A Pharma Brand Marketer s Guide to Predictive ROI Modeling EVIDENCE FOR SUCCESS: As seen in

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1 As seen in EVIDENCE FOR SUCCESS: A Pharma Brand Marketer s Guide to Predictive ROI Modeling By Ken Truman VP, Insights & Connections, MedThink Communications

2 20/20 hindsight doesn t cut it anymore. As a pharma marketer, forecasting and planning often feel like there should be a crystal ball involved. Do you know what impact a 15% cut to next year s marketing budgets will have on your brand s sales? Would you have a systematic approach for shifting your marketing channel mix, or would you just pick off one or two (perceived) low-value tactics? Traditional pharma ROI analyses rely on promotional response models that look back at historical data and report the impact of individual channels on ROI. Yet few pharma marketers take advantage of the opportunity to leverage the knowledge acquired from this data to proactively inform the next planning process. In fact, according to an August 2016 report by The CMO Survey, only 28% of pharma CMOs leverage any kind of analytics to determine what their marketing mix should look like. Only 28% of pharma CMOs leverage any kind of analytics to determine their marketing mix The practice of leveraging retrospective models to inform prospective planning is not new. Other industries (and some practitioners in pharma) call this practice marketing mix modeling (MMM). But for a variety of reasons, the end recipients (pharma brand marketers) of MMM data too often get stuck in a historical-focused mindset (of proving the ROI). The more interesting question is, what can we do with these models using a forward-thinking mindset (improving ROI)? Principally, we can 1. Run what if scenarios to determine at what level profit is maximized at various investment levels 2. Determine optimal allocation of marketing dollars to individual channels to maximize ROI In this case, what we re doing is predicting ROI, how to maximize it, and what to expect. For this reason, we refer to this practice as predictive ROI modeling when speaking with clients. How to move from reactive ROI to proactive ROO measures. When is the right time to measure marketing channel ROI? Before you start marketing. Yet many MMM practitioners are brought in at the end of a program, left to reassemble the broken pieces from a fragmented landscape of data sources and channels. According to a 2013 white paper by the Council for Research Excellence, some marketing mix modelers spend up to 70% of their time on data collection alone. With so much time devoted to assembling the historical data, it s no surprise that MMMs are not often leveraged for predictive ROI modeling. The foundation of a solid predictive ROI model is a return-on-outcomes (ROO) measurement model framework. What is an ROO measurement model framework? It s a structured way of linking your strategic imperatives to your tactics, key performance indicators (KPIs), and ultimately, your desired outcomes.

3 What does ROO do? Why ROO vs ROI? In the world of pharma marketing, we re often tasked with changing physician/ patient mindsets that may not have an immediate impact on ROI, but will influence it in the long term. Measuring and modeling short- vs long-term impact is key. ROO accounts for both mindset and behavioral changes and provides definitive information on outcomes associated with marketing dollars. Change in mindset Increase perception of condition X as chronic rather than episodic Increase awareness of rare disease Change in behavior Increase patient adherence to medication Increase physician writing It s imperative in tactical planning to align the key drivers unique to each tactic with your desired outcomes. This serves two purposes: 1) all parties are in agreement on how individual tactics will be measured to inform optimization decisions once tactics are in market; 2) marketers can be diligent about data collection and ensure the right metrics are captured to be inputs in future predictive ROI models. Beyond ensuring that measurable KPIs are aligned with each channel, additional steps should be taken to ensure measurable ROI is possible. This includes: Leveraging test/control methodology when feasible Variance in spend across channels and time to prevent multicollinearity Multicollinearity arises when marketing activity across channels is flighted in exactly the same manner, making assessing the individual impact of each channel difficult to tease apart Having the right measurement model framework ensures you re capturing the right data sources to inform your predictive ROI models, as well as optimizing tactics once in market. And while it requires extensive planning and discipline to get it right, the benefits of developing a measurement model framework far outweigh its costs.

