Kustomer is a SaaS-based customer relationship platform designed to integrate conversations, transactions, and a company's proprietary data in one single system, capturing all aspects of the customer journey. We sat down with Jeremy Suriel, CTO & Co-Founder of Kustomer, to learn more.
Tell us about Kustomer
My co-founder and I worked together for 20 years in customer support. Over time, we’ve seen major changes in the industry - social media gave consumers a voice, users started communicating through text, mobile computing took off - and companies weren’t listening to their customers through these new channels.
Recognizing these changes, Kustomer was launched in 2015 as a CRM platform to improve the customer experience. Our goal is to help companies compile customer information into one place, automate business processes, address the pain points behind customer support systems, and enable users to make smarter, data driven decisions.
What are you building with MongoDB?
We are building an application that allows Kustomer users to get a complete picture of their customer’s activity from the first interaction through the entire journey. This insight allows customer support representatives to provide a better, more personalized experience to the end user. With Kustomer, users are able to combine conversations, custom objects, and track events in an easy-to-use interface. They are able to collect historical data behind every account from every channel, get insight into the customer sentiment, and more.
We could have chosen any data storage engine for this application. We briefly considered MySQL, Postgres, and DynamoDB, however, when compared to the alternatives, MongoDB was the stand out in two key areas. First, we needed to store complicated data in a simple way. MongoDB’s flexible data model allowed us to have independent tenants in our platform with the ability for each customer to define the structure of their data based on their specific requirements. Relational data stores didn’t give us this option and DynamoDB lacked some key features and flexibility like easily adding secondary compound indexes to an existing data model.
We were also excited to leverage MongoDB’s WiredTiger storage engine with improved performance and concurrency. Overall, MongoDB was a no-brainer for us.
Please describe your application stack. What technologies or services are you using?
We have a microservice-based architecture with MongoDB as the primary database storing the majority of our data. Our infrastructure is running in AWS where we follow standard best practices.
Services are continuously deployed with zero-downtime from CircleCI to Amazon Elastic Container Service (ECS) running our docker-based microservice containers.
All services running with an AWS VPC, Multi-AZ for high availability with auto-scaling and traffic distributed through AWS ELB/ALBs.
API gateways sit in front of all our microservices, handling authentication, authorization, and auditing.
Customer Search & Segmentation, which is a core functionality of our platform, is powered by Elasticsearch.
We rely on AWS Kinesis Data Streams to collect and process events.
We use AWS Lambda functions to help customers populate AWS Redshift and create real-time dashboards. We’re also developing a Snowflake integration for other analytics use cases.
Finally, we use Terraform to automatically configure our cloud-based dev, qa, staging, and production environments.
We leverage MongoDB Enterprise Advanced for ongoing support and for the additional software that helps us with database operations. For example, we use the included Cloud Manager product to manage our database backups. The tool helps us upgrade our clusters, connect our alerts to Slack, and more. Our favorite feature of MongoDB Cloud Manager is the profiling/metrics dashboard that allows us to see everything that is happening within our deployment at all times and perform very specific queries to get greater insights into performance.
How is MongoDB performing for you?
MongoDB continues to perform well as our application and usage grows. We now have 1-4 millisecond reads and sub-millisecond writes. Our data volume has grown 80% since last quarter and we currently have 30+ MongoDB databases with well over 100 collections. We may explore sharding one or more of our services’ MongoDB collections and/or migrating to MongoDB Atlas in the future.
Overall we’ve experienced great benefits with MongoDB. We have great response times, are able to get the talent we need, are easily able to personalize our product to our customers’ needs, and more. Our company would not be where we are today if we had based our application on any other database.