Increase Sales with Real-Time Data

Our team implemented a real-time data platform to increase sales for our client through better business intelligence and faster responses.

Context

Client resold goods that frequently fluctuated in price and availability. As a result, client’s data was frequently out of sync with partners, which resulted in a poor customer experience.

Solution

Create an event backbone that supports push and poll architectures. From there, migrate client to an event-driven architecture, updating processes, policies, and automation along the way.

Details

Firstly, our team designed and implemented an event streaming backbone with Kafka.

From there, our team planned out a topic strategy. Next, we created and integrated connectors to stream data into the Kafka topics.

Once all of the data sources were integrated, we developed stream processing and ETL services to enrich data, setup alerts, and kickoff automation.

Lastly, our team developed runbooks and trained client subject matter experts to successfully manage operations.

Overall, some of the most important technologies that led to the success of this transformation included:

  • Kafka
  • Zookeeper
  • Kubernetes
  • Java
  • Python
  • Jenkins
  • Grafana
  • Prometheus

Results

In summary, our client was able to achieve a 98% listing accuracy and improve the third step in their sales funnel (of 7) by 20%. Additionally, our client was able to add better visibility to their data sources and automations, reducing triage effort.

Ultimately, our client realized higher conversion rates (+44%), improved customer satisfaction, and increased customer retention.

Many projects involve similar technologies and processes, here are some other case studies you may find useful:

More on Real-Time Data and Event Streaming

Generally, most modern organizations aim to be highly reactive to specific stimuli. In the technology world, these stimuli are referred to as ‘events’.

Similarly, when a system is called ‘event-driven’, it means that ‘Y should occur when X happens’.

What is the benefit?

In most cases, this style of operation is considered the most efficient use of resources.

This approach allows the data to drive processes, as opposed to processes driving data. In short, this allows for increased parallelism, modular development, and non-destructive analytics.

Surprisingly, this strategy tends to be one of the most underrated ways to improve efficiency and automation. If this subject piques your interest, check out some of these articles:

If your organization is considering event streaming, we would love to learn more!

– Team Llama 🦙