Dr Con Menictas and Brian Fine present 'Exclusive Research How to Successfully Market Products and Services In Times of Economic Instability' at Mumbrella360 2022.

1. 2022 TRS Conference
Mumbrella 20-21 July 2022
The Impact of Financial Uncertainty on Marketing
Con Menictas Brian Fine

2. 2022 TRS Conference
Defining Uncertainty

3. 2022 TRS Conference
1. Conceptually: Something that is doubtful or that causes one to feel doubtful.
2. Financially: The fear of not being able to service:
1. Debt
2. Standard of living
3. Wealth preservation
4. Wealth accumulation
3. Economically: The absence of national confidence:
1. Financials
2. Production
3. Manufacturing
4. Independence
Defining Uncertainty

4. 2022 TRS Conference
Things around us that cause
Uncertainty

5. $3,200,000
Average
Household
Wealth
Top
20%
1
Bottom
20%
ABS
Quintile
$35,000
12
Income
Ratio
The wealthy bought expensive things.
Everyday people did not.
Everyone buys expensive things, on debt,
except for the very few.
Previously
Today
Causes of Uncertainty: Wealth and Income

6. 22
Europe 57
11
Pharma
10
ROW
USA
Australia
100
27
26
Singapore
15
6
26
Refined
Fuel
Korea
Japan
ROW
Australia
100
16.7
Thailand
Malaysia
United Kingdom
81.8
China
51.6
26.8
Japan
18.9
17.3
13.6
149.6
Germany
Singapore
New Zealand
Republic of Korea
ROW
421.4
United States
16.9
14.6
13.6
Imports
A$B
Causes of Uncertainty: Precarious Reliance on Imports

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8.0
8.5
9.0
9.5
10.0
5
6
7
8
9
10
Jan-20
Jan-19
Jul-20
Jul-19
Jan-21
Jul-21
Jul-22
Jan-22
AUS
5
6
7
8
9
10
10
11
12
13
14
15
Jan-19
Jul-20
Jul-19
Jan-20
Jan-21
Jul-21
Jan-22
Jul-22
10
11
12
13
14
15
10
11
12
13
14
15
Jan-20
Jan-19
Jul-21
Jan-21
Jul-19
Jul-20
Jan-22
Jul-22
NSW QLD
VIC WA SA
Causes of Uncertainty: Unemployment Stress

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4
4
5
3
2
7
19
9
16
14
23
4
8
4
3
3
5
15
13
16
14
21
5
10
5
4
2
5
16
17
16
17
20
4
9
7
4
2
9
15
25
17
17
22
10
7
7
7
4
10
20
24
18
19
26
8
5
10
8
6
9
21
27
21
27
31
11
8
10
9
6
14
20
29
21
27
37
Dec-21
135
Disadvantaged Fringe
Dec-19
37
Young Growing Families
Jun-19
Stressed Seniors
Jun-22
Rural Families
Jun-20
Battling Urban
Dec-20
Exclusive Professionals 47
51
59
143
Young Affluents
Jun-21
25
Multicultural Established
Mature Stable Families
179
Suburban Main Stream
47
126
Wealthy Seniors 124
Causes of Uncertainty: Rental Stress Driver (6-Month Bands)

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8
4
7
10
10
11
9
15
4
14
1
8
2
7
10
9
9
10
13
4
14
1
11
4
9
13
11
11
12
16
5
18
1
11
4
9
12
12
13
13
17
6
20
1
12
4
8
13
11
12
13
17
6
19
12
5
9
14
12
12
13
17
7
20
1
12
4
10
13
13
14
14
18
7
20
1
Exclusive Professionals
125
Rural Families
Multicultural Established
57
Suburban Main Stream
Battling Urban
82
Disadvantaged Fringe
1
Stressed Seniors
Jun-22
Dec-21
Jun-21
Dec-20
78
Jun-20
Dec-19
Jun-19
74
29
83
84
112
7
Young Growing Families
Young Affluents
Mature Stable Families
Wealthy Seniors 40
Causes of Uncertainty: Mortgage Stress Driver (6-Month Bands)

