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    Today’s increasingly digital society is causing an evolution in pharma marketing strategies. Numerous digital channels have sprung up due to technological advancements, and they are changing doctors’ and patients’ habits. Digital platforms such as emails, digital ads, and paid search are available for pharma companies to target doctors based on their preferences.1 These channels are not deployed to substitute traditional channels, but as part of a synergistic multi-channel marketing strategy, to complement them.2

    Effective implementation of digital marketing techniques, along with the traditional methods, provides marketers with a potent mix to expand market reach, better engage doctors, and thus gain a higher marketing return on investment (ROI). For example, if a sales rep visited a doctor after the doctor saw a digital ad, it increases the likelihood of their product to be prescribed. With such synergies, the final effect of the two channels is greater than the sum of their individual effects. Therefore, the integrated marketing strategy not only increases customer engagement and expands market reach, but also provides a higher return on marketing investment.3 An accurate ROI assessment will provide a channel-wise contribution, giving channels their due credit and helping to determine optimal budget allocation for future campaigns.4

    Issues with Measuring Channel Interactions

    Measuring the marketing ROI can be tricky, as some of these channels are not really aimed at affecting the prescription directly. They are meant to build brand awareness and perception and interact with traditional channels to affect sales.

    Measuring this interaction effect between channels also has certain challenges:
    Promo MixHow does the interaction effect change as the investment mix changes?
    Promo ChannelsHow does one split the interaction effect among the interacting channels?

    This blog addresses one approach that can tackle these challenges.

    Dynamic Asymptote Approach to Measure Channel Interactions

    Channels tend to interact continuously. As soon as promotion begins in one channel, it creates a stir in the market. Doctors are more likely to prescribe that drug, increasing the effectiveness of other channels, even if to a minor extent.

    Consider the example of an interaction between rep calls and healthcare physicians (HCPs) digital ads. ‘Continuous interaction’ means that the interaction effect of digital ads on calls should be a function of the increasing ad spend. The asymptote (maximum impact of a channel on sales) of calls needs to be defined as a function of an increasing number of ad clicks. Now, when ad spend goes up, either the same doctors see more ads, or newer (likely low value) doctors are seeing the ads for the first time. In both the scenarios, the relation between asymptote and clicks must be that of diminishing return, with a saturation point. This type of relationship is best explained using a negative exponential curve.

    It is theoretically possible that all channels interact with all other channels. Hence, the total number of interaction terms could go up to ‘nXn,’ with ‘n’ being the number of channels. Some of these interaction terms will not make business sense. Brainstorming sessions will help decide how many interaction terms to use. All perspectives must be considered (including feedback from sales and marketing teams, prior modeling experience of the analytics team, existing literature on the subject, etc.) while deciding on how many interaction terms to model.

    This would help explain all the causal and hypothetical relationships between the promotion channels and the basis on which an empirical model can be designed.

    This approach allows splitting the interaction effect among the interacting channels. The part of the impact attributable to the increase in asymptote of channel 1, due to channel 2, will also be credited to channel 2. The remainder is credited to channel 1. Therefore, we are not only measuring the effect of marketing more accurately but also crediting this effect to the respective channels more accurately, resulting in better ROI estimation.

    Figure 1. Call response on NRx varying with investments in digital ads5

    Call Response on NRx

    While this is a better approach to capture the response of different marketing channels and linking them to sales, it has certain limitations, including:

    1. Some digital campaigns are run for short durations, and hence, do not have many modeling data points. Due to the lack of data, the model might not throw-up statistically significant results for those channels.

    2. The results might be challenging to interpret and visualize in the case of multiple interaction terms.

    Despite its limitations, there are strategic advantages to marketers, as the approach:

    1. Allows for better selection of channels for promotion

    Marketers often find it difficult to explain the success of a few promotion channels, particularly for the channels which do not directly affect sales.6 The dynamic asymptote approach can help justify the spend on these promotion channels by accurately measuring their indirect impact on other channels that finally lead to sales.

    2. Allows marketers to better time their campaigns

    Measuring the interaction effect makes it possible for marketers to make better decisions on timing parallel channel promotions. Meaning, if the DTC TV and DTC print promotions are interacting with each other, it makes sense to run the DTC print promotion when the effect of the DTC TV promotion is on (i.e., during the TV promotion shelf-life).

    The asymptote approach can also potentially assist pharma companies to automate their real-time campaign management solutions which enable them to make decisions on ‘when’ and ‘which’ campaigns to run based on their interaction effect.

    Learn More - " How Should A Marketing Mix Analysis Influence The Next-Best Actions? "


    As the pharma marketing landscape continues to evolve, the objective of marketing is changing from sales-focused to value-focused. Promotion to other stakeholders i.e. payers and patients are consequently turning more sophisticated. Further, some channels are specifically targeted towards payers to increase drug coverage in their plans and improve health and economic outcomes. Meaning, interactions happen not just between promotion channels to a single stakeholder, but also between promotion to different stakeholders. Therefore, there is a direct benefit of measuring interactions across stakeholders and channels to budgeting decisions.

    Pharma marketing has evolved over many years. Pharma practitioners are observing the onset of newer promotion tactics and ways to target different stakeholders. New tactics need to be supplemented by more modern measurement methodologies, so researchers can further transform the existing approach and increase the accuracy of measuring promotion response and marketing ROI.

    About the Authors

    Varun Jain works on marketing analytics projects and technology solutions for top pharma clients. He has led large marketing mix and budget optimization assignments across various therapeutic areas such as vaccines, oncology, and rare diseases. His focus has been on running analytics for various kinds of marketing problems across traditional and digital channels. 

    Adarsh Gautam has worked with multiple pharmaceutical clients and helped them in their marketing and operations decisions by bringing cutting edge-data analytics and technical expertise. He has also led several marketing mix and sales force sizing projects during his stint at Axtria. 


    1. Chressanthis G and Naphade K. Is It Time To Adjust The Pharma PDE Sales Force Optimization Model? Axtria Research Hub, published online April 2017, available at http://insights.axtria.com/whitepaper-is-it-time-to-adjust-the-pharma-pde-sales-force-optimization-model.

    2. Paul J. Why You Should Adopt a Cross-Channel Marketing Strategy. Candidsky, published online February 20, 2018, available at https://www.candidsky.com/blog/adopt-cross-channel-marketing-strategy/.

    3. Evolving Pharmaceutical Marketing Strategies: Digital as a Key Component of Multichannel Marketing, GBI Research, Jan 2017, available at http://gbiresearch.com/report-store/market-reports/cbr-pharma-insights/evolving-pharmaceutical-marketing-strategies-digital-as-a-key-component-of-multichannel-marketing.

    4. Prasad A, Kalyan R and Russell S. Planning Marketing-Mix Strategies in the Presence of Interaction Effects. The Institute for Operations Research and the Management Sciences 2015; 24: 25-34. Available at https://pubsonline.informs.org/doi/abs/10.1287/mksc.1040.0083.

    5. Wood L. Evolving Pharmaceutical Marketing Strategies 2017: Digital Trends are Changing Marketing Strategies Within the Pharmaceutical Field – Research and Markets, Business Wire, published online March 02, 2017, available at https://www.businesswire.com/news/home/20170302005998/en/Evolving-Pharmaceutical-Marketing-Strategies-2017-Digital-Trends.

    6. Amy Gallo: A Refresher on Marketing ROI, Harvard Business Review, Jul 2017, available at https://hbr.org/2017/07/a-refresher-on-marketing-roi.

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