Aravo Solutions Case Studies Optimizing Customer Engagement with Databricks Lakehouse: A Case Study on Iterable

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	Optimizing Customer Engagement with Databricks Lakehouse: A Case Study on Iterable Aravo Solutions

Optimizing Customer Engagement with Databricks Lakehouse: A Case Study on Iterable

Aravo Solutions

Technology Category	Analytics & Modeling - Machine Learning Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries	Cement Equipment & Machinery
Applicable Functions	Maintenance Sales & Marketing
Use Cases	Predictive Maintenance Rapid Prototyping
Services	Data Science Services Hardware Design & Engineering Services
Challenge	Iterable, a company that helps brands optimize and humanize their marketing, was facing challenges with its data infrastructure. The company needed to build personalized and automated customer experiences for its clients, which required harnessing diverse, complex data sets and facilitating rapid prototyping of machine learning models. However, the infrastructure they initially built with AWS native tools, including EMR, was resource-intensive, costly to maintain, and created significant operational overhead. This made it difficult for Iterable to scale the level of data ingestion and rapid prototyping of machine learning models needed to support its customer requirements and respond quickly to changes in the market. Furthermore, the company's AI solutions had to account for diverse data variables, drifts in the model, new regulatory changes, and a growing demand for more privacy protection. Read More
About Customer	Iterable is a marketing company that helps more than 1,000 brands optimize and humanize their marketing in today’s competitive landscape. The company empowers marketers with the ability to deliver individualized cross-channel communications for customers at every stage along their journey with a brand. They accomplish this through customer segmentation and personalization, and campaign optimizations such as identifying the best times to send messages to customers and which engagement channels are working best. Iterable is committed to constant innovation, creating and deploying cutting-edge solutions that help brands stay ahead of their competition and meet the growing and evolving expectations of their customers. Read More
Solution	Iterable turned to Databricks Lakehouse to overcome these challenges. Databricks Lakehouse enabled Iterable to wrangle their diverse data to create Predictive Goals, a data-enriching segmentation tool that allows marketers to create bespoke, goal-oriented predictive segments with their first-party data. The platform facilitated rapid prototyping and collaboration throughout the model lifecycle, from simplified access to data and automated cluster management to streamlined experimentation and iteration on models. Delta Lake, a component of the lakehouse platform, provided Iterable with the means to create a unified view of all its data in a secure and compliant manner. This allowed Iterable to glean an accurate and complete view of its customers’ needs and develop effective solutions to meet them. With reliable and consistent data being fed by over 5,000 pipelines, the data science team used MLflow to train, experiment and track hundreds of models across more than 2,000 projects for the company’s customers. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Platform as a Service (PaaS) - Application Development Platforms

Applicable Industries

Cement

Equipment & Machinery

Applicable Functions

Maintenance

Sales & Marketing

Use Cases

Predictive Maintenance

Rapid Prototyping

Services

Data Science Services

Hardware Design & Engineering Services

Challenge

Iterable, a company that helps brands optimize and humanize their marketing, was facing challenges with its data infrastructure. The company needed to build personalized and automated customer experiences for its clients, which required harnessing diverse, complex data sets and facilitating rapid prototyping of machine learning models. However, the infrastructure they initially built with AWS native tools, including EMR, was resource-intensive, costly to maintain, and created significant operational overhead. This made it difficult for Iterable to scale the level of data ingestion and rapid prototyping of machine learning models needed to support its customer requirements and respond quickly to changes in the market. Furthermore, the company's AI solutions had to account for diverse data variables, drifts in the model, new regulatory changes, and a growing demand for more privacy protection.

About Customer

Iterable is a marketing company that helps more than 1,000 brands optimize and humanize their marketing in today’s competitive landscape. The company empowers marketers with the ability to deliver individualized cross-channel communications for customers at every stage along their journey with a brand. They accomplish this through customer segmentation and personalization, and campaign optimizations such as identifying the best times to send messages to customers and which engagement channels are working best. Iterable is committed to constant innovation, creating and deploying cutting-edge solutions that help brands stay ahead of their competition and meet the growing and evolving expectations of their customers.

Solution

Iterable turned to Databricks Lakehouse to overcome these challenges. Databricks Lakehouse enabled Iterable to wrangle their diverse data to create Predictive Goals, a data-enriching segmentation tool that allows marketers to create bespoke, goal-oriented predictive segments with their first-party data. The platform facilitated rapid prototyping and collaboration throughout the model lifecycle, from simplified access to data and automated cluster management to streamlined experimentation and iteration on models. Delta Lake, a component of the lakehouse platform, provided Iterable with the means to create a unified view of all its data in a secure and compliant manner. This allowed Iterable to glean an accurate and complete view of its customers’ needs and develop effective solutions to meet them. With reliable and consistent data being fed by over 5,000 pipelines, the data science team used MLflow to train, experiment and track hundreds of models across more than 2,000 projects for the company’s customers.

Impact #1	The adoption of Databricks Lakehouse has brought about significant operational benefits for Iterable. The platform has made the data infrastructure revitalization process quick and painless for the AI team, and has helped instill data engineering and machine learning best practices for Iterable’s wide Product and Engineering teams. This ensures Databricks-inspired optimizations continue across the organization. With a centralized view of all data, code and models, Iterable’s marketers now have a limitless capacity to create personalized user experiences at scale. The company can now handle its variable data volumes and rapidly growing machine learning road map, ensuring consistency and reliability in the data provided to create better models for continued innovations that aid in customer conversion, engagement and retention.

Impact #1

The adoption of Databricks Lakehouse has brought about significant operational benefits for Iterable. The platform has made the data infrastructure revitalization process quick and painless for the AI team, and has helped instill data engineering and machine learning best practices for Iterable’s wide Product and Engineering teams. This ensures Databricks-inspired optimizations continue across the organization. With a centralized view of all data, code and models, Iterable’s marketers now have a limitless capacity to create personalized user experiences at scale. The company can now handle its variable data volumes and rapidly growing machine learning road map, ensuring consistency and reliability in the data provided to create better models for continued innovations that aid in customer conversion, engagement and retention.

Benefit #1	2x faster pace of new feature releases
Benefit #2	30% increase in customer engagement due to optimized message delivery
Benefit #3	10x increase in lakehouse adoption expands AI innovation

Benefit #1

2x faster pace of new feature releases

Benefit #2

30% increase in customer engagement due to optimized message delivery

Benefit #3

10x increase in lakehouse adoption expands AI innovation

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Overview

Optimizing Customer Engagement with Databricks Lakehouse: A Case Study on Iterable

Operational Impact

Quantitative Benefit