Proprietary Research Can Give You Credibility -- Here's How to Do It Correctly
It's hard to put a price tag on the importance of proprietary research, but there are some numbers out there that are good indicators: Forrester Research's 2019 first quarter report, for example, showed that proprietary research contributed to more than a $23 million increase in revenue.
When founding our own company, my partners and I spent years accumulating research to help us convince insurers that people with healthy lifestyles deserved lower insurance rates. This was a powerful idea that we believed would revolutionize the thinking of many in the industry. It was especially powerful because we had proprietary research to back it up.
The doors that research opened
When we began, there wasn't a clear way to distinguish between people who actively chose to live healthy lifestyles and those who were naturally healthy. Not only that, but there wasn’t a way to measure or quantify these differences, either. No one had created a “credit score” for health. So we decided to perform our own research and do just that.
We assembled a team of medical, health and fitness experts to create a cutting-edge assessment, and they wrote more than 30,000 quiz questions about a variety of health topics. From this quiz, we started gathering real-time data that showed functional knowledge to be far more impactful than self-assessment in virtually every single way when it came to health.
Performing proprietary research was an essential part of our success as we were getting started, and it gave us the credibility that helped us establish our place in the life insurance industry. Research can give companies the opportunity to not only get started, but also differentiate themselves in crowded fields.
Easier said than done
Conducting proprietary research has its challenges, though. It's not easy. Determining what is real and credible versus what was published just to get clicks is a tall order. That was a major reason behind our choice to pull in experts when building our team -- they were able to identify limitations or misinterpretations and alert us so our research could be better informed.
There are also limitations to what data can be collected or aggregated. When building our proprietary database, we compared our information to public data so we could make more nimble and adaptive correlations, which allowed for faster innovations in a space that is otherwise often slow to change.
Navigating the logistics of conducting research
The benefits of conducting and owning research are ample, but in order to reap them, entrepreneurs need to navigate the logistical and ethical minefields that inevitably come up when doing so. The following steps will ensure a safe path forward:
1. Make sure the data collection model works for the business model. A method of aggregating data should in no way be based on self-assessment. People lie or fib when someone asks them to evaluate something about themselves. Think of it this way: The SAT doesn't ask students whether they're good at spatial reasoning; it makes them demonstrate their knowledge of it and prove it instead. The same logic and method should apply to all research.
Further, any approach to research should make sense for each specific business model. It might seem easier to sell an app to millennials, but they would likely be the users on the back end who would default on their payments and undercut a business's bottom line. Be intentional about how research is performed and who is a part of it in order to make the best decision.
For example, Beam Dental has a smart toothbrush that records exactly how much time and with what frequency a person brushes his or her teeth. The company can then utilize that data for better dental care and insurance modeling. It's a smart research plan that produces better data and results.
2. Cultivate market differentiation.
Incumbents aren't sitting around when it comes to data innovation. They're moving just as fast as others are. But companies that build something unique can prevent bigger players in the market from catching on, replicating the (smaller) companies' ideas and pushing them aside in the market.
So, adopt new processes and technologies to stay ahead and stand out. Ancestry.com built a lot of its datasets manually by having employees go to local government offices where birth and death records were being kept in hand-written files. The records were public information but weren't going to be digitized by anyone else and couldn't be removed from the offices that were holding them. Because of this, Ancestry employees transcribed the records by hand in order to build the company’s proprietary dataset, which set it apart because it chose to do things differently.
Similarly, Goldman Sachs set itself apart in the personal loan space. It poured significant money into building the Marcus loan, which is totally customizable and technologically savvy. The firm is even using mobile developers to create its all-digital retail bank and hopes to offer native mobile apps in the future. It didn't wait to stand out. It took action and found success.
3. Prioritize building an experienced team.
Not everything has to be built from scratch, and community data frameworks are available for those building their own datasets. But engineering and machine-learning teams can actually be quite lean when it comes to managing information, and younger engineers might need more experience to understand how to ethically and equitably handle large datasets for an organization. For this reason, invest in a high-quality, experienced data team that has grown during the past decades of technological development to avoid unknown errors in the long-term output of data.
General Mills, for example, stays in a state of constant recruiting for positions in its growth insights and analytics department. The company understands that as new needs and questions arise, the team analyzing and conducting research should grow, too.
With these steps in mind, companies can conduct their own research in a way that lends them credibility and differentiates them in their markets. And if Forrester Research’s first-quarter increase to the tune of $23 million is any indicator, proprietary research will be a worthwhile investment.
Interestingly, Healthcare.com is working to find a new way of defining datasets, too. Incumbent health technology companies struggle with older data that isn't easily manipulated, but Healthcare.com is building datasets without needing access to private data, which is something clients don't always feel comfortable with. The company is creating a frictionless client experience and innovating its way into being a leader in the way things are done