Product Development


April 26, 2023

How to Use Quantitative Insights to Make Data-Driven Decisions

How to Use Quantitative Insights to Make Data-Driven Decisions

How to Use Quantitative Insights to Make Data-Driven Decisions

Jake Dluhy-Smith

CEO, Co-Founder

In the dual-track agile process, product development is divided into two tracks: discovery and delivery. In the discovery track, insights play a crucial role in helping product development teams better understand their users and make informed product decisions based on real data. 

At OAK’S LAB, we divide insights into two main types:

  1. Quantitative insights cover how we analyze data on the performance of the product and conduct market research to inform our product decisions.
  2. Qualitative insights cover how we gather data from users to understand the reasons why certain behaviors are happening within the product.

This article is focused on quantitative insights, and in our next article as part of the OAK’S LAB WAY series, we will focus on qualitative insights.

What are quantitative insights? 

Quantitative insights are learnings that are obtained through various methods including data analysis, A/B testing, and market research. They provide data on users, product performance, and business metrics. Quantitative insights should consist of the key metrics that teams use to measure success and determine whether they are helping the business achieve its goals.

How we use quantitative insights at OAK’S LAB

Quantitative insights aid our understanding of the business value of a new idea, and how it contributes to the startup’s goals, helping with prioritization. It is important to work with the data available on a regular basis to catch, understand, and solve issues that could affect the users, the product, or the business. 

Our product development teams split quantitative insights into three categories:

1) Users - insights about people that are using the product. Some examples include:

  • User demographics: age, gender, location, occupation, income, etc.
  • User behavior: time spent on the product, frequency of use, user flow, etc.
  • User satisfaction: satisfaction scores, NPS (Net Promoter Score), etc.
  • User acquisition: how users find the product, channels that are most effective for acquisition, etc.

2) Product - insights on the performance of the product. Some examples include:

  • Feature usage: which features are most used, least used, or not used at all.
  • Conversion rates: how many users convert from free to paid, or from trial to paid.
  • Performance metrics: how quickly the product loads, how many errors occur, etc.
  • Engagement metrics: how long users stay on the product, how often they come back, etc.

3) Business - insights on the performance of the startup. Some examples include:

  • Revenue: how much revenue the product generates, where the revenue comes from, etc.
  • Cost analysis: how much it costs to develop, maintain, and market the product.
  • Market share: how much of the market the product currently has, compared to competitors.
  • Return on Investment (ROI): how much profit the product generates compared to the investment made to develop and market it.

Tips for quantitative insights

Following the tips mentioned below can help teams use quantitative insights effectively and build successful products. 

  1. Get to the root. Rather than simply accepting data at face value, it is important to explore further and identify the underlying issue. Doing so will allow you to gain a more comprehensive understanding of the reasons behind the data and decipher why the numbers reflect certain levels.
  2. Consider the quantitative insights alongside qualitative insights. Quantitative insights need to be combined with other qualitative insights to gain a deeper understanding of user behavior. For example, if a new graph is released on a customer-facing dashboard, and quantitative insights show that users are spending a lot longer on the page and are focused on the new graph, qualitative insights like customer interviews can help determine whether users like the graph or find it confusing.
  3. Rate the importance of your metrics for the situation. It is not possible to monitor and focus on all metrics equally; some will be more important for the industry, customer value, and other factors. Therefore, prioritize a few core metrics and use secondary metrics to gain further insight and inform product decisions.
  4. Set aside a recurring timeslot to discuss quantitative insights. Beyond gathering insights, ensure you have the time to discuss the inputs and apply them to features or ideas. This helps to build products with the user’s problem in mind and produce the intended outcomes.
  5. Be aware of your biases. Try to remain neutral and avoid looking for certain things that you want to see in the data.

Quantitative insights provide valuable data for making informed product decisions. By gathering insights about users, product performance, and business metrics, teams can prioritize initiatives and improve the user experience. 

In our next article, part of the OAK’S LAB WAY series, we will focus on qualitative insights.

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