{"id":1018720,"date":"2025-10-06T16:22:53","date_gmt":"2025-10-06T15:22:53","guid":{"rendered":"https:\/\/staging-artefact.digitalseeder.com\/?post_type=blog&p=1018720"},"modified":"2025-10-14T13:49:44","modified_gmt":"2025-10-14T12:49:44","slug":"smarter-decisions-stronger-roi-marketing-measurement-in-pharmaceutical","status":"publish","type":"blog","link":"https:\/\/staging-artefact.digitalseeder.com\/zh\/blog\/smarter-decisions-stronger-roi-marketing-measurement-in-pharmaceutical\/","title":{"rendered":"\u66f4\u660e\u667a\u7684\u51b3\u7b56\uff0c\u66f4\u9ad8\u7684\u6295\u8d44\u56de\u62a5\u7387\uff1a\u533b\u836f\u884c\u4e1a\u7684\u8425\u9500\u6d4b\u91cf"},"content":{"rendered":"

Measuring to decide better<\/b><\/span><\/h3>\n

Marketing Mix Modeling (MMM) is a cornerstone of modern marketing measurement, providing a holistic view of how different activities contribute to sales and ROI. In a context where CMOs must demonstrate impact and collaborate closely with CFOs on budget allocation, MMM, together with incrementality testing and attribution, forms a complementary toolkit for data-driven decision-making.<\/span><\/p>\n

    \n
  • Marketing Mix Modeling (MMM)<\/b>: Quantifies the impact of marketing on sales based on historical data, separates baseline demand from incremental effects, and guides budget allocation.<\/span><\/li>\n
  • Incrementality Testing<\/b>: Proves causality by comparing exposed vs. control groups, validating which activities truly drive sales.<\/span><\/li>\n
  • Attribution<\/b>: Assigns credit across promotional touchpoints (emails, remote calls, congresses, webinars), clarifies HCP engagement journeys, and complements both MMM and incrementality testing.<\/span><\/li>\n<\/ul>\n

    Together, these methods form a comprehensive framework: MMM sets the strategic view, incrementality testing confirms effectiveness, and attribution explains cross-channel dynamics.<\/span><\/p>\n

    Pharma\u2019s key questions<\/b><\/span><\/h3>\n

    In pharmaceuticals, MMM is increasingly mobilized to answer three strategic questions:<\/span><\/p>\n

      \n
    • Impact: <\/b>What is the real effect of promotional activities on prescription volumes?<\/span><\/li>\n<\/ul>\n
        \n
      • Channel performance: <\/b>Which channels drive the strongest incremental impact, and which are reaching saturation?<\/span>
        \n<\/b>Budget optimization: <\/b>How should resources be reallocated to maximize ROI and sustain growth?<\/span><\/li>\n<\/ul>\n

        These questions matter in a context where promotional spend often exceeds hundreds of millions, and where compliance rules severely restrict the available marketing levers.<\/span><\/p>\n

        Four challenges to crack<\/b><\/span><\/h3>\n

        Implementing MMM in pharma holds strong potential but requires overcoming four major hurdles:<\/span><\/p>\n

          \n
        • Data granularity: <\/b>Access is often limited to monthly, aggregated sales data; promotional datasets are fragmented; Medical Science Liaisons (MSLs) activities cannot be modeled directly for compliance reasons.<\/span><\/li>\n<\/ul>\n
            \n
          • Data foundation: <\/b>Omnichannel complexity (interactions with HCPs) and siloed systems make integration difficult; manual processes create inconsistencies and errors.<\/span><\/li>\n<\/ul>\n
              \n
            • Hypothesis-driven design: <\/b>Precise spend data is rarely available; event costs require broad estimation; competitor activity cannot be fully captured.<\/span><\/li>\n
            • Business adoption: <\/b>Success depends on trust from brand teams; simplifying outputs, ensuring transparency, and training are critical to avoid the \u201cblack box\u201d effect.<\/span><\/li>\n<\/ul>\n

              Client case<\/b>: Marketing Mix Modeling for a pharmaceutical brand in France<\/b><\/span><\/p>\n

              With Thomas FILAIRE, from the Paris office<\/span><\/p>\n

              A leading pharmaceutical brand in France applied Marketing Mix Modeling (MMM) <\/span>to optimize promotional and non-promotional spend, measure cross-channel impact, and guide strategic decision-making.<\/span><\/p>\n

              Our client faced 3 challenges:<\/b><\/p>\n

                \n
              1. Measuring Impact:<\/b> accurately attributing sales uplift to specific promotional and non-promotional activities is complex, especially in a highly regulated environment<\/span><\/li>\n
              2. Data Readiness: <\/b>ensuring data completeness, quality, and granularity across multiple sources (sales, promotional interactions, medical activities, external factors) is a prerequisite for robust modeling<\/span><\/li>\n
              3. Compliance:<\/b> medical activities cannot be used as direct drivers in the model due to regulatory constraints, but their influence is captured through control variables<\/span><\/li>\n<\/ol>\n

