When you design a product experiment, you want to test how different factors affect your desired outcomes, such as user behavior, satisfaction, or revenue. But how do you decide which factors to manipulate and measure in your experiment? In this article, you will learn some effective ways to identify key variables in a product experiment.
A hypothesis is a tentative explanation or prediction about how something works or what will happen. It helps you focus your experiment on a specific problem or opportunity, and guides your choice of variables. For example, if you want to test how a new feature affects user engagement, your hypothesis might be: "Adding a gamified reward system to our app will increase the average time spent per session by 10%." Then, you can identify the key variables that relate to your hypothesis, such as the feature itself, the time spent per session, and the baseline engagement rate.
While you should formulate a hypothesis before delving into extensive research, it's crucial to ensure that your hypothesis is well-informed. Prioritize researching and staying updated on pertinent market trends and data related to your product before formulating a hypothesis for your experiment. This can uncover insights into critical aspects of your product that might benefit from a more detailed experiment. When choosing a hypothesis, also weigh the feasibility of testing it. This can ensure that the results of your experiment provide useful insights.
A hypothesis is a provisional explanation or prediction regarding how something functions or the anticipated outcome. It directs your experiment towards a particular problem or opportunity and influences variable selection. For instance, if testing the impact of a new feature on user engagement, a hypothesis could be: "Incorporating a gamified reward system into our app will boost average session time by 10%." Identify key variables related to the hypothesis, like the feature, session time, and baseline engagement rate.
Furthermore, a skillfully formulated hypothesis not only guides the course of experimentation but also enables measurable outcomes, thereby elevating the scientific rigor of the research. It serves as a crucial instrument in the scientific method, establishing a basis for the objective analysis and interpretation of results.
Initial Hypothesis: Formulate a hypothesis about the key factors that may influence product acceptance, based on market research and user knowledge. Identification of Key Variables: Conduct pilot tests to gather data on various aspects of the product, analyzing metrics such as user feedback, adoption rate, and purchasing behavior to pinpoint the most impactful variables.
The first step is clearly defining the objective, then moving on to research- reviewing existing studies, competitor analysis, and trends. Next is brainstorming with teammates and Conducting small-scale tests to explore hypotheses. Collect direct input from users.
The ICE framework is a useful tool for evaluating and prioritizing variables based on three criteria: impact, confidence, and ease. Impact is how much the variable will affect the outcome; confidence is how certain the effect will be; and ease is how feasible it is to implement and measure the variable. You can assign each variable a score from 1 to 10 on each criterion, then multiply them together to get a total score. The higher the score, the more likely it is that the variable will be a key one for your experiment. For example, if you have two variables: A) changing the color of a button, and B) adding a testimonial section, you might score them as follows: A) Impact: 3, Confidence: 5, Ease: 9, Total: 135; B) Impact: 7, Confidence: 6, Ease: 4, Total: 168. According to the ICE framework, variable B has a higher priority and potential than variable A.
The ICE framework is a valuable tool for evaluating and prioritizing variables based on impact, confidence, and ease. Impact assesses the variable's effect on the outcome; confidence gauges the certainty of the effect, and ease evaluates implementation feasibility. Assign scores from 1 to 10 for each criterion, multiply them for a total score. Higher scores indicate greater likelihood of a key variable. For instance, comparing two variables—A) changing button color and B) adding a testimonial section—scores might be: A) Impact: 3, Confidence: 5, Ease: 9, Total: 135; B) Impact: 7, Confidence: 6, Ease: 4, Total: 168. As per the ICE framework, variable B holds higher priority and potential than variable A.
ICE Framework: Use the ICE (Impact, Confidence, Ease) to assess and rank potential variables in terms of impact, confidence in assessment, and ease of implementation. Prioritize variables with higher scores, focusing efforts on those that strike a balance between significant impact, high confidence, and easy implementation.
While using the ICE framework one must be aware that it is an estimate. Misjudging the impact, underestimating complexity, and overestimating execution will result in incorrect prioritization. Use data and historical insights to assess the impact. Consult with cross-functional teams while assessing complexity and start with small-scale tests then progressively refine the approach to build confidence.
Exploratory analysis is a process of examining your existing data to discover patterns, trends, and correlations that can inform your experiment design. You can use various methods, such as descriptive statistics, visualization, or clustering, to identify the variables that have the most influence or variation on your outcome. For example, if you want to test how a pricing change affects your conversion rate, you might use exploratory analysis to find out how different segments of your customers respond to different price points, or how seasonality or competition affect your sales. This can help you select the most relevant variables for your experiment, such as the price level, the customer segment, or the time period.
Exploratory analysis involves examining existing data to unveil patterns, trends, and correlations guiding experiment design. Utilize methods like descriptive statistics, visualization, or clustering to pinpoint influential variables on the outcome. For instance, testing a pricing change's impact on conversion rates might involve exploring how customer segments respond to various price points or assessing the influence of seasonality and competition on sales. This aids in selecting pertinent variables for the experiment, such as price level, customer segment, or time period.
Start with Exploratory Data Analysis to identify patterns and relationships among variables. Use visualizations and descriptive statistics to highlight the most influential variables in product performance.
I would add that my experience has shown that exploratory analysis is critical when developing predictive models in a business setting. By using techniques such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), I've been able to reduce dimensionality and visualize complex datasets. This process often reveals unexpected insights and relationships that are not apparent from traditional analysis, leading to more informed decision-making and strategic experimentation.
We need to use a hybrid approach using statistical techniques and domain expertise for comprehensive variable selection to ensure all the key variables have been included to understand the dynamics of the experiment.
Another way to identify key variables in a product experiment is to consult with experts and users who have relevant knowledge or experience with your product or domain. You can use various methods, such as interviews, surveys, or feedback sessions, to gather their opinions and insights on what factors matter most for your outcome. For example, if you want to test how a new layout affects your website usability, you might ask web designers or usability experts to review your prototype and suggest improvements, or you might conduct user testing with a sample of your target audience and observe their behavior and satisfaction. This can help you validate or refine your variables based on real-world feedback and evidence.
By following these four ways, you can identify the key variables in a product experiment that will help you answer your hypothesis and optimize your product performance.
To identify key variables in a product experiment, seek input from experts and users with relevant knowledge or experience. Employ methods like interviews, surveys, or feedback sessions to gather opinions and insights on crucial factors for your outcome. For instance, testing a new website layout's impact on usability could involve consulting web designers or usability experts for suggestions or conducting user testing with your target audience. This validates or refines variables based on real-world feedback and evidence. By following these approaches, you can pinpoint key variables in a product experiment, aiding in hypothesis testing and optimizing product performance.
I would add that leveraging user and expert feedback has been indispensable in my experience, especially when validating assumptions in predictive model development. Engaging domain experts and end-users through structured interviews and usability tests has often led to critical adjustments in model features and parameters. This collaborative approach ensures that the models I develop are not only technically sound but also practically relevant and user-centric.
To make a product successful, it takes a magical touch. Mix of data and instincts aka mix of arts and science. There is no fixed methodology to it. Strategy always evolves. I will go with something like preto-typing to enact the scenario and decide on the product fitment. War gaming can also help here
Well based on the industries sector you have, i can share other methods that can determined key factor as well, i called it trial and error, i.e for food RnD such as temperature, technique or ingredients