assessing the impact of unmodeled marketing activities … pharmaceutical...spring 2008 dma...

Spring 2008 DMA AnAlyticS AnnuAl JournAl

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Abstract

Due to data availability and modeling technique limitations, not all marketing activities were considered in past pharmaceutical marketing studies. Using a Monte Carlo simulation approach, this study assessed how the omission of other factors in the model influences the estimates of detailing impact and the associated return on investment (ROI).1 We found that a single factor detailing response model could induce substantial bias in assessing detailing ROI. Additionally, the severity of this bias depends on how much the omitted factor is related to detailing activities, and how much the omitted factor affects physicians’ prescribing decisions. Identifying the relationship between ROI assessment bias and the related factors presented the opportunities of correcting this bias in certain cases.

Introduction

This paper examines the impact of omitting relevant variables from a predictive sales model, and proposes a method of correcting estimates for included variables, in order to measure their impact on sales more accurately. The adjustment is extended to correct practical metrics such as ROI. Using Monte Carlo simulated data, this research identifies the relationship among three dimensions: the correlation between the omitted and included predictors, the impact of the omitted variable, and the bias introduced by omission of a relevant predictor from the model. In certain cases, the bias will be seen to have significant economic consequences. These relationships are used to develop a

practical method for adjusting sales models to predict the impact of a predictor variable accurately, (for example the impact of detailing on sales, and its affect on ROI, particularly when a relevant variable is omitted). This paper goes on to compare how well the single factor detailing response model, common in the pharmaceutical industry, and the Marketing Mix Modeling approach, perform in terms of detailing ROI estimation bias.

In developing models that determine the impact of individual promotional activities on sales in the pharmaceutical world, all relevant elements need to be considered. Factors such as market share, competitive environment, positioning of the product as well as marketing activities, should be taken into account. Pharmaceutical manufacturers utilize a variety of marketing vehicles to promote their products to consumers and physicians. In order to reach physicians, pharmaceutical manufacturers engage in a number of promotional activities: Detailing (in-person visits by the sales force), Sampling (samples of products delivered to physicians’ offices), Journal Ads, Meetings & Events (conferences), Coupons, and Direct Mailing. Consumers are most often targeted via Direct-To-Consumer (DTC) advertisements and public relations media. However, researchers often find they can not obtain all the relevant data needed to produce a comprehensive marketing mix model. In practice, researchers must omit some relevant variables due to a lack of data or appropriate modeling techniques. It is also common in the industry to use a single-factor approach to quantify the effects of these promotional activities, a practice which may limit the usefulness of the results. For example, a single factor model that uses only detailing to predict new prescriptions is not able

Assessing the Impact of Unmodeled Marketing Activities on Pharmaceutical Detailing ROI

Hong Jin, Jim Ryan, and Stephen Vail

Analytic Partners


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to incorporate the effects of other relevant factors and may systematically overestimate the impact of detailing on new prescriptions. By not specifying other important factors in the model, such as sampling or DTC ,which also impact sales, manufacturers frequently overestimate the impact of detailing, which absorbs the impacts of the omitted relevant factors. This bias in measuring the effect of detailing will directly affect the accuracy of detailing ROI evaluation.

In contrast, the Marketing Mix Modeling approach may be better suited to accurately estimate the effects of individual promotional activities that affect physicians’ prescribing decisions, because its methodology is able to estimate simultaneously the impact of multiple activities and their interactions. However, even with this technique, the researcher is sometimes confronted with the problem of having to leave out variables which may have a significant impact on sales.

Research on the Impact of Pharmaceutical Marketing

There are many published articles on pharmaceutical promotion response analysis. Manchanda et al. (2005) summarized key business issues and analytical techniques. Among all pharmaceutical promotion activities, detailing has been the most popular topic. The majority of research focuses on specific modeling techniques used to assess the effectiveness of pharmaceutical marketing activities. Lilien et al. (1981) used a Bayesian approach to analyze the impact of detailing on physicians’ prescribing decisions. Parsons and Abeele (1981) applied a multiplicative regression model to demonstrate diminishing return in sales call responses. Manchanda and Chintagunta (2004) investigated a physician-specific response model using panel data to analyze individual physicians’ responses to salesforce detailing calls and call quality. Chintagunta and Desiraju (2005) investigated the relationship between price and detailing in international markets.

