How Packaging Makes us Fat: Volume Estimation Heuristics...

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How Packaging Makes us Fat: Volume Estimation Heuristics and Size Preferences

(with Nailya Ordabayeva, Erasmus )16/5/2013

Pierre CHANDON

L’Oréal Chaired Professor of Marketing, Innovation and Creativity.

Director, INSEAD Social Science Research Center

Overview of presentation

How Packaging Makes us Fat

Background Trends in package and portion sizes.

The psychophysics of volume estimation.

Perception of 1D vs. 3D Supersizing (Chandon & Ordabayeva 2009)

Stronger underestimation of 3D vs. 1D supersizing

Influences consumption and size choices.

Predicting & Managing Downsizing Impressions (Ordabayeva & Chandon 2013)

Additive change model predicts perceived downsizing.

Additive change model can be used for “smart downsizing”.

Asymmetries in judging size increases vs. decreases (ongoing work)

Estimations, production of size increases, decreases.

Effects of valence, reference level, expertise.

Conclusions

2

Background

Package and portion sizes aren’t what they used to be

Near Lake Tahoe, 2004?? oz

18996.5 oz

19006.5 oz

19156.5 oz

19576.5 oz

19676.5 oz

19076.5 oz

19256.5 oz

Background

No or Ambiguous Size Labels“Extra Large”

“Small” (Burger King)

“Medium” (McDonald’s)

Size branding impacts consumption

114

93

86Downsell Normal Grand King

Classic Petit Normal Grand

Upsell Mini Petit Normal

(Chandon et al. 2013)

Calories ordered

3

100 cl, 34 oz430 cal

80’s

120 cl, 40 oz516 cal

90’s

190 cl, 62 oz817 cal

00’s

19 cl, 6.5 oz81 cal

50 cl, 17 oz215 cal

25 cl 8.5 oz108 cal

40 cl, 14 oz172 cal

Background

Supersized Packages

France

94 cl, 32 oz62 cl, 21 oz35 cl, 12 oz

USA

Background

Product Range Effects

53

48

45

(Sharpe et al. 2008, Chandon et al. 2013)

“Vicious” range(without the smallest)

Extended range

« Virtuous » range(without the largestcup)

0 cl

6%94%

67%33%

Calories ordered

4

Background

Strong Consumer Reactance to DownsizingMarketers downsize to hide cost increases and reduce

packaging costs

From 18 to 16.3 oz.

Some consumers hate downsizing

70% oppose Bloomberg’s

proposed ban on drinks > 16 oz.

Consumer complain about downsizing from 16 to 12 oz.

Literature Review

Psychophysics of Volume Estimation

Size effects: The Empirical law of sensation

Size and volume estimations follow an inelastic power function of actual size People under-estimate actual size changes (Plateau 1872, Stevens 1986).

Shape effects

Lower elasticity for 3D objects (Teghtsoonian 1965; Frayman & Dawson 1981).

Elongated objects appear bigger (Raghubir & Krishna 1999, Wansink & van Ittersum 2003).

0

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500 3000

Est

imate

d S

ize

Actual Size

Squares (b=.8)

Lines (b=.9)

Cubes (b=.6)

ESTSIZE = a*(ACTSIZE)^b, where b<1

Unresolved issues

1. Is it the shape of the object or the shape of the volume change that matters (how many dimensions change)? (Chandon & Ordabayeva 2009)

2. Can we model and manage size impressions and acceptance of smaller portions? (Ordabayeva & Chandon 2013)

3. Are we as sensitive to size increases as size decreases? How do we estimate sizes? (ongoing work)

5

196

326 (actual)

154

124

0

100

200

300

400

0 100 200 300 400

Perc

eiv

ed

volu

me

(cl

)

Actual volume (cl)

Chandon & Ordabayeva 2009

Underestimation of Increasing Sizes, esp. in 3D

People underestimate volume change, especially when all 3D changes.

