My IFB - Case Study

Business Requirement 

IFB operates in a highly competitive home appliance market in India, competing with both established Indian brands and European manufacturers. While IFB products offered advanced functionality, the overall user experience across products felt fragmented, inconsistent, and difficult to understand—especially for younger, digitally native users.

The business objective was to create a unified companion application that would bring IFB’s product and service experiences onto a single platform. This platform aimed to improve brand relevance among the 25–35 age group, simplify complex appliance interactions, and strengthen post-purchase engagement. In parallel, the initiative sought to improve customer service transparency, reduce dependency on printed manuals, and make smart appliances accessible to users without technical or wireless expertise.

Key Business Goals

• Attract and engage next-generation customers (25–35 years)

• Create a consistent and unified experience across IFB products

• Improve customer service efficiency and issue transparency

• Reduce operational costs by digitizing product manuals

My Design Process 

Every project demands a different design approach depending on its scope, constraints, and maturity. Since My IFB was designed as a companion app from scratch for a diverse user base, the focus was on deeply understanding real user behavior while aligning closely with business and engineering constraints.

I followed a structured yet flexible design process, adapting methods based on the problem being solved at each stage. Rather than treating the process as linear, key phases overlapped to allow continuous validation and iteration.

Stakeholder Study

A stakeholder study was conducted early in the project to align business objectives, technical constraints, timelines, and brand expectations. The goal was to understand how different teams—product, engineering, design, and business—defined success for the companion app and where existing limitations might impact the user experience.

Through these discussions, it became clear that the project needed to balance ambitious experience goals with practical constraints such as limited timelines and a small design team. As a result, the design process was intentionally simplified. Certain exploratory activities were deprioritized, and instead of running parallel A/B design explorations, the team focused on designing a single, well-validated solution from start to finish.

This alignment helped ensure that user needs and business priorities were addressed together, enabling a solution that was both user-centric and feasible to execute within the project scope.

Method used:

• FAST framework to quickly align on goals, constraints, and success criteria

• SWOT analysis to understand market positioning and competitive pressures

• Collaborative brainstorming sessions with cross-functional stakeholders

• Domain expert interviews to understand appliance behavior, service workflows, and technical limitations

Key Outcomes from Stakeholder Alignment

The stakeholder alignment phase helped identify key opportunities to improve IFB’s end-to-end customer experience beyond the mobile application. The following strategic directions were agreed upon and directly influenced the product roadmap:

• Unified digital experience: Establishing a single companion mobile app as the primary interface for appliance control, service management, and user engagement.

• Omnichannel consistency: Aligning website, mobile app, and customer support touchpoints to reduce fragmented experiences.

• Improved issue transparency: Introducing clearer problem acknowledgment and status visibility across digital channels, including app and Play Store feedback.

• Proactive customer support: Shifting from reactive service handling to proactive communication and faster resolution workflows.

• Next-generation brand alignment: Evolving visual and interaction patterns to better resonate with younger, digitally native users.

• IoT-led integration: Laying the foundation for integrating current and future IFB products into a single IoT-enabled ecosystem.

• Centralized customer records: Moving toward an in-house system for sales and customer product history to support personalized experiences.

After aligning on business objectives, we conducted both primary and secondary research to understand how users interact with washing machines in real-life contexts. The goal was to uncover behavioral patterns, pain points, and unmet needs that could not be addressed through assumptions alone.

Primary research included 1:1 user interviews and in-home field observations, allowing us to observe how users operate washing machines, make decisions, and respond to errors or uncertainties. Secondary research focused on market trends, competitor offerings, and brand perception to identify gaps and opportunities in the current ecosystem.

Based on the research findings, users were grouped by behavior, familiarity with technology, and usage patterns rather than demographics alone. To build shared empathy and align the team, we synthesized these insights into two core user personas that represented the most impactful user groups for the product.

Research Methods:

• 1:1 user interviews & field studies to understand real-world appliance usage

• Brand value comparison to assess IFB’s perception against competitors

• Market and competitive audit to identify feature gaps and experience benchmarks

• Heuristic evaluation of competitor apps to understand usability strengths and weaknesses 

To understand IFB’s position in the competitive home appliance market, we conducted a brand value comparison across major Indian and global players. The analysis mapped brands across two key dimensions: Conventional vs. Innovative experiences and Value for Money vs. Premium positioning.

