Set Metrics in an Advertising-Sales Relationship using

The Unbundling of Industry Mind-Set Metrics:

Investigating the Effect of Own and Competitor

Mind-Set Metrics in an Advertising-Sales Relationship using

Dynamic Hierarchical Factor Models

Peter van Voornveld

S2785978

MSc Marketing Intelligence & Management Supervisor: dr. K. Dehmamy

Second reader: prof. dr. J.E. Wieringa

Introduction

Data about the value of a brand in the “hearts and minds” of

consumers is increasing.1

Mind-set metrics represent complex intermediate mind-set states, which evolve in conjunction with competitor mind-set metrics. 2 • Industry movement 3

• Batch dependent measurement errors 4

Incorporating mind-set metrics of the focal brand and its competitor into modelling efforts is challenging.

• Collinearity & Overparameterization 5,6

Through the application of a dynamic hierarchical factor model, the interconnected nature of industry mind-set metrics can be unbundled.

Incorporating this latent knowledge could improve the predictive

performance of sales in the advertisement-sales relationship. 7

Research Question

Do own and competitor intermediate mind-set states

extracted using a dynamic hierarchical factor model provide a

useful basis to improve the ability to predict sales in the

Data Description

• The dataset is provided by MeMo² and ***.

• Motor insurance division

• 117 weeks of data: January 01, 2016, to March 26, 2018.

• Car insurance sales, advertising channels: television, radio and online.

• Competitors: COMPETITOR 1, COMPETITOR 2, COMPETITOR 3, COMPETITOR 4, COMPETITOR 5,

COMPETITOR 6, COMPETITOR 7 and COMPETITOR 8.

• Mind-metrics: Top of mind awareness, spontaneous brand awareness, aided brand awareness, aided

campaign awareness, brand consideration and brand preference.

Model Choice

Dynamic Hierarchical Factor Model (DHFM)

• Captures movement on different hierarchy levels: 1. Market dynamics (3)

2. Mind-set states dynamics (2,2) 3. Competitor dynamics (1)

4. Mind-set metric dynamics (1)

Factor Augmented Vector AutoRegressive model with

Exogenous variables (FAVARX)

Interpretation: Impulse Response Functions, Generalized Forecast Error Variance Decomposition and Forecasting

• FAVARX takes into account the dynamics and feedback

effects of the intermediate mind-set states and the

competitive actions. 7

• FAVARX allows to incorporate endogenous and exogenous variables. 8

Results

• Variance Decomposition

• The mind-set state level mostly explains spontaneous brand awareness, aided campaign awareness and brand preference of ***. • The mind-set state level mostly explains variance the competitors COMPETITOR 3, COMPETITOR 1, COMPETITOR 2 and

COMPETITOR 8.

• Comparing the predictive performance of the FAVARX vs. the benchmark model

• The FAVARX model outperforms the VARX model based on the average squared prediction error: FAVARX 0.303, VARX 0.379 • Both models outperform a naïve based on the Theil’s U-statistic: FAVARX 0.842, VARX 0.942

108 109 110 111 112 113 114 115 116 117 -1,25 -1,00 -0,75 -0,50 -0,25 0,00 0,25 0,50 0,75 1,00 1,25

Results

Hypothesis Expected relationship Findings

H1: Effect of advertising on own mind-set metrics + Television on own factor 1: Negative effect

Television on own factor 2 & radio on own factor 1 and 2: No effect H2: Effect of advertising on sales + Television: Positive effect

Radio: Positive and negative effects Online: No effect

H3: Effect of own mind-set metrics on sales + Own mind-set factor 1 and 2: No effect H4: Effect of competitor mind-set metrics on sales - Competitor mind-set factor 2: Negative effect

Competitor mind-set factor 1: No effect

Sales Television Radio Mind-set factors

59,9% 13,9% 6,4% 3,8% 5,4% 5,1% 5,5% 19,8%

FAVARX

VARX

Competitor mind-set factor 1 Competitor mind-set factor 2

Generalized Forecast Error Variance Decomposition

Discussion and Conclusion

Do own and competitor intermediate mind-set states extracted using a dynamic hierarchical factor model

provide a useful basis to improve the ability to predict sales in the advertising-sales relationship?

The performance did not improve significantly. Although non-significant the performance did improve by 25,1% based on

the average squared prediction error statistic.

• Television show to have a positive effect, where radio has a mixed effect and online has no effect.

• Type of industry

• Composition campaign

Managerial Implications

Data Collection Sales Growth Competitors

Tracking own and competitor

mind-set metric could be very

useful.

- Short term sales predictions

- Signalling proxy of competitor

branding efforts

*** should reconsider the

usefulness of radio for

growing sales.

To analyse the branding

performance of a company, the

relevance of competitor

branding efforts should not be

underestimated.

Limitation and Future Research

• Dataset

• Sales are based on registrations.

• Different type of industry to test the relevance of the research framework.

• FAVARX estimation

• Non-normality was found in the residuals.

• Not all draws are checked for the model assumptions.

• Not accounted for industry relevant factors, like car sales.

• Interpretation

Thanks for your attention!

References

1. Bowman, D., & Gatignon, H. (2009). Market Response and Marketing Mix Models: Trends and Research Opportunities.

Foundations and Trends in Marketing, 4(3), 129–207.

2. Park, J., Dillon, W. R., Lee, S., & Chaiy, S. (2015). Getting the Most Out of Customer Satisfaction Tracking: A Dynamic Multilevel

Structural Equation Model of Key Performance Metrics (Working Paper).

3. Rajagopal. (2008). Measuring brand performance through metrics application. Measuring Business Excellence, 12(4), 29–38.

4. Sridhar, S., Naik, P. A., & Kelkar, A. (2017). Metrics unreliability and marketing overspending. International Journal of Research

in Marketing, 34(4), 761–779.

5. Farley, J. U., Lehmann, D. R., & Mann, L. H. (1998). Designing the Next Study for Maximum Impact. Journal of Marketing

Research, 35(4), 496–501.

6. Gelper, S., Wilms, I., & Croux, C. (2016). Identifying Demand Effects in a Large Network of Product Categories. Journal of

Retailing, 92(1), 25–39.

7. Srinivasan, S., Vanhuele, M., & Pauwels, K. (2010). Mind-Set Metrics in Market Response Models: An Integrative Approach.

Journal of Marketing Research, 47(4), 672–684.