Bots Customer Case (1)

A central customer view to gain complete insights

About the Company

BOTS’ mission is to bring trading algorithms to the people. Professional traders commonly use financial algorithms. More than 75% of today’s trades on exchanges are executed with algorithms. BOTS harnesses the algorithms’ power – pop it in a smartphone, any smartphone – and place it right under everyone’s fingertips. BOTS connects people with all available trading instruments and lets them trade in cryptocurrencies. The market potential is in the billions of transactions each month.

Bots logo
“Crystalloids guided us through the entire process from roadmap to writing a detailed and prioritized backlog. The team was very knowledgeable about BI and every aspect of data engineering. We are happy with the deliverables and will hire Crystalloids again next time.”
Jens van der Sman

Jens van der Sman
Business Intelligence Manager at BOTS

The Challenge

BOTS is a fast-growing scale-up company. Not all enterprise data sources were available for reporting. The data and the insights were fragmented. This made it impossible to deliver comprehensive insights into the company's various business personas, such as finance, eCommerce, marketing, and product development. Consensus on the requirements of the BI tool for people working with different solutions was necessary so that everyone was aligned and focused on the same answer. There was no central place where the data was stored and transformed. This makes it hard yet essential to understand the data and make all the data that flows in various systems and applications available and valuable. The data and definitions needed to be better understood, operational procedures required to be adjusted, and plans were not directly available for extracting data. When these parts are improved, they contribute to the company's maturity. 

The Goal

  • Design a BI roadmap for the future
  • Reliable insights into the performance metrics 
  • Share and democratize insights with all employees
  • Have all enterprise data available in a central place
  • Have all data prepared for use in dashboarding tools
  • Reduce the number of visualization tools

The Solution

To make all data sources available for reporting, all data in the customer domain were collected and transformed into a centralized environment on the Google Cloud Platform. This way, BOTS could get valuable insights from the data in their reporting tools. Crystalloids built a data warehouse to get all the data to a central place. The main view could then be created from the customer-related data, which includes payments, deposits, withdrawals, subscriptions, trading, registrations, the first installation of bots, and wallet balances. 

For BI reporting and modeling, the Looker Data Platform was selected. With Looker, BOTS can build their views to get valuable insights. An advantage of Looker is that the data can be managed and defined in Looker. With Looker ML, users can do more customized data manipulation. 

The solution architecture is as follows:

Bots architecture

The Process

Crystalloids and BOTS started with multiple sessions to define a detailed and prioritized backlog. As a result, a precise estimation of work, tasks, dependencies, and deliverables, such as a solution architecture and definitions of done, was delivered.


The Result

One of the results that Crystalloids achieved is that BOTS has better insights now into their customer journey because of the comprehensive views. Next to that, all data sources now lead to one customer identity. Also, new data was fetched, which contributed to new insights. In Looker, they can build customized views to get precisely the information they need. The data warehouse was equipped, and the data was prepared for use in Looker. BOTS has a better understanding now of the customer domain metrics and dimensions. One of the bottlenecks was the onboarding process of new customers. They can now see where a customer drops out. Based on this, they can improve the customer journey and decrease the number of dropouts.


GCP Products

  • Google Cloud dataflow
  • Google Cloud storage
  • Google Cloud BigQuery
  • Google Cloud Functions
  • Google Data Studio
  • Looker data platform

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