Prasad A. Naik

Understanding the Impact of Synergy in Multimedia Communications

Many advertisers adopt the integrated marketing communications perspective that emphasizes the importance of synergy in planning multimedia activities. However, the role of synergy in multimedia communications is not well understood. Thus, the authors investigate the theoretical and empirical effects of synergy by extending a commonly used dynamic advertising model to multimedia environments. They illustrate how advertisers can estimate and infer the effectiveness of and synergy among multimedia communications by applying Kalman filtering methodology. Using market data on Dockers brand advertising, the authors first calibrate the extended model to establish the presence of synergy between television and print advertisements in consumer markets. Second, they derive theoretical propositions to understand the impact of synergy on media budget, media mix, and advertising carryover. One of the propositions reveals that as synergy increases, advertisers should not only increase the media budget but also allocate more funds to the less effective activity. The authors also discuss the implications for advertising overspending. Finally, the authors generalize the model to include multiple media, differential carryover, and asymmetrical synergy, and they identify topics for further research.

The effects of expert quality evaluations versus brand name on price premiums

Investigates the extent to which expert evaluations of quality impact price premiums of national brands over the store brands. Using data from Consumer Reports, finds that the average quality of store brands exceeds the average quality of national brands in 22 out of 78 product categories. Yet store brands typically do not charge price premiums, while national brands do (28.7 percent price premium on average). When national brands have higher quality, however, they increase the price premium from 28.7 percent to 50.4 percent on average. Regression analysis predicts that a national brand would command 37 percent price premium over a store brand that offers the same quality, a finding that highlights the handsome returns on building brand equity.

Markov-Switching Model Selection Using Kullback-Leibler Divergence

In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. In applying Akaike information criterion (AIC), which is an estimate of KL divergence, we find that AIC retains too many states and variables in the model. Hence, we derive a new information criterion, Markov switching criterion (MSC), which yields a marked improvement in state determination and variable selection because it imposes an appropriate penalty to mitigate the over-retention of states in the Markov chain. MSC performs well in Monte Carlo studies with single and multiple states, small and large samples, and low and high noise. Furthermore, it not only applies to Markov-switching regression models, but also performs well in Markov- switching autoregression models. Finally, the usefulness of MSC is illustrated via applications to the U.S. business cycle and the effectiveness of media advertising.

Partial least squares estimator for single-index models

The partial least squares (PLS) approach first constructs new explanatory variables, known as factors (or components), which are linear combinations of available predictor variables. A small subset of these factors is then chosen and retained for prediction. We study the performance of PLS in estimating single-index models, especially when the predictor variables exhibit high collinearity. We show that PLS estimates are consistent up to a constant of proportionality. We present three simulation studies that compare the performance of PLS in estimating single-index models with that of sliced inverse regression (SIR). In the first two studies, we find that PLS performs better than SIR when collinearity exists. In the third study, we learn that PLS performs well even when there are multiple dependent variables, the link function is non-linear and the shape of the functional form is not known.

Extending the Akaike Information Criterion to Mixture Regression Models

We examine the problem of jointly selecting the number of components and variables in finite mixture regression models. We find that the Akaike information criterion is unsatisfactory for this purpose because it overestimates the number of components, which in turn results in incorrect variables being retained in the model. Therefore, we derive a new information criterion, the mixture regression criterion (MRC), that yields marked improvement in model selection due to what we call the “clustering penalty function.” Moreover, we prove the asymptotic efficiency of the MRC. We show that it performs well in Monte Carlo studies for the same or different covariates across components with equal or unequal sample sizes. We also present an empirical example on sales territory management to illustrate the application and efficacy of the MRC. Finally, we generalize the MRC to mixture quasi-likelihood and mixture autoregressive models, thus extending its applicability to non-Gaussian models, discrete responses, and dependent data.

Markov-switching model selection using Kullback-Leibler divergence

Abstract In Markov-switching regression models, we use Kullback–Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. Specifically, we derive a new information criterion, Markov switching criterion (MSC), which is an estimate of KL divergence. MSC imposes an appropriate penalty to mitigate the over-retention of states in the Markov chain, and it performs well in Monte Carlo studies with single and multiple states, small and large samples, and low and high noise. We illustrate the usefulness of MSC via applications to the U.S. business cycle and to media advertising.

