What’s the best way to get inside of the mind of a customer or audience you’re serving? Traditionally, we’d hire market research experts or run a survey. These methods are limited by scale, but with AI we may be able to dive deeper into real-time customer conversations to derive insights that speed the feedback look and accelerate the innovation cycle. Remesh, led by our guest Andrew Konya, is tackling this head on.
In this episode, we discuss how AI can understand large population in-depth, finding an instant audience, training on the most useful data sets, and go-to-market strategy for startups.
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Show Notes with Timestamps (VIDEO):
1:21 Remesh’s focus on helping customer understand populations (i.e. customers, voters, etc)
3:11 Leveraging ML to scale conversational understanding from 1:1 to 1:n
6:08 Engaging with the Remesh platform to understand an audience
9:19 Finding an instant audience to drive real-time insights via live conversation
10:49 Providing insight from a massive data set. Making predictions with small data sample
13:28 Where Remesh started in building it’s data set
15:51 Lesson learned in building data sets representative of audience
18:01 Managing customer conversation around prediction accuracy of AI algorithm
21:19 Initial go-to-market strategy and its evolution
24:14 Measuring and explaining ROI in customer conversation
26:14 Hiring strategies for AI talent
Next Episode: Sebastian Jimenez, Founder & CEO of Rillavoice