How to think about product/startup research

Why good research is > than perfect research -- and how you can move quicker

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Anyway, let’s dive into the topic of research — specifically how to think about product and startup research.

Research is absolutely critical to making informed strategic and product decisions. And for a PM, research must be carefully balanced with speed, efficacy and an iterative process. Spending more time than necessary on it has a cost, and starting any research without having a plan is like getting in a car and driving without a destination. Without a sense of direction, your time spent on research can easily spiral and you can end up nowhere useful, or spending way more time than you should be trying to get more information.  

Research takes time. It takes time to thoroughly analyze markets and customers, document all your findings and then coherently present them with ample evidence. The challenge that product managers have is that time isn’t on your side -- so, how can you balance speed and agility with sufficient research in order to make informed, data-driven, strategic and product decisions? 

Start by changing your perception from “Do we know everything about X?” to “Do we know enough about X to make a decision?”. 

Like many things in product management, perfect is the enemy of the good. And unless you're building rocket, this is how you should think about research.

Below are five steps to be fast and accurate enough in your research to make an informed decision and execute. 

Develop your assumptions and hypotheses 🧪

Write down your assumptions. Your assumptions are what you implicitly accept as true in order for your product to work.  Take Airbnb, the first shared accommodation platform that had an extremely contrarian assumption -- people will pay to stay on a stranger’s couch. This was an assumption that needed to be true for the early version of Airbnb to work. 

Rank your assumptions by risk and complexity. Once you have your assumptions written down, it’s useful to put them in a table with two other variables: risk and complexity.

Risk means how important it is for this assumption to be true for your product to work. High risk means it is fundamental to the idea. Complexity means how hard it is to test and validate.

Once you have this table, start forming hypotheses, and then researching/testing the ones that are high risk and easy complexity first. 

A hypothesis is at the heart of effective research. It captures the essence of what you believe to be true under a certain condition, and why that’s likely the case. It differs from your assumption in that it is explicit, and testable. A simple and comprehensive format is: 

We believe that_(target user) will_(predicted action)_because _(reason)_.

A key function of a product manager is to test and validate high-impact hypotheses enough to make a decision and execute as fast as possible. 

Our mock Airbnb assumption can be converted to this  hypothesis.

We believe that travellers will book accommodation online and stay on a strangers couchbecause it’s cheaper than a hotel and a more personal experience.

A hypothesis is your north star. Without a clear one, it’s easy to go into research blind and directionless. Your hypothesis tells you what you need to learn, drives your key questions for research, and highlights which evidence will empower a decision. 

By making your assumptions as well as your hypotheses explicit, you increase the clarity of your approach and the chance for learning. 

Create your question deck 🤔

Create a question deck. Once you have your assumptions and hypotheses and know what you need to validate, your question deck is your map for figuring out how you’re going to do it. First, write down the high-level topics to learn about. Then, write down the key questions you and your team need answers and evidence for. Then prioritize the importance of getting answers to those questions. This list of topics and questions will determine your timeline and the level of research needed. 

Following the same example, here would be some example questions in the deck:

Research is iterative 🪜

Like building a product, what you’re researching and learning about should be iterative and should change based on what you’re discovering. You want your questions and priorities to adapt as you move through the cycles of validated learning. 

You should frequently run a self-check and ask yourself if what you’re researching now is productive to your goal. 

Be aware of analysis paralysis 🧟

We’re all prone to information overload. And as often is the case, less is more. Going after too many sources and trying to analyze myriad data will most likely be counterproductive, lead to analysis paralysis and kill your research pace. 

Your goal isn’t to be 100% right. The decision that follows from the research is still a bet. Your goal is to mitigate the risk and understand the opportunity enough to justify putting money and resources behind it. 

Make a decision 🎬

Perfect is the enemy of good. Once you have gathered enough information and evidence around your hypothesis, the next step is to apply those findings to the next actionable step, and to do that quickly. A simple decision framework I like to use here is SPADE

[S] Setting: What needs to be decided?

[P] People: Who needs to be involved in the decision?

[A] Alternatives: What are the possible alternatives?

[D] Decision: What’s the decision?

[E] Explanation: Why was that the decision?

Once you’ve done research and analysis and have insights, take the next step and set up a meeting with relevant stakeholders as soon as possible. This keeps what you have to share relevant, and shows you’re somebody who gets stuff done. 

Have any thoughts, questions or feedback? 👇

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Every week, I write about product, startups, growth, working with people, and anything else that I’ve found helps me have a happier and more meaningful career in product management

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