Hightouch, a fast-growing software company that pioneered the concept of Reverse ETL, announced the release of Hightouch Audiences: an interface for marketers to build audiences on top of data across their company and then sync that data to various marketing tools. Hightouch Audiences supplements the original Reverse ETL product with an additional interface making data accessible to marketers and other users across a company regardless of SQL ability.
Previously, the data warehouse was only accessible to SQL-savvy team members. With Hightouch Audiences, the power of the data warehouse is democratized so that anyone can personalize customer experiences for use cases such as lifecycle marketing, targeted paid ads and lookalike audiences of high value customers.
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“The launch of Audiences brings us one step closer to accomplishing our vision of making data accessible to everyone within a company,” said Kashish Gupta, co-founder of Hightouch. “Before Hightouch, taking action on data involved exporting a CSV and then uploading that CSV to an ad or email tool. Now marketers and other business users can pull the audience directly from a central source of truth like a data warehouse, and then sync that audience to their many marketing tools in realtime”
Autotrader and Compare Club are part of the initial set of customers that Hightouch Audiences serves, and they have been design partners during the development process. For these customers, Hightouch Audiences allows their marketing teams to activate data without relying on their data teams for SQL queries day to day.
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“One of the biggest blockers to success in marketing is speed of experimentation and iteration. Getting data to run campaigns has been a limiting factor, as marketers have to rely on other stakeholders. Hightouch Audiences enables marketers to self-serve data, leading to more experimentation, creativity and ROI within marketing teams,” said Fareed Mosavat, VP of Programs at Reforge and a former Director of Product at Slack.
Hightouch’s approach to data integration, called Operational Analytics, extends the usage of the data warehouse beyond its typical purpose analytics into new use cases that enable data to power workflows such as sending emails and alerting sales reps.
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