Research has also been done to assess the impact of other marketing activities. For example, Wilkes et al. (2000) discussed the impact of DTC on prescription drug sales. Neslin (2001) and Wittink (2002) analyzed ROIs across key marketing channels. NarayananNarayanan et al. (2004) investigated the role marketing mix interactions and their impact on ROI assessment. Mizik and Jacobson (2004) used a dynamic fixed-effects distributed lag regression to assess the impact of detailing and sampling on physicians’ prescribing behaviors.

Among empirical works of pharmaceutical promotion response analysis found in the literature, no single research disclosed whether all marketing activities were included, nor did any explore the impact of leaving out some marketing activities from their models. Due to the lack of consistency in controlling which activities were included or omitted, different studies generated conflicting results. For example, Parsons and Abeele (1981) found the estimated effect of detailing was negative while most studies found positive impact of detailing on prescription drug sales.

This study attempts to address when and how a missing marketing activity may affect the promotion response and ROI of the modeled marketing activities.

Empirical Study Setup

Relationships Among Pharmaceutical Marketing Activities

In order to gain a better understanding of the following empirical studies, we discuss the relationships among key pharmaceutical marketing activities. Based on the promotional targets, pharmaceutical marketing efforts can be grouped into Physician-Driven and Consumer-Driven activities. Physician-driven marketing channels include Detailing, Sampling, Journal Ads, Meetings & Events (M&E), Coupons, Direct Mail, and eDetailing. Direct-To-Consumer marketing activities include TV, Magazine, Radio, Internet advertisements and Public Relations media broadcasting. It is unlikely a single product can leverage all of the above-mentioned marketing vehicles. In general, a brand marketing team chooses a set of marketing channels based on the product’s market potential, financial strength, and therapeutic nature. Among the key marketing channels, the relationship among various marketing activities can vary. In general, the types of interrelationship can be summarized as follows:

1. Detailing, sampling, and M&E are the primary physician-Detailing, sampling, and M&E are the primary physician-driven marketing channels. Therefore their marketing activities are well-coordinated, and therefore positively correlated.

2. Direct mailing and eDetailing are complementaryDirect mailing and eDetailing are complementary physician-driven marketing channels and are typically provided to physicians who have not received sufficient detailing or other types of physician-driven promotions.

3. Journals Ads, DTC and PR are mass media marketingJournals Ads, DTC and PR are mass media marketing


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channels, and their marketing activities are not necessarily directly correlated with physician-driven marketing activities.

In addition, one brand’s prescription drug sales may be negatively impacted by competitors’ marketing promotion activities. Most pharmaceutical companies choose their physician targets (for detailing and/or sampling promotion) based on their respective category prescription volume. Physicians who write a large number of prescriptions are generally targeted by all major pharmaceutical manufacturers in the same therapy area. Hence, a pharmaceutical manufacturer’s detailing and sampling promotion activities could be positively correlated with their competitors’ promotion activities.

Assumptions About The Sales-Marketing Relationship

In order to examine the differences between the marketing mix modeling approach and the single-factor approach, we conducted empirical studies under a set of hypothetical business scenarios. We assume in all scenarios that new prescription drug sales are influenced by detailing and other marketing activities that are positively or negatively correlated with detailing, including Sampling, Direct Mailing, Corporate PR, Competitor Promotion, and a set of specific market conditions (seasonality, brand equity and preference, etc.). Among these factors, we further assume that:

1. Detailing, sampling and direct mailing have a positiveDetailing, sampling and direct mailing have a positive impact on physicians writing new prescriptions in favor of the promoted drug. On the other hand, competitors’ detailing affects physicians’ prescribing decision negatively, in favor of products other than the concerned drug.

2. Corporate PR may not have a significant direct impactCorporate PR may not have a significant direct impact on physicians’ prescribing decisions. It is included in the model to demonstrate the impact of a potential “over-specification” in the marketing mix models.

3. Detailing is positively correlated with sampling, corporateDetailing is positively correlated with sampling, corporate PR and even competitors’ detailing promotion activities, but negatively correlated with direct mailing, which is leveraged as a complementary promotion vehicle when detailing promotion is too costly.