A New Dimension Reduction Approach for Data-Rich Marketing Environments: Sliced Inverse Regression

In data-rich marketing environments (e.g., direct marketing or new product design), managers face an ever-growing need to reduce the number of variables effectively. To accomplish this goal, the authors introduce a new method called sliced inverse regression (SIR), which finds factors by taking into account the information contained in both the dependent and independent variables. Sliced inverse regression objectively identifies appropriate factors through simple statistical tests for determining the number of factors to retain and for assessing the significance of factor-loading coefficients. The authors make conceptual connections between SIR and several existing approaches, including principal components regression (PCR) and partial least squares regression (PLSR). Using Monte Carlo experiments, the authors demonstrate that SIR performs better than these approaches. Two empirical examples—designing a new executive business program and direct marketing by a catalog company—are presented to illustrate the application of SIR and to show that it outperforms both PLSR and PCR in these cases. In addition, the authors discuss how direct marketers can apply SIR to analyze large databases and to thus target customers effectively. In conclusion, SIR is a promising methodology in data-intensive marketing environments.

Long-term Profit Impact Of Integrated Marketing Communications Program

The concept of Integrated Marketing Communications (IMC) emphasizes the role of synergy, which arises when the combined effect of multiple activities exceeds the sum of their individual effects. In this paper, we investigate the effects of synergy on the profitability of IMC programs in uncertain markets. We develop a dynamic multimedia model that incorporates both synergy and uncertainty, and use it to determine the optimal IMC program. Our results generalize previous findings to uncertain markets, illuminate the profit implications of IMC programs, and explain the catalytic effects of synergy in IMC contexts. Specifically, we find that the expected long-term profit of the advertised brand increases as synergy increases. Furthermore, managers should allocate a non-zero budget to a catalytic activity even if it is completely ineffective. Finally, these findings continue to hold in an uncertain duopoly market.

Discovering How Advertising Grows Sales and Builds Brands

Advertising nudges consumers along the think–feel–do hierarchy of intermediate effects of advertising to induce sales. Because intermediate effects—cognition, affect, and experience—are unobservable constructs, brand managers use a battery of mind-set metrics to assess how advertising builds brands. However, extant sales response models explain how advertising grows sales but ignore the role of intermediate effects in building brands. To link these dual contributions of advertising, the authors propose an integrated framework that augments the dynamic advertising–sales response model by integrating the hierarchy, dynamic evolution, and purchase reinforcement of intermediate effects. Methodologically, the new approach incorporates the intermediate effects as factors from mind-set metrics while filtering out measurement noise, extracts the factor loadings, estimates the dynamic evolution of the factors, and infers their sequence in any hypothesized hierarchy by embedding their impact in a dynamic advertising–sales response model. The authors apply the proposed model and associated method to a major brand to discover the brands operating hierarchy (advertising → experience → cognition → affect ↔ sales). The results provide the first empirical evidence that intermediate effects are indeed dynamic constructs, that purchase reinforcement effects exist not only for experience but also for other intermediate effects, and that advertising simultaneously contributes to both sales growth and brand building. Thus, both researchers and managers should consider using the proposed framework to capture advertisings dual contributions of building brands and growing sales.

Dynamic Marketing Budgeting for Platform Firms: Theory, Evidence, and Application

Few studies address the marketing budgeting problems of platform firms operating in two-sided markets with cross-market network effects, such that demand from one customer group in the platform influences the demand from the other customer group. Yet such firms (e.g., newspapers whose customers are both subscribers and advertisers) are prevalent in the marketplace and invest significantly in marketing. To enable such firms to make effective marketing decisions, the authors delineate the desired features of a platform firms marketing response model, specify a new response model, and validate it using market data from a local newspaper. The results show that the firm faces reinforcing cross-market effects, its demand from both groups depends on marketing investments, and the model exhibits good forecasting capability. The authors use the estimated response model to determine optimal marketing investments over a finite planning horizon and find that the firm should significantly increase its newsroom and sales force investments. With this model-based recommendation, the firms management increased its newsroom budget by 18%. Further normative analysis sheds light on how cross-market and carryover effects alter classical one-sided marketing budgeting rules.