From Excel Sheet to 13,000 Customers: How Sean Tepper Built Tykr

The podcast discusses Ticker, a SaaS platform that simplifies stock investing by focusing on product-driven outcomes rather than just outputs. It highlights the transition from a personal Excel spreadsheet (initiated in 2016) to a fully developed platform launched in 2020, emphasizing long-term fundamental analysis and automated scoring systems. Key features include a traffic light rating system for stock evaluation, a confidence scoring model using AI, and a seven-out-of-seven rating framework. The platform analyzes over 100 data points per stock across four years of quarterly trends, aiming to provide users with clear guidance through "why pages" that explain ratings rather than offering opaque scores. It also addresses challenges in scaling, such as transitioning from manual to AI-driven tools, integrating with broker APIs (which faced technical hurdles), and prioritizing user education to build trust.

The discussion covers Tickers growth strategies, including SEO-driven content, YouTube outreach, an affiliate program, and partnerships with brokers to enable direct stock trading. Metrics like visitor-to-trial conversion (4%), trial-to-paid conversion (over 50%), and a low churn rate (5%) underscore its customer-centric model. The platforms focus on long-term investment signals, simplified onboarding, and psychological strategies like trust pilots and risk reversal (e.g., 30-day free trials) are highlighted as critical to user acquisition. Additionally, the podcast explores the shift from algorithmic ratings to educational features inspired by platforms like Duolingo, ensuring users understand their investment decisions. AI integration, such as the 4M Confidence Booster and an AI investing helper, streamlines analysis while maintaining human oversight to avoid overreliance on automation.

Future plans include expanding broker integrations and trading capabilities, with the goal of achieving rapid growth through partnerships. The platforms methodology emphasizes consistency in financial performance, avoiding short-term trading, and using user feedback to refine its approach. Challenges like outdated broker APIs and balancing automation with user control are noted, alongside the importance of aligning teams with company goals and prioritizing collaborative leadership. The podcast underscores Tickers mission to empower users through simplicity, education, and transparency, contrasting with traditional financial advice and emphasizing the value of transaction-based revenue models for institutional clients.

• What if you implemented AI-driven confidence scoring to accelerate stock evaluation?

  • Move: Integrate a simplified version of the 4M Confidence Booster (Math, Meaning, Moat, Management) into your product, using automated scoring to provide instant stock ratings.
  • Why Now?: Users demand faster, data-backed decisions, and your current manual analysis takes 15 minutes per stockthis could reduce time to under 60 seconds.
  • Expected Upside: 30% faster onboarding for users, 20% higher trial-to-paid conversion (as Ticker achieved over 50% historically), and a competitive edge by avoiding full automation while offering guidance.

• What if you redesigned onboarding to prioritize "aha moments" with a checklist?

  • Move: Introduce a pre-filled checklist with two steps completed (e.g., account setup + first stock evaluation) to create psychological momentum.
  • Why Now?: Tickers "checklist feature" reduced cognitive load and achieved 12% average annual returnsreplicating this could boost low-ticket product retention.
  • Expected Upside: 15% faster trial-to-paid conversion, reduced churn (current Ticker churn is 5%, vs. 5.75% B2B SaaS benchmark), and higher user satisfaction via immediate value.

• What if you simplified broker integrations by focusing on key data points instead of full transaction history?

  • Move: Develop a lightweight API to sync only stock price and holdings data per asset, avoiding 1990s-era API limitations.
  • Why Now?: Users request trading capabilities (as Ticker plans to add broker connections), but full integration is technically complexthis minimal approach prioritizes accuracy.
  • Expected Upside: Reduces friction in onboarding for 13,000+ customers, enables 100%+ more monthly transactions (key for institutional clients), and paves the way for future full integration.

• Prioritize Measuring Impact Over Output: Track how your software's features (e.g., traffic light ratings, AI confidence scores) directly influence user outcomes, such as improved investment decisions or higher customer retention, rather than just counting delivered features.
• Build a Feedback-Driven Iterative Product: Start with a simple prototype (e.g., an Excel spreadsheet or MVP) and refine it based on continuous user feedback, like Tickers evolution from 2016 spreadsheets to a 2020 SaaS platform with 13,000+ customers.
• Automate Decision-Making with Data-Backed Systems: Develop automated scoring or recommendation systems (e.g., Tickers 4M AI confidence booster) to simplify complex processes, reducing manual analysis time and aligning with user needs for clarity and speed.
• Optimize Conversion Metrics with Proven Strategies: Focus on improving key metrics like visitor to trial conversion (target 2%+), trial to paid conversion (aim for 20%), and churn rate (<5.75%) using tactics like SEO (300+ articles), YouTube (6,000 subs), and affiliate programs (30% recurring commissions).
• Enhance Trust Through Transparency and Education: Incorporate data transparency (e.g., showing historical performance vs. benchmarks) and educational content (e.g., "why pages," checklists) to reduce user friction, as seen in Tickers onboarding strategies and 30-day free trial with money-back guarantees.