A generic form of the above Sales-Marketing relationship can be expressed as;

NRx = B + βD

fD(Details) + β

S f

S(Samples) + β

M f

M(Mails)

+βP

fP(PR) + β

C f

C(COMP_Details)

where ;

• NRx stands for new prescriptions of the concerned brand

• B represents baseline new prescription volume in absence of all marketing promotions

• F

i() is the functional form linking the ith marketing activity

with new prescriptions

• βD measures the impact of detailing on new prescriptions

• β

S measures the impact of sampling on new prescriptions

• βM measures the impact of direct mailing on new

prescriptions

• βP measures the impact of corporate PR on new

prescriptions

• β

C measures the impact of competitors’ detailing on new

prescriptions

Contrary to the above comprehensive marketing mix model, a single factor detailing response model may take the following format NRx = B SF +βSF

Df

D(Details), assuming the function

form of detailing, f D(), is the same as that in the above

marketing mix model.

Simulation Scenarios

In order to demonstrate the difference between the marketing mix modeling approach and the single-factor detailing response analysis, we generated a set of simulation scenarios based on a dataset from a major pharmaceutical manufacturer. The single factor detailing response model includes only detailing as the sole predictor, while the marketing mix model considers all marketing activities including Corporate PR, which is assumed to have no direct impact on physicians’ prescribing decisions. For each method, we will compare the respective estimated detailing ROI against the true ROI value to determine which approach produces a more accurate assessment on detailing impact.

For the sake of simplicity, we assume a linear relationship of marketing activity on sales. In all simulated scenarios, the impact of detailing is assumed to be equivalent to ROI = 1*.

* In order not to disclose customer data, the detailing ROI result is normalized in this study. The consequent ROI bias calculations will be based on ROI indexes, but the relative bias results (percentage difference) should be the same.


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Scenario 0 examines whether Marketing Mix Modeling has any advantage in detailing ROI assessment when detailing promotion and other marketing activities are completely uncorrelated.

Scenario 1 reveals the impact on detailing ROI assessment when excluding a marketing activity (sampling in this scenario) that is positively correlated with detailing and has positive impact on physicians’s prescribing decisions.

Scenario 2 shows the impact on detailing ROI assessment when missing a marketing activity (direct mailing in this scenario) that is negatively correlated with detailing and has positive impact on physicians’s prescribing decisions.

Scenario 3 exhibits the impact on detailing ROI assessment when missing competitors’ detailing activities, which are positively correlated with detailing but have negative impact on physicians’ prescribing decisions.

Scenario 4 addresses the potential “over-specification” issue in Marketing Mix Models that include a marketing activity (Corporate PR in this scenario) that has no impact on physicians’ prescribing decisions.

Simulation Methods

At the core of the simulation, a procedure was run iteratively to generate synthetic data sets. In all data sets, the number of new prescriptions is a function of several factors corresponding to activities typically found in the pharmaceutical marketing mix. The mean value and spread of each marketing activity and its impact on new drug sales are derived based on empirical studies of pharmaceutical marketing promotion response analyses, and these values are controlled parameters in Monte Carlo simulations that generate data points for each scenario defined above.

In each scenario, two parameters for a certain factor are altered over a specific suitable range. The two parameters of a marketing activity are: its influence on new prescriptions (model coefficient) and its correlation with detailing. For each coefficient-correlation value pair, we fit the simulated datasets using two models: the single factor detailing response regression and the full MMM regression. Coefficient estimates are retrieved and used in ROI calculation. We perform 400 repetitions for each value pair to ensure that we are able to

compute a reliable estimate of the average bias of the detailing ROI in each scenario.

ROI Bias – Simulation Results Evaluation Metric

In this study, we use detailing ROI bias as the metric to evaluate the estimation results from the two modeling approaches. ROI bias is defined as the percentage difference between the estimated ROI and the hypothetical ROI, calculated based on the true parameter. Specifically, ROI Bias = Estimated ROI / Hypothetic ROI – 1.

Given the above-defined sales-marketing promotionrelationship, ROI can be expressed as

βD f

D(Details)*Price / NRx / *Margin Detailing Costs,

where unit price (per NRx ), profit margin and detailing costs are same in both estimated and hypothetic ROI calculations. Hence, ROI bias is equivalent to parameter estimation relative error,

β̂D/ β

D-1 .

In the following empirical studies, we compare ROI biases from the two models: the Single-Factor detailing model, and the Marketing Mix Model. In addition, we will also investigate the relationship between ROI bias and A) the correlation between detailing and the missing marketing activity, and B) the impact of the missing activity on new prescriptions sales.

