The GRAMS Framework: A Structured Approach to UX Research and Service Design

Introduction

The GRAMS framework provides a structured methodology for conducting user research, facilitating qualitative data collection, and refining user insights for service design and UX research. Over the past decade, it has been adopted by researchers, product teams, and marketers as an effective tool for understanding user behaviour. GRAMS enables researchers to systematically frame interviews, conduct structured data collection, and develop refined persona maps based on empirical findings.

This paper presents:

• A historical overview of the GRAMS framework.

• A comparison with Empathy Maps and Jobs to Be Done.

• A structured guide for implementing GRAMS across four key research phases:

  1. Structuring interview preparation.

  2. Conducting user interviews.

  3. Analysing and categorizing qualitative data.

  4. Creating detailed persona maps.

Historical Context of the GRAMS Framework

The GRAMS framework emerged from research challenges associated with unstructured qualitative data collection. Conventional methods often resulted in fragmented insights that were difficult to synthesize into actionable outcomes. GRAMS was developed to address these limitations by structuring the inquiry process into five key elements: Goals, Reality, Alternatives, Motivations, and Struggles. Over the years, the framework has been refined through its application in user research, service design, and product development initiatives.

Comparative Analysis: GRAMS vs. Alternative Research Frameworks

Jobs to Be Done (JTBD)

JTBD focuses on defining the functional, emotional, and social aspects of why users engage with a product or service. While effective, it does not always provide the structured approach necessary for conducting in-depth qualitative interviews.

Advantages of GRAMS over JTBD:

• GRAMS extends beyond defining the user’s goal by integrating a broader spectrum of behavioral insights.

• It allows for systematic comparison of user alternatives and decision-making processes.

Empathy Maps

Empathy Maps primarily capture what users say, think, feel, and do, but they do not offer a structured mechanism for data analysis.

Advantages of GRAMS over Empathy Maps:

• GRAMS provides a systematic method for structuring interview questions.

• It introduces a benchmarking component through the analysis of alternative user behaviors.

Implementing the GRAMS Framework

Phase 1: Structuring Interview Preparation

Common Challenges in Interview Preparation

Researchers often struggle with:

• Overly broad interview guides that generate excessive data.

• Lack of focus in the interview process, resulting in inconsistent findings.

• Difficulty ensuring comprehensive coverage of relevant user insights.

GRAMS Approach to Interview Preparation

The GRAMS framework structures interview questions around five critical components:

1. Goals – What the user aims to achieve.

2. Reality – Current methods used to achieve this goal.

3. Alternatives – Other approaches the user has considered.

4. Motivations – Underlying drivers for their choices.

5. Struggles – Barriers preventing success.

Example 1: B2C – Fitness App Development

• What are your fitness objectives?

• How do you currently track your workouts?

• Have you tried other fitness tracking methods?

• Why is fitness tracking important to you?

• What challenges do you face in maintaining a consistent routine?

Example 2: B2B – Enterprise Software Adoption

• What efficiency improvements are you aiming for?

• What tools do you currently use for workflow management?

• What alternatives have been considered before implementing new software?

• What drives your need for an improved system?

• What obstacles prevent full adoption within your organization?

Phase 2: Conducting User Interviews

Common Challenges in User Interviews

• Difficulty in maintaining interview structure.

• Participants deviating from key topics.

• Capturing detailed and relevant responses.

GRAMS Approach to Conducting Interviews

By maintaining the GRAMS sequence, interviewers can ensure consistency while allowing flexibility for participants to elaborate on their experiences.

Example 1: B2C – Subscription-Based Learning Platform

• What skills are you hoping to gain from this platform?

• What learning methods have you used previously?

• Have you considered other e-learning platforms?

• What personal or career motivations drive your learning?

• What barriers prevent you from fully engaging with the platform?

Example 2: B2B – Cloud Service Provider Selection

• What business objectives are driving your need for cloud services?

• How is your organization currently managing cloud infrastructure?

• What providers were considered before making a decision?

• What internal or external motivations influenced the decision?

• What challenges have arisen in transitioning to cloud-based solutions?

Phase 3: Analysing and Categorizing Qualitative Data

Common Challenges in Data Analysis

• Difficulty in categorising open-ended responses.

• Identifying patterns in qualitative data.

• Ensuring insights are actionable and relevant.

GRAMS Approach to Data Analysis

Responses are categorized under each of the five GRAMS elements, making it easier to extract key themes.

Example 1: B2C – Mobile Banking Application

GRAMS Category Findings Goals Increase financial literacy Reality Uses traditional banking with paper-based tracking Alternatives Has considered budgeting apps but finds them complex Motivations Wants financial independence and control Struggles Fear of mismanaging money, difficulty understanding banking fees

Example 2: B2B – Procurement Software

GRAMS Category Findings Goals Streamline procurement processes Reality Uses spreadsheets for managing orders Alternatives Has evaluated enterprise resource planning (ERP) systems Motivations Cost reduction, process automation Struggles Lack of integration, steep learning curve for employees

Phase 4: Creating Data-Driven Persona Maps

Common Challenges in Persona Mapping

• Personas often lack behavioural depth.

• Limited alignment with real-world decision-making.

• Stakeholders struggle to derive actionable insights from personas.

GRAMS Approach to Persona Mapping

GRAMS-based personas integrate structured research findings, ensuring they are evidence-based and applicable to business decision-making.

Example 1: B2C – Online Grocery Shopping Background: A young professional with a busy schedule. Goals: Reduce time spent on grocery shopping. Reality: Uses a mix of in-store and online shopping. Alternatives: Has considered subscription-based grocery delivery. Motivations: Values convenience and variety. Struggles: Concerned about delivery accuracy and product quality.

Example 2: B2B – HR Software Adoption Background: HR manager in a mid-sized company. Goals: Improve employee experience with streamlined HR processes. Reality: Uses outdated manual systems. Alternatives: Evaluated several HR tech platforms but found them costly. Motivations: Increase efficiency, compliance with labor laws. Struggles: Resistance from leadership, lack of IT support.

Conclusion

The GRAMS framework offers a structured and replicable approach for conducting user research. By focusing on user goals, existing realities, alternatives, motivations, and struggles, researchers can derive meaningful insights that drive product innovation, service design, and customer experience improvements. Whether in UX research, product development, or B2B strategy, GRAMS provides an essential methodology for structured qualitative analysis.