The Truth Curve is a powerful visualization tool that helps us determine the appropriate level of effort to invest in experiments and validate our hypotheses (Jeff Gothelf, “The Truth Curve,” February 25, 2023). The curve consists of two axes:
- the Y axis represents the level of evidence supporting our hypothesis
- the X axis represents the increasing effort and scope dedicated to the experiment.
1.1) The Land of Wishful Thinking:
In the initial stages of exploring a new hypothesis, we find ourselves in the Land of Wishful Thinking. With limited evidence, our goal is to learn quickly and inexpensively. This phase involves utilizing various techniques to test our product, ranging from observation to optimization. Some of these techniques include:
- Paper prototyping allows us to create and test possible designs quickly and cost-effectively. BlueDuck Labs shows us a great example of how just using simple drawings can gather precious insights that will have a huge impact on your development.
- A pre-order page enables potential customers to express their interest in a product or service before its official launch, providing valuable insights into market demand.
- Explainer videos illustrate how a product or service works, even if the product is not fully functional yet. They help convey the value proposition and generate early interest.
- Dropbox raised $48 million in funding before they even built their product. They did this by creating an explainer video that explained what Dropbox was and how it would work. The video was well-produced and visually appealing, and it helped to raise awareness of the company and its product. The video was a key factor in Dropbox’s success, and it is a valuable tool for any startup (Dropbox Raised $48,000,000 With Their Explainer Video, motioncue.com).
- Landing page tests serve as advertisements for our idea, presenting the value proposition, call to action, and conversion mechanism.
- Feature Fake experiments create the illusion of a feature through buttons or links, leading to a controlled outcome. They help manage customer expectations and avoid disappointment.
- Spotify leveraged this method to test their hypothesis that users are willing to pay more for premium audio quality. By offering an upgrade option and indicating that it was coming soon, they measured the sign-up rate to determine the demand for the feature before investing in its development. (Spotify Is Now Testing Its HiFi Feature Ahead of Launch, Makeofus.com)
- Concierge MVP involves personally providing all functions of the product or service to individual customers, solving their problems one-on-one. It allows us to validate the viability of the offering before scaling it.
- Rent the Runway, for example, validated its online dress rental model by offering an in-person service to college students, allowing them to try on dresses before renting. This effective concierge MVP not only validated the riskiest hypothesis but also engaged customers, gathering invaluable feedback to refine the business (Building a minimum viable product, Harvard Business Review).
Wizard of Oz:
- Wizard of Oz experiments teach us how to deliver a service without building the entire infrastructure. For example, we can create a website where users can purchase products, while manually handling the logistics behind the scenes.
- Zappos, for instance, kick-started its journey by showcasing shoe photos from local stores on a website to gauge demand for an online store. This hands-on approach fostered valuable customer interaction during the product design phase. Directly observing real customers provides actionable insights surpassing hypothetical surveys and accelerates the identification of genuine solutions to customer problems. (15 ways to test your minimum viable product, The Next Web).
1.2) Building Confidence:
Lightweight Experiments As we collect positive evidence through these lightweight experiments, our confidence grows. These techniques help us assess interest, validate assumptions, and gain valuable insights, justifying further investment in subsequent experiments.
2.1) Problem/Solution Fit:
Advancing on the Truth Curve, we transition to the phase of finding problem/solution fit. Here, we focus on testing whether our product effectively solves a real problem for customers. The emphasis shifts from “Should we build it?” to “Can we build a sustainable business with this idea?” Experimentation methods for this stage may include clickable prototypes or Wizard of Oz experiments, where the front end is fully developed while the back end is manually operated.
With increasing positive feedback and evidence, we enter the realm of product/market fit. In this stage, we determine if the solution is viable as a sustainable business and worth scaling. The question evolves from “Can we build it?” to “Should we build it?” Achieving product/market fit requires more substantial investment and rigorous validation through user testing, A/B testing, and other experiments (Product Market Fit, productplan.com).
3.1) Stop and Correct Your Course:
Throughout the product development journey, it is crucial to pay attention to the feedback and data collected from experiments. If negative feedback or flaws in our assumptions emerge, we must be willing to stop and reassess the viability of our idea. This moment calls for potential pivots or even the decision to abandon the concept entirely and pursue alternative hypotheses.
3.2) Adjusting the Learning Questions:
As we progress on the Truth Curve, the questions we ask about our hypothesis change. In the early stages, we focus on problem/solution fit. As we move towards product/market fit, the emphasis shifts to sustainability and business viability. Each stage demands a reassessment of the most important learnings and the least amount of work needed to acquire them.