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12%
8%
12%
12%
8%
9%
8%
10%
8%
9%
To Spend Less
4%
Mature Stable Family
Exclusive Professionals
Suburb Main Stream
Young Affluent
Wealthy Seniors
Rural Family
Multicultural Established
Young Growing Families
Stressed Seniors
Battling Urban
Disadvantaged Fringe
100
Reasons for Shopping at ALDI June 2022 Data
84% 16%
Shopped
Normally
Shopped
ALDI
76%
24%
100
Switched (3.84%)
ALDI
Spend Less (12.16%)
18%
8%
15%
8%
7%
9%
11%
9%
10%
Mature Stable Family
Young Affluent
2%
Suburb Main Stream
2%
Exclusive Professionals
Rural Family
Multicultural Established
Young Growing Families
Battling Urban
Wealthy Seniors
Disadvantaged Fringe
100
Stressed Seniors
Latest Supermarket Shift in Shopping Behaviour
Switched

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Uncertainty’s Impact on
Demand and Marketing Effectiveness

12. 2022 TRS Conference Impact on the Demand of Quality and Luxury Brands
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Luxury Purchases
Quality Purchases
Utility Purchases
Financial and Economic Uncertainty Index (0-100)
Preference
Index
(0-100)
Panic-driven
discounting and
bundling to
preserve margin
by Quality and
Luxury Products
and Services
1 2 3 4
Currently
Here

13. 2022 TRS Conference Impact on Marketing Effectiveness
2000 2005 2010 2015 2020 2025
Economic Uncertainty
Marketing Effectiveness

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Advanced Market Research
Methodologies to Dig Into Consumer
Motivation to Overcome Uncertainty

15. Psychometrics
Psychophysics
Mental Capacities and Processes
1
Latent
Information
Statistically Extracted
3
Choice
Contextualization
Complete Set of Competing
Alternatives
2
Advanced Aspects of Consumer Decision Making

16. Compensatory (C)
versus
Non-compensatory
(N)
Information
Ignored
(Yes or No)
Consistent (C)
versus
Selective (S)
Attribute-based
(AT) versus
Alternative Based
(AL)
Evaluation
Formed
(Yes or No)
Quantitative (QN)
versus
Qualitative (QL)
WADD C N C AL Y QN
ADDIF C N C AT Y QN
EQW C Y C AL Y QN
EBA N Y S AT N QL
SAT N Y S AL N QL
LEX N Y S AT N QL
MCD C Y C AT Y QN
FRQ C Y C AL Y QN
Legend
WADD = weighted additive;
ADDIF = additive difference;
EQW = equal weight;
EBA = elimination by aspects;
SAT = satisficing;
LEX = lexicographic;
MCD = majority of confirming dimensions; and
FRQ = frequency of good and bad features.
Decision Heuristics
Heuristic
Strategy

17. Item Decision Strategies (Incomplete Set)
1
Weighted additive rule (WADD) where the values of each alternative on their features are
considered along with their relative importance.
2
Equal weight heuristic (EQW) where individual importances are ignored and an averaging of
importance of features prevails.
3
Satisficing heuristic (SAT) where alternatives are considered individually in their existing
order and are eliminated progressively.
4
Lexicographic heuristic (LEX) which determines the most important feature is considered
first and then other features are considered comparatively.
5
Elimination by aspects (EBA) heuristic (Kahneman & Tversky, 1972) where the most
important attribute is determined in a probabilistic manner to a cut-off criterion and then other
alternatives are examined comparatively. This process is continued down the hierarchy of
importance for each attribute.
Decision Strategies

