People conduct interviews in research to gather in-depth, qualitative insights directly from participants. Interviews allow researchers to explore experiences, opinions, and motivations in detail, providing richer context than surveys or numerical data. They enable flexibility, allowing follow-up questions for deeper understanding. Additionally, interviews help capture nuances, emotions, and personal perspectives, making them valuable for studies in the commercial sector, social sciences, healthcare, business, and anywhere where you need to find out how people experience your product or service.
Pros & Cons of Interviews
Interviews are a valuable research tool, providing rich, qualitative data that helps uncover emergent theories, build relationships, and gain a deeper understanding of a topic or community. They offer a way to grasp the bigger picture and lay the groundwork for effective change. However, interviews can be time-consuming, resource-intensive, and expensive, with challenges such as participant reluctance, interviewer bias, and the need for specialised training in thematic coding and analysis. SenseMaker® is a smarter way to capture real experiences and enjoy all the benefits of real interview-led research without the associated time, training and energy costs all while reducing the risk of bias.
Read on to learn more about the benefits of SenseMaker®, or skip ahead to the table comparing SenseMaker® versus Traditional Interviews.
SenseMaker® isn’t just another survey tool – it’s a revolution in how we gather real-life experiences. Instead of rigid questions and rehearsed answers, SenseMaker® allows people to share their own stories in their own words. Using predefined labels, respondents categorise their own experiences, making analysis faster, more accurate, and free from interviewer bias.
SenseMaker® is a Smarter Way to Capture Experiences because
With digital access via mobile apps and online surveys, SenseMaker® reaches a wider audience than traditional interviews ever could. The anonymity and convenience encourage openness – no more polite or pressured responses. Instead, you get insights straight from the source.
SenseMaker® is a Large-Scale & Accessible Data Collection Tool that
Traditional interviews rely on note-taking, transcription, and interviewer interpretation—all of which introduce bias and risk losing key details. Interviewers may unintentionally influence responses, shaping narratives instead of capturing them. SenseMaker® removes the middleman, letting respondents frame their own experiences in ways that truly reflect their perspectives.
By using structured signifiers, SenseMaker® ensures responses are categorised consistently. No more sifting through transcripts and guessing themes – patterns emerge naturally, giving you richer, more reliable insights without the hassle.
So Why is SenseMaker® more reliable than Traditional Interviews?
Traditional interviews require painstaking manual coding and subjective interpretation. SenseMaker® streamlines this process by automatically identifying themes, emotional tones, and shared experiences across large datasets. Decision-makers get actionable insights faster, with a clearer understanding of emerging trends and potential challenges.
If you Analyse Themes & Patterns with SenseMaker® you get
Forget messy filing systems and endless spreadsheets. SenseMaker’s digital platform stores every response in an organised, searchable format. Built-in visualisation tools generate charts, graphs, and reports with a click – so you can present findings clearly without spending hours formatting data.
Easy Storage & Instant Visualisation with SenseMaker® includes
More authentic stories. More accurate insights. Less bias. Less manual work. If you’re still relying on traditional interviews, it’s time to upgrade. SenseMaker® delivers deeper understanding and actionable intelligence – faster and more efficiently than ever before.
| Feature | SenseMaker | Traditional Interviews |
|---|---|---|
| Primary Purpose | Identifies patterns in narratives through participant self-interpretation. Your theories can be tested against what the data is showing. | Varies. Typically develops theories inductively from gathering qualitative data. |
| Approach | Combines qualitative and quantitative analysis by using structured signifiers (Triads, Dyads, etc.). | Purely qualitative and exploratory; may include iterative coding of data if the interviewer has received training. |
| Data Collection Process | Participants submit self-signified narratives via structured digital tools. | Researchers conduct interviews and may also make observations and take field notes. |
| Type of Data Collected | Short narratives, micro-stories, and structured responses. | Interviews which will vary in degrees of structure. Some interviewing processes will allow open-ended responses and allow observations by the interviewer. |
| Sample Size | Scalable (hundreds or thousands of responses). | Smaller samples due to time/resource cost. |
| Speed of Analysis | Fast (automated dashboards & self-signification). | Slow (manual writing, interpretation, and analysis). |
| How Data is Analysed | Pattern detection through visual dashboards, heatmaps, and statistical tests. Automated coding via Triads & Dyads. | Either through informal methods or the interviewer can receive training in manual coding processes through thematic coding (open, axial, selective coding). |
| Statistical & Visual Analysis | Automatically generated heatmaps, cobweb diagrams, word clouds, cross-data correlations. All downloadable as graphics. | Analysis must be done manually by a trained interviewer, for example, manual thematic clustering, memos, and comparison. |
| Bias Reduction | Minimises researcher bias by having respondents interpret their own stories. | Higher risk of bias, as the researcher codes and interprets responses. |
| Role of Interviewer | Minimal intervention; participants categorise their data. | Interviewer-driven. Formal training in coding and categorisation is required for theme analysis to be effective. Coding needs to be done extensively by hand. |
| Output | Identifies patterns, clusters, and trends in narratives – allowing you to focus on generating theories of change. | Interviewers generate theories based on emerging patterns in data. Data collection risks being biased or even incomplete leading to premature decision making. |
| Survey Tools | Triads, Dyads, Open-text, Image-based prompts. | Interviews, focus groups, and field notes. |
| Scalability | Can process large volumes of responses. | Typically not scalable. High time and resource cos, so best for small studies. Requires significant expertise and cross-checking. |
| Cost | A single cost that is fixed no matter how much data you collect, the price for a 3 month NFP licence: i.e £900 only | Varies. Can be difficult to predict ahead of time. The time cost of the analysis stage can increase exponentially with the amount of data you collect. |
Cognitive Edge Ltd. & Cognitive Edge Pte. trading as The Cynefin Company and The Cynefin Centre.
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