/ #Case Study #Jawabkom 

Jawabkom Case Study: How to use rakam to analyze Firebase data better


Executive Summary

“Jawabkom – The first Arabic online platform that provides individual expert consultations” – Yahoo

Jawabkom is an online consultation platform that relies heavily on product analytics to make correct data-driven decisions in their product development cycle. However, their previous way of getting these insights was slow and not scalable. They needed easy and modern ways to enable their teams to get real-time insights from their data warehouse, and that is when we came on board.

About Jawabkom

Jawabkom is one of the fastest-growing websites in the online consultations niche among Arabic nations. Millions of people use Jawabkom to get affordable and timely one-on-one answers from their many verified Experts.

Founded in 2014, Jawabkom is already one of the top 100 most visited websites in all 22 Arab nations. Jawabkom received several Awards, and was named “One of the Most Influential Websites in the Arab World” by UAE’s e-Government.

Challenges of Their Old Stack

(Firebase + BigQuery + SQL + Spreadsheet)

  • Limited Customization Options

As Jawabkom’s products grew and matured, the teams working behind the scenes also started to have more complex questions that they wanted to ask about their data. They couldn’t succeed in getting these insights from FirebaseUI, and this was due to its limited customization options. Firebase doesn’t give much flexibility to its users to customize its UI for their needs. By that, they couldn’t ask questions like the monthly retention rate for a specific feature or the weekly LTV growth rate.

  • Complexity and Technicality

In Jawabkom’s quest of finding a lasting solution, they discovered that Firebase supports export to BigQuery (Google’s cloud data warehouse solution). After linking their Firebase account to BigQuery, they also started loading all their company data from different tools and platforms to their BigQuery warehouse. Once they had all their company data inside their BigQuery, they began to draw the product analytics insights by writing SQL queries. Unfortunately, this was a short-term solution as their non-technical teams couldn’t write SQL. Therefore, the technical teams were overwhelmed by writing the queries to get insights from their data.

“Linking with BigQuery enabled us to get answers to our questions, but it was restricting the number of people working with data, leading to less data-driven decisions across the company. The only people who could get insights from our data were the technical people who know SQL - this accounts for only 20% of our headcount and leaves 80% of us dependent on technical teams when working with data.”
Batin Duz, Project Manager at Jawabcom

The technical teams started to spend 70% – 80% of their time writing SQL queries and visualizing the data on excel sheets for non-technical teams. Whenever simple customizations, like filtering their data by country was needed, technical teams had to edit the query or, in some cases, rewrite the whole query from scratch. They eventually realized that that wasn’t the most scalable way to work with data, so they added rakam to their stack.

How We Helped

(Firebase + BigQuery + rakam)

Automation and Detailed Insights

Jawakomb connected rakam to their BigQuery warehouse and installed rakam’s firebase recipe, which includes predefined models for firebase events and attributes. After, rakam automatically created several dashboards to provide them with quick insights into their data and give them five reporting interfaces:

Segmentation: A deep analysis of metrics and users by drilling, filtering and pivoting the data.

Funnel: A step-by-step analysis of user behavior that determines where and why they drop off or convert.

Flow: Visualizes the complete user journey in a Sankey chart to easily discover the common paths users take on your products.

Retention: A cohort-based analysis that shows what brings users back.

SQL: An SQL interface where you can build custom queries and reports using SQL.

“With Rakam, we came a very long way of democratizing data within our organization. We enabled teams to utilize, consume and manipulate the data without the need for technical skills, and all of our product decisions started to be driven by data coming straight from our data warehouse, which became the single source of truth for us.”
Batin Duz, Project Manager at Jawabcom


  • Simplicity and Speed

With rakam’s non-technical friendly and customizable UI, the questions Jawabkom teams have been trying to answer by writing long and complex SQL queries on BigQuery now take only a few clicks and dropdown selections on rakam. Asides from drastically reducing the time spent on writing queries, this also enabled everyone in their company to get real-time insights on their user behavior and product usage.

“I have been spending 60-70% of my time writing SQL queries to prepare the reports and dashboards that our product teams are requesting. But with rakam, product teams can go to UI and get the insights they want themselves without waiting for me to write SQL. My responsibility now is to make sure the models and metrics are modeled and defined correctly to answer our end-users’ questions.”
Batin Duz, Project Manager at Jawabcom

  • No SQL Barrier = Data Democratization

Product teams at Jawabcom heavily relied on technical teams to get any insights on customer behavior. They first needed to explain to their technical teams what kind of insight they required from their data and later wait for them to write the query to get this insight. Depending on the complexity of the question, this process could take one day to several weeks. Now, teams at Jawabkom including the non-technical teams can go to rakam and build complex funnel and retention reports using rakam’s built-in features without the barrier of SQL.


Emre Semercioglu