18. Item Aspects of task complexity (Incomplete Set)
1 The number of alternatives to consider e.g., Loyalty Icons v Loyalty Points
2
The number of attributes for each of the alternatives in the alternative set in leading to
information overload (Meyer and Johnson 1989).
3
Time pressure to complete the task where the less the time the more complex the task will
seem (Wright 1974) e.g., Sale items where the sale will run out.
4
Compatibility effects where the reference deems a bias, for instance, when a product is
evaluated in terms of its dollar value regarding its features, or in relation to its durability
(Slovic, Griffin et al. 1990);
5
Available versus processable information where information that is not organized in an
intelligible manner will not be processed as easily as information logically organized (Russo
1977), nor will information that is deemed incomplete be processed easily such as partially
described options (Ford and Smith 1987); e.g., vague, immature and illusory advertising.
6
The format of information display, such as numerical versus linguistic where numerical
information invites a different strategy to linguistic (Huber 1980; Stone and Schkade 1991).
Task Complexity

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Examples of Advanced Research and Statistical
Methodologies for Incremental Gain
(1)Loyalty
(2)Supermarkets
(3)Property Sales

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Example 1
Loyalty Choice Model

21. Choice Task 1 Toyota Hyundai
Body Type SUV Sedan
Air Conditioning Climate Controlled Standard
Price $40,000 $30,000
Choice Task 2 Toyota Hyundai
Body Type Sedan Sedan
Air Conditioning Climate Controlled Standard
Price $30,000 $40,000
Choice Task 3 Toyota Hyundai
Body Type Sedan SUV
Air Conditioning Standard Climate Controlled
Price $30,000 $30,000
Choose →
Choose →
Choose →
Looking at a Simple Car Example of a Choice Experiment

22. The client wanted to identify members that are sensitized
to the uptake of new products and services
o The client uses a variety of predictive models to identify
potential EDM responsive members e.g., RFM:
o How do we improve on RFM rates?
High Recency High Frequency High Earn = Good
The Problem: Improving on RFM Targeting for Icon Usage Optimization

23. o 256 potential combinations.
o Each respondent completed eight exercises.
o A ‘none’ option was also provided for respondents to choose from.
o A total sample of 7,135 respondents completed the survey.
o A final 6,637 observations were deemed suitable for analysis.
o We removed time stamped violations, invariant, atypical and problematic
observations.
The Construction of the Choice Experiment

24. Alternative, Attribute
and Levels Matrix
Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5
Attributes
Company’s New
Product
Competing Programs in the Market
1 2 4 5
Non-Price Related 1 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Non-Price Related 2 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Non-Price Related 3 {1,2} {1,2} {1,2} {1,2} {1,2}
Non-Price Related 4 {1,2} {1,2} {1,2} {1,2} {1,2}
Uncertain Non-Price Related 1 {1,2} {1,2} {1,2} {1,2} {1,2}
Uncertain Non-Price Related 2 {1,2} {1,2} {1,2} {1,2} {1,2}
Uncertain Non-Price Related 3 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Uncertain Non-Price Related 4 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Fixed Price-Related 1 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Fixed Price-Related 2 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Fixed Price-Related 3 {1,2} {1,2} {1,2} {1,2} {1,2}
Fixed Price-Related 4 {1,2} {1,2} {1,2} {1,2} {1,2}
Variable Price-Related 1 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Variable Price-Related 2 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Variable Price-Related 3 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Variable Price-Related 4 {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4} {1,2,3,4}
Experimental Alternatives, Features and Levels Matrix

25. Loyalty
Program
Members
RFM
Index
Choice Experiment
Index
Actual New Product
Take-Up
1 0 1 1
2 1 1 1
. . . .
. . . .
. . . .
6,637 1 1 1
Total -> 597 1,546 2,143
𝑅𝐹𝑀 =
597
2,143
= 27.86%; 𝐶ℎ𝑜𝑖𝑐𝑒 =
1,546
2,143
= 0.7214%
Analysis

26. 1 2 3 4 5 6
Segments Membership RFM Choice Model Lift On RFM Lift Difference
1 2,332,860 203,730 257,534 53,804 26%
2 1,697,990 172,301 204,153 31,852 18%
3 1,152,114 130,920 152,714 21,794 17%
4 667,559 83,064 97,083 14,019 17%
5 402,531 52,463 62,846 10,383 20%
Total 6,253,054 642,478 774,330 131,852 ~20%
Retrospective Results

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Example 2
Supermarket Preference

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The Problem: Increasing supermarket shopper loyalty in financially vulnerable cohorts
The client wanted to identify members that have a
propensity to switch to or prefer to use a competitor
o The client uses an HNW score of shoppers on small and
large baskets as a Loyalty Indicator:
o How do we identify Loyal and Disloyalty propensity?
High Recency High Frequency High Spend =

29. Latent
Score
Survey Question 1
Survey Question 2
Survey Question 3
The true score
per customer
What is a Latent Variable?

