Amplify your data-driven decision making in Pharma commercial operations

March 1, 2023

Simple four-step guide


One of the most important aspects of your job as a commercial leader in the pharmaceutical industry is to make decisions that drive growth and impact the bottom line. When it comes to making data-driven decisions, however, it’s not always easy to build processes and culture that bring you the best return on investment. To help you overcome these hurdles, we’ve created this four-step guide.

Sales and marketing functions in pharmaceutical companies have actually been in some sense even front-runners in using for example sales data to steer their actions in a very coherent way. Analytical methods and data sources are however constantly evolving, especially in the form of big data and real-world data, and there are more and more ways to use that data in making your operations more efficient.

One of the key factors in successful data-driven Pharma operations is committing to a systematic process which builds on continuous improvement and encourages a positive data culture. Applying these steps will put you on the right track in building a truly data-driven organization.

Step 1. Always start with the business question

The first step to data-driven decision making is to start with the business question. Always. What are you trying to achieve? What is the business question you are trying to answer or what business goal are you trying to make a reality? A clear and well-defined business question will help ensure that everyone involved in answering it has a shared understanding of what success looks like.

This should include:

  • What is the goal?
  • How do we measure success?
  • How long do we have to achieve it?
  • Who else needs to be involved in answering this question (internal stakeholders, external parties)?

Many healthcare leaders make the mistake of starting to build a data infrastructure, collecting loads of data and hiring all sorts of data people before figuring out what they really want to achieve with all that. Instead, you should first figure out the end goal and then build your capabilities to make it a reality.

If you are only just starting with becoming data driven, you can get underway by choosing just one or two simple questions to answer. These can be for example: “How can I identify an unmet medical need?” or “What methods can I use to verify the effects of my marketing actions?”. You don’t need to make huge investments or change everything in your way of working, but just try to build a better process for answering one or two questions first.

Step 2. Create a process for collecting and analyzing data

The next step is to get the right data for the analysis, but you also need to focus on people and processes. All these things come together to help you make better decisions in your business.

  • Identify the data you need to collect (e.g., “In addition to product sales numbers I will collect prescription numbers geographically over time and compare them to the marketing actions made during that period)
  • Collect this data from multiple sources, including internal systems like CRM and ERP, third party services like Salesforce or Google Analytics, external data vendors like RemedyBytes or even manually by asking your team members or clients questions about their workflows and activities.
  • Verify the quality of the data, clean and pre-process it to ensure that it is accurate, complete, and consistent.
  • Analyze this information using the right tools and statistical and analytical techniques that make sense for your business (these can include simple tools such as Excel pivot tables/charts).
  • End result of the analysis should be actionable insights that help you achieve the business goal.
  • Key question related to people is to ask who in your organization will provide these insights. You could for example hire data people (analysts, data scientists etc.), train your own people, or get the knowledge from external sources.
  • Don’t forget to create documentation! It is important to document the data collection, preparation, analysis, and interpretation processes to ensure that they are repeatable and transparent. This involves documenting the data sources, methods used, analytical techniques, and results obtained.
Step 3. Make your data encourage action

Even the best analysis is useless if you don’t take it to action. Put extra effort in making things easy for your team to act on the data.

  • Present data in a clear and understandable way.. Use charts, graphs, and other visualizations that make it easy to interpret and draw conclusions from the data.
  • Make recommendations. Make it easy to spot what is essential in the data. Instead of showing just numbers, show recommended actions.
  • Provide support, training and encouragement for data-driven culture. Make sure your people understand the data and the tools, align their individual goals with the company targets and remember to reward and celebrate good performance.
Step 4. Create a feedback loop

In the final step, you’ll test your hypothesis and measure the results. This can be done via A/B testing or monitoring and analyzing the relevant data. Once you’ve collected enough data, compare it with what you expected to see and make any necessary adjustments to your strategy. Then repeat!

One great tip for managing your decision-making process comes from Google’s Chief Decision Scientist Cassie Kozyrkov, who says you should decide what your actions will be before you see the data. It’s always easy to explain the data to fit your preconceived ideas so giving yourself clear thresholds for action helps to fight the confirmation bias. For example, you can decide that “If this marketing campaign has improved our product’s market share by 2%, we’ll continue the campaign. Otherwise, we’ll stop doing it.”

The process is simple but requires discipline and patience. There are no shortcuts or quick fixes here but by following these steps you will be able to amplify your data-driven decision making capabilities as a team member or a leader in Pharma!


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