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Strategic Communications Advisory For Visionary Founders
Conversation
Highlights
Welcome to another episode of Category Visionaries, the show that explores the future of tech with innovative B2B founders. In today’s episode, we’re speaking with Daniel Erickson, CEO and Founder of Viable, a customer feedback analysis platform that has raised $9 million in funding.
Key topics discussed in this episode:
- Daniel’s experience working in the chaotic cannabis industry at Eaze, navigating regulatory battles and rapid expansion
- The lessons learned from Rahul Vohra at Superhuman, particularly the importance of studying games to design effective workflows, processes, and habits within a product
- The impact of James Clear’s “Atomic Habits” on Daniel’s personal and professional life, emphasizing the power of small actions in building lasting habits
- Viable’s origin story, starting as a tool to help startups find product-market fit using surveys and evolving into an enterprise-focused platform for aggregating and analyzing unstructured customer feedback data
- The company’s core offerings, including a question-answering system that provides insights backed by customer data and a reporting feature that organizes feedback into organically discovered themes
- The enterprise sales process, leveraging investor networks, personalized outreach, and content marketing to drive leads and close deals
- The Product team as the primary user of Viable, benefiting from the ability to access distilled insights from various customer-facing teams without manual analysis
- Viable’s positioning in the emerging “experience analysis” or “generative analysis” category, differentiating itself by building an analyst rather than tools for analysts
- The challenges of being a category creator, including the need for customer education and the lack of established budget lines, and the importance of finding investors who value category creation
- The three-year vision of enabling more interactivity and goal-oriented analysis, tying customer feedback to company objectives
- The importance of building a data moat through user-generated content, ratings, and annotated training datasets to support the development of new AI-powered features
Actionable
Takeaways
Identify Manual Pain Points Ripe for Automation:
Look for time-consuming, manual processes within your target market that could be streamlined or automated using AI and other technologies. Focus on solving these pain points to deliver immediate value to customers.
Adapt Your Product and Target Market Based on Feedback:
Be willing to pivot your product and target market based on early customer feedback and usage patterns. In Viable's case, this meant shifting from a startup-focused tool for measuring product-market fit to an enterprise-focused platform for analyzing diverse customer feedback.
Leverage Multiple Channels for Enterprise Sales:
Drive enterprise sales by tapping into your investor network for early leads, crafting personalized outreach to potential buyers, and investing in content marketing to build brand awareness and credibility.
Educate Customers on the Value of Your Category-Creating Solution:
When pioneering a new category, prioritize customer education to help them understand the value of your solution and how it differs from the status quo. Highlight the time and cost savings, as well as the strategic benefits, of adopting your product.
Build a Data Moat to Support AI-Powered Features:
Create a competitive advantage by building a robust dataset of user-generated content, ratings, and annotated examples that can be used to train and improve your AI models over time. This data moat will enable you to develop more advanced, interactive features that set you apart from potential competitors.
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