By Pranay Madan
Developing and bringing a product or service to the life science market requires constant monitoring of market trends and competitive threats as well as the agility to make shifts in your strategy. Generally, the market intelligence that’s behind a successful life science market strategy isn’t public, but if you could take a peek, you’d likely find these four best practices.
Best Practice #1: Base your market research approach on the depth and uniqueness of your business need.
Letting depth and uniqueness guide your selection of optimal market research methods can help you determine if a high-level piece of data will suffice or if you’ll need something more granular. As a guideline, the higher the cost of a product or service, the more granular the market research needs to be.
Depth refers to the number of ways data can be sliced and analyzed. For example, if you operate in the genomics markets, a high-level assessment of that market might be, “The market is growing at 20 percent.” To add more depth, or granularity, you might ask, “What’s driving that 20 percent growth rate?” Looking deeper, you may discover that the clinical business segment is growing at 40 percent, which is driving a majority of the high-level growth. You can go even deeper and ask, “Why is the clinical business segment experiencing so much growth?”
Depth is also about understanding the parts as opposed to having only a broad sense of the whole. For example, a high-level question might be, “What type of pie is this — apple or cherry?” More granular questions would be, “What are the exact ingredients that make up this pie? How much sugar? How much flour?”
Uniqueness refers to the number of people who might be interested in that information. For example, many people are interested in the size of the clinical NGS oncology diagnostics market, but you might be one of the only people who’s seeking specific feedback on a new product that your company offers.
Best Practice #2: Track a cross-section of primary and secondary data sources.
An effective business intelligence strategy requires tracking a multitude of primary and secondary data sources, including leading companies and research institutions. Before you commit to one strategy over another, weigh the advantages and disadvantages of paying for primary data — like interviews with KOLs, users, or industry insiders — or using secondary data sources — like SEC filings, investor presentations, or analyst coverage —which are public and often free. Primary data may be expensive, but it can answer your questions quickly and directly. Secondary data is a great value but only if you take the time to clean it up and analyze it properly for your use case. Tracking a cross-section of data sources will reveal either an obvious direction or a clearer idea of what pieces your strategy is missing.
Best Practice #3: Create universal access to data within your organization.
Giving all departments access to your company’s data increases efficiency. Without access, there may be a lot of blind spots across the organization because the company doesn’t know what it knows. Let’s say a product manager is successful in one segment of a market and then sees a new application in a market that doesn’t align with his/her business unit. The new application is slotted into a second product manager’s business unit. But if life science research data isn’t being shared openly, the second product manager is going to have a hard time understanding the best way to compete in that market segment. Product managers have been known to pay for custom research and then keep it siloed. It’s better to pull it out of storage and add it to the company’s core research.
Best Practice #4: Ask your data very specific questions.
Let’s say you’ve compiled some data, and now it’s time to interpret it. How do you work it into something actionable? First, avoid the rookie mistake of asking a general question and expecting specific answers. Instead, treat your data like an industry expert. Ask it, outloud, some very specific questions. Using these vocalized questions can help you structure your analysis and queries.
After you’ve done an analysis, step back and look at the data with fresh eyes to determine where it has answered your questions and where you need more granularity or more data to fill in the gaps. The goal of this process is to simplify the follow-up work you need to do. If your data answered 12 of 15 questions, then you need only enough data to answer three remaining questions. Perhaps those are quick questions that you can ask to a handful of customers. You may not need to conduct a huge research effort to gather a bunch of granular data. Know what your data can and cannot tell you.
Remember that establishing any market strategy takes the one element that’s in short supply for most people: time. If you’re tempted by shortcuts, ask yourself the question posed by Coach John Wooden, “If you don’t have the time to do it right, when will you have the time to do it over?”
About the Author: Pranay Madan is a Data Product Manager at DeciBio Consulting where he develops and curates business intelligence products and services for companies in the research tools, clinical diagnostics, and health technology markets. He holds a Bachelors of Engineering from Panjab University and a Masters of Business & Science (MBS) from the Keck Graduate Institute.