The Dynamics Of Product Decision Making In Startups — Principles And Frameworks
The art of decision-making isn’t always about capturing the elusive “best” decision — it’s about making the best use of available information, building trust among stakeholders, and acting with conviction.
Early-stage startups are frequently faced with competing demands and priorities. The backlog of products can quickly become difficult to manage. What factors do you consider when deciding which features to prioritize? Which stakeholders should you prioritize? How do you strike a balance between immediate and long term objectives?
The problem is that high-stakes decisions are rarely so clear-cut. It’s difficult to reach agreement on what the “best” option even is on a team; with too many options, leaders can become paralyzed by indecision, wasting valuable time and opportunities. How do you make a decision that balances speed and sagacity? Should you rely more on data or your intuition? How do you reconcile differing points of view among intelligent, strong-willed operators without stirring up trouble?
To better understand decision making in startups, the factors behind the decisions need to be investigated and how the decisions affect the startup, both in terms of building products and the ability to recruit and retain talents.
Understanding The Dynamics
The initial phase is commonly chaotic and “very, very driven by the organizational power dynamics.” Priorities may shift from week to week, with directives coming from the Executives — because the founders and executives wield the most power, followed by sales and marketing. “If you’re a lowly designer, researcher, tech, or product person at this stage, you frequently lack the ability to challenge the backlog.”
Then, as the company matures, they realize that throwing things at the wall has a low success rate. They begin to strengthen their design team’s research capabilities. The power structure “becomes slightly more balanced” at that point.
However, it will take time for the research team to shift the power balance. Initially, the research is summative, such as examining how the product is currently performing. By deploying deeper, ethnographic research to understand problems and user needs, the research function “becomes a great tool for problem discovery” as it becomes more sophisticated.
That is when the design and research teams begin to exert greater influence over the backlog. The power dynamic has shifted from a “leadership-driven feature factory” to prioritization based on “formative exploratory research” from design teams.
A successful startup is dependent on the interaction of an idea ( usually a product or service), a market, people, and capital.
PRINCIPLES AND FRAMEWORKS FOR DECISION MAKING
- “Business plan” is good but a “Learning plan” is better. A learning plan, not a business plan, is what an entrepreneur requires. You have a theory, there are some things you know and some things you don’t. You must understand what you don’t know in your hypothesis and what assumptions you are making, and then devise a method to test the hypothesis at the lowest possible cost and risk. In general, things will not go as planned. So, what do you do now? You learned something by testing the hypothesis in the real world, and you can now use that knowledge to improve your product. The number of times you can do this is determined by your drive, capital, and people. Plan less and accomplish more. Your business plan is incomplete if you don’t make a learning strategy.
- Understand why people use products. A major issue in the tech industry is a lack of understanding of causation. Almost annually, companies release new versions of their products that include new features. Nobody looks at how many features in the previous version were used and liked. Companies add new features instead of improving the features that people already use. This will continue until a competitor comes out with a much simpler product!
A startup must gain a clear understanding of why people use products and how to improve it. If a new product has two major features, say A and B, launch three versions of the product: A, B, and AB. This will assist you in determining which feature is more valued by users. - Hack risks, probability, options. Innovation is a high-risk endeavor. You cannot calculate the precise probability of success for your startup based on historical data so you create flexibility. Don’t become obsessed with your concept. Make your product/service architecture flexible so that you can test different options. Learn how to apply your learnings to different markets and consider combining it with existing products. It is profitable to test options.
- Appropriate positioning is everything. Be innovative in positioning the startup to stand out. Choose a positioning category where the money can be utilized and create something unique. Choose the right environment that gives you access to capital, connections, and expertise.
- Find a balance between intuition and analytics. Companies “very quickly move from no research to an over-reliance on metrics, data, and research” and soon “get bogged down in not being willing to make any hunch decisions” because everything has to be validated before they move ahead. The good news is that it’s possible to find and maintain a middle ground between those two extremes.
- Integrate design and research. One way to do this ensuring that research, product and design teams work together to get actionable insights.
- Focus more on making right decisions. In startup mode, everything comes at you quickly and you tend to react immediately. If you’re a manager and make a bad decision, you simply reverse it. In scale-up mode, however, you have a choice: “You can do things fast or you can do things right.” There’s always a balance, but in scale-up mode you need to shift toward doing things right more often than doing things fast.
- Use A/B testing to reconcile different points of views. When two strong options are open, rather than making a decision, test both theories and let data be the judge.” This method may appear to favor data-driven people at first glance, but it empowers each PM to push ideas forward.