This comparison revealed that while several competitors strongly emphasized either innovation or affordability, there was a clear opportunity for IFB to strengthen its position as a balanced brand, offering reliable, value-driven products while improving perceived innovation through digital experiences.

The insights from this exercise helped define the role of the companion app: not as a feature-heavy experiment, but as a strategic lever to elevate IFB’s brand perception, modernize user interactions, and differentiate the experience without compromising trust or usability.

Key Insight:

IFB’s hardware was perceived as reliable and value-driven, but its digital experience lagged behind innovation-led competitors, creating an opportunity for the companion app to bridge this gap.

To identify experience gaps and set a clear benchmark for the My IFB companion app, we conducted a competitive audit of leading global and Indian home appliance brands. The analysis focused on both business positioning and end-user experience, with particular emphasis on washing workflows, usability, and navigation clarity.

Each competitor was evaluated using consistent criteria across product offering, target audience, digital capabilities, and overall usability. This helped uncover patterns in how competitors approached connected appliance experiences and where users commonly faced friction.

Key Insights:

• Most competitors offered feature-rich apps, but washing flows were often complex and required multiple decision steps.

• Innovation-led brands prioritized remote control and diagnostics, but struggled with clarity and ease of use.

• Value-focused brands delivered reliable functionality but lacked modern interaction patterns and guidance.

• No competitor clearly balanced simplicity, transparency, and smart guidance within a single washing experience.

Opportunity Identified:

• Design a washing experience that reduces cognitive load by shifting from control-heavy flows to intent-driven interactions, without sacrificing advanced functionality.

To objectively assess the usability and experience quality of existing companion apps, we conducted a heuristic evaluation of leading competitor products. The goal was to identify common usability patterns, strengths, and friction points across the ecosystem and understand where IFB’s experience stood in comparison.

Each application was evaluated using a consistent set of usability heuristics, including task orientation, navigation clarity, content quality, trust and credibility, and overall interaction structure. This allowed for a qualitative comparison of experience maturity rather than a feature-by-feature comparison.

Key Observations:

• Most competitor apps performed well in feature depth but struggled with task clarity, especially during core washing workflows.

• Navigation patterns were often inconsistent, requiring users to explore multiple sections to complete a single task.

• Content and writing quality varied significantly, affecting user confidence and trust during error handling or setup.

• IFB’s existing experience showed strong trust and reliability cues but lagged behind innovation-led competitors in clarity and guidance.

Opportunity Identified:

• Simplify task flows by prioritizing intent-driven actions, reduce navigation complexity, and improve contextual guidance during critical moments of use.

Conclusion:

• Identified a mismatch between IFB’s perceived brand positioning and its actual market opportunity based on brand, competitive, and heuristic analysis.

• Discovered that while IFB was often benchmarked against innovation-led brands such as Samsung, its closest experiential and value-based competitor was Electrolux.

• Recognized the need to reposition IFB’s digital experience rather than competing on feature complexity alone.

• Defined clarity, usability, and trust as the primary levers for differentiation in the companion app.

• Used these insights to shape the design direction, focusing on: Simplifying core washing workflows, Improving contextual guidance and feedback, Positioning IFB as a modern yet reliable brand for next-generation users

After completing primary and secondary research, findings were synthesized across interviews, field observations, competitive analysis, and heuristic evaluations to identify recurring behavioral patterns and usability gaps.

Insights were clustered using aggregated empathy mapping, allowing us to understand what users consistently think, feel, say, and do while interacting with washing machines and related digital touchpoints. This helped surface emotional friction points that were not always verbally expressed during interviews.

Based on these insights, we created two core personas and mapped the current-state user journey to visualize end-to-end interactions across setup, wash configuration, monitoring, and post-wash stages. We further translated these journeys into user flows to understand decision paths, dependencies, and moments of cognitive overload.

This synthesis helped identify the most critical user problems and informed the prioritization of design efforts for the companion app.

