“Fivetran has allowed us to not worry about orchestration or configuring Airflow. We can focus on making sure the data transformations are useful and the metrics are accurate.” — Shubhankar Srivastava, Co-founder, Houseware
- Industry: Software Development
- Company Size: <20
- Departments supported: Sales, Marketing, Customer Support
- Sources: Salesforce, Stripe, Outreach, Pipedrive, Amplitude, Apache Kafka, Google Analytics, Intercom, Segment, Hubspot, MongoDB, PostgreSQL
- Destination: Snowflake, BigQuery
- Additional components: dbt Core
Houseware provides a platform and toolkit for its customers to build internal data products that go beyond the scope of general analytics and data visualization tools. Instead of providing its users with tables, rows, columns, schemas—which is what’s exposed in most BI tools—Houseware delivers metrics. Examples of metrics include ARR, NRR, customer churn, conversion rate, and other KPIs familiar to almost every business.
Most importantly, the data applications that Houseware users build are designed to address the problem that every BI tool promises to solve but rarely does: data-informed action.
Shopping behavior triggers targeted emails to be sent to buyers, support tickets tell sales reps which accounts need attention so customer churn can be reduced, actions taken by users inside a product activate custom experiences to be delivered to users.
These are the types of data applications and workflows that are enabled by the Houseware platform.
- Lack of data insight, reliability and availability
- Inefficient marketing campaigns and ad spend
- Inability to turn data into customer retention optimizations
- Decreased trust in data due to errors
Joining tables used to take two to three days of effort before Fivetran, but now can be done in three to four hours.
- Users had to learn data analytics tools and database methods, such as table joins, and develop custom metrics from scratch
- Data dashboards and analytics often broke down, took too long to produce results, or required too much custom programming
- Poor APIs and data pipelines limited the types of analytics that developers could construct to meet customer needs
- Tools produced insights without any actionable recommendations
- Building data connectors required lots of programming time and effort
- Powered by Fivetran enables Houseware customers to easily connect their data to the Houseware platform
- More than 200 pre-built data connectors with comprehensive documentation
- Integration with dbt to automate data transformation processes
- Free 14 day for each new connector Houseware adds to its implementation
- Support for handling data at scale with well-crafted APIs that Houseware can build their product on top of
“During our proof of concept, Fivetran had tremendous proactive support and was able to predict usage and pricing on scaled-up volumes before we actually experienced that level of usage.” — Shubhankar Srivastava, Co-founder, Houseware
- Customers can quickly turn interesting data insights into actionable and operational results
- Using the language of metrics and templates rather than rows and columns makes data analysis more approachable and actionable
- Free trials enable Houseware to build attractive proof of concepts for new customers
- Houseware can build tools to enable follow-up sales calls based on actual product usage
- Data access is no longer the limiting factor in building custom data analytics apps
- Much shorter time to value when creating data-driven applications
- Ability to leverage numerous data integrations using Fivetran Connect Cards and GitHub endpoints that work with legacy products as well as older (and less-capable) APIs
“Fivetran cares about the trust and reliability of its data connectors and puts them at the center of their business. That translates into trust from our customers as they move through their own data journeys.” — Shubhankar Srivastava, Co-founder, Houseware
- A solution that allowed developers to create apps without deep technical knowledge of data source APIs
- Wide collection of data integration APIs and connectors needed to cover various client software tool configurations
- Being able to easily interpret and understand customer trends revealed by data patterns
- Being able to adapt workflows to different client and customer needs in how they obtain and process their data
- Save on data engineering/devops labor costs through massive automation of common tasks, especially at scaling up their customer base
- Build a solid platform that can be used in the future as the basis for a customer self-service portal