Generative AI has revolutionised many SaaS products, and Novus Insights, a leading research firm, sought to leverage its power. Trusting our capabilities in SaaS UX/UI design, they hired Tcules to develop a cutting-edge market research product designed to become a leader in the research SaaS space. The project aimed to create a Generative AI-powered platform that not only facilitates data gathering through surveys and ad tests but also delivers actionable insights through data visualisations driven by both survey results and industry data. This case study delves into how generative AI played a transformative role in the survey creation process.

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We wanted to leverage the capabilities of AI to improvise the traditional survey creation process to enhance efficiency and accuracy while reducing the time and efforts.

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Traditional survey creation methods often involved manual efforts, consuming significant time and resources, in creating questionnaires, editing each question and thinking through the question and answer types. And not all brand managers possess the necessary expertise in survey design and data analysis, leading to suboptimal results.

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To talk about the primary user personas, the brand managers struggle to create insightful surveys quickly and efficiently. Traditional methods are time-consuming, require expertise, and often result in subpar data. Novus Insights, a leading research firm headquartered in Singapore, partnered with us to address this challenge by harnessing the power of Generative AI in their SaaS product.

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With a vision of playing a pivotal role in advancing AI in the Market Research Industry, Novus’ goal was to expedite the process of survey creation by harnessing the capabilities of Generative AI.

Existing Challenges in the industry

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1. Time-consuming Process
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   Traditional survey creation methods often involved manual efforts, consuming significant time and resources, in creating questionnaires, editing each question and thinking through the question and answer types.
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2. Expertise Requirement
   
   Not all brand managers possess the necessary expertise in survey design and data analysis, leading to suboptimal results.
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3. Data Quality
   
   Ensuring the quality and relevance of survey questions to extract meaningful insights posed a significant challenge.
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4. Adaptability
   
   The solution needed to be adaptable to various industries and research objectives while maintaining consistency and reliability.

Approach

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When the client proposed the idea of leveraging AI to enhance the survey creation process, we worked on the proof of concept and our immediate focus was to rigorously assess the feasibility, desirability, and viability of the proposed solution before moving forward with the MVP. After getting validation on all the aspects, we created an MVP of the whole product by employing a user-centred approach to develop all the modules.

Feasibility assessment

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To determine the technical feasibility of integrating AI into the survey creation process, we conducted in-depth research and analysis. This involved evaluating the current state of AI technology, assessing available resources and expertise, and identifying potential challenges and limitations especially for interacting with chat based generative AI tools. By collaborating closely with data scientists and solution architects, we gained valuable insights into the technical requirements and feasibility constraints of the proposed AI solution.

Desirability assessment

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Understanding the desirability of AI-powered features was paramount to ensuring user acceptance and adoption. We employed a user-centred approach, engaging with key stakeholders and target users, particularly Brand Managers and Assistant Brand Managers. Through stakeholder interviews we gathered valuable feedback on the user preferences, pain points, and their expectations. This iterative process allowed us to refine the proposed solution to better align with user needs and preferences.

Viability assessment

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In addition to technical feasibility and user desirability, we also assessed the economic viability and business sustainability of integrating AI into the survey creation process. This involved conducting market research, analysing the competitive landscape, and evaluating potential return on investment. By collaborating with business analysts and strategists, we gained insights into market demand, revenue potential, and cost implications. This holistic assessment helped us ensure that the proposed AI solution was not only technically feasible and user-desirable but also economically viable and strategically aligned with the client's goals and objectives.

Process

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Research & analysis

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As this project was not about solving a problem and more about introducing and implementing technological advancements, we conducted secondary research by analysing the positioning of market research automation platforms with and without AI integration in their platforms. We also considered other AI integrated platforms to understand the general relatability of the users with the prompt based inputs for GenAI. We analysed all the platforms with different parameters to prioritise the functionalities that should be added in the final solution.

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Persona identification and contemplating

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From continuous collaboration with the stakeholders, we expanded our understanding of survey creation process and user personas, i.e., Brand managers and Assistant brand managers. We considered personas based on the size of the organisations, as the responsibilities vary depending on hats a brand manager has to wear in different organisational setups based on their experience. Dissecting the personas helped us in channelling their pov into ours to better understand their way of working and what could potentially improve their survey creation experience.

User flow, use cases, and journey mapping

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We mapped the existing workflow of the users to understand the opportunity areas and improvised the flow. We mapped the journey of the user based on the improvised flow which then resulted in coming up with information based solutions at each step in the journey for better adaptability.

Information design based on the user journey

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After detailed study of user journeys based on all the possible use cases and user flows, we could prioritise the information for each step of the survey creation process. With continuous collaboration with stakeholders we leveraged on their knowledge of the users and domain which then helped us in fixing the user journeys based on all the possible use cases, user flows. We could then prioritise the information for each step of the survey creation process.

Information design based on the user journey

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The solution focused on leveraging AI in the survey creation process where most delay and friction occurs, i.e., designing a survey questionnaire.

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AI prompt to generate a survey questionnaire

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Leveraged advanced AI algorithms to develop a model where users can simply enter:

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1. Goals/objectives of the survey
2. Demographics of the target audience
3. Preferred sections in the questionnaire

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AI powered suggestions

Gen AI analyses previous prompts and provides intelligent suggestions for survey questions based on industry best practices and data trends after a Survey questionnaire is generated.

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Customisation options

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Users can also customise survey questionnaires to align with their specific research objectives and target audience if the generated results do not fulfil their requirements.

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Scalability

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The solution is scalable and adaptable to accommodate the evolving needs of different industries, research projects, and also the advancements in AI model.

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Accuracy

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The AI-powered suggestions have improved the quality and relevance of survey questions, leading to more insightful data analysis.