100% increase ≈ 60-70% perceived increase.

Similar results when asking about calories, weight, or volume.

3D: 160 (-51%)

1D: 241 (-26%)

S70 cl

M?

L?

XL?

1D: h =: +67%

3D: h, w, l = +19%


Effects on Dosage (Magnitude Production Task)

Task: Triple existing dose.

Results:

1D: Poured x 3.04

(1% overdose)

3D: Poured x 3.63

(+21% overdose)

Because volume changes appear smaller in 3D, people pour more into 3D conical glasses.

250 ml

1D:Cylindrical containers

3D:Conicalcontainers

100 ml20 ml

6


People are More Likely to Supersize in 1D and to Downsize in 3D

Control (55% choose red)

22 Cl, €.80

22 Cl, €.80

1D Supersizing(100% supersize)

33 Cl, €.80

22 Cl, €.80

3D Supersizing (68% supersize)

33 Cl, €.80

22 Cl, €.80

50 Cl, €.80

33 Cl, €.60

50 Cl, €.80

33 Cl, €.60

3D Downsizing(69% downsize)1D Downsizing

(48% downsize)

Original menu

50 Cl, €.80

50 Cl, €.60

Ordabayeva & Chandon 2013

Modeling Perceived Size

Two sources of error1. Biased estimation of changes in

individual dimensions (height, length, width)

People have biased estimates of each dimension but correctly multiply them (e.g., horizontal-vertical “T” illusion).

2. Biased integration rule

People don’t correctly multiply the changes in all three dimensions

1. Compounds changes in 2 (vs. 3) dimensions (surface area model)

2. Look at average change in each dimension (contour model)

3. Add instead of multiplying the % change in each dimension (addchange model).

Tests (and potential remedies)

Test whether people accurately estimate height, length, and width.

Test whether improved attention to dimensions helps.

Test effects of shape of size change (3D, vs. 2D, vs. 1D vs. elongation)

7

2.0

.50

1.74

.57

1.78

.56

1.59

.63

1.26

.79

0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Pre

dict

ed p

erce

ived

vol

ume

Actual volume

Multiplicative model


Modeling Size Impression

Additive change heuristic

Perceived size is a function of the sum of the % change in height, width, and length.

+ 26%(h) + 26% (w) +26% (l) = +78%

Actually: 1.26*1.26*1.26 = 2 = +100%


Contour model

Surface area model

Additive change model

Typical observations


Models Predictions for 3D Changes

3D changes

2.0

.50

1.78

.56

1.59

.63

1.26

.79

0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Pre

dict

ed p

erce

ived

vol

ume

Actual volume

Contour

1 ∑

1 ∑′ ′

′′

Multiplicative

Additive change

Surface area

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Models Predictions for 2D Changes2.0

.50

1.83

.55

1.60

.63

1.28

.80

0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Pre

dict

ed p

erce

ived

vol

ume

Actual volume

Multiplicative

Additive % change

Surface area

Contour

They all improve

These worsen

Additive change improves

2D changes


Models Predictions for 1D Changes2.0

.50

1.67

.67

1.33

.83

0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Pre

dict

ed p

erce

ived

vol

ume

Actual volume

Multiplicative,additive change

Surface area

Contour

They all improve

These worsen

Additive change model improves

1D changes

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.85

1.00

1.051.16

0.5

1.0

1.5

2.0

0.5 1.0

Pre

dict

ed p

erce

ived

vol

ume

Actual volume

Multiplicative

Additive % change

Surface area

Contour


Models Predictions for Elongated Downsizing

Elongated downsizing where increase in height = sum of decrease in length and width.

100x100x100 => 150x80x80.

Additive change model predicts no detection of downsizing.

Other models predict increasing size perceptions!

1D (height only) 2D (width and length) 3D

Candles

Candies

Study 1: 1D vs. 2D vs. 3D DownsizingComparison of Volume Estimation Models

222 participants

Estimation mode: decomposition (first estimate each dimension, then total size) vs. holistic (estimate total size)

?(190g)

885g?

