With regulatory expectations rising and data governance under the microscope, pharma leaders are understandably concerned about data ownership and transparency.

For years, brands have been comfortable outsourcing their data and measurement initiatives to external partners and tech solutions, but there is an increasing trend towards in-housing these capabilities to ensure full visibility and control across these business-critical workstreams.

Prepackaged solutions often lack transparency, exacerbating these concerns. If marketers cannot see the assumptions made by external models, how can they truly understand exactly what is driving their results? And without the opportunity to audit, how can they trust that insights are correct?

As the volume of metrics increases and data security remains an ongoing priority, best-in-class measurement must be built within an organisation’s own environment.

While it requires internal expertise and technical resource, developing this internal capability gives brands full autonomy over how the model is set up, ensuring privacy, auditability and full control over sensitive commercial and patient-related data, as well as fostering internal trust – without ongoing licensing costs.

An advanced MMM gives marketing teams the ability to run dynamic scenario planning, automated quality assurance and actionable BI insights, without reliance on black-box vendors.

In order to identify relevant opportunities, improve effectiveness and drive measurable returns, marketers must have access to rich, trusted data sets, and this is a significant challenge for pharma.

Diverse legacy data stacks, disconnected sources and siloed working approaches mean that data is often inconsistent or unstructured, making effective measurement and optimisation almost impossible for marketers.

In order to close this gap, pharma companies must start with a comprehensive data audit to understand how data flows across departments, identify silos and integration gaps and assess data quality, access and governance.
Centralised ownership and strong data infrastructure are the first step towards increasing marketing effectiveness, and this becomes vital where companies are deploying automation and AI.

It is no longer enough to rely on a single method of measurement, and the most effective strategies rely on a combination of MMM, multi-touch attribution (MTA) and incrementality testing. Together, these strategies can give pharma marketers a complete and accurate picture of performance drivers. Each has its own strengths and weaknesses, and it is vital to understand where they can be used for maximum advantage.

While MMM looks to analyse the entire business environment, understanding how different customer touchpoints impact interactions, conversions and sales remain crucial. Some level of MTA will always be needed to monitor and optimise daily activity, supporting bidding and reporting functions within marketing platforms.

However, restrictions around first- and third-party data mean that MTA can no longer provide marketers with the full picture, particularly when it comes to upper-funnel activity. MMM can plug those gaps.

When launching a new campaign, channel or product, insights from both MMM and MTA may not always deliver enough immediate information to make effective decisions. Brands can make assumptions based on the success of previous activity, but each launch is different and historic data has its limits. In these scenarios, incrementality testing can prove valuable.

In terms of deriving a definitive, data-driven view of effectiveness, incrementality testing remains the most robust measurement method available. Setting up strict control and exposed groups allows marketers to isolate the impact of specific variables, such as a channel or pricing change, and measure the effect of changes on sales.

Used in conjunction with MMM, incrementality testing can validate findings and identify areas where a model can be optimised or refined – and if used regularly, with further segmentation, it can reveal even deeper insights such as the value of different approaches for engaging HCPs in different fields.

When fifty-five paired MMM with incrementality testing for a global brand, the advanced, granular insights empowered the team to make bolder, data-backed decisions. The approach led to a 20% increase in marketing efficiency, saving €4 million by reallocating budgets towards high-potential levers while reducing the brand’s marketing carbon footprint by 20%.

As the marketing ecosystem evolves and fragments, measurement strategies must become more sophisticated.

MMM is often discounted as a valuable tool in this market due to historic limitations, but with open-source next-generation models many of the problems that today’s marketers face can be solved, especially when combined with MTA and incrementality testing in a coordinated, complementary way.

But measurement alone is not enough: the insights generated must be integrated into a defined measurement and activation framework to ensure that the correct decisions are made from these outputs.

As global pharma brands expand their AI-driven commercial models and global manufacturing footprints, advanced MMM, as part of a versatile and sophisticated marketing toolkit, becomes a strategic enabler by aligning investment with true incremental impact and supporting evidence-based decision-making at scale.