How do you design a gtm experiment to test your value proposition?

A clearly articulated problem statement allows you to understand what issues your potential customers are facing, and how your product or service can address those issues. This ensures that your solution has a market fit and that there's a real demand for what you're offering. Misunderstanding the problem often leads to developing solutions for non-existent or irrelevant problems, thus wasting valuable time and resources.

Try to ground this in a real world person or problem. This person, this job title. Ideally if you can find a job post as a starting point for this that's even better. Now you aren't coming up with something theoretical, you're creating something actual. Huge time saver.

Design a GTM experiment by setting clear objectives and segmenting your target market. Create variations of your value proposition and choose relevant channels for dissemination. Use A/B testing to compare variations, focusing on engagement, conversion rates, and feedback. Analyze data to determine the most effective value proposition. Iterate based on insights to refine your GTM strategy. This approach ensures your value proposition aligns with customer preferences, optimizing your market approach.

Designing a go-to-market (GTM) experiment involves several key steps to effectively test your value proposition. First, define clear objectives for what you aim to learn. Segment your target market to identify a representative sample group. Create variations of your value proposition to test different messages and benefits. Use a mix of channels (social media, email, landing pages) to reach your audience. Implement A/B testing to compare results between variations, focusing on metrics like conversion rates, engagement, and feedback. Collect and analyze data to understand which value proposition resonates most. Finally, iterate based on findings to refine your GTM strategy.

Defining your problem involves clearly articulating the issue your product or service addresses and identifying the customer segment it targets, enabling focused efforts and resource allocation. Utilizing frameworks such as the problem statement or jobs-to-be-done helps articulate customer needs, pains, and gains, streamlining solution development.

One cheat code: Ask your top sales and CS team members how they prepare for a first call. Don't ask for hypotheses. Ask what goes off in their head based on what they see. The more nuanced the answer the more you can use for experimentation "Oh if they have XYZ title on Linkedin and their website has ABC FAQ I know they've tried to solve it with X and now struggling with Y". These statements tell you where to find "heuristics" not "hypotheses" and in my experience are 2-5x more likely to succeed.

Formulating your hypothesis involves crafting a clear, specific, measurable, and testable statement that predicts how your value proposition will address your customer's problem and deliver value. Utilizing a structured template like "We believe that [customer segment] will [expected outcome] if we [value proposition]" helps articulate your assumptions effectively, such as "We believe that online shoppers will buy more products if we offer free shipping and easy returns."

The more research you do while formulating the hypothesis, the higher the likelihood of initial value propositions being close to what it needs to be by the end of the experiment. We are lucky to live in the age of AI and widely available information, and these are great sources while formulating the hypothesis. Some of the not so often used, but very valuable sources are online events, topics for live events and keynotes, interviews, podcasts by influencers in the industry. If you based your initial hypothesis around that, it will typically resonate. Its important to strike a balance between research and experimentation, and there should be a structured way of collecting the information, including customer feedback

When formulating hypotheses, apply systems thinking to account for interconnected factors and potential consequences. Incorporate behavioral insights to inform decision-making, prioritize pain points over gains, and embrace trade-offs. Seek diverse perspectives to drive innovation and test multiple hypotheses to better understand customer preferences. Establish a learning agenda to refine hypotheses, address knowledge gaps, and align with overall objectives.

?? Develop a clear, specific, measurable, and testable hypothesis about how your value proposition will solve the customer's problem. A well-structured hypothesis might follow the format: "We believe that [customer segment] will [expected outcome] if we [value proposition]." This hypothesis guides the direction of your experiment.

My favourite experiment for Value Proposition is sponsoring a newsletter which your ideal customer is likely reading. The top reasons are: 1. Known benchmarks for important metrics (Open rate, Click Through Rate etc.) - This will let you test the overall message quickly and accurately. For example, if their CTR% is 7% and your sponsored ad only got 2%, your UVP messaging may be off. You can iterate and test again. 2. Their audience is highly segmented so you can test if your UVP (Unique Value Proposition) sticks with different segments. 3. Low budget - You can find opportunities to sponsor these in low sums ($300-$1000) with high confidence that your audience will at least see your message. Good luck :)

Selecting the appropriate experiment to validate your hypothesis involves considering factors like your objectives, available resources, and developmental stage. Options such as surveys, interviews, landing pages, prototypes, beta tests, and A/B tests offer varied insights, enabling you to choose the method best suited to gather reliable feedback efficiently while minimizing costs and time.

When choosing experiments, tailor the type to your hypothesis complexity, optimizing for learning, not just validation. Leverage low-cost, lean experiments and consider the entire customer journey. Embrace mixed-method approaches and incorporate competitive benchmarking. Plan for scaling and iteration to ensure continuous learning and value proposition evolution throughout your go-to-market journey.