4 Choosing the right model for predicting ROI. There are a multitude of models available in the modeler s toolkit, from traditional regression models to more advanced techniques such as structural equation models (SEMs) and agent-based models (ABMs). So, as a pharma marketer, which model is the right model? The truth is that there is no one right approach. Although a 2014 article from the Harvard Business Review argues that simpler models are more appropriate for human consumption, we believe that a deep understanding of channel-level data (ie, inputs for the model) is more important than model sophistication. If you don t know why a paid search impression is a poor model input, then your issues may go beyond your model toolkit. What should your model account for? All personal and nonpersonal promotional activity (using the right channel-level KPI agreed to in your ROO measurement model framework) Thresholding and diminishing returns effects Interaction effects between channels Lagging effects External factors RETURNS INVESTMENT Of course, in an ideal world, you would be able to measure all of these. In reality, it may not be possible to model and/or capture all the data all the time. However, it is essential to account for as many of the parameters as you can in order to create an MMM that can do more than evaluate past performance. CASE STUDY Bigger isn t always better: a predictive ROI modeling lesson in diminishing returns. CHALLENGE One of our clients, a midsized biopharmaceutical company, acquired a brand from a large pharma company. The brand was the category leader and got there, in part, with much larger promotional budgets than our client was accustomed to spending. INSIGHT A predictive ROI model incorporating thresholding and diminishing returns effects revealed that the brand was over-invested in digital advertising, and that the ideal investment level was 50% of historical spend. SOLUTION Because the predictive ROI model revealed that every dollar spent above the historical level resulted in less than $1 in incremental revenue, budgets were reduced by half, yet higher ROI was predicted. RESULTS One year later the brand continues to be the market leader, with >50% yearover-year growth, in line with model projections, while reducing the client s digital media budget by half.

5 Forecasting and scenario planning. Once your predictive ROI model is in place, it can be leveraged to inform future tactical plans. You can examine the impact of increased or decreased investment levels what is marginal revenue generated vs marginal cost? What is the optimal allocation of budget to maximize ROI? In order to make the model most effective, remember that it is a tool to help understand cause and effect, not the end answer. It is important to involve key stakeholders across your organization and agency partners, as every year there are likely to be changes to market conditions, potential changes to your creative campaign, an evolving media landscape, etc, all of which can have an impact not accounted for in the model. Review model results with these stakeholders implausible outputs could be the result of a misspecified model. However, misspecified models are rare if you ve done a diligent job of adhering to an ROO measurement model framework. If you truly understand how your tactical KPIs are linked to your desired outcomes, you can build a communication plan to achieve the highest predicted ROI for your budget. CASE STUDY The confidence to try new channels: a predictive ROI modeling case study. CHALLENGE A client marketing a cardiovascular brand with poor uptake due to late market entry wanted to test DTC promotion, but had no experience in DTC. As budget was limited, they needed assurance of potential ROI prior to program launch. INSIGHT This is a situation in which simpler models with interpretable parameter estimates are ideal. Leveraging historical predictive ROI models from other DTC initiatives, a model was built for our client s brand with new parameter estimates informed by a variety of data sources, including Disease prevalence rate by market Density of prescribers of client s brand by market Benchmarks for patient response to DTC promotion Benchmarks for physician receptivity on prescribing brands based on patient request Brand claims approval rates by market Other data captured via market research SOLUTION A multichannel DTC promotional campaign was deployed that linked KPIs to new prescriptions. Predictive modeling forecasted a projected minimum 2:1 ROI (at 80% confidence level). RESULTS A successful pilot DTC initiative that delivered a 2.5:1 ROI.

6 Predictive modeling: moving forward. Predictive ROI modeling built on a solid ROO measurement model framework is an essential tool to help brand marketers go from looking back to looking forward. From simple thumbs up/thumbs down assessments of tactics, to understanding how to optimize a channel to achieve highest ROI, to determining the ideal channel mix, predictive ROI modeling can move you from measuring the past to making decisions based on how it will impact the future with confidence. Historical and best assumptions Forecasting and scenario planning Re-estimate ROI model Tactical plan Outcomes KPIs Ready to get the most out of your predictive ROI modeling? Contact us! Angie Miller, SVP amiller@medthink.com