Alteryx: Customer Profiles, GTM Strategy & Product Roadmap

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Now, let's discuss your experience at Alteryx, starting with your efforts to land new accounts. Could you share your experience with that? How challenging was it to introduce Alteryx into a new account?

The people we typically targeted were those who had data stored in various platforms and were using a platform like Microsoft Excel for basic data preparation, blending, cleansing, and VLOOKUP type exercises. Our sales approach was to reach out to these greenfield accounts, often via LinkedIn, email, and phone. We would provide the prospective customer with examples of their industry peers or use cases relevant to their specific vertical, with the aim of initiating a conversation or a meeting.

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That's really helpful to understand. So, it sounds like there was a good utility for the product overall. You mentioned the interest within these accounts. How universal was it? Were most of these buyer profiles relatively interested in something like Alteryx, or was the interest scattered?

The sales play revealed a division in businesses regarding who saw value in Alteryx, and it was primarily those in analyst roles. These are the individuals typically burdened with manual tasks, such as compiling data from various sources like data warehouses or repositories and pulling them into Excel. They often end up with an Excel spreadsheet with 10 million rows, and any attempt to manipulate it results in a loading circle and eventually a system crash.

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That's really helpful to understand. So, it sounds like there was a good utility for the product overall. You mentioned the interest within these accounts. How universal was it? Were most of these buyer profiles relatively interested in something like Alteryx, or was the interest scattered?

However, not everyone was a fan. Data scientists, or those who prefer to work with code, were less enthusiastic. Alteryx has always promoted itself as code-free and code-friendly, which appeals to non-technical people. But this approach is not always appreciated by those working in data science or data engineering, as it can bypass their expertise.

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