Econometrics for marketing

ECONOMETRICS: UNLOCKING MARKETING GROWTH POTENTIALS
Joseph Sungjun Jang
August 2021

ECONOMETRICS
A quantitative study that evaluates and enhances consumer impact on economy.
ECONOMETRICS IN MARKETING
Bridges marketing and sales, assessing and scaling marketing impact on sales.
© Joseph Sungjun Jang

“Half my advertising spend is wasted;
the trouble is, I don’t know which half.”
– John Wanamaker
“We want to enable Audience First
Marketing; how do we measure its
commercial impact?”
– Marketers with customer-centric mindset
“the long term is not simply the adding
up of short terms.”
– Peter Drucker
WHAT KEEPS MARKETERS UP AT NIGHT
MARKETING MIX MODELING
Measures and enhances marketing profitability
AUDIENCE FIRST MIX MODELING
Evaluates sales and profit penetration by audience segment
BRAND RESPONSE MODELING
Balances marketing mix to drive both short- and long-term sales
ECONOMETRICS SOLUTIONS

MARKETING MIX MODELING (MMM)
Econometrics study that measures and enhances marketing profitability

MMM decomposes sales and guides marketing to be more profitable
SALES
OLV
(Online video)
CCI
(Consumer confidence index)
Strong relationship,
but variance differ
Weak relationship
between sales and OLV
Centralizing all metrics
to find sales impact
BASELINE
SALES
SALES
OPERATIONS
MARKETING
BASELINE
• Business seasonality
• Competitive pressure
• Consumer index
• Pandemic and recession
• Owned digital asset
− Organic social
− Organic video
− App
SALES OPERATIONS
• Distribution
• Sales channel expansion
• Pricing
• Promotion
MARKETING
• TV
• Online video
• Display
• Paid social
• Paid search
• Ecommerce
• Affiliate
HOLISTIC SALES IMPACT MEASUREMENT SALES DECOMPOSITION MARKET MIX OPTIMIZER
Demo data sources: Aggregate Marketing System Simulator developed at Google, Federal Reserve Economic Data, Google Trends

SALES BY BUSINESS DRIVERS MARKETING IMPACT BY ACTIVITY
COVID
Competitive
Competitive
Brand media
Brand media
Performance media
Performance media
Trade marketing
Trade marketing
Owned media
Owned media
2019 2020
Base
• Brand seasonality
• Economic factors
• Product presence
Base
• Product presence
Nonmarketing
contribution
74.3%
Marketing
contribution
25.7%
MMM uncovers opportunities to grow marketing’s impact on sales
Efficiency Effectiveness
Marketing activity FY 2019 ROI Contribution Spend (in $M)
TV $2.71 $64.9 $24.0
OOH $2.04 $9.6 $4.7
Online video $2.23 $27.5 $12.3
Branded contents $2.76 $5.8 $2.1
Brand display $2.21 $19.3 $7.1
Engagement paid social $1.88 $6.0 $3.2
Interactive units $2.42 $2.7 $1.1
Nonbrand paid search $2.84 $13.4 $4.7
Performance display $3.47 $13.2 $3.8
Conversion paid social $3.51 $7.5 $2.2
Loyalty program $4.75 $24.8 $5.2
Brand search $4.60 $11.3 $2.5
E-mail $4.48 $7.6 $1.7
Max-reach conversion $3.65 $56.9 $15.6
In-market conversion $6.11 $51.0 $8.4
Cross-sell conversion $5.12 $29.2 $5.7
Affiliate marketing $4.03 $6.9 $1.7
Journey
Drive reach
Create engagement
Increase demand
Prompt high affinity
Yield conversion
UPPER
MIDDLE
LOWER

2-Jan-16 2-Jul-16 2-Jan-17 2-Jul-17
Advertising Sales
$0.0
$0.5
$1.0
$1.5
$2.0
$2.5
$0
$20
$40
$60
$80
$100
$0 $30 $60 $90 $120
ROI
and
marginal
ROI
Sales
(in
$M)
Spend (in $M)
Return ROI marginal ROI
2-Jan-16 2-Jul-16 2-Jan-17 2-Jul-17
Advertising
Sales
Adstock (Consumer response)
Current ROI
Current mROI
(marginal ROI)
X
Unprofitable
(saturation start)
X
X
Current
spend
Advertising (Ad) vs. sales Ad to consumer response Consumer response to profit metrics
Lagged impact
Diminishing
returns
Lagged impact
How each marketing activity’s sales impact and ROI are assessed

