Research Programming

A program is a collection of different experiments that, when mapped together, empowers cohesive and intelligent decision-making around a specific goal or outcome.

1. Define Your Target Goal/Outcome

  • Start with the big picture: Clearly articulate the ultimate goal of your research program. This could be anything from increasing product adoption, optimizing customer experiences, or understanding the drivers behind consumer behavior.
  • Be specific: Instead of a vague goal like “increase sales,” aim for something more defined, like “identify which features of our mobile app drive the highest user engagement among Gen Z.”

2. Break the Goal into Research Questions

  • Think of your program like a puzzle: Each experiment should address one piece of the larger question. Break your goal into smaller, testable research questions that, together, will give you a full understanding of the problem.

Example: If your goal is to improve customer loyalty, some research questions might be:

  1. What messaging encourages repeat purchases?
  2. How does the ease of navigation in the app influence user retention?
  3. Which payment methods result in the highest checkout completion rates?
  • Prioritize: Rank these questions based on their impact or feasibility to guide the order in which you run experiments.

3. Design Each Experiment with a Specific Hypothesis

  • Create a hypothesis for each experiment: Each one should have a clear, testable statement that directly relates to your research questions. For example, “Offering free shipping will increase repeat purchases by 15%” or “A streamlined checkout process will reduce cart abandonment.”
  • Keep it manageable: Each experiment should focus on one or two variables to ensure clarity and actionable results.
  • Consider dependencies: Some experiments may build on others. For instance, understanding how navigation impacts retention might be more useful once you’ve identified which features users value most.

4. Map Out the Experimental Path

  • Create a roadmap: Structure your research program in phases. Group related experiments together and determine which need to be conducted first, second, and so on. This allows insights from earlier experiments to inform later ones.

Example:

  • Phase 1: Focus on product features and app navigation to learn what drives usage.
  • Phase 2: Test messaging and incentives to influence behavior based on usage patterns.
  • Phase 3: Examine payment methods and pricing to optimize conversion based on previous insights.

5. Design with Flexibility

  • Allow room to pivot: Not all experiments will turn out as expected, and some may reveal surprising insights. Build flexibility into your program to adjust based on what you learn.
  • Iterative cycles: Some questions may need to be revisited as new information is uncovered. For instance, if one experiment shows millennials prefer a certain feature, you might decide to test different messaging related to that feature in a follow-up experiment.

6. Utilize Subconscious.ai’s Tools for Causal Modeling

  • Leverage Subconscious.ai’s strengths: The platform excels in running iterative experiments and modeling cause-and-effect relationships. Each experiment can be designed using randomized controlled trials (RCTs), ensuring reliable results that feed into the broader program.
  • Use synthetic respondents: Simulate different consumer groups to test how various segments respond to changes, building a rich dataset covering multiple perspectives.

7. Data Collection and Analysis

  • Standardize data collection: Ensure that data collection and analysis across experiments are consistent. This enables easy comparison of results from different studies and identification of patterns.
  • Causal Insights: Subconscious.ai provides deeper insights beyond just “what happened”—it reveals why it happened, which is crucial for refining your approach throughout your program.

8. Iterate and Adapt

  • Review after each phase: After each batch of experiments, analyze the findings as a whole. Did the results answer your research questions? Do you need to adjust your approach for the next phase?
  • Refine questions and hypotheses based on insights. If initial assumptions were off, or new areas need exploration, adjust your approach. This process is flexible and iterative.

9. Use a Central Repository for Findings

  • Document everything: Keep a record of hypotheses, experiments, and results in a centralized place. This makes it easy to reference previous work and build on it in future experiments.
  • Example: Use a research dashboard or file system to quickly see which hypotheses were supported, which weren’t, and what new questions arose from the data.

10. Wrap It All Together

  • Bring it back to the big picture: After several rounds of experiments and gathered data, put all the pieces together. How do insights from each experiment help answer the original goal? What patterns emerged? Were new opportunities discovered?
  • Synthesize actionable recommendations: Summarize key insights across your program and translate them into concrete, actionable recommendations for your business or research focus.

Example of a Framed Research Program:

Goal: Increase mobile app engagement among millennials by 20%.

  1. Phase 1: Test which app features (social sharing, notifications, etc.) drive the most engagement.
  2. Phase 2: Test different messaging strategies (fun vs. functional) to increase feature adoption.
  3. Phase 3: Explore pricing strategies and in-app offers to boost conversion rates based on user engagement data.

Each phase informs the next. By the end, you’ll understand not just what drives engagement, but why—and more importantly, how to leverage that knowledge to achieve your goal.

By structuring your research program in this way, you create a strategic, data-driven roadmap that evolves with insights, keeping focus on the ultimate outcome.