Even if surveys, interviews, landing pages, prototypes, beta tests, and A/B tests all have their own time and place, Sometimes there simply isnt enough time to do time consuming activities. Especially in a startup environment. A/B tests gets you straight into the "action" and you can see directly which one works best. Other ways to do the same could be Cold email outreaches, Buying a slot in a niche newsletter or showing up at an industry event and just talking to alot of people. Running a small controlled version of your thesis before scaling it. But if you have a lot of time and resources surveys and interviews with a large sample of your ICP of course minimises the risks of your experiments.

?? Decide on the type of experiment that best suits your hypothesis and goals. Options include surveys, interviews, landing pages, prototypes, beta tests, or A/B tests. The chosen method should provide reliable feedback, be aligned with the hypothesis, and be resource-efficient.

When designing experiments, prioritize actionable metrics and establish a control group or baseline for comparison. Incorporate A/B testing and minimize confounding factors to ensure accurate results. Foster a culture of experimentation among your team and plan for unintended consequences. Conduct a premortem analysis to proactively address risks and increase the chances of successful experiments that inform your value proposition and go-to-market strategy.

?? Outline your experiment's specifics, including its goal, key performance indicators (KPIs), success criteria, duration, and procedure. This design stage ensures a structured approach to executing the experiment and measuring its results.

In designing your experiment, outline the experiment goal, identify key performance indicators (KPIs) to measure hypothesis validation, set success criteria, determine the experiment duration, and establish the experiment procedure to ensure clear execution and effective measurement of results.

Plan the details of your experiment carefully. Define the metrics you'll track, the duration of the test, and the sample size. For an A/B test, this might involve creating two versions of your value proposition and presenting them to different segments of your audience. Ensure you have a clear plan for data collection and analysis.

Execute your experiment according to the established procedure, ensuring adequate sample size, data quality, and analysis tools for meaningful insights. Monitor progress closely and be prepared to make adjustments, such as variable changes or experiment duration extensions, to optimize outcomes as needed.

When running experiments, focus on obtaining a representative sample of your target audience and implement continuous monitoring to ensure progress and performance. Establish clear communication channels with stakeholders, prioritize data privacy and security, and leverage real-time analytics for timely insights. Encourage adaptability in your experiment design and conduct interim analyses to identify patterns or concerns, enabling data-driven decisions and compelling value propositions.

?? Implement the experiment, adhering to the defined procedure. Ensure adequate sample size and data quality, and be prepared to make necessary adjustments during the experiment's run. Monitoring progress is crucial for obtaining meaningful data.

Execute the experiment while closely monitoring the relevant metrics. Collect data systematically to ensure you can draw valid conclusions. Analyze the results to see if they support your hypothesis. Use these insights to refine your value proposition, making adjustments based on what you’ve learned. This iterative process helps ensure your GTM strategy is grounded in real customer feedback and behavior.

Upon collecting data, interpret and evaluate its implications in relation to your hypothesis, comparing actual outcomes with predetermined success criteria and KPIs. Identify patterns and insights, documenting key learnings, challenges, and recommendations for future experimentation or actions.

?? After collecting data, analyze the results to determine if your hypothesis is validated or not. Compare actual performance against your success criteria and KPIs, looking for trends and insights. Document your learnings and challenges, and use this analysis to inform future experiments and strategic decisions.

Employing tools like the problem statement and jobs-to-be-done framework can further refine this understanding. The problem statement helps in articulating the problem succinctly, while the jobs-to-be-done framework focuses on understanding tasks that customers are trying to accomplish (jobs), why they're finding it difficult (pains), and what they stand to gain by resolving these difficulties (gains).

?? To design a good GTM experiment for your value proposition, start by clearly understanding the problem you are solving. Check if other companies in the market are solving the same problem. If not, you might have a unique advantage. Aim to go beyond just solving the problem; always try to improve and set yourself apart from competitors (raise the bar). Accurate targeting is key, as it affects the quality of the data you gather and the insights you get. Make sure your experiment is well-planned to collect useful data and refine your value proposition.

I would make sure that your clearly articulated problem statement is connected to your strategic narrative or story as to make sure that is actually part of your value proposition. The test that out for what you think is your ICP.

I have seen cases where companies chose the audience and allowed a small amount of loud feedback to take them away from the big picture. Be careful not to make the wrong decisions by following a too narrow/improperly targeted segment and a value proposition that is not worded properly. Rather start with smaller segments if you have the resources for it, and then use the higher performing communication for a wider audience