PROFIT CURVES BY MARKETING ACTIVITY MARKETING MIX ENHACEMENT
$0
$9
$18
$27
$0 $9 $18 $27
Return
(in
$M)
Spend (in $M)
TV
OLV
Display
Paid social
Brand search
Nonbrand search
Affiliate
35.3%
29.5%
19.3%
25.7%
18.1% 15.3%
7.7% 8.9%
10.7%
8.3%
6.7%
9.5%
2.2% 2.8%
Modeled mix
Modeled ROI: $2.92
Optimal mix
Projected ROI: $3.07
(+5.1% lift in ROI)
Affiliate
Nonbrand search
Brand search
Paid social
Display
OLV
TV
Profit curves enhance future marketing mix

BRAND PORTFOLIO DEMAND PLANNING MARKET PENETRATION
24.8% 23.3%
20.0%
17.9%
17.7%
15.2%
12.7%
15.9%
10.6%
12.5%
5.0% 7.2%
5.7%
7.1%
3.5% 0.9%
Plan Optimal
Not supported
Exclusives
Endorsement
Masterbrand
New launches
Growing brands
Second dominant
Dominant brand
Season 1 Season 2 Season 3 Season 4 Season 5 Season 6
Plan mix
Optimal mix
Planned marketing driven sales
Optimal marketing driven sales
EFFECTIVE
EFFICIENT
NASCENT
HIGH MARGIN
CONTENDING
EFFECTIVE
DIMNISHED EFFICIENCY
Marketing
ROI
Marketing contribution
US
KR
DE
UK
CA
FI
DK
JP
FR
ES
PL
Model elevates business portfolios to be data-driven

Enhancing marketing mix bridges marketing and financial projections
$42 $37 $40
$18
$16
$17
$18
$20
$22
$29
$32
$33
Planned mix ($107M) $105M optimal mix
(-$2MM from plan)
$112M optimal mix
(+$5MM from plan)
Lower funnel conversion
(Performance conversion)
Lower funnel reach
(Performance targeting)
Mid funnel
(Engagement media)
Upper funel
(Brand reach media)
Planned marketing mix
Lower budget, same impact
(lower budget, higher ROI)
Higher budget per sales target
(higher budget, optimal ROI)
Marketing spend $107M $105M $112M
Marketing driven sales $323M $323M $340M
ROI $3.02 $3.08 $3.04

BRAND RESPONSE MODELING (BRM)
Econometrics study that balances marketing mix to drive short- and long-term sales

MARKETING MIX MODEL (MMM) OUTPUT BASE RESPOSNE MODEL (BRM) OUTPUT
Unexplained
(Model error)
Sales operations
Marketing
Long-term marketing
Organic base
Unexplained
(Model error)
Sales operations
Marketing
Base
Base
BRM expands MMM to assess long-term marketing efficacy
Organic base driver Breakdown
Financial brand value impact 64.4%
Consumer purchase power 16.6%
Increase in consumers’ credit 8.1%
Summer and winter Holidays 7.6%
Decline in unemployment rate 3.2%

35%
37%
32%
41%
20%
25%
18%
19%
22%
20%
24%
18%
23%
18%
26%
22%
Balance
Long-term mix
(BRM)
Optimal mix
(MMM)
Planned mix
Upper funel
(Brand reach media)
Mid funnel
(Engagement media)
Lower funnel reach
(Unbranded conversion)
Lower funnel conversion
(Branded conversion)
Optimal profit mix
(MMM suggestion)
• Projected ROI: $3.15
• ∆% ROI vs. planned: +11.3%
Long-term effectiveness mix
(BRM suggestion)
• ∆% ROI vs. planned: -1.7%
Balance
• ∆% ROI vs. planned: +5.2%
Planned mix
Projected ROI: $2.83
BRM balances short- and long-term brand growth

Why balancing marketing mix is so important…
Skewing marketing investment to be short- or long-term create sales gaps
$0
$50
$100
$150
$200
$250
$0 $50 $100 $150 $200 $250 $300 $350
Sales
from
marketing
(in
$M)
Spend (in $M)
Marketing: optimize ROI
Sales: year-over-year growth
Finance: P&L focus
Marketing: build customer value
Sales: compound growth rate
Finance: balance sheet focus
Profit gap
(Higher profit at current investment level)
Incremental gap
(Higher elasticity and more margin)
Current
spend

