Market Mix Modeling (MMX): CPG Vs Pharma

Introduction

In today's dynamic marketplace, understanding the effectiveness of marketing strategies is crucial for businesses striving to maximize their return on investment (ROI). Market Mix Modeling (MMX) serves as a pivotal tool in this regard, enabling companies to analyze historical trends and determine which strategies have yielded the best results. This analysis not only informs future marketing investments but also optimizes spend allocation. While the core principles apply universally, unique business realities in consumer-packaged goods (CPG) versus pharmaceutical manufacturing necessitate distinct approaches when leveraging MMX models effectively.

Virtually every major CPG and pharma company now employs some form of MMX modeling given its power to drive productivity amid rising media costs and fragmented customer journeys. However, the specific data environments, channel dynamics, target metrics and implementation roadmaps differ vastly between these sectors. Recognizing and properly accommodating these divergences is critical for organizations striving to extract full value from their MMX initiatives.

Challenges

CPG:

• Data Availability: CPG companies generally have access to a wealth of consumer data from retailers and market research firms, the true differentiator lies in the granularity of store-level, or brick-level, data. In the United States, CPG companies enjoy access to comprehensive and highly granular store-level datasets, enabling precise, data-driven decision-making. However, in European markets, brick-level data tends to be sparser and less detailed, posing challenges for CPG companies aiming to gain deep insights into consumer behavior and accurately measure marketing impact at a granular level.
• Diverse Product Portfolio: CPG companies often manage a wide range of products, each necessitating unique marketing strategies. This diversity requires a versatile approach, capable of handling multiple product lines and marketing channels.
• Short Purchase Cycles: Consumers in the CPG sector make frequent purchasing decisions. This necessitates rapid feedback on marketing effectiveness and frequent adjustments to campaigns and promotions.
• High Competition: The intense competition in the CPG market demands constant innovation and adjustment in marketing tactics to stay ahead.

Pharma:

• Data Availability: Pharma companies face stricter data privacy regulations and limitations in accessing patient-level data, making it challenging to obtain comprehensive and granular data for marketing purposes.
• Regulatory Constraints and Advertising Limitations: The pharmaceutical industry is subject to strict regulations governing marketing practices, advertising claims, and promotional activities. In many countries, direct-to-consumer advertising of prescription drugs is restricted or prohibited, United States and New Zealand are the only two countries in the world where direct-to-consumer (DTC) advertising of prescription drugs is legal.
• Longer Sales Cycles: Pharmaceutical products typically have longer development and approval processes, which affect the timing and nature of marketing efforts.
• Stakeholder Complexity: Multiple stakeholders, including healthcare professionals (HCPs), patients, and insurance providers, influence purchasing decisions, adding layers of complexity to the marketing strategy.

Objectives of Using MMX Models

Both the CPG and Pharma industries utilize MMX models to assess the efficacy of their promotional strategies and optimize marketing budget allocation across channels and ultimately maximize their return on investment (ROI). However, distinct differences exist in their specific objectives:

CPG:

• Managing the intricate dynamics of price and promotion at the store level
• Optimizing media mix allocation across channels like TV, digital, and non-digital
• Gaining deeper insights into consumer behavior to enable targeted marketing efforts

Pharma:

• Optimizing promotion channels, including personal promotions by sales reps, digital channels, speaker programs, conferences, and TV ads
• Enhancing omnichannel marketing strategies to reach and effectively engage key stakeholders, including healthcare professionals (HCPs) and patients
• Accounting for managed care dynamics, such as formulary status changes, IDN influence, and regional variations in formulary coverage and control exerted by IDNs.
• Measuring promotion effectiveness at granular sub-national levels, including personal promotions by sales force teams
• Guiding brand planning through resource optimization and the development of integrated marketing strategies

Datasets Considered

When building MMX models for the CPG and pharmaceutical industries, the datasets used reveal both commonalities and differences. Both sectors rely on sales and media data to optimize strategies and track performance. However, the specific types of data and channels differ considerably between them.

CPG industry uses point-of-sale (POS) data, consumer behavior insights, and detailed store-level pricing data. Promotional activities are tracked across digital platforms and social media, emphasizing direct consumer engagement.

In contrast, Pharma focus on prescription data (Rx), and patient-level data to understand prescribing behavior and outcomes. Promotion data in this industry includes personal promotion, speaker programs, managed care interactions, etc., reflecting the complexity of healthcare systems.

Target Measurement (Modeling) and Methodology

Not surprisingly given the above dynamics, the actual target variables and modeling methodologies employed in CPG versus pharma MMX models differ significantly.

In the CPG industry, the primary target variable for MMX models is sales data. This includes point-of-sale (POS) transactions and household panel data, which capture consumer purchase behaviors. Modeling techniques such as regression analysis, time series analysis, and machine learning algorithms are employed to assess the impact of marketing activities like promotions, media spend, and pricing strategies on sales outcomes. These models help optimize marketing strategies and maximize return on investment (ROI) by providing insights into which marketing actions drive the most significant sales impact.

In the pharmaceutical industry, the target variables for MMX models are typically prescription (Rx) data and the likelihood of healthcare providers (HCPs) prescribing a specific drug. These models often measure prescription volumes, sales data, and various channels used. Advanced modeling techniques such as logistic regression, survival analysis, and longitudinal data analysis are used to evaluate the influence of various marketing efforts on prescribing behavior.

These models help pharmaceutical companies understand how promotional activities, including sales force detailing, medical conferences, and direct-to-consumer (DTC) advertising, impact HCP prescribing habits and patient outcomes. By integrating these diverse data sources, pharmaceutical companies can optimize their marketing strategies and ensure effective allocation of resources.

Implementation and Operationalizing Insights

Beyond just modeling, the implementation roadmap for operationalizing MMX insights differs vastly between CPG and pharma:

In CPG, model outputs funnel into centralized marketing functions that can nimbly adapt multimedia mix investment levels, repricing/promotion mechanics, and adjust brand positioning and activations with their integrated agency ecosystems.

For pharma companies, execution is far more complex given matrix organizational structures. Driving alignment on evolving promotion strategies, sales force sizing/deployment and spend shifts requires intensive change management across fragmented internal stakeholders (marketing, sales leadership, payer teams) as well as a disparate set of external vendors, agencies, and data suppliers.

Indeed, mastering these implementation nuances alongside robust MMX model construction is what separates the truly elite marketing analytics practices from more commoditized players. As data environments and go-to-market complexities continue evolving, properly accommodating CPG-pharma modeling distinctions will only grow more critical for driving efficient commercial growth.

Conclusion

While leveraging advanced MMX models has become table stakes for both CPG and pharma companies, realizing full potential requires tailoring methodologies to the unique sector dynamics and operational realities. MathCo's cross-industry experience reveals stark differences in data environments, channel mixes, target metrics, structural modeling approaches and implementation roadmaps that must be accounted for.

From household panel data and brand identities to patient journeys and therapeutic paradigms - the CPG versus pharma divide is vast. World-class MMX modeling demands building modeling systems from the ground up, customized to each sector's KPIs, data availability and decision-maker vernaculars. Those able to master these nuances while aligning insights with streamlined execution will be best positioned to extract maximum commercial value from MMX investments.

As regulation, digital acceleration and new data pipelines continually reshape both industries, staying ahead of the curve on emergent MMX modeling practices will be imperative for driving sustained competitive advantage.

Authors:

Snehamoy (Sneh) Mukherjee, Partner, MathCo

Sarath Murali , Associate Principal, MathCo

Kosuri madhuri, Management Consultant, MathCo