Lifecycle of a problem statement: Defining a good one, & the approach to solve it

Leveraging analytics to drive the product involves 2 challenges:

1. Formulating a good problem statement
2. Adopting an effective approach to solve it

If you’re doing any one of them wrong, you’re likely not getting much value out of analytics.

Crafting the right problem statement

The quality of your problem statement directly influences the efficacy of your analytics.

There are 2 parts to this process:

1. A well-defined problem
2. Prioritization of the problem

Well-defined problem

There are 3 characteristics of a well-defined problem:

• Specificity: Avoid vague descriptions. Clearly articulate what aspect of the product needs improvement, such as "increase the checkout conversion rate" instead of "improve sales."
• Measurability: Attach a quantifiable metric to the problem. This could be an improvement percentage, a specific number increase, or a reduction in user complaints.
• Solvability: Ensure that the problem is actionable. This means having enough data to analyze and the ability to implement potential solutions.

Examples of Problem Statements:

• Bad: "Make the website better." This is too ambiguous and lacks direction for actionable measures.
• Good: "Optimise the onboarding rate by reducing user friction" This statement is clear, measurable, and directly targets a specific area for improvement.

Prioritization

Resources & time are always limited - Hence, prioritizing problems effectively is crucial.

Use the weighted EIL framework:

• Effort (0.2 Weightage): Estimated resources and time required to solve the problem.
• Impact (0.4 Weight): Potential benefits and value added by solving the problem.
• Long-Term Use (0.4 Weight): Sustainability and relevance of the solution over time. Ex: If you’re going to change a user flow in the next 2 months, you might not want to focus on optimizing it right now)

Score each factor from 1 to 10, apply the weights, and calculate a total prioritization score. Start with the problem with the highest score.

Adopting an effective approach to solving the problem

With a clear, prioritized problem statement, the next step is to focus on the approach to solve it.

Hypothesis development

Start by listing all possible reasons for the problem. This should be an exhaustive process where:

1. You hypothesize potential reasons
2. You determine what data you need to test each hypothesis

Data analysis

Dive into the data to confirm or refute your hypotheses. This involves extracting relevant metrics and analyzing patterns and trends.

💡 Majority of analytics teams stop after identifying trends, but the real value of analytics comes from diving deeper to come up with actionable.

Deep dive for actionable

It’s important to move beyond just identifying trends and to understand why they occur. This will help you craft experiments to test, UI/ UX changes to make, etc.

Implementation planning

Once you have a clear understanding of what needs to be done, outline specific action steps. Collaborate with the product/ tech team to ensure these solutions are implemented effectively.

Measurement

After implementing changes, measure their impact on predefined metrics to assess success. This final step is critical to closing the loop and setting the stage for continuous improvement.

Crafting the right problem statement and adopting a strategic approach are fundamental to using analytics effectively in product development. This way, you can ensure that analytics provide actionable insights that drive product success and user satisfaction.

Hope this was helpful. If you’re looking for any help with Mixpanel or analytics, feel free to reach out using any of the below methods.

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