Explaining experimental changes in consumer behaviour in ... · 0.00 0.20 0.40 0.60 0.80 1.00 New...

Explaining experimental changes in consumer

behaviour in realistic settings using observational data

Adriaan Kole, René de Wijk, Daniëlla Stijnen, Anna Maaskant

Wageningen UR: Food & Bio based Research

Consumer Sciences & Intelligent Systems group

Restaurant of the Future

A normal university cafetaria?

Look inside…

Diners being observed

A multifunctional research facility

• Restaurant for 200 persons• Grand café• Research kitchen• Sensory laboratory• Mood rooms• Mind room

• 45 video cameras• 7 video analysis workstations• 3 km of cabling

Restaurant of the Future in the press

Laboratories for sensory and physiological research

• Measure physiological and emotional responses

• Study effects of food odor, taste and texture

The Restaurant; designed to study the effects of changing

environments on consumer behaviour

• Light• Odor• Temperature• Buffet lay out• Price• Assortment…. Etc,

Observational research

• Observe food selection and consumption

• Study effects of environmental and social variables

The behavioral approach: Food choice in Restaurant

300 registered consumers:

Age (yrs) 40.88

% Males 51%

BMI 23.76

NeophobiaScore 26.56

Education High

Some results

� We know the nutritional content of the products,

we know who is buying what:

� Clusters of consumers based on nutritional intake

� Consistency of repeated lunch selections

� Lunch composition; the effect of soup on the

other lunch selections

Cluster analysis

Three clusters of consumers

1 2 3

Lunch energy from carbohydrates (%) 31 42 53

Lunch energy from protein (%) 16 19 19

Lunch energy from fat (%) 52 37 24

Relatively healthy food choice

carbohydrates : fat : protein 50%:25%:25%

Cluster analysis

Three clusters of consumers

1 2 3

Lunch energy from carbohydrates (%) 31 42 53

Lunch energy from protein (%) 16 19 19

Lunch energy from fat (%) 52 37 24

Need more carbohydrates and less fat

Cluster analysis

Three clusters of consumers

1 2 3

Lunch energy from carbohydrates (%) 31 42 53

Lunch energy from protein (%) 16 19 19

Lunch energy from fat (%) 52 37 24

Needs MUCH more carbohydrates and MUCH less fat

Cluster analysis

Three lusters of consumers

1 2 3

Lunch energy from carbohydrates (%) 31 42 53

Lunch energy from protein (%) 16 19 19

Lunch energy from fat (%) 52 37 24

Basis for health intervention:

how to move consumers from cluster 1 to 3.

Consistency calculated for:

� Nutrients: fat, carbohydrates & proteins

� Energy

� Price

� Weight

� Number of lunch items

� Location of lunch items

� Type of lunch items

80

90

100

Sna

ck

Bre

ad

Sou

p

Sal

ad

San

dwic

h filli

ng

Drin

ksS

andw

ich

Des

sert

sH

ot m

eals

But

ter

Fruit

Type of food/drink

Co

ns

iste

nc

y o

f re

pe

at

pu

rch

as

e (

%)

0

10

20

30

40

50

60

70

Ra

te o

f re

pe

at

pu

rch

as

e (

%)

Consistency

Rate

Some lunch items attract fewer, but loyal consumers

80

90

100

Sna

ck

Bre

ad

Sou

p

Sal

ad

San

dwic

h filli

ng

Drin

ksS

andw

ich

Des

sert

sH

ot m

eals

But

ter

Fruit

Type of food/drink

Co

ns

iste

nc

y o

f re

pe

at

pu

rch

as

e (

%)

0

10

20

30

40

50

60

70

Ra

te o

f re

pe

at

pu

rch

as

e (

%)

Consistency

Rate

… while other lunch items attract more, but less

loyal consumers

Most consumers are consistent with regard to their

choice of buffets and ……

0

5

10

15

20

25

30

35

40

45

50

50 51-60 61-70 71-80 81-90 91-99 100

Consistency of repeat purchases (%)

Nu

mb

er

of

co

ns

um

ers

(%

of

tota

l)

… with regard to lunch price, weight & number of items

0

0.2

0.4

0.6

0.8

1

1.2

Pric

eW

eight

No.

produ

cts

Con

sist

ency

buffe

t

Con

sist

ency

food/d

rink

Ene

rgy

in lu

nchP

rote

ins

FatS

atura

ted fa

tTra

ns fa

t

Uns

atura

ted fa

t

Multi

ple u

nsat

urate

df Fat

Car

bohyd

rate

s

Mono/d

isac

char

ides

Die

tary

fiber N

aCo

eff

icie

nt

of

va

ria

tio

n (

st.

de

v/m

ea

n)

Consumers are not consistent with regard to the

nutritional composition of their lunches.