30. NPS
Satisfaction
Recommendation
U1: Psych Models
U2: Spend Models
Brand
Investment
Consistency
Service
Credibility
Quality
Clarity
Value
Brand
Preference/
Utility
Economic
Uncertainty
Churn/Switch
Uncertainty
Utilitarianism
Financial
Uncertainty
The Complete Latent Structure: A Structural Equation Model (SEM)

31. ID
Brand
Investment
Consistency
Customer
Service
Credibility
Perceived
Quality
Clarity
Perceived
Risk
Utility
ID
Brand
Investment
Consistency
Customer
Service
Credibility
Perceived
Quality
Clarity
Perceived
Risk
Utility
ID
Brand
Investment
Consistency
Customer
Service
Credibility
Perceived
Quality
Clarity
Perceived
Risk
Utility
ID
Brand
Investment
Consistency
Customer
Service
Credibility
Perceived
Quality
Clarity
Perceived
Risk
Utility
1 0.94 0.54 0.88 0.71 0.61 0.97 0.56 0.45 1 0.82 0.11 0.14 0.94 0.47 0.43 0.87 0.71 1 0.87 0.91 0.43 0.86 0.70 0.37 0.93 0.92 1 0.39 0.50 0.56 0.46 0.13 0.17 0.95 0.85
2 0.26 0.14 0.08 0.50 0.06 0.32 0.00 0.23 2 0.64 0.85 0.48 0.68 0.76 0.55 0.13 0.50 2 0.98 0.59 0.48 0.19 0.19 0.73 0.02 0.46 2 0.44 0.33 0.26 0.29 0.82 0.37 0.61 0.38
3 0.71 0.74 0.46 0.84 0.23 0.72 0.61 0.65 3 0.38 0.40 0.53 0.46 0.72 0.21 0.33 0.34 3 0.84 0.69 0.03 0.12 0.20 0.86 0.59 0.38 3 0.93 0.61 0.19 0.20 0.90 0.26 0.71 0.16
4 0.21 0.81 0.54 0.76 0.44 0.29 0.79 0.25 4 0.76 0.75 0.85 0.42 0.19 0.82 0.82 0.57 4 0.15 0.25 0.72 0.66 0.69 0.41 0.62 0.55 4 0.29 0.13 0.22 0.19 0.06 0.71 0.04 0.59
5 0.81 0.31 0.73 0.42 0.48 0.88 0.08 0.66 5 0.22 0.41 0.96 0.47 0.63 1.00 0.56 1.00 5 0.10 0.61 0.55 0.88 0.25 0.34 0.26 0.36 5 0.92 0.26 0.13 0.29 0.03 0.05 0.04 0.04
6 0.82 0.80 0.55 0.39 0.40 0.17 0.69 0.40 6 0.10 0.61 0.73 0.77 0.50 0.27 0.10 0.21 6 0.96 0.99 0.33 0.54 0.13 0.03 0.57 0.19 6 0.96 0.76 0.39 0.52 0.77 0.40 0.54 0.23
7 0.63 0.38 0.42 0.90 0.60 0.89 0.10 0.83 7 0.03 0.69 0.72 0.99 0.80 0.53 0.65 0.82 7 0.19 0.94 0.83 0.59 0.32 0.66 0.57 0.97 7 0.43 0.95 0.25 0.42 0.99 0.80 0.52 0.71
8 0.95 0.03 0.41 0.76 0.08 0.77 0.70 0.52 8 0.45 0.37 0.66 0.62 0.80 0.48 0.85 0.66 8 0.57 0.16 0.49 0.91 0.60 0.14 0.31 0.07 8 0.42 0.10 0.03 0.20 0.03 0.28 0.38 0.97
9 0.14 0.66 0.53 0.83 0.33 0.00 0.20 0.52 9 0.03 0.16 0.93 0.77 0.83 0.86 0.15 0.40 9 0.15 0.05 0.55 0.37 0.54 0.39 0.14 0.20 9 0.86 0.43 0.06 0.66 0.25 0.84 0.32 0.67
10 0.38 0.38 0.87 0.96 0.33 0.69 0.26 0.84 10 0.11 0.93 0.80 0.40 0.43 0.19 0.99 0.70 10 0.32 0.13 0.83 0.83 0.46 0.05 0.08 0.02 10 0.30 0.01 0.46 0.53 0.84 0.80 0.37 0.55
Competitor Supermarket 1
Client Supermarket Competitor Supermarket 2 Competitor Supermarket 3
The Hidden Supermarket Preferences Per Customer
Loyal
ID 10
Disloyal
propensity
IDs 1, 7
Disloyal
propensity
ID 5
Disloyal
propensity
IDs 1, 8

