Experimentation and learning are essential for creating successful AI products and projects. AI products and projects often involve complex problems, data, algorithms, and systems that require careful analysis, design, testing, and evaluation. Experimentation and learning can help you to explore different hypotheses, assumptions, and solutions, and to validate or invalidate them with data and feedback. Experimentation and learning can also help you to discover new insights, opportunities, and challenges, and to adapt and improve your AI products and projects accordingly.
However, experimentation and learning can also be challenging, especially when it comes to AI products and projects. Experimentation and learning can be time-consuming, costly, risky, and uncertain. Experimentation and learning can also be overwhelming, as you may have to deal with a large number of experiments, data, results, and learnings. How can you experiment and learn effectively and efficiently for AI products and projects, and avoid getting lost or confused by the details?
One possible way is to use an Exploration Map. An Exploration Map is a visual tool that helps you to plan, track, and communicate your experiments and learnings for AI products and projects. An Exploration Map provides you with an overview of the experiments carried out and shows, for example, in which areas additional experiments should be carried out. The map also provides information about the expectations of an experiment and its effect on the target group.
In this blog post, we will explain what an Exploration Map is, how to create and use one, and how it can help you to experiment and learn effectively and efficiently for AI products and projects.
What is an Exploration Map?
An Exploration Map is a visual tool that helps you to plan, track, and communicate your experiments and learnings for AI products and projects. An Exploration Map is based on the Lean Startup methodology, which advocates for building, measuring, and learning from experiments to create products and services that meet the needs and expectations of customers.
An Exploration Map consists of four main elements:
Hypotheses: These are the statements or assumptions that you want to test or validate with your experiments. Hypotheses can be about the problem, the solution, the value proposition, the customer segment, the channel, the revenue model, or any other aspect of your AI product or project. Hypotheses should be specific, measurable, and falsifiable, meaning that they can be proven true or false with data and feedback.
Experiments: These are the actions or activities that you perform to test or validate your hypotheses. Experiments can be qualitative or quantitative, such as interviews, surveys, prototypes, MVPs, A/B tests, or analytics. Experiments should be designed to generate the most learning with the least effort, cost, and risk.
Results: These are the outcomes or outputs of your experiments. Results can be data or feedback, such as metrics, indicators, observations, insights, or opinions. Results should be analyzed and interpreted to determine whether they support or contradict your hypotheses, and to what extent.
Learnings: These are the conclusions or implications of your results. Learnings can be validations or invalidations, such as confirmations, rejections, refinements, or pivots of your hypotheses. Learnings should be used to inform your next actions or decisions, such as iterating, scaling, or stopping your AI product or project.
An Exploration Map can be represented as a table or a matrix, where each row corresponds to a hypothesis, and each column corresponds to an element of the Exploration Map. For example, see figure 1.
Figure 1: An example of an Exploration Map
Hypothesis | Experiment | Result | Learning |
Customers are willing to pay for an AI-powered concept map tool that helps them to research, learn, and create | Conduct a landing page test with a value proposition and a pricing plan | 1000 visitors, 10% sign up rate, 5% conversion rate, average revenue per user $10 | Validated, customers are interested and willing to pay for the product |
Customers prefer a mind-map like interface for visualization and exploration | Create a prototype with a mind-map like interface and test it with 10 potential customers | 8 out of 10 customers liked the interface and found it intuitive and easy to use | Validated, customers prefer a mind-map like interface |
Customers need a chatbot that can generate ideas and suggestions based on text prompts or custom input | Create a chatbot prototype that uses ChatGPT to generate ideas and suggestions and test it with 10 potential customers | 6 out of 10 customers found the chatbot helpful and creative, 4 out of 10 customers found the chatbot irrelevant or confusing | Partially validated, customers need a chatbot, but it needs to be improved |
An Exploration Map can help you to:
Plan your experiments and learnings in a systematic and structured way
Track your progress and status of your experiments and learnings in a clear and concise way
Communicate your experiments and learnings to others in a visual and engaging way
How to create and use an Exploration Map?