0

0.2

0.4

0.6

0.8

1

1.2

Pric

eW

eight

No.

produ

cts

Con

sist

ency

buffe

t

Con

sist

ency

food/d

rink

Ene

rgy

in lu

nchP

rote

ins

FatS

atura

ted fa

tTra

ns fa

t

Uns

atura

ted fa

t

Multi

ple u

nsat

urate

df Fat

Car

bohyd

rate

s

Mono/d

isac

char

ides

Die

tary

fiber N

aCo

eff

icie

nt

of

va

ria

tio

n (

st.

de

v/m

ea

n)

Summary

Consumers pay relatively little attention to their nutritional

needs in terms of energy and nutritional composition.

They rely more on factors such as portion size and habitual

routes along buffets.

Some results: consumption patterns

� Lunch composition; the effect of soup on the

other lunch selections. � Hypothesis: Soup is supposed to be relatively satiating. Hence, we

expect fewer calories on a soup tray compared to a non-soup tray.

Some background information

Soup and non-soup consumers are similar with

regard to their personal characteristics.

Soup and non-soup lunches are similar with regard

to energy from macro-nutrients.

What else is in the lunch?(Percent energy from product categories)

Non-soup lunches contain more bread, sandwich

fillings and snacks

No Soup SoupSandwich fillings 6.3 2.5Sandwiches 6.8 3.9Butter 1.9 1.1Bread 19.0 10.6Dessert 9.9 3.7Drinks 23.8 23.9Fruit 0.8 0.4Green Salad 4.6 1.6Meal Salad 4.6 2.9Snack 14.8 11.6Soup 0.0 34.1Hot meal 7.1 3.2

What else is in the lunch?: Total energy

Over 200 lunches, the difference adds up to 10000 calories, or

approximately less 3 LB bodyweight if everything else is

constant(1 LB bodyweight = appr. 3500 cals).

1867 kJoules 1677 kJoules

Lunch

Without soup With soup

Conclusions of the soup case

� Results support the satiating properties of soup.

� However, if soup eaters consume fewer calories,

why are they not thinner…..

� …. Or are they compensating on other meals?

Routine buffets: Faster visits result in more purchases

% visits resulting

in purchase

Time of visit

w.o.purchase

(s)

Bread 86 5,4

Fruits & Juices 91 2,6

Sandwich Fillings 75 4,0

Salads 51 5,0

Soups 72 9,1

Sandwiches 37 7,4

Desserts 65 3,7

Snacks 62 11,5

r= -0.8, sig.

Routine buffets:

% visits resulting

in purchase

Time of visit

w.o.purchase

(s)

Bread 86 5,4

Fruits & Juices 91 2,6

Sandwich Fillings 75 4,0

Salads 51 5,0

Soups 72 9,1

Sandwiches 37 7,4

Desserts 65 3,7

Snacks 62 11,5

Interventions should focus on less habitual foods which are thoroughly inspected first.

Tracing walking patterns

Challenge

Observe the walking patterns of visitors

to learn their attention patterns during

choosing behaviour

Solution

LED-based tracking solution

Camera’s can observe individuals, even

when camera’s do not overlap

Preliminary result

Individual tracks showing browsing

behaviour and choosing times

Aggregated tracks showing hot zones

Bread

BreadSoups

Sandwiches

Coffee & Tea

Desserts

Lemonades

Cash register

Sn

ac

ks

Soups

Bre

ad

Sandwich

Fillings

Juices

Juices

Sa

lad

sS

an

dw

ich

es

Le

mo

na

de

s

Coffee & Tea

De

ss

erts

Cash register

Entrance

The Standard

Route

Tracking results: a possible basis for interventions

� Food choice behaviour is not random

� Food choice behaviour may be partly related to

proximity of buffets

� Combinations of buffets suggest equivalence of

choices (e.g., snacks vs sandwiches)

