QUICK SUMMARY
The session introduces a novel approach to de-risking innovation through prelaunched.com, a platform that validates products by having potential customers place small deposits on early-stage concepts. This method provides authentic market validation by gathering insights directly from people who demonstrate purchase intent rather than relying on traditional survey respondents. The approach helps companies make better innovation decisions by collecting real behavioral data, understanding customer motivations, and identifying which concepts have the best market potential before full development.
KEY QUOTES
- “Skin in the game makes innovation less risky.”
- “What if instead of professional respondents, we could gather insights directly from people who are ready to buy.”
- “Whenever somebody places even $1, that’s the best validation ever. That really speaks about that somebody really wants to buy these products, and that’s very authentic.”
FULL SESSION SUMMARY
The Innovation Challenge
The presenter begins by highlighting a concerning trend in the CPG sector: breakthrough innovation has declined from 50% to approximately 30% over the past decade, with companies increasingly focusing on renovations rather than true innovations. Despite 90% of Fortune 500 executives citing innovation as their top priority (according to a Deloitte survey), many companies remain hesitant to pursue innovative products. This hesitation stems from various factors including post-pandemic pressures, value chain issues, risk aversion, and technology constraints.
The Data Trust Problem
A fundamental issue identified is the lack of trust in traditional market research data. The presenter shares personal experience from helping companies launch innovative products through their agency, noting initial successes followed by repeated failures. The core problem: traditional research methods rely on paid respondents whose opinions don’t necessarily translate to actual purchase behavior. The presenter emphasizes that it’s not just about fraud or bias, but that people genuinely don’t know what they’ll do in the future, especially regarding innovative products they’ve never encountered before.
Skin in the Game
The presenter introduces prelaunch.com, a platform built on a fundamentally different approach to innovation validation. Instead of asking people what they might buy, the platform asks them to place small deposits ($1-$20) on early-stage product concepts. This creates “skin in the game” and provides a much stronger signal of genuine purchase intent.
The process works by:
- Brands placing early-stage concepts on the platform
- Driving authentic potential customers to these concept pages through advertising
- Offering incentives (discounts, early access) for customers who place reservation deposits
- Collecting feedback from both those who place deposits and those who decline
Data Collection and Analysis
Beyond the initial validation through deposits, the platform collects rich qualitative and quantitative data:
- Surveys with those who place deposits to understand their motivations
- Feedback from those who decline to understand objections
- In-depth interviews with interested customers
- AI-powered WhatsApp conversations for iterative data collection
- In-home testing with product samples
This data is analyzed using machine learning to identify customer clusters and create detailed customer personas representing real potential buyers. The platform also benchmarks products against thousands of others, creating a “pre-launch score” based on 35 different metrics to help brands understand their concept’s potential.
Practical Applications
The presenter explains that the platform can be used at various stages of product development:
- Early stage: Comparing 10 different product ideas to identify which has the most potential
- Late stage: Fine-tuning positioning, messaging, and target markets before launch
A case study is shared about a large appliance company that was planning to produce a smaller version of a home gardening solution based on traditional survey data showing customers preferred the smaller, more compact, cheaper option. However, the prelaunch platform revealed that actual buyers preferred the larger version because the smaller one lacked sufficient capacity to be practical. This insight potentially saved the company “tens of millions of dollars” by preventing the production of a product that wouldn’t succeed in the market.
Affordability and Versatility
In response to audience questions, the presenter notes that the subscription-based service is approximately “10 times less than the average solutions in the market.” They also confirm that the approach works for products across price points—from items costing tens of thousands of dollars down to products in the $5-10 range—as long as the product is truly innovative and unique.
KEY TAKEAWAYS
- Traditional market research often fails to predict innovation success because opinions don’t translate to purchase behavior.
- Having potential customers place even small deposits creates “skin in the game” and provides more reliable validation than surveys.
- Collecting feedback from those who decline to purchase is as valuable as data from those who show interest.
- Machine learning analysis of customer data can identify distinct customer segments and create accurate buyer personas.
- The prelaunch approach works across price points and industries, provided the product offers genuine innovation.
- Iterative testing of different hypotheses (pricing, positioning, features) allows for data-driven refinement before full market launch.
- The gap between what people say they want and what they’ll actually buy can be substantial, especially for innovative products.
DELIVERY ON EVENT FOCUS: Aligning Innovation with Business Strategy
The prelaunch approach directly addresses the challenge of aligning innovation with business strategy by providing reliable data on which innovations are most likely to succeed in the market. By validating concepts early with real purchase intent, companies can allocate resources more effectively toward innovations with proven market potential. This reduces the risk of investing in products that won’t generate returns and ensures innovation efforts are directed toward opportunities with genuine business value. The platform’s benchmarking system also helps companies understand how their innovations compare to others in the market, further informing strategic decision-making.
DELIVERY ON EVENT THEME: Harvesting Innovation and Sowing the Seeds of Future Growth
The prelaunch methodology embodies the event theme by providing a systematic approach to “harvesting” the most promising innovations from a company’s idea pool. By testing multiple concepts early and identifying which ones generate genuine purchase intent, companies can focus their resources on developing the innovations most likely to yield growth. The iterative nature of the platform allows companies to continually refine their concepts based on real customer feedback, effectively “sowing the seeds” for future success by ensuring products are aligned with actual market needs before full development and launch.
ACTION ITEMS FOR INNOVATION EXPERTS & CORPORATE CHANGEMAKERS
- Reassess validation methods: Evaluate current market research approaches and consider incorporating “skin in the game” methodologies that measure actual purchase intent rather than stated preferences.
- Test multiple concepts early: Rather than committing to a single innovation path, test several concepts with potential customers to identify which generates the strongest market response.
- Collect rejection data: Implement systems to gather feedback from people who show initial interest but decide not to purchase, as this provides crucial insights about barriers to adoption.
- Implement iterative testing: Develop processes for continuously refining product concepts based on customer feedback before full-scale development.
- Leverage AI for deeper insights: Explore AI-powered conversation tools to engage potential customers and gather richer qualitative data about their needs and preferences.
- Create authentic customer personas: Build customer personas based on behavioral data and actual purchase intent rather than demographic assumptions.
- Challenge internal assumptions: Use real market validation data to question and test internal assumptions about what customers want, especially for innovative products.