Method used:

• Affinity mapping to cluster qualitative research insights

• Aggregated empathy mapping to capture shared emotional and cognitive patterns

• Persona synthesis based on behavioral segmentation

• Current-state journey mapping to identify friction points

• User flow mapping to analyze task complexity and decision paths

Based on research synthesis and aggregated empathy mapping, we identified two primary user archetypes that represented the most impactful and recurring behavioral patterns. These personas were created to align the team on user motivations, pain points, and expectations, and were used as reference points throughout design and decision-making.

To understand how users currently experience laundry end-to-end, we mapped Harsh’s real-world journey across pre-laundry, washing, and post-laundry stages. The goal was to identify moments of confusion, decision fatigue, and emotional friction that occur without digital assistance.

This journey helped reveal where users rely on assumptions, memory, or trial-and-error highlighting clear opportunities where a companion app could reduce cognitive load and increase confidence.

Key Insights:

• Users rely heavily on memory and past mistakes to make wash decisions

• High cognitive load while deciding wash type, load separation, and drying method

• Lack of confidence during wash execution leads to repeated checking

• No clear feedback loop to reassure users that the selected wash is correct

• Emotional stress peaks during decision-heavy moments, not during machine operation

Insight:

• Laundry frustration is driven more by decision-making uncertainty than by the physical act of washing.

To synthesize qualitative insights across interviews and field observations, we created an aggregated empathy map that captured recurring user thoughts, emotions, behaviors, and pain points throughout the laundry experience.

Rather than focusing on individual responses, this exercise helped identify common emotional patterns—such as uncertainty during wash selection, anxiety about incorrect settings, and the need for reassurance once the cycle starts. These insights allowed us to move beyond what users said to understand what they consistently felt and did across contexts.

Key Insights:

• Users frequently feel uncertain and anxious while selecting wash settings

• Confidence increases only after the wash visibly progresses or completes

• Users rely on memory and past mistakes rather than system guidance

• Lack of clear feedback creates repeated checking behavior

• Successful washes lead to relief rather than delight — indicating low emotional ceiling

Insight:

• Users want reassurance and guidance, not more control.

To understand how users currently operate the washing machine and navigate the existing mobile experience, we mapped the end-to-end user flow for a typical wash cycle. The objective was to identify decision-heavy steps, repeated actions, and points where users rely on memory or trial-and-error.

This flow highlights the complexity users face while configuring a single wash—often requiring multiple selections, confirmations, and corrections before starting the cycle. These insights helped us pinpoint where the experience could be simplified, guided, or automated through the companion app.

Key Observations:

• Users must make too many micro-decisions for a single wash cycle

• Similar decisions (water level, temperature, rinse) are repeated across stages

• Errors are often detected late, resulting in rework or rewash

• The flow assumes prior knowledge and confidence from the user

• Cognitive load is high before the wash even starts

Insight:

• The problem is not lack of features, it is excessive decision-making.

Conclusion:

• The current washing experience places a high cognitive load on users before the wash cycle even begins.

• Users are required to make multiple repetitive decisions (water level, temperature, rinse, dry) for a single task.

• The flow assumes prior knowledge and confidence, forcing users to rely on memory and past mistakes.

• Errors are often discovered late in the process, leading to rework or rewash cycles.

• Decision-heavy interactions increase hesitation, uncertainty, and repeated checking behavior.

• The problem is not a lack of features, but over-configuration and lack of guidance.

• These insights highlighted the need for a companion app that: Simplifies wash setup, Reduces decision-making effort, Provides guided, intent-driven interactions, Builds confidence through clear feedback and reassurance

After identifying the core user pain points, we transitioned into the ideation phase with a clear focus on reducing decision complexity, improving confidence, and enabling effortless task completion.

We began by defining the information architecture to establish a clear structural foundation for the app and ensure that key tasks were easy to discover and access. To explore how users might interact with the product in real-world scenarios, we created product storyboards, including edge cases, to visualize usage across different contexts and constraints.

Building on these scenarios, we conducted Crazy 8 ideation sessions to rapidly explore multiple solution approaches without prematurely committing to a single direction. This helped us generate a wide range of ideas while staying grounded in the identified user problems.

Promising concepts were translated into hand-drawn sketches and low-fidelity wireframes, which were used for early validation and user feedback before moving into detailed design.

Method used:

• Information Architecture

• Product Storyboarding

• Crazy 8 Ideation

• Low-fidelity Wireframing

To translate research insights into a scalable product structure, we designed a comprehensive information architecture that prioritizes task clarity, quick access to core actions, and long-term platform scalability.

