Building A New Startup? Let Data Drive It
This will be the first in a series of posts on entrepreneurship that provides guidance on how to use data to guide your efforts and take the first steps to turning an idea into a marketable product.
One of the most consistent passions throughout the course of my professional life has been supporting entrepreneurship in the software industry. Being an entrepreneur is a difficult endeavor: nearly everyone has great ideas, but few manage to see them through to fruition.
My experience has allowed me to interface with entrepreneurs regularly, and I want to put down my perspective on what leads to entrepreneurial success.
Benefits of the Data-Driven Business Plan
Before we get into concrete steps entrepreneurs need to take, I want to briefly explain the top two reasons being data-driven can be beneficial, even in the earliest stages of growing a business.
Data allows you to maintain your objectivity. Entrepreneurs tend towards getting wedded to their ideas, ignoring warning signs and missing opportunities that don’t support their belief that THEIR solution is THE solution to the problem they are trying to solve. Meticulously collecting feedback and user data, and then using that data to drive business decisions will help entrepreneurs stay open to feedback and opportunities for product refinement, improving their flexibility and ultimately, their product.
Data will help you tell your story to investors and customers down the road. Collecting market, user, and product data as early as possible means that, when the time comes, an entrepreneur doesn’t have to rely on solely their own beliefs to sell their product or vision to investors. They will have hard quantified evidence that demonstrates the impact their idea has had, and projections about future growth and revenue will be rooted in valuable, real-world data.
Qualify Your Product. Quantify Your Market.
The first steps a budding entrepreneur must take are to qualify their product and quantify their market. Even this first step can be challenging, as entrepreneurs must maintain their belief in their product while remaining open to critical feedback.
Laying a foundation of good data practices during this step will lead to rewards later on in the venture. All data that can be collected, both quantifiable and qualifiable, should be collected.
An entrepreneur really needs to shake their network to find good sources of unbiased feedback. This initial research phase is critical, and every conversation with the right person is going to be a valuable asset. Industry influencers, potential partners, mentors, and even knowledgeable family and friends can all inform the earliest phase of bringing an idea to market.
Staying objective is key to success in this stage. If your resources are indicating that your product may not be the best solution for the problem you are trying to solve, or that the problem itself is negligible: believe them and adjust accordingly. Use feedback and market research to remain flexible in this stage; it is easiest to adjust an idea before investing in it and building momentum in a particular direction.
Some entrepreneurs fall into the trap of protecting their ideas too closely; they believe the idea has real value that can be stolen. Largely, this fear is unfounded and only serves to isolate an entrepreneur from valuable sources of feedback and data. Even talking to potential competitors should not be ruled out. It is worth repeating: every conversation about the product has value.
This is an entrepreneurs chance to fully explore their idea before they write the first line of code, and fine tune it so that they can be sure going forward that the idea is being developed for the market, and not themselves.
Develop a Minimum Viable Product
Once you quantified the potential market and qualified your product to serve market needs, the next step is to develop a minimum viable product (MVP). This is a minimized product that you can put into the hands of customers in order to extract data on how to optimize the final product before it goes into full scale production.
My suggestion is that entrepreneurs in this stage stick to the “Rule of Three” and provide customers with three critical functionalities that will serve as the backbone for the end product. Ideally the three functionalities you choose will come from survey data from potential customers. The minimum number of respondents to gain practical insight should be around 100. As you ask individuals for their insight, commonalities should appear and serve to direct your efforts.
Once you have determined the three critical functionalities, development can begin. Again, this development process should be data-driven and flexible. This stage is all about quantification and trial-and-error, and consistently collected data from these early users can direct your efforts to improve your product.
An entrepreneur needs to understand if they are solving the problem they intended to address, how many users are engaging with the product, how often they are using it, and which users are extracting the most value from their product.
Developing KPIs to track and quantify this data is absolutely necessary, and will offer enormous returns when you are trying to sell this story to investors or customers as you move forward. To read about how to think about developing your initial KPIs, please read my post here.
What’s Next?
After the MVP has been in the hands of customers for a long enough duration to extract valuable information and focus their product, the next step is to decide whether to grow their business organically or seek outside investment through equity rounds.
At this stage hopefully the entrepreneur has laid the groundwork for building a data-driven organization, and will use the data collected so far to inform their decision about which path to take. They have a small team and working product, with KPIs to show its impact.
In my next post I will outline the advantages and disadvantages of the two main strategies for growing your venture, as well as insight into how to base those decisions off of data collected thus far in the process.