Computational Results

ROI Assessment Accuracy under Each Scenario

Scenario 0: In Table 0 (below) there is no statistically significant difference between detailing ROI estimates obtained from the Single-Factor and Marketing Mix Model approaches. In other words, the MMM approach has no accuracy advantage over the Single-Factor when the omitted marketing activities are completely uncorrelated with the detailing promotion that is included in the model.

Table 0: Detailing ROI Assessment with

Uncorrelated Marketing Activities


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Scenario 1: Table � (top) shows that the Single-Factor approach overestimates the detailing ROI by absorbing the impact from the sampling promotion, which is positively correlated with detailing activities. ROI bias becomes larger as the detailing-sampling correlation and the sampling impact increase, as shown in Figure � (left). Detailing ROI is overestimated by less than 13.8% when the correlation is 9% and the sampling impact is low. However, detailing ROI is over-assessed more than 13 times when sampling is highly correlated with detailing and when sampling has high impact on physicians’ prescribing decisions. On the other hand, the ROI bias using the Marketing Mix Modeling approach is much smaller, and the bias does not increase with the detailing-sampling correlation and the sampling impact level.

Scenario 2: In contrast to Scenario 1, this scenario shows that the Single-Factor approach underestimates the detailing ROI, as evidenced in Table 2 (pg. 34). The ROI underestimation becomes more severe as direct mailing and detailing activities are more correlated and direct mailing promotion has larger impact on physicians’ prescribing decisions. As seen in Figure 2, (pg. 34) detailing ROI is underestimated by 5.2% when the correlation is -9% and direct mailing promotion impact is low. The estimated detailing impact becomes negative

Table 1: Impact of Missing Sampling on Detailing ROI Bias (Scenario 1)

Figure 1: Detailing ROI Bias and Detailing-Sampling Correlation and Sampling Impact


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when direct mailing is highly correlated with detailing promotion and with larger impact on physicians’ prescribing decisions.

Scenario 3: Similar to Scenario 2, this scenario shows that the Single-Factor approach underestimates the detailing ROI, as evidenced in Table � (pg. 35.) The detailing ROI underestimation becomes more severe when the competitors’ detailing promotion and own detailing activities are more correlated and the competitors’ detailing has larger negative impact on physicians’ prescribing decisions. As seen in Figure �, (pg. 35), detailing ROI is underestimated by less than 4.7% when the correlation is 9% and the competitive promotion impact is low. The estimated detailing impact becomes negative when the competitive promotion is highly correlated with its own detailing activities and has larger impact on physicians’ prescribing decisions.

Scenario 4: This scenario (pg. 36) demonstrates that including an extra non-factor (Corporate PR) in a Marketing Mix Model has little impact on assessing detailing response and ROI, even if Corporate PR and detailing activities are positively correlated. The above simulation results also show that the correlation between the non-factor (Corporate PR) and detailing does not affect the detailing ROI assessment accuracy.

Table 2: Impact of Missing Direct Mailing on Detailing ROI Bias (Scenario 2)

Figure 2: Detailing ROI Bias and Detailing-Direct Mailing Correlation and Direct Mailing Impact


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Detailing ROI Adjustment

The empirical studies prove that the Single-Factor detailing response analysis either overestimates or underestimates the detailing ROI when

• marketing activities left out are correlated with detailing promotion, and

• marketing activities left out affect physicians’ prescribing decisions.

However, the Marketing Mix Modeling approach evaluates detailing ROI more accurately even under the above conditions.

The simulation results also show that the ROI bias increases with:

• the correlation level between detailing and the concerned left out marketing activities, and;

• the level of impact of the left out marketing activities on physicians’ prescribing decisions.

The above mentioned relationship presents the opportunity of adjusting detailing ROI estimates from a Single-Factor model based on the estimated correlation and impact level of the left out marketing activities. For example, the simulation results indicate that the Single-Factor approach underestimates detailing ROI by 52.3% when missing the competitors’ detailing activities, which are:

• 27% correlated with its own detailing promotion, and;

• the impact of competitors’ detailing is 40% compared to its own detailing promotion.

In most pharmaceutical marketing environments it is impossible to obtain the competitors’ detailing activities for all target physicians, which in turn prevents including reliable competitors’ detailing data in physician-level promotion response analysis.