(317g)?

(530g)?

(190g)885g

?(317g)

?(530g)

?(190g)

885g?

(317g)?

(530g)

?(30g)

140g?

(50g)?

(84g)?

(30g)140g

?(50g)

?(84g)

?(30g)

140g?

(50g)?

(84g)

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Study 1: 1D vs. 2D vs. 3D Downsizing

Accurate Estimations of Height, Width, Length

Estimates of single dimensions are accurate.

If anything, change in height, width, and length are overestimated.

These errors cannot explain the underestimation of size change.

Condition Total size Actual dimensions Estimated dimensions

Height Width Length Height Width Length

1D

1.00 1.00 1.00 1.00 1.00 1.00 1.00 .60 .60 1.00 1.00 .63 .94 .94 .36 .36 1.00 1.00 .39 .90 .87 .21 .21 1.00 1.00 .24 .87 .83

2D

1.00 1.00 1.00 1.00 1.00 1.00 1.00 .60 1.00 .77 .77 .94 .72 .73 .36 1.00 .60 .60 .92 .54 .57 .21 1.00 .46 .46 .91 .37 .46

3D

1.00 1.00 1.00 1.00 1.00 1.00 1.00 .60 .84 .84 .84 .81 .76 .78 .36 .71 .71 .71 .64 .59 .61 .21 .60 .60 .60 .51 .45 .48

Study 1: 1D vs. 2D vs. 3D DownsizingAdditive Change Model Fits Data Best

No difference between decomposition and holistic conditions

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Study 2: 1D vs. Elongated Downsizing

“Smart” Package Downsizing Using the Predictions of the AddChange Model

885g?

(814g)885g

?(743g)

885g?

(673g)

16% downsizing 8% downsizing

1D downsizing

“Smart” downsizing

(dimensions calculated with the AddChange model

to predict no perceived change)

24% downsizing

• Half of 195 participants could weigh products by hand before estimation.• Two replications: Candles and soap. Visual aptitude and gender.


Elongation Hides Volume Downsizing

.79

.87

.91

.98.99 1.00

.80

.88

.94

.91

.96

.68

.76

.84

.92

1.00

.68 .76 .84 .92 1.00

Est

imat

ed s

ize

(mul

tiple

of t

he r

efer

ence

)

Actual size (multiple of the reference)

1D downsizing;visual estimation only

Elongated downsizing; visualestimation only

1D downsizing;visual & haptic estimation

Elongated downsizing; visual& haptic estimation

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Elongation Hides Volume Downsizing

0.7

0.8

0.9

1

1.1

0.7 0.8 0.9 1 1.1

WT

P (

mul

tiple

of t

he o

rigin

al p

rice)

Actual size (multiple of the original size)

elongated visual only

visual + weight

visual + weight1D visual only

Same effects for WTP

Study 3: Using Perceptual Biases to Fight Overeating

Smart Downsizing with Real Products

A company wants to reduce quantity from 8 to 5 lbs. Test with professional packages and loyal adult consumers.

Comparison with competition

Choice vs. comp. 57% 46% 39%

Elongation (h) Elongation (w) Shorter (1D) Original (8lbs)

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Excel Tool

Using AddChange Model to Choose Optimal Downsizing Dimensions

Excel Tool

Using AddChange Model to Choose Optimal Downsizing Dimensions

3D representation of the original and final objects

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Current Puzzle:

Perception of Up- vs. Down-sizing

Are estimations of increasing sizes as elastic as the estimations of decreasing sizes?

190g 190g 190g?

(885g)?

(317g)?

(530g)?

(885g)?

(317g)?

(530g)?

(885g)?

(317g)?

(530g)

?(190g)

885g?

(317g)?

(530g)?

(190g)885g

?(317g)

?(530g)

?(190g)

885g?

(317g)?