Balancing marketing bridges profit and incremental gaps
$0
$50
$100
$150
$200
$250
$0 $50 $100 $150 $200 $250 $300 $350
Sales
from
marketing
(in
$M)
Spend (in $M)
Marketing: optimize ROI
Sales: year-over-year growth
Finance: P&L focus
Marketing: build customer value
Sales: compound growth rate
Finance: balance sheet focus
Balance
Profit gap
(Higher profit at current investment level)
Incremental gap
(Higher elasticity and more margin)
Current
spend

AUDIENCE FIRST MIX MODELING
Complementary MMM analysis that facilitates Audience First Marketing

Audience mix modeling framework
Model input Model outputs Model takeaway
Audience segment Share of spend
Share of
Sales contribution %
ROI Optimal mix
Core 29.8% 25.4% $2.02 27.4%
Retargeting 14.3% 14.8% $2.55 12.8%
New product core 7.5% 6.6% $2.30 8.5%
Cross audience targeting 6.5% 5.1% $2.25 5.8%
Contextual targeting 3.8% 3.5% $2.60 4.7%
Behavioral targeting 2.6% 2.0% $3.14 1.7%
Incremental lookalikes 1.2% 0.8% $2.16 0.9%
VIP members 1.1% 2.3% $3.76 2.3%
Performance max reach 17.9% 22.3% $2.80 21.2%
Performance in-market 10.2% 9.8% $3.33 8.7%
Performance cross-sell 5.0% 7.4% $5.71 6.3%
• Model KPI: marketing driven sales sourced from MMM
• Model input: spend by customer segment
• Data requirement: clean audience level spend for 52+ weeks
AUDIENCE MIX MODELING STRUCTURE
Demo data sources: Aggregate Marketing System Simulator developed at Google, Federal Reserve Economic Data, Google Trends, Fabricated/simulated audience data

Multi-objective optimization
Annual ROI
Long-term
efficacy
Audience
penetration*
19.7%
2.6%
14.9%
7.9%
11.7%
5.8%
10.8%
3.7%
1.7%
12.3%
6.8%
2.0%
26.9%
3.0%
12.1%
14.5%
10.2%
2.2%
4.8%
5.6%
1.9%
7.1%
9.3%
2.3%
24.4%
2.9%
12.9%
11.7%
10.5%
3.7%
7.3%
4.9%
1.9%
9.2%
8.4%
2.2%
23.7%
2.8%
13.3%
11.4%
10.8%
3.9%
7.6%
4.7%
1.8%
9.5%
8.2%
2.2%
TV OOH OLV Brand
display
Paid social Interactive
units
Performance
display
Brand search E-mail Reach
e-commerce
Cross-sell
e-commerce
Affiliate
marketing
Audience penetration Long-term ROI Annual ROI Balance
UPPER FUNNEL MIDDLE FUNNEL LOWER FUNNEL
*Definition of audience pentation can differ by audience reach, optimal audience frequency, consumer survey results, consumer panel data outputs, or audience segmentation research results
It would require a partnership with clients’ media agency, consumer survey agency, or usage of 3rd party audience syndication platforms
Demo data sources: Aggregate Marketing System Simulator developed at Google, Federal Reserve Economic Data, Google Trends, Fabricated/simulated audience data

Business priority setting framework tied to marketing portfolio planning
Framework reference
Bauer, Thomas, et al. Marketing Performance: How Marketers Drive Profitable Growth. Wiley, 2016.
HBR's 10 Must Reads on Strategic Marketing. Harvard Business Review Press, 2013.
Kim, W., and Renee Mauborgne. Blue Ocean Strategy, Expanded EDITION: How to Create Uncontested Market Space and Make the Competition Irrelevant. Harvard Business Review Press, 2015.

