Poster Presentation at 42nd ISMS(INFORMS Marketing Science Conference), which is a renowned world-wide conference when it comes to marketing science & management (http://meetings2.informs.org/wordpress/nationalharbor2020)

1. Marketing Resource Allocation
across Media Channels in the Game Industry
ISMS 1678
Sejin Jang, Devsisters
Jeong-Eun Yoo, Hanyang University
Juhong Park, Devsisters
Hyun Shin, Hanyang University

2. Outline
 Literature Review
 Customer Lifetime Value (CLV) / Customer Equity (CE)
 Resource Allocation
 Data
 Industry Background
 Data Description
 Methodology
 Vector Auto-regressive (VAR) Modeling Approach
 Model: Active User & Paying User
 Results
 Descriptive Statistics
 Estimation & Empirical Findings
 Conclusion
 Summary of Findings
 Implications & Future Research

3. Customer Lifetime Value / Customer Equity
Framework
 Marketing aims at acquiring new users or retaining existing ones.
 CLV/CE framework views customers as an asset where CLV considers how much
value a customer can generate, and CE is a sum of CLV of customers
(Dwyer, 1997; Gupta, Lehmann & Stuart, 2004; Reinartz & Kumar, 2003; Wiesel,
Skiera & Villanueva, 2008; Rust, Lemon & Zeithaml, 2004).
 According to CLV/CE approach, marketing expenditure can be regarded as an
investment (Berger at al., 2002).

4. Resource Allocation
 Prior researchers emphasized an importance of measuring ROI in terms of
advertising spending (Kripalano & Shapiro, 1973).
 Optimal resource allocation of advertising spending matters (Mantrala, 1992).
 How to better allocate marketing budget across media channels becomes an
important issue.

5. 본 자료는 데브시스터즈가 소유한 자산으로 외부 반출 및 수정을 엄격하게 금지합니다. 이를 어길 시에는 민.형사상 조치를 받을 수 있습니다.
 Period
2016, Oct 17 ~ 2019, June 30 (weekly data, 139 weeks)
 Country
South Korea
 Metrics
Marketing spending, Paying users, Active users, Revenue
 Marketing channel
Facebook, Google, Naver Webtoon
 Exogenous (control)
Holiday, Game updates, Featured
Data
<Devsisters’s Cookie Run: OvenBreak>

6. The Vector Autoregressive (VAR) Modeling Approach
 Long-run effectiveness of each channel can be computed as a form of an elasticity, using
impulse response function analyses (Dekimpe & Hanssens, 1995, 1999)
 Endogeneity among variables can be captured
 Exogenous variables can be considered

7. Model 1
𝑀𝐾𝑇_𝐹𝐵𝑡
𝑀𝐾𝑇_𝑁𝐹𝐵𝑡
𝑃𝑈_𝐹𝐵𝑡
𝑃𝑈_𝑂𝑟𝑔𝑎𝑛𝑖𝑐𝑡
𝑅𝐸𝑉_𝐹𝐵𝑡
𝑅𝐸𝑉_𝑂𝑟𝑔𝑎𝑛𝑖𝑐𝑡
𝜋11
j
𝜋12
j
𝜋13
j
𝜋14
j
𝜋15
j
𝜋16
j
𝜋21
j
𝜋22
j
𝜋23
j
𝜋24
j
𝜋25
j
𝜋26
j
⋮
𝜋61
j
𝜋62
j
𝜋63
j
𝜋64
j
𝜋65
j
𝜋66
j
𝑀𝐾𝑇_𝐹𝐵𝑡−𝑗
𝑀𝐾𝑇_𝑁𝐹𝐵𝑡−𝑗
𝑃𝑈_𝐹𝐵𝑡−𝑗
𝑃𝑈_𝑂𝑟𝑔𝑎𝑛𝑖𝑐𝑡−𝑗
𝑅𝐸𝑉_𝐹𝐵𝑡−𝑗
𝑅𝐸𝑉_𝑂𝑟𝑔𝑎𝑛𝑖𝑐𝑡−𝑗
ℎ𝑜𝑙𝑖𝑑𝑎𝑦1,𝑡 + 𝑢𝑝𝑑𝑎𝑡𝑒1,𝑡 + 𝑢𝑝𝑑𝑎𝑡𝑒_𝑙𝑎𝑔1,𝑡 + 𝑓𝑒𝑎𝑡𝑢𝑟𝑒𝑑1,𝑡
ℎ𝑜𝑙𝑖𝑑𝑎𝑦2,𝑡 + 𝑢𝑝𝑑𝑎𝑡𝑒2,𝑡 + 𝑢𝑝𝑑𝑎𝑡𝑒_𝑙𝑎𝑔2,𝑡 + 𝑓𝑒𝑎𝑡𝑢𝑟𝑒𝑑2,𝑡
⋮
ℎ𝑜𝑙𝑖𝑑𝑎𝑦6,𝑡 + 𝑢𝑝𝑑𝑎𝑡𝑒6,𝑡 + 𝑢𝑝𝑑𝑎𝑡𝑒_𝑙𝑎𝑔6,𝑡 + 𝑓𝑒𝑎𝑡𝑢𝑟𝑒𝑑6,𝑡
𝑗=1
𝐽
=
+
Vector Autoregression Model for Paying User Analysis
𝑐1
𝑐2
𝑐3
𝑐4
𝑐5
𝑐6
𝐸𝑇1,𝑡
𝐸𝑇2,𝑡
𝐸𝑇3,𝑡
𝐸𝑇4,𝑡
𝐸𝑇5,𝑡
𝐸𝑇6,𝑡
𝜖1,𝑡
𝜖2,𝑡
𝜖3,𝑡
𝜖4,𝑡
𝜖5,𝑡
𝜖6,𝑡
++
+
, 𝜖𝑖~𝑖𝑖𝑑 𝑁(0, 𝜎2
)

8. Results: Elasticity Analysis of Direct Acquisition Users
Marketing spending effects on the # of users (AU vs. PU)
Shock MKT_FB MKT_GG MKT_NW MKT_FB MKT_GG MKT_NW
Response AU_FB AU_GG AU_NW PU_FB PU_GG PU_NW
Elasticity 0.446 0.088 1.160 1.028 0.150 1.406
Per unit
46.108 7.535 28.053 46.108 7.535 28.053
88.605 28.118 27.664 5.351 0.175 0.624
Divided by shock
1.000 1.000 1.000 1.000 1.000 1.000
1.922 3.731 0.986 0.116 0.023 0.022

9. Conclusion: Summary of Findings
 Marketing investment on Facebook works effectively for both AU and PU. Accordingly,
the ROI is higher compared with other channels. And the dollar value of one user is
also higher compared with other channels
 Marketing investment on Google is effective for AU rather than PU, meaning that it is
not as profitable as expected
 Marketing spending on Naver webtoon has a great influence on organic users and
organic revenue. In case of Naver, it was difficult to accurately measure the value of
one user due to the characteristics of the channel. Thus, the effect of the marketing
cost may have been underestimated so far.

10. Thanks 