The IA was structured to support users with varying confidence levels, allowing quick actions such as appliance control and service tracking to remain easily accessible, while secondary features like recipes, store items, and settings were logically grouped to reduce cognitive load.

Key decisions included separating appliance control, support services, and commerce into distinct sections, ensuring users could complete critical tasks without navigating through unnecessary complexity. This structure also allowed the app to evolve beyond a single appliance and support IFB’s broader smart home ecosystem in the future.

The information architecture served as the foundation for wireframing and interaction design, ensuring consistency across flows and reducing friction throughout the user journey.

IA Design Principles:

• Task-first navigation for frequent actions

• Progressive disclosure for advanced features

• Clear separation between control, support, and commerce

• Scalable structure for multiple appliance categories

To explore how users interact with the product in real-life contexts, we used storyboarding as a lightweight yet powerful tool to visualize user behavior beyond the screen.

We created two types of storyboards:

• Big-picture stories to understand the end-to-end experience of using the washing machine across different environments, routines, and time constraints.

• Focused stories to zoom into critical interaction moments such as notifications, remote control actions, and system feedback.

This approach helped us identify moments where users needed reassurance, timely reminders, and reduced manual involvement—especially during multitasking or when away from home. Storyboarding allowed the team to align on realistic usage scenarios and informed subsequent ideation and interaction design decisions.

Key Outcomes from Storyboarding:

• Identified the need for proactive reminders and status updates

• Validated remote control as a confidence-building feature

• Highlighted moments of anxiety when users are away from the appliance

• Informed notification timing and tone for the mobile app

To rapidly explore multiple solutions for the key user pain points identified during research, we conducted a Crazy 8 ideation exercise with the design team.

In this time-boxed activity, each participant sketched eight solution ideas in eight minutes, focusing on different ways the product could reduce user effort, improve confidence, and support multitasking during laundry. After the sketching phase, the team collaboratively reviewed and voted on the most promising concepts.

This exercise helped us move quickly from problem understanding to solution exploration, ensuring we considered a wide range of ideas before converging on a primary design direction.

Key Outcomes from Crazy 8:

• Identified notification-led interactions as a core value driver

• Highlighted opportunities for simplifying wash setup and monitoring

• Surfaced the need for proactive system guidance instead of manual checking

• Informed early layout and interaction patterns for wireframes

To translate early concepts into a tangible structure, we created paper wireframes to explore layout, hierarchy, and navigation before moving into digital design.

Paper wireframing allowed us to quickly iterate on key user flows—such as onboarding, appliance management, and service actions—without being constrained by visual details. This helped the team validate core assumptions, identify gaps in navigation, and refine interactions that directly addressed user pain points like low confidence, frequent monitoring, and complex setup.

These wireframes formed the foundation for low-fidelity prototypes and were later refined through internal reviews and early usability feedback.

Key Learnings from Paper Wireframes:

• Simplified appliance selection and categorization reduced cognitive load

• Early validation of onboarding flow improved clarity and trust

• Identified redundant steps that could be removed before prototyping

• Helped align stakeholders on scope and interaction priorities

Based on learnings from paper wireframes, we translated the validated structure into low-fidelity digital wireframes to define screen layouts, navigation patterns, and interaction flows in greater detail.

These wireframes were intentionally kept visually minimal to avoid aesthetic bias during user feedback sessions. This allowed users to focus on task clarity, flow comprehension, and decision-making rather than visual design. Through early usability walkthroughs, we validated key journeys such as onboarding, appliance management, and service requests, and identified areas where users needed clearer guidance and reduced steps.

The insights from this phase directly informed interaction refinements before moving into high-fidelity design.

What We Validated Using Digital Wireframes:

• Onboarding clarity and appliance setup flow

• Discoverability of key actions (start wash, service, status)

• Reduction of unnecessary steps in frequent tasks

• Alignment between physical machine usage and app controls

After validating the low-fidelity wireframes, we conducted usability testing to evaluate how effectively users could complete core laundry tasks using the proposed experience. At this stage, the focus was on interaction clarity and task completion, not visual design, to minimize bias.