JOSEPH SUNGJUN JANG
linkedin.com/in/josephsungjunjang/
sungjooncafe.com

References
Blank, Steven G. The Four Steps to the Epiphany: Successful Strategies for Products That Win. K & S Ranch, 2013.
Blut, Markus, et al. “Testing Retail Marketing-Mix Effects on Patronage: A Meta-Analysis.” Journal of Retailing, vol. 94, no. 2, 2018, pp. 113–135.
Boulding, W., & Staelin, R. Identifying generalizable effects of strategic actions on firm performance: The case of demand-side returns to R&D spending. Marketing Science, 14(3): G222-G236.
Broadbent. Simon (1984), "Modelling with Adstock," Journal of the Market Research Society. 26 (October). 295-312
Cavusgil, S. T., & Zou, S. 1994. Marketing strategy-performance relationship: An investigation of the empirical link in export market ventures. Journal of Marketing, 58(1): 1-21.
Dekimpe, M.G., and Hanssens, D.M. 1995. "The Persistence of Marketing Effects on Sales," Marketing Science (14:1), pp. 1-21.
Hanssens, Dominique M., et al. Market Response Models: Econometric and Time Series Analysis. Kluwer Academic Publishers, 2004.
Johnson, Richard Arnold, and Dean W. Wichern. Applied Multivariate Statistical Analysis. Pearson, 2007.
Lim, Weng Marc. “A Marketing Mix Typology for INTEGRATED Care: The 10 Ps.” Journal of Strategic Marketing, vol. 29, no. 5, 2020, pp. 453–469.
Mangobe, Magaliе Mboyo, and Vasil Ilyich Bespiatykh. “Marketing Mix in e-Commerce.” EurasianUnionScientists, vol. 4, no. 6(75), 2020, pp. 10–12.
O'Sullivan, Arthur, et al. Economics: Principles, Applications, and Tools. Pearson Prentice Hall
Peterson, M. F., Arregle, J.-L., & Martin, X. 2012. Multilevel models in international business research. Journal of International Business Studies, 43(5): 451-457.
Steenkamp, Jan-Benedict. “Global Marketing Mix Decisions: Global Integration, Not Standardization.” Global Brand Strategy, 2017, pp. 75–109.
Tellis, Gerard J. “Modeling Marketing Mix.” The Handbook of Marketing Research, 2006, pp. 506–522.
Villas-Boas, J. Miguel, and Russell S. Winer. “Endogeneity in Brand Choice Models.” Management Science, vol. 45, no. 10, 1999, pp. 1324–1338.

Various econometrics in marketing
Y=β0+ �
i=1
n
βiBi+ �
i=1
n
βiMi+ϵ
Y=μ+γ+ψ+ �
i=1
n
βiBi+ �
i=1
n
βiMi+ϵ
Yp=μp+γp+ψp+ �
i=1
n
βiBi + �
i=1
n
βiF
p
Mi
p
+ �
i=1
n
βiR
p
Mi
p
+ϵ
Yp=μp+γp+ψp+ �
i=1
n
βiBi + �
t=1
T
�
i=1
n
βitF
p
Xi
p
+ �
t=1
T
�
i=1
n
βitR
p
Xi
p
+ϵ
LM, LM.FIT, PLM
DLM, RUCM
LME4, STAN
MARS, STAN
Public R packages relevant
to our proprietary packages
Multivariate regression: measure marketing contribution on sales and ROI by marketing activity
Dynamic linear model (DLM): measure brand value in cash, marketing contribution, and ROI by marketing activity
Multilevel DLM: on top of DLM, measure brand value, marketing contribution, and ROI by product line
Multilevel DLM with state space: on top of multilevel DLM, measure marketing ROI by demand period (or business cycle)
1
2
3
4

Various type of marketing profitability response curves
$0.0
$0.5
$1.0
$1.5
$2.0
$2.5
$0
$20
$40
$60
$80
$100
$0 $30 $60 $90 $120
ROMI
and
mROMI
Sales
(in
$MM)
Investment (in $MM)
Return ROMI mROMI
X
Current ROMI
Current mROMI
(marginal ROMI)
Break even
ROMI
X
Unprofitable
(saturation start)
X
X
Exponential
distribution
Logistic distribution Gompertz distribution Shifted gompertz distribution
Rayleigh distribution
Lomax distribution Nakagami distribution
Fréchet distribution
Many continuous probability distributions’ CDFs (cumulative distribution function) mimic law of
advertising’s market penetration and diminishing returns
All curve visual sources: Wikipedia to respective cumulative distribution function pages

Econometrics for marketing

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