How do you test and validate your launch assumptions before spending your budget?

Some common types of launch assumptions: Product assumptions: About your product itself, such as its features, benefits, and appeal to consumers. E.g, you assume that your product is unique, that it solves a real problem for consumers, or that it is priced competitively. Market assumptions: About the market for your product, such as its size, growth potential, and competitive landscape. E.g, you assume that your product is entering a large and growing market, that there is no direct competition. Target audience assumptions: About your target audience, such as their demographics, needs, behaviors. E.g, you assume that your target audience is tech-savvy, that they are interested in new products, or that they are willing to try new things.

Before you launch, check your assumptions, These are basically your best guesses about who will buy your product, why they'll love it, and how they'll find it. Think about things like Who exactly needs your product? What problem does it solve? How much will they pay?

Let's imagine we want to launch a new project management tool targeting marketing agencies. 🎯 Key Assumptions: ❓ Problem-Solution Fit: Agencies struggle with managing complex projects across multiple clients and teams. (High Criticality, Medium Uncertainty) ❓ Value Proposition: Our tool's superior collaboration features and streamlined workflows will increase agency efficiency. (High Criticality, High Uncertainty) ❓Pricing Strategy: Agencies are willing to pay a premium for a solution that demonstrably boosts productivity. (Medium Criticality, Medium Uncertainty) ❓ Marketing Channels: Industry publications and online communities are the most effective channels to reach decision-makers. (Medium Criticality, High Uncertainty)

Before you launch, check your assumptions! These are your guesses about who wants your product, why they need it, and how they'll find it. Write them down and figure out how likely they are to be true. Focusing on the most uncertain assumptions will help you test what truly matters for a successful launch.

To test and validate launch assumptions before committing budget: Customer Interviews: I dive into real conversations with potential users to uncover their pain points and validate our assumptions. Pilot Programs: We run small-scale pilots to gather feedback and refine our approach based on actual user interactions. A/B Testing: By experimenting with different versions of our product or messaging, we gauge what resonates best with our target audience. Data Analysis: We analyze existing data to validate assumptions and identify trends before investing further. This approach ensures that we’re aligned with market needs and reduces risk before full-scale launch.

My biggest learning while I was an entrepreneur was to test a new product/feature with a landing page before I built the product/feature. Here is a 3-step process that I recommend one follow: 1. Build a sales/landing page with a CTA (payment gateway, book a meeting, or any action that would validate the customer's willingness to purchase the product. 2. Drive all traffic to the landing page: Whether you use paid marketing or organic/existing traffic, direct all your traffic to engage with your sales page. 3. Learn, update your sales page & repeat the process: As you get feedback from potential customers, update the sales page to test new features and messaging. When you find a message that finds customer-messaging fit, start building.

Following the example of the project management tool, here are some design experiments: Assumption 1 (A): Problem-Solution Fit Experiment (E): user interviews with marketing agency PMs to understand their current pain points. Metric (M): frequency of keywords related to common PM problems. A 2: Value Prop. E: present a prototype showing core features and workflows. M: Measure user engagement, such as time spent or completion rates of task. A 3: Pricing Strategy E: Offer a freemium model with limited features. M: Analyze user upgrade rates to paid plans, tracking conversion rates. A 4: Marketing Channels E: Run targeted ad campaigns on industry online communities. M: Compare CTRs, website traffic, and leads generated from each channel.

Designing experiments for validating your assumptions effectively. 1) Define your hypothesis: Start with a clear statement of what you believe to be true. 2) Set objectives: Establish specific goals for what you want to learn. 3) Choose your method: Consider tools like surveys, landing pages, or MVPs to gather data. 4) Identify metrics: Determine how you will measure success—be it user engagement, conversion rates, or feedback quality. 4) Iterate & learn: Use the insights gained to refine your ideas and run subsequent experiments. This structured approach not only minimizes risk but also fosters a culture of learning and adaptation.

Continuing with the PM tool example: Experiment 1: Validating Problem-Solution Fit Execution: Conduct in-depth interviews with 10-15 marketing agency PMs, focusing on their current workflow challenges. Data Collection: Utilize a structured interview format to capture qualitative data on their pain points & desired solutions. Analyze the recorded interviews to identify recurring themes & areas of frustration. Exp.2: Evaluating Value Proposition Exec.: Develop an interactive prototype showcasing the tool's core features and functionalities. Share it with a targeted group of agencies. Data Collection: Track user engagement metrics within the prototype, such as time spent on specific features, completion rates of tasks, and user feedbacks.

1. Create an MVP to test assumptions from the main pain points defined. 2. Use surveys, interviews, and landing pages to gauge interest and gather feedback from potential customers. Make sure you not only interview current customers but also prospects. 3. Conduct small-scale experiments or pilot tests to validate assumptions. This would be using a small "outbound" team and running ads to your ICP. If it works, it can easily be scaled since the product and the marketing have been validated. 4. Iterate based on findings to refine the product-market fit before scaling up. This approach minimizes resource expenditure while maximizing learning and validation. That's why I created a company to validate and de-risk MVP launches.

Use surveys, landing pages, or social media to gather data. The goal is to get real user feedback to see if they like your idea and are willing to pay for it.

For running your experiments, you have a few options: If you don't have an audience, you can use paid media to drive traffic to the experiment. If you already have users, you can invite your power users to participate in the experiment, or you can set up an environment to conduct A/B testing on your website or platform. It's essential to use analytics, feedback, and interviews to measure participant responses. Remember to ensure an adequate amount of time and a sufficient sample size for reliable results.

Analyze your results objectively. Use visual aids like charts and graphs to identify patterns or trends. Apply statistical tests to assess the significance of your findings. Avoid confirmation bias and be honest about what the data tells you.

I would consider: - Visualize data: Utilize charts and graphs to identify trends and outliers. - Statistical tests: Apply t-tests or chi-square tests to assess significance and confidence. - Be objective: Remain honest about your findings to avoid confirmation bias. This process not only enhances understanding but also informs future strategies. Embrace the insights—both positive and negative—for continuous improvement.

Turn your test results into gold! See what worked well and what didn't. Maybe your target audience is different than you thought, or your price point is off. Use this info to refine your launch strategy.

Turn your data into $$ Once you did all previous steps, consider: 1. Analyze results: Dive deep into the data to understand what worked and what didn't. 2. Challenge assumptions: Be willing to pivot if your initial assumptions prove incorrect. 3. Adapt strategies: Adjust your product, target market, or pricing based on real feedback. 4. Test new ideas: Design experiments to validate your updated hypotheses. Stay flexible and continuously iterate; that’s where true growth happens.

Launch day is just the beginning! Use what you learned from testing to improve your approach. Put more muscle behind what's working and ditch what's not. Think of it like fine-tuning a machine - make small adjustments for better results.

Additionally, consider the following: - Seek feedback from target customers early on. - Keep a close eye on competitors' actions. - Have a contingency plan in case assumptions don't hold. - Stay agile and be ready to pivot if needed.

Leave the theory part and do a pilot. If it works then your assumptions are true otherwise go back, rework your products. Also, during the pilot make sure you capture the VOC properly - Ideally don't depend on others. During the pilot the data has to flow directly to you.