32. 1 2 3 4 5 6 7 8
Baskets Based On 12-Month Review
Essential Items per Basket Only
𝐷𝑖𝑠𝑡𝑛 Untreated
T 2.5%
C 2.5%
Control Growth
8.2% Small | 5.7% Main
Test
Growth
Lift
(%)
Ave Lift
(%)
Small
Basket
Couples With Children 21.0% 15,750,011 196,875 16,144 23,625 46.34%
19.9%
Couples Without Children 37.5% 28,125,12 351,563 28,828 35,156 21.95%
One Parent Families 11.1% 8,342,246 104,278 8,551 9,594 12.20%
Other Families 1.3% 962,567 12,032 987 1,059 7.32%
Group Household 6.1% 4,567,500 57,094 4,682 5,709 21.95%
Lone Person 23.0% 17,250,000 215,625 17,681 19,406 9.76%
Main
Basket
Couples With Children 32.4% 56,711,230 708,890 40,407 58,129 43.86%
16.4%
Couples Without Children 26.5% 46,417,112 580,214 33,072 40,615 22.81%
One Parent Families 11.1% 19,465,241 243,316 13,869 14,599 5.26%
Other Families 1.3% 2,245,989 28,075 1,600 1,713 7.02%
Group Household 4.3% 7,486,631 93,583 5,334 6,083 14.04%
Lone Person 24.4% 42,673,797 533,422 30,405 32,005 5.26%
Performance by Shopper Cohort During Uncertainty

33. 2022 TRS Conference
Example 3
Property Content Development Model

34. 2022 TRS Conference
The Problem: Increasing the incidence of potential buyers and the survival of the offer
The client wanted to better identify deal closure and the
duration of property offers for 1st-timers and investors
o The client used an industry list of enhanced property buyer
data and real estate profiles.
o How to we improve on both identifying good buyers and
improve on marketing content?
Employment Income =
Lifecycle

35. 12 Month Period OR CTR TUR UR
Property Development Ads 20.39% 2.66% 0.55% 0.17%
Targeted Choice + SEM 28.51% 4.24% 0.72% 0.13%
Difference YOY 39.82% 59.40% 30.91% -23.53%
50%
15%
10%
15%
10%
Psychological Data for
Latent Uncertainty
Economic Data for
External Uncertainty
CRM
Financial Data for
Internal Uncertainty
Geo Dems
Data Sources to capture
uncertainty indicators
in order to optimize content
Increased the Impact of 1st Property and Investment EDMs off Lists
Base 1.51% 2.16%

36. CRM
+ Survey-Based
Heuristics
Data + Survey-Based
Latent
Perceptions Data
+ Consumer
Psychological
Data
+
Real-Time
Financial
Data
Real-Time Financials and Understanding Decision-Making to Overcome Uncertainty

37. 2022 TRS Conference
Mumbrella 20-21 July 2022
THANK YOU!
Con Menictas Brian Fine