                We developed a mix marketing modeling tool, based on 3 layers:<\/b><\/p>\n

                  \n
                1. Model Structure:<\/b> A Bayesian regression model quantified the impact of marketing activities on sales, considering baseline sales, incremental sales driven by marketing, and control variables such as seasonality, market trends, and specific events (e.g. COVID-19)<\/span><\/li>\n
                2. Transformations: <\/b>The model incorporated ad stock (carry over) and saturation effects to account for the lag and diminishing returns of marketing activities<\/span><\/li>\n
                3. Business priors:<\/b> Business knowledge and previous studies informed the model\u2019s assumptions, improving its robustness and relevance<\/span><\/li>\n<\/ol>\n

                  This product, unlocked several long term gains, from decision-making to organizational resilience:<\/b><\/p>\n

                    \n
                  • Channel Effectiveness:<\/b> The model revealed that promotional activities accounted for 17.1% of sales, with F2F calls as the top contributor (13.4%), followed by climb events (2.3%) and promotional congresses (1.4%)<\/span><\/li>\n
                  • ROI Optimization:<\/b> Promotional congresses and other interactions (emails, remote\/phone calls) had the highest ROI (3.25 and 3.59, respectively), while F2F calls were robust but saturated (ROI: 1.45).<\/span><\/li>\n
                  • Actionable Insights:<\/b> The brand team was able to adjust commercial and medical plans, reallocate budgets, and shape future strategy based on data-driven insights. Recommendations included reducing investment in saturated channels and increasing spend where ROI was highest<\/span><\/li>\n
                  • Continuous Improvement:<\/b> The model enabled ongoing monitoring and refinement of marketing strategies, ensuring that the organization remained agile and responsive to market changes<\/span><\/li>\n<\/ul>\n

                    With marketing <\/span>expense<\/b> from <\/span>100 millions to 150 millions on the product<\/b>, and a <\/span>~10% of the marketing spent optimization<\/b>, <\/span>decision-making gains<\/b> are estimated to <\/span>~10 to 15 millions euros<\/b>. <\/span>Considering project cost of ~\u20ac200,000<\/b>, that is scalable vertically to other brands and geography for ~\u20ac100,000 ; without any additional IT costs. The <\/span>ROI of the overall project is a ~5,000%<\/b>, as an order of magnitude for the first year.<\/span><\/p>\n

                    The future of Marketing measurement in Pharma<\/b><\/span><\/h3>\n

                    Marketing measurement is moving toward more integrated and sophisticated approaches. For pharma, this evolution will strengthen the role of MMM while ensuring alignment with compliance and strategic impact.<\/span><\/p>\n

                      \n
                    • Integrated frameworks<\/b>: Combining MMM with attribution and incrementality testing to balance short- and long-term optimization.<\/span><\/li>\n
                    • Data quality<\/b>: Stronger governance to harmonize CRM, promotional, and external data, with full transparency.<\/span><\/li>\n
                    • Evolving channels<\/b>: Adapting measurement to digital touchpoints, webinars, and new HCP engagement platforms.<\/span><\/li>\n
                    • In-house capabilities<\/b>: Building internal MMM and testing expertise to embed a test-and-learn culture.<\/span><\/li>\n
                    • Privacy-first<\/b>: Leveraging first-party data while safeguarding patient and HCP privacy.<\/span><\/li>\n
                    • Quality of engagement<\/b>: Shifting focus from activity volume to the depth and effectiveness of HCP interactions.<\/span>
                      \n<\/span><\/li>\n<\/ul>\n

                      Overall, pharma will need to combine technical sophistication with organizational maturity to ensure marketing investments deliver measurable and sustainable value.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

                      In today\u2019s data driven marketing landscape, Customer Data Platforms (CDPs) have become an essential component of a successful strategy. A CDP is a centralised system that collects, cleanses, and unifies customer data from various sources to build comprehensive customer profiles.<\/p>","protected":false},"featured_media":1023610,"parent":0,"template":"","meta":{"_acf_changed":false},"blog-category":[2997,21931],"blog-language":[2991],"class_list":["post-1018720","blog","type-blog","status-publish","has-post-thumbnail","hentry","blog-category-data-marketing","blog-category-healthcare","blog-language-en"],"acf":[],"yoast_head":"\nSmarter decisions, stronger ROI: Marketing Measurement in Pharmaceutical<\/title>\n<meta name=\"description\" content=\"Marketing Mix Modeling (MMM) is a cornerstone of modern marketing measurement, providing a holistic view of how different activities contribute to sales and ROI.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" 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