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Driving adoption of data to build better products, growth etc.

During my time at Jobseeker Company, I worked on projects that I cannot disclose due to NDA restrictions. Please contact me for more information at
rezayes98@gmail.com⁠
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Product teams need to continuously monitor their product usage data to understand the user pain points and iteratively improve the product experience. As a product manager, I am responsible for delivering tools that improve visibility, make it easier for the team to comprehend, and derive actionable insights from the product data. Here is a summary of how my team and I accomplished this:

Identify your product assumptions. Create a list of data points required to prove or disprove those assumptions.

Ensure that the above data points are being recorded.

Build APIs to query data and model it in the required format.

Share product usage data with the team and help them derive insights out of it.

Connect those APIs with visualization tools and then create widgets and dashboards as per need.

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Now that we have an (almost) working dashboard, its usage is limited within the team due to the need to manually query and generate relevant data for their work. The existing dashboard is primarily for HR and Management. So, does the team start using data for their everyday decision making? No, because they have to manually query and generate it, and non-technical teams often rely on the engineering team for help with this.

Here are some of the challenges I faced when I was trying to drive adoption for data dashboards inside my team:

People heavily rely on their intuition for making decisions and do not refer to data even though it is available.

This is normal human behavior!⁠

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During meetings, data was often needed to propel the conversation forward but people would proceed with behind the envelope calculations for the sake of everyone’s time.

Over a period of time, each function of the product had its own record. For e.g. engineering, dev-ops, sales, and marketing had their own data records running. This created data silos and no one had the bird’s eye view of whether the product was heading in the right direction.

And, we humans tend to ask questions and validate hypotheses with our colleagues and trust them rather than looking for facts and information ourselves.

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Welcome Back-Office Assistant

In order to sustain the data driven culture and cater to all the natural human tendencies, I realized that visual interfaces were the way to go forward for the first step. I chose to recreate our own Back-Office, powered by

Redash⁠

, to develop my own skills and integrate them with the APIs that were already created to query the data for the dashboards. This could be accessed by every team in the organization.

I created an MVP

Within a week, I had an MVP ready which could answer questions like:

How many users fill the inquiry form in the last 7 days?

How much the total conversions from submitting the inquiry to hot leads in the last month?

How many wrong target users or fraud are filling the inquiry form in the last month?

I took feedback from different team members and created a list of questions that everyone wanted to be answered on a day to day basis. I slowly started connecting APIs from different data sources with this one skill.

I ensured that the MVP served all the product functions

After a couple of weeks of further development, the skill was ready to answer questions like:

What is the uptime of the product in the last 1 month? — DevOps

What is the mean response time of our APIs? — Engineering

How many unique user sessions did we have on our website in the last month? — Marketing

What is the value of our current sales pipeline? — Sales

How many users have completed the user funnel? — Product

All of this took roughly 3–4 weeks, given the fact that all of the APIs were available to query the required data.

Benefits of using the visual interface

With

Redash⁠

data visualization, I ensured that:

No meeting or conversation was ever paused due to the lack of a data point.

Reduce the friction of manually querying and sourcing. Getting data as soon as a question popped up in someone’s head. Essentially, cater to the innate human tendency of asking questions.

Data visualization transforms complex data sets into clear, visual representations. This aids in easier comprehension of intricate information during meetings, ensuring that all stakeholders, regardless of their technical background, can grasp the insights quickly.

I continuously improved the skill

Through logs, I closely monitored what were the top questions that the team members were asking and the skill did not have an answer to or wasn’t responding to as per need. I kept adding those questions to the skill to ensure that team members kept coming back to the Back-Office for all their data needs.

Over a period of months, the team not only had access to data but were using it for their day-to-day needs of understanding how our users were using the product. With this arsenal, the team was able to improve the user conversion funnel from 10% to more than 50% over the period of 6 months!

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