less than 50%)

Quick Quiz

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Survival rates vs. Nielsen BASES’ database

0

10

20

30

40

50

60

70

80

<20 20-39 40-59 60-79 80+

~2x~2x

Overall Concept Purchase IntentA

vera

ge

In

-Mar

ket

Su

rviv

al R

ate

BASES Database Ranking

Bottom Average Top


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0

10

20

30

40

50

60

70

80

<20 20-39 40-59 60-79 80+

~15x~15x

Overall After-Use Purchase IntentA

vera

ge

In

-Mar

ket

Su

rviv

al R

ate


Bottom Average Top


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0

10

20

30

40

50

60

70

80

<20 20-39 40-59 60-79 80+

~2x~2x

Ave

rag

e I

n-M

arke

t S

urv

ival

Rat

e


Bottom Average Top

After-Use Value Rating


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0

10

20

30

40

50

60

70

80

<20 20-39 40-59 60-79 80+

Concept Uniqueness RatingA

vera

ge

In

-Mar

ket

Su

rviv

al R

ate


Bottom Average Top



Questions?Thank you!

Documents

How Companies can Select Winning Ideas and Forecast Sales Before Launching New Products