(530g)

2D (width and length) 3D (height, width, length)1D (height only)

Downsizing

condition

Supersizing

condition

Current Puzzle:

Perception of Up- vs. Down-sizing

Replication with candies

30g?

(140g)?

(50g)?

(84g)

?(30g)

140g?

(50g)?

(84g)

2D (width and length) 3D (height, width, length)1D (height only)

Downsizing

condition

Supersizing

condition

?(30g)

140g?

(50g)?

(84g)?

(30g)140g

?(50g)

?(84g)

30g?

(140g)?

(50g)?

(84g)30g

?(140g)

?(50g)

?(84g)

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Study 1

Size Estimates (all conditions)

3.2

1.2

0

1

2

3

4

5

0 1 2 3 4 5

Es

tim

ate

d s

ize

(m

ult

iple

of

the

sm

all

es

t s

ize

)

Actual size (multiple of the smallest size)

Downsizing: EST = 1.27×(ACT^.87)

Supersizing: EST = 1.01×(ACT^.74)

Lower elasticity and accuracy for estimation of increasing sizes than for estimations of decreasing sizes

Asymmetry parameter bdown/bup = 1.18

The loss aversion parameter in CPT is typically around 2.

Study 1

Size Estimates (1D vs. 2D vs. 3D)

0

1

2

3

4

5

0 1 2 3 4 5

Es

tim

ate

d s

ize

(m

ult

iple

of

the

sm

all

es

t s

ize

)


1D: Downsizing EST = .98×(ACT^1.04)

1D: Supersizing EST = 1.01×(ACT^.85)

2D: Downsizing EST = 1.22×(ACT^.91)

2D: Supersizing EST = 1.02×(ACT^.76)

3D: Downsizing EST = 1.62×(ACT^.71)

3D: Supersizing EST = 1.0 ×(ACT^.63)

Asymmetry (bdown/bup)1D: 1.222D: 1.203D: 1.13

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Study 1

No Effect of Drawing Attention to Individual Dimensions (Decomposition condition)

0

1

2

3

4

5

0 1 2 3 4 5

Es

tim

ate

d s

ize

(m

ult

iple

of

the

sm

all

es

t s

ize

)


Hol: Downsizing EST = 1.24×(ACT^.88)

Hol: Supersizing EST = 1.01×(ACT^.74)

Dec: Downsizing EST = 1.29×(ACT^.87)

Dec: Supersizing EST = 1.01×(ACT^.74)

Supersizing condition

Downsizing condition

260 kcal ? (520 kcal)

? (130 kcal)

260 kcal

Study 2

With the Same Reference, Single Estimation

Procedure

Use same reference size.

Ask for single estimation.

Same factor change, up & down

Professional photos from market research company.

7 products: Rice, pasta, chips, currants, whipped cream, stuffed tomato, soup, salad.

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56 Cal ? Cal

? Cal 56 Cal



50 Cal ? Cal

? Cal 50 Cal

18



164 Cal ? Cal

? Cal 164 Cal



316 Cal ? Cal

? Cal 316 Cal

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250 Cal ? Cal

? Cal 250 Cal

Study 2

Size Estimates

0.56

1.41

0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Est

imat

ed p

ort

ion

(m

ult

iple

of

refe

ren

ce p

ort

ion

)

Actual portion (multiple of reference portion)

Supersizing: EST = 1×(ACT^.50)

Downsizing: EST = 1×(ACT^.83)

Asymmetry (bdown/bup) = 1.66

Probably because of photos vs. real products

No systematic product differences (confounds of healthiness and size)

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Study 3

Decoupling Size Direction and Valence

Valence manipulation (tasting)

Sugary or salty iced tea

Pleasant or disgusting JB

N = 145 participants

Magnitude estimation vs. production

Add or subtract 33%, then 67% (iced tea)

Add or subtract 45%, then 90% (JB)

Study 3

Decoupling Size Direction and Valence

?(-33%)

120ml

Iced tea Jelly Beans

Downsizing

Supersizing

?(-67%)

?(+33%)

120ml?