Figure 3: Impact of Competitor Promotion on Own Detailing ROI Bias

Table 3: Impact of Missing Competitor Promotions on Detailing ROI Bias (Scenario 3)


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However, small-scale surveys, competitive intelligence study or analog studies may provide the needed information of competitors’ detailing activities, including its impact on physicians’ decisions and correlation with its own detailing promotion. If the correlation is 27% and the impact is 40% of its own detailing promotion, the detailing ROI should be adjusted up by 52.3%.

Contributions and Conclusions

This research investigates the impact of model specification on the ROI assessment biases, under various business scenarios. The contributions of this study can be summarized as follows:

1. This research demonstrates that the Marketing Mix Modeling approach is a better suited approach than the Single-Factor approach in assessing pharmaceutical promotion response and ROI.

2. The research also reveals the scenarios when the Single-Factor approach mis-estimates the primary marketing activity (detailing in our case) in the model.

3. The research proves that over-specification in Marketing Mix Models (i.e. including an extra non-factor in the model) has little impact on estimating the ROI of the marketing activity included in the model.

4. The research shows that the level of ROI bias (in the Single Factor model) depends on the omitted marketing activity’s impact on sales and correlation with the marketing activity included in the model.

5. The research presents a way of adjusting ROI assessment bias from a Single-Factor model based on external information of the left out marketing activity’s impact on sales and the correlation with the marketing activity included in the model.

This study investigates an important and practical problem in pharmaceutical promotion response analysis: the impact of missing marketing activities on the ROI assessment of the marketing activities included in the model. In real business

cases, it is common that some marketing activities have to be left out of the promotion response analysis, due to data availability, time constraints, or cost considerations. This research provides valuable insights assisting practitioners to determine if an ROI assessment is a true reflection of the effectiveness of the concerned marketing activity. The findings in the research can be generalized and applied to other industries where different elements of marketing mix may be correlated with each other. n

REFERENCES: 1. Chingtagunta, P., Desiraju, R., 2005. “Strategic Pricing and Detailing

Behavior in Internal Markets,” Marketing Science, 2� (1) 67-80. 2. Lilien, G., Rao, A., Kalish, S., 1981. “Bayesian Estimation and Control of

Detailing Effort in a Repeat Purchase Environment,” Management Science, 2� (5) 493-507.

3. Manchanda, P., Wittink, D., Ching, A., Cleanthous, P., Ding, M., Dong, X., Leeflang, P., Misra, S., Mizik, N., Narayanan, S., Steenburgh, T., Wieringa, J., Wosinska, M., Xie, Y., 2005. “Understanding Firm, Physician and Consumer Choice Behavior in the Pharmaceutical Industry, “Marketing Letters, �� (3/4) 293-308.

4. Manchanda, P., Chintagunta, P., 2004. “Responsiveness of Physician Prescription Behavior to Salesforce Effort: An Individual Level Analysis,” Marketing Letters, �� (2-3) 129-145.

5. Mizik, N., Jacobson, R., 2004. “Are Physicians ‘Easy Marks’? —

Quantifying the Effects of Detailing and Sampling on New Prescriptions,” Management Science, �0 (12) 1704-1715.

6. Narayanan, S., Desiraju, R., Chintagunta, P., 2004. “Return on Investment Implications for Pharmaceutical Promotional Expenditures: The Role of Marketing-Mix Interactions,” Journal of Marketing, �8 (October 2004) 90-105.

7. Neslin, S., 2001. “ROI Analysis of Pharmaceutical Promotion (RAPP): An Independent Study,” Available at http://www.rxpromoroi.org/rapp/ index.html.

8. Parsons, L., Abeele, P., 1981. “Analysis of Sales Call Effectiveness,” Journal of Marketing Research, �8 (February 1981) 107-113.

9. Rosenthal, M., Berndt, E., Donohue, J., Epstein, A., Frank, R., 2003. “Demand Effects of Recent Changes in Prescription Drug Promotion,”

10. Wilkes, M., Bell, R., Kravitz, R., 2000. “Direct-To-Consuder Prescription Drug Advertising: Trends, Impact, And Implciations,” Health Affairs, �� (2) 110-128.

11. Wittink, D., 2002. “Analysis of ROI for Pharmaceutical Promotion (ARPP),” Available at http://www.rxpromoroi.org/arpp/index.html.

Table 4: Impact of Including Non-Factor on Detailing ROI Assessment (Scenario 4)

assessing the impact of unmodeled marketing activities … pharmaceutical...spring 2008 dma...

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