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Self-checkout machines are now a staple of modern retail, offering customers the promise of convenience and autonomy. However, the reality often falls short. During a visit to Sainsbury’s, I observed numerous customers encountering frustrations, struggling with touchscreens, unclear prompts, and system errors. These challenges frequently led to manual intervention, slowing down the process and contradicting the core objective of self-service.
These recurring issues raised important questions: What design flaws were causing these difficulties, and how could the system be improved to deliver a smoother, more intuitive experience?
Recognizing the opportunity to enhance the customer journey, I undertook this project to thoroughly investigate user pain points and propose practical design solutions. My goal was to deliver insights and recommendations that would not only improve system usability but also ensure the checkout process was accessible and efficient for all users. This case study documents my research process, key findings, and actionable improvements that could transform the self-checkout experience at Sainsbury’s.
Research Question: How can Sainsbury’s self-checkout machines be redesigned to provide a smoother, more intuitive, and inclusive user experience?
The question emerged from real-world observations and user complaints—particularly regarding the machines’ lack of accessibility, the need for staff intervention, and overall user frustration.
My goals for this research were to:
I recruited a diverse pool of participants to ensure broad representation of user experiences. My recruitment strategy included outreach through social media and local community groups.
Criteria:
Process:
To gain a comprehensive understanding of the problem, I used a mixed-methods approach that combined qualitative and quantitative techniques. This allowed me to cross-reference findings from different methods and validate recurring issues.
To establish a foundational understanding of self-service technology, I conducted a literature review by analyzing academic papers, industry reports, and user experience studies. Key focus areas included
I designed a survey to gather quantitative data on user experiences with Sainsbury’s self-checkout machines. The survey received 19 responses (with 17 valid after filtering out incomplete entries). Participants were primarily between 18-45 years old and used the machines on a weekly or bi-weekly basis.
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Key Survey Findings:
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After analysing the responses from the participants, I concluded that the reliability component had the lowest customer satisfaction. This indicates that a more dependable and robust operating system and equipment are still needed for the existing Sainsbury's self-checkout machine.
I used the insights gathered to examine potential issues and scenarios that users may encounter with Sainsbury's self-checkout machines. Based on this analysis, I conducted 5 semi-structured interviews with participants aged 20-45. These interviews focused on capturing the emotional and practical aspects of user frustration.
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Common Themes from Interviews:
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Observational Research
To identify the current issues related to human factors in the self-checkout machine, I opted for an observational research approach. I conducted research by observing customer behaviour at the Sainsbury's supermarket located at New Cross Road. Cameras and mobile phones were used to conduct the observations with the consent of the customers. By employing observational methodologies and conducting timely research, I was able to identify several pain points experienced by customers and gain tangible insight into how users interact with self-checkout machines.
Key Observations:
I adopted a thematic analysis approach to synthesize findings from all research methods.
Step-by-Step Analysis:
Affinity Mapping: Identified connections between survey results, interviews, and observations.
Pattern Validation: Cross-referenced recurring pain points across methodologies.
Rather than jumping into design solutions, this stage focused on forming evidence-based hypotheses that could guide future iterations. Drawing directly from observational research, interviews, and survey data, I crafted hypotheses linking specific user pain points with potential improvements. These hypotheses serve as testable predictions anchored in user behavior that articulate how and why proposed changes might improve the self-checkout experience.
As a personal project, my primary goal was to generate research-backed recommendations that could improve the user experience of Sainsbury’s self-checkout machines. While I did not have direct influence on implementing changes, my research provides practical solutions grounded in real user experiences and data.
The research process reinforces how minor design adjustments could make a significant difference in improving user satisfaction. Based on participant feedback, addressing these areas could lead to a 25% improvement in customer satisfaction and faster checkout flow during peak hours.
This project allowed me to translate real-world observations into meaningful solutions, strengthening my ability to conduct independent research and propose impactful design recommendations.
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