Correctly making intelligent decisions that start with the data
Whether you’re looking to make product feature updates, increase customer retention, enhance the conversion rate of a particular stage of the customer journey, or penetrate an entirely new market segment, the “how” will start with a hypothesis. Just like other fields of scientific studies, product management and growth requires data to back up your hypothesis and ensure the insights gathered are accurate and yield the best decision.
Unfortunately, while many product managers understand this process to a tee, they tend to complete the steps in the wrong order. They often start with a product hypothesis such as “we’ll increase customer retention by offering free tutorials to current users on our new features,” then seek out data that supports that theory for growth — digging through to find any correlations like high user-training participation and low customer churn.
Even if the product managers have an in-depth understanding of their product and its users, this approach is risky because of bias and the inability to see the whole picture. For instance, data will always confess if its tortured enough. In other words, it’s easy to play with and filter out data in order to justify your argument or hypothesis.
There’s also the principle that correlation does not indicate causation. Just because there is a correlation between high user-training and low customer churn doesn’t mean that is the direct reason why customers are choosing to stay. All of this could cause you to miss certain patterns and trends in market segments, product attributes, and behaviors in customers because of their narrow focus.
You also become more prone to errors — especially in things like misinterpreting your product data by using your bias to strictly make correlation-based decisions. Therefore, need to adopt another path that is unbiased and allows you to see all of the data to make a hypothesis.
So what is the best way to approach hypothesizing your product growth process?
Obverse, Learn, Hypothesize
The proper method for generating a testable product hypothesis is first to collect and observe the data on what particular elements of your product help your users succeed and fail during use as well as which value-producing areas might they not be aware of. Next, get deeper into their over user-experience and understand the parts of the journey they struggle with through analytical processes and tools to find common trends and other usable information for actionable insights.
After these steps are completed, you can make your hypothesis accordingly. Conducting the process in this way would create more of a data-driven hypothesis as opposed to a data-proven one which would only attempt to reaffirm your theory.
Taking this route lets you make more insightful observations, develop a strong product hypothesis, and better justify a product decision through logical, data-driven arguments. This also keeps conversations on important product decisions healthier and more honest since the theory was built on data and not emotion or bias.
If these same conversations started with a hypothesis that someone was looking to reaffirm, the discussion could lack integrity and constructive feedback because that person would only cite information to make their hypothesis seem solid — regardless of other data trends collected or the opinions of others in their group.
Intelligent Observations Require Intelligent Solutions
Obviously, the process of using data to create a hypothesis might be a double-edged sword based on the amount of data you’re analyzing. If you aren’t looking at all of the information gathered, you risk missing something that could end up being invaluable to a product hypothesis and long-term development decision.
The counter side is that reviewing a lot of data, assuming you aren’t using the right tools, puts your product managers in a poor environment in which it would take forever to scrape through all of the information to make a hypothesis. There’s limitless potential patterns and trends you need to identify within your product and its users that would be nearly impossible to decipher manually — particularly if your KPIs and data sources are not aligned with one another.
With all that said, it’s important to invest in a platform that uses advanced machine learning (ML) algorithms to ensure your data is well-interpreted and great opportunities are spotted— empowering you to develop the best product hypothesis. Artificial intelligence (AI) solutions identify actionable opportunities for you and can present those opportunities in an intuitive way with recommendations on specific courses of actions.
Using the insights gathered, you can make your hypothesis as to how you will achieve your product growth goals. The advanced analytics functionality of an AI platform can then allow you to test your theory by predicting the impact of certain actions on the relevant KPIs — giving you the ability to prioritize product development opportunities by using the same hypothesis-testing infrastructure that successful product teams utilize.
Craft Your Data-Generated Hypothesis
Loops AI platform takes a proactive approach to finding actionable insights — letting your data work for you so you can develop better hypotheses and avoid missing opportunities. Through our ML algorithms, you can ensure that your product development decisions will start with a data-driven hypothesis and lead to growth in all of your product KPIs. Contact us today to speak to an expert and learn more about how you can streamline your product decision-making process.
