Large scale research: 5 lessons to implement to enhance your business outcomes

In today's digital landscape, creating seamless and intuitive user experiences that resonate with customers is crucial for business success. Business leaders and decision makers rely heavily on the research conducted by product teams to uncover insights and patterns about user behaviour. The research outcomes help businesses to understand more about their customer and market position, informing strategic decisions on where to invest in the future to drive revenue and customer satisfaction.

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Research can range from smaller investigation into how to build or enhance a feature for existing customers, to large scale exploratory research to uncover the market demand for a new product or service. When it comes to large scale research, often businesses have overly long research approaches that produce bad data because they don’t put the right people with the right approach onto these types of projects.

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I recently worked on a large scale research project with the goal of exploring the opportunity to bring a new product to market. The research involved 30 hours of interviews, 3 surveys and 2 focus groups and presenting recommendations back to the business to determine if this was the right space to invest.

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Reflecting on this experience, here are 5 things that you should check your team are doing to get better results, to help them run successful research studies that generate actionable insights to drive business growth, improve efficiencies and save costs.

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1. Define a process for mapping research findings back to hypotheses and key questions‍

In the discovery phase, begin by clearly outlining what is already know or assumed about the problem space, and identify what knowledge is needed to bridge any gaps. Write these out as a series of key questions and hypothesis that will help to scope and guide the investigation. This will allow the team to define a focused research strategy with appropriate methods that enhance their understanding of the context.

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When planning each research activity, always keep the key questions in mind. For instance, use semi-structured approaches for interviews and surveys in a way that ensures the participant responses align with the initial research questions. This approach will make it easier to analyse the data and draw well-informed conclusions about participant goals and motivations. By maintaining this structured approach, it will ensure consistency and reliability in the research findings.

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2. Use trusted platforms for participant recruitment

When recruiting for roles that do not require specialised skills, leverage user testing platforms to efficiently match with suitable participants. This method is quick, simple, and cost-effective. If the research requires specialist roles, screener questions can be used to ensure the team is paired with individuals who are suitable for the study. If incentives are being provided to participants on this type of platform, the team should be cautious as often individuals are able to bypass the screener questions by guessing the answers and successfully making it onto the study. To prevent this, be sure to use platforms or partners that ensure high-quality participants. Although this may require a larger budget allocation, the superior quality of results will justify the investment.

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By ensuring we recruit the right participants, we can obtain more accurate and reliable data, leading to better-informed decisions that reflect customer needs, and more successful outcomes for the business.

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3. Leverage AI where possible

AI can significantly accelerate our research process, particularly in data collection and analysis. With participant consent, use AI note-taking tools during interviews to generate transcripts and group conversations into key themes. Test a few tools internally before starting the research to find the best fit. Be cautious of potential AI misinterpretations; always cross-reference AI-generated summaries with notes or replay the recordings to ensure accuracy. Examples of useful tools include Fathom AI, Supernormal, and Otter AI.

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By integrating AI into our research workflow, we can enhance efficiency and reduce time and cost, allowing us to focus more on deriving actionable insights.

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4. Develop a strategy for analysing research

Diverse perspectives in research can lead to the temptation of prioritising individual opinions as recommendations. Instead, focus on identifying patterns by understanding how many participants share similar views. Define a tagging strategy to categorise data by persona type, feature suggestion, or any emerging themes that are coming through in the research. This helps in weighting opinions and making informed decisions. Affinity mapping can be an effective tool to analyse patterns and trends across large datasets.

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Defining a structured analysis strategy ensures that our recommendations are backed by robust data, leading to more reliable recommendations to make data-driven business decisions.

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5. Plan for presenting research findings to stakeholders

Analysing and presenting research findings to stakeholders can be challenging, and it is key to understand stakeholder needs and the forums where findings will be shared. Some stakeholders may require detailed reports, while executives may prefer concise summaries to facilitate quick budget decisions. Create multiple versions of the findings report: a comprehensive document with an executive summary and a shorter presentation highlighting key findings and recommendations. Keep this report as a living document, updated regularly with team contributions.

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Tailoring the presentation of our research findings to the needs of different stakeholders ensures that our insights are effectively communicated and can drive strategic decisions.

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Conclusion

Conducting large-scale user research is complex but essential for creating user experiences that drive business success. The key takeaways include defining a well-structured research plan, leveraging appropriate tools, and developing effective strategies for data analysis and stakeholder communication. By implementing these suggestions, your team can move through the research process efficiently, generating actionable insights to the business to inform strategic decisions that drive revenue.

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