Summary 2

1. Restaurant well-suited for longitudinal studies on:

1. (variations in) consumer preferences/food choice

2. Repeat purchasing of specific products

3. Effects of behavioral and/or environmental

interventions with regard to food choice

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New Healthy Welfare-

Friendly

Selected chicken product

Re

lati

ve

tim

e d

uri

ng

me

nu

se

lec

tio

n (

0-1

.0)

ColaCucumber

DrinbBuffetFrenchFriesFriedOnionKetchup

LettuceMayoOnionReadHealthyReadNew

ReadWelfare-friendlySelectedChickenTomatoVegetablesWater

Interventions: the Effect of product information on routing

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0.20

0.40

0.60

0.80

1.00

New Healthy Welfare-

Friendly

Selected chicken product

Re

lati

ve

tim

e d

uri

ng

me

nu

se

lec

tio

n (

0-1

.0)

ColaCucumber

DrinbBuffetFrenchFriesFriedOnionKetchup

LettuceMayoOnionReadHealthyReadNew

ReadWelfare-friendlySelectedChickenTomatoVegetablesWater

Order of food choice varies with the type of chicken product.

Different consumers, different strategies?

Interventions: the Effect of product information on routing

� Citrus odour: more combination meals chosen

� Vanilla odour: more often fish/meat with staple foods

P=0.05P=0.03

P=0.02

20

30

40

50

60

70

80

90

100

% o

f vis

ito

rs

CitrusAroma

VanillaAroma

Bron: FBR/CICS

Intervention: effect ambient aromas on choice

Intervention: Effect of ads on food choice

� Photos of salads decrease demand for desserts.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

NoDessert FruitDessert VanillaDessert

% o

f vis

ito

rs

SaladPhoto

StirrFryPhoto

Bron: FBR/CICS

Intervention: calorie labeling

400

450

500

550

600

Average lunch energy (kCals)

Reference period Information period

Bron: FBR/CICS

About 30% of the consumers eats less calories

... And 70% increases calorie intake

Bron: FBR/CICS

Recuctions: toppings, bread, meals, cold snacks

Increase: salads, warm snacks

Interventions: CO2 labeling

Effect of new technology on salad sales

� Humidifier increases attention for

the salad buffet

� Humidifier decreases salad sales

(food technology phobia)

Bron: FBR/CICS

46

54

59

0

10

20

30

40

50

60

70

Experimental condition

Pe

rce

nta

ge

ap

pro

ac

h p

er

da

y

No humidif ier

Half humidif ier

Full humidif ier

59

50

51

44

46

48

50

52

54

56

58

60

Experimental condition

Perc

en

tag

e c

ho

ice p

er

ap

pro

ach

No humidifier

Half humidifier

Full humidifier

Effect of product on food choice

Eating gesture analysis

Challenge:

observe the eating gestures to learn about

eating habits and emotions (liking/disliking)

Solution

Formalised framework describing which

eating gestures exist

Computer vision software to detect

occurring gestures

Preliminary result

Individual eating gestures and eating

patterns can automatically be detected

Smaller bites result in smaller meals

small large free100

200

300

400

500LS

HS

P < 0.001 P < 0.05

P < 0.05

Inta

ke (

g)

30% reduction in grams eaten when taking small bites compared to large bites

Bron: Dieuwerke Bolhuis. WUR/HV

Eating faster is eating more, esp. with smaller bite sizes

200

300

400

500

600

Short(20 s/100g)

Long(60 s/100g)

a a

bc

LB 6.7 bites/100g

HB 20 bites/100g

Inta

ke

(g

)

Bron: Dieuwerke Bolhuis. WUR/HV

Lab: bite sizes can be reduced by using stronger flavours

Conclusions and discussion

� Effects can be described empirically (this is what we

observe that consumers do): observational measures

� We can influence what people do and observe.

Conclusions and discussion

� Usually psychological theory explain effects based on how

people are and how their brain works.

� If we only observe – we don’t know these things

� How can we bridge the gap between lab and real life.

Conclusions and discussion

� Or do we not need to, since only the effects matter??

Thank you for your attention !

� Contact: adriaan.kole@wur.nl