We tested the flows with participants from different departments and regions to reflect diverse usage contexts. Sessions included moderated interviews and task-based usability testing.

Conclusion:

• 7 out of 8 participants faced  problems in onboarding.

• 6 out of 8 participants unable to explain the problem faced by typing alone.

• Users are of different parts of India so requesting their native language in App.

• User took around 3-4 Min to find the appropriate product from the list to buy.

• Unable to identify their machine.

After usability testing, we synthesized all user feedback using affinity mapping. Observations, pain points, and behavioral patterns were grouped into thematic clusters to identify recurring problems across users.

While multiple feature requests emerged, not all could be implemented immediately. To ensure focus and feasibility, we evaluated each cluster based on:

• Frequency of occurrence

• Impact on core user workflows

• Technical and delivery feasibility for the first MVP

This process helped us distinguish critical experience blockers from enhancement opportunities.

After synthesizing insights from usability testing and affinity mapping, we identified multiple opportunities to improve the product experience. However, not all features could be implemented at once.

To ensure focus and feasibility for the first release, we prioritized features using a scoring framework based on:

• User happiness impact

• Business impact

• Criticality to user needs

• Technical feasibility

Each feature was evaluated against these criteria and mapped to a primary persona. Based on the final score, features were categorized into P0 (Must-have), P1 (High priority), and P2 (Nice-to-have).

After implementing the prioritized features, we conducted a second round of usability testing to validate whether the design changes effectively resolved previously identified user problems.

After implementing the prioritized features, we conducted a second round of usability testing to validate whether the design changes effectively resolved previously identified user problems.

Test Objective:

• Validate improvements made based on affinity mapping and feature prioritization.

• Measure reduction in user confusion, task time, and dependency on external help.

• Ensure the redesigned experience supports first-time and low-confidence users.

Test Scope:

• Participants: 8 users (existing + first-time IFB customers)

• Method: Moderated remote usability testing

• Prototype fidelity: Mid–High fidelity

Key flows tested:

• App onboarding & language selection

• Appliance registration (barcode scan)

• Buying appliances with ratings

• Raising a complaint with image support

• Added Onboarding & Language Support :  Easy to learn onboarding screens added to provide some context to understand the app in a easy way to new users of applications. This minimises confusion to both new and old customers and an educated user is better buy things from our plateform.

Problem: New users lacked clarity about the app’s purpose and core value, leading to low confidence and early drop-offs.

Added Onboarding Screens: Introduced step-by-step onboarding to clearly communicate key capabilities and set user expectations before first use.

Problem: Users struggled to navigate the app in English, causing misunderstandings during setup and feature usage.

Added Language Selection (Multi-Language Support) : Added native language options to reduce cognitive load and make the experience accessible across regions and demographics. 

Problem: Users found it difficult and error-prone to manually enter serial numbers from manuals or machine panels. 

Added Barcode Scanner for Appliance Registration: Enabled barcode scanning with contextual guidance, significantly reducing registration time and user errors. 

Problem: Users lacked trust and confidence while purchasing appliances due to the absence of peer validation. 

Added Product Ratings in Online Store : Introduced star ratings to provide social proof and support faster, more confident purchase decisions. 

Problem: Users were unable to accurately describe appliance issues using text, leading to delayed or incorrect support resolution. 

Added In-App Camera for Complaint Reporting: Added image capture within complaints, allowing users to visually communicate problems and improve diagnostic accuracy. 

Design system is the Brand of any company and so the My IFB is the first gen application for the brand so the Proimary design system was done by us on primary basis to cover up all the existing digital branding features of the brand in the application so that the app can be scalable in future and mordern in long run. 

Later IFB hired a dedicated branding company to do the extended design system of the brand and later open for all designer to scale up different apps and products whithin IFB ecosystem, This makes the digital products more alinged and uniform which lastl makes more trust towards the brand.

This project marked Generation 1 of the My IFB app (2018), where the design system was intentionally scoped to washing machine use cases. As IFB expands the app to support additional home appliances and services, the design system is structured to scale across multiple verticals while maintaining consistency.

Looking ahead, IFB plans to introduce AI-powered chatbot and voice-assisted experiences to further simplify appliance management and customer support. These future capabilities are designed to integrate seamlessly into the existing system, enabling continuous evolution of both the product experience and the design framework.