To create and use an Exploration Map, you can follow these steps:
Step 1: Define your hypotheses: Start by defining the hypotheses that you want to test or validate with your experiments. You can use the Business Model Canvas, the Value Proposition Canvas, or the Lean Canvas to identify and articulate your hypotheses. You can also use tools such as Creately VIZ, Taskade, or Talknotes to generate hypotheses automatically based on text prompts or custom input. Write down your hypotheses in the first column of the Exploration Map.
Step 2: Design your experiments: Next, design the experiments that you will perform to test or validate your hypotheses. You can use the Experiment Canvas, the Experiment Board, or the Test Card to design and document your experiments. You can also use tools such as [Orchidea] to evaluate your experiments based on various criteria, such as feasibility, desirability, viability, or impact. Write down your experiments in the second column of the Exploration Map.
Step 3: Execute your experiments: Then, execute your experiments and collect the results. You can use tools such as [Google Forms], [SurveyMonkey], [Typeform], or [Qualtrics] to conduct surveys or interviews. You can use tools such as [Figma], [Sketch], [Adobe XD], or [InVision] to create prototypes or MVPs. You can use tools such as [Google Analytics], [Mixpanel], [Amplitude], or [Heap] to track and analyze metrics or indicators. Write down your results in the third column of the Exploration Map.
Step 4: Analyze your results: Next, analyze your results and interpret them to determine whether they support or contradict your hypotheses, and to what extent. You can use tools such as [Tableau], [Power BI], [Looker], or [Data Studio] to visualize and explore your data. You can use tools such as [SPSS], [R], [Python], or [MATLAB] to perform statistical or mathematical analysis. You can also use tools such as Creately VIZ, Taskade, or Talknotes to expand your results in a snap, whether you want to generate similar items, counter points, or by using custom instructions. Write down your learnings in the fourth column of the Exploration Map.
Step 5: Update your Exploration Map: Finally, update your Exploration Map based on your learnings. You can use tools such as [Miro], [Mural], or [Jamboard] to create and edit your Exploration Map online and collaborate with your team. You can also use tools such as Creately VIZ, Taskade, or Talknotes to convert your Exploration Map into different types of diagrams or visual frameworks, such as mind maps, Kanban boards, or flowcharts, to help you organize and categorize your learnings. You can also use tools such as [Orchidea] to prioritize your learnings based on their potential or value, and to identify the next steps or actions based on your learnings.
Here is a possible paragraph based on the structure of the above Exploration map template:
The Exploration map template is a visual tool that helps you to plan, track, and communicate your experiments and learnings for AI products and projects. The template consists of five main steps, each labeled and accompanied by icons for visual representation. The first step, “Enter experiments,” involves carrying out all experiments approved or planned in the Exploration stage. The second step, “Discuss position,” is a phase where discussions on positioning in the basin occur. In the third step, “Prototyping,” there’s a definition of the target position for the next experiment. The fourth step involves adding feedback and making adjustments if necessary based on the outcomes of the experiments. Finally, in the fifth step labeled “Findings,” one is required to deliver relevant findings.
The Exploration map template helps you to follow a systematic and structured process for conducting experiments and innovations. The template helps you to define your hypotheses, design and execute your experiments, analyze your results, and update your learnings. The template also helps you to visualize and communicate your experiments and learnings to others in a clear and concise way.
By creating and using an Exploration Map, you can experiment and learn effectively and efficiently for AI products and projects. You can also leverage the power of AI to generate, expand, and evaluate your hypotheses, experiments, results, and learnings.
Conclusion
Exploration Map is a visual tool that helps you to plan, track, and communicate your experiments and learnings for AI products and projects. Exploration Map provides you with an overview of the experiments carried out and shows, for example, in which areas additional experiments should be carried out. The map also provides information about the expectations of an experiment and its effect on the target group. Exploration Map is based on the Lean Startup methodology, which advocates for building, measuring, and learning from experiments to create products and services that meet the needs and expectations of customers.
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