Hightouch launches AI Decisioning
Introduction
Composable CDP Hightouch recently unveiled a new enterprise AI product called AI Decisioning, which operates as a native Snowflake app. This innovative solution harnesses Snowflake Cortex AI, a powerful suite of tools designed to run large language models against unstructured data.
Key Features
AI Decisioning is engineered to continuously analyze customer data stored in Snowflake, leveraging the vast amount of information available to identify optimal ways to engage with individual customers based on various attributes and behaviors. The product seamlessly integrates with popular platforms like Salesforce Marketing Cloud, Braze, and Iterable for seamless activation. It aims to move beyond traditional targeted segments by offering personalized engagement strategies for each customer.
“When marketers build audiences for campaigns, they inherently lump together many individuals based on overly broad generalizations and their best guess on customer behavior. AI Decisioning removes the guesswork.”
Brian Kotlyar, VP of marketing and growth at Hightouch (in a release)
Unique Approach
Hightouch differentiates its AI Decisioning product by adopting a composable approach. Brian Kotlyar, the VP of marketing and growth at Hightouch, emphasizes the importance of this approach, which involves never storing or duplicating data, working with existing data and schemas, integrating seamlessly with martech platforms, and offering customers a pay-as-you-go model based on their specific needs.
The new product transforms a data warehouse or lake into an AI Decisioning hub, providing enterprises with a comprehensive solution to enhance customer engagement strategies and drive better outcomes.
Industry Response
Industry experts have lauded Hightouch’s innovative approach to AI-powered decisioning. Craig Howard, chief solutions officer at Actable, commended the company for expanding its orchestration capabilities and introducing a new level of decision management functionality. While acknowledging the potential of AI Decisioning, he also highlighted the importance of data organization and quality for the success of such solutions.
Greg Krehbiel echoed similar sentiments, emphasizing the significance of data quality and the ability to act on insights generated by AI Decisioning. He suggested that AI could uncover new customer segments unknown to the marketing department, thereby optimizing marketing efforts.
Human-Centric Approach
Despite the advanced AI capabilities of AI Decisioning, human oversight remains crucial. Human marketers define goals and outcomes, while data teams manage the data accessed by the AI solution. This human-in-the-loop approach ensures that strategic decisions align with business objectives and customer preferences.
Dig Deeper: What the Composability Revolution Means for CDPs
FAQs
1. How does AI Decisioning differentiate itself from existing AI-powered customer engagement solutions?
AI Decisioning stands out by offering a composable approach that focuses on leveraging existing data structures and integrating seamlessly with martech platforms. It aims to provide personalized engagement strategies at scale based on individual customer behaviors.
2. What role does Snowflake Cortex AI play in the functionality of AI Decisioning?
Snowflake Cortex AI powers AI Decisioning by running large language models against unstructured data available in Snowflake, enabling the system to analyze and experiment with customer data to optimize engagement strategies.
3. How does AI Decisioning integrate with popular marketing platforms like Salesforce Marketing Cloud and Braze?
AI Decisioning seamlessly integrates with platforms like Salesforce Marketing Cloud and Braze for activation, allowing marketers to execute personalized engagement strategies based on insights generated by the AI solution.
4. What are the key considerations for enterprises looking to implement AI Decisioning?
Enterprises should focus on organizing and maintaining high-quality data to maximize the effectiveness of AI Decisioning. Consistent metadata and meaningful data structures are essential for the successful implementation of the solution.
5. How does AI Decisioning balance automation with human oversight in customer engagement strategies?
While AI Decisioning automates the analysis and experimentation process, human marketers and data teams play a crucial role in setting goals, managing data, and ensuring that engagement strategies align with business objectives and customer preferences.
By incorporating these FAQs, the article provides additional insights and addresses common questions that readers may have about AI Decisioning and its implications for enterprise marketing strategies.