(+67%)

?(-45%)

105g?

(-90%)

?(+45%)

105g?

(+90%)

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0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Est

imat

ed p

ort

ion

(m

ult

iple

of

refe

ren

ce p

ort

ion

)


Supersizing: EST = 1.0×(ACT^.65)

Downsizing: EST = 1.01×(ACT^1.16)

Study 3

Size Estimates


0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Est

imat

ed p

ort

ion

(m

ult

iple

of

refe

ren

ce p

ort

ion

)


estimation: unpleasant observed

estimation: pleasant observed

Pleasant Supersizing: EST = .98×(ACT^.70)

Pleasant Downsizing: EST = .97×(ACT^.98)

Unpleasant Supersizing: EST = 1.01×(ACT^.61)

Unpleasant Downsizing: EST = 1.01×(ACT^1.24)

Study 3

Pleasantness does not Influence Size Estimates

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0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Pro

du

ced

po

rtio

n (

mu

ltip

le o

f re

fere

nce

po

rtio

n)


Supersizing: EST = 1.0×(ACT^1.04)

Downsizing: EST = .92×(ACT^.89)

Study 3

Lower Asymmetry for Size Production Task

Asymmetry (bup/bdown) = 1.17

0.0

0.5

1.0

1.5

2.0

0.0 0.5 1.0 1.5 2.0

Pro

du

ced

po

rtio

n (

mu

ltip

le o

f re

fere

nce

po

rtio

n)


dosage: unpleasant observed

dosage: pleasant observed

Unpleasant Supersizing: EST = .92×(ACT^1.12)

Pleasant Supersizing: EST = .99×(ACT^1.02)

Unpleasant Downsizing: EST = .92×(ACT^.88)

Pleasant Downsizing: EST = .87×(ACT^.77)

Study 3

Pleasantness does not Influence Size Production

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Study 4

Expertise

136 cooking school students

Pasta:

165g (x 3): 55g or 495g

Sugar:

140g + 75% (35g) or – 75% (245g)

Study 4

Lower Asymmetry for Size Estimates of Experts

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Pre

dict

ed p

erce

ived

vol

ume

Actual volume

estimation: observed

estimation: predicted

b = .84

b = .90


No effects of location, cooking experience, math aptitude

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Study 4

Lower Asymmetry for Size Production of Experts

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Pre

dict

ed p

erce

ived

vol

ume

Actual volume

production: observed

production: predicted

b = .88

b = .80

Asymmetry (bup/bdown) = 1.10

No effects of location, cooking experience, math aptitude

Explanations?

Attention? Drawing attention to height, width, length did not help.

Lower asymmetry for cooks, especially for magnitude production.

Future studies with training and feedback.

Estimation strategy? Multiplication is easier than fractionation.

Future studies with eye-movement analyses.

Motivation? Manipulating product valence did not help.

Approach/avoidance Approaching objects seem closer than receding ones (Lewin 1935).

Approaching noises have a higher pitch (Doppler 1942)

Approaching events seem closer than past ones (Caruso et al. 2013).

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Summary

Key results

People underestimate size changes because they fail to compound changes across dimensions.

Not because of incorrect attention to changing dimensions.

Not because of failure to estimate each dimension.

This happens because people add instead of multiplying the changes in each dimension.

Size change perceptions are steeper and more linear for downsizing than for supersizing. Why?

Summary

This is how we can help: Provide salient size information in units that people understand and

complement with other sensory information (haptics).

To improve accuracy: linearize the volume estimation task from 3D to 2D to 1D.

To improve acceptance of downsizing: Elongate products. This works because people fail to compound the effects of two diminishing dimensions and compensate them with the steep increase in one dimension.

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A Big Thank you!

How Packaging Makes us Fat: Volume Estimation Heuristics...

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