Industry

Customer Lifecycle

Year

2021

Deliverables

Research · UX/UI · Prototyping

Research · UX/UI · Prototyping

01

Problem

Data complexity

Users frequently experience difficulty in interpreting insights due to the volume and diversity of available information. The absence of an intuitive structure in the interface resulted in slower decision-making and frustration during data exploration. To improve comprehension and lessen mental strain, users required a clear, meaningful method of interacting with complex datasets.

Lack of personalisation

Users had different goals, roles, and responsibilities, yet the system delivered the same default overview to everyone. This uniform approach overloaded users with irrelevant information and prevented them from customising their dashboards to align with their specific preferences, priorities, and contexts at every stage of their needs. The absence of personalisation discouraged engagement and limited the tool’s overall effectiveness.

Data complexity

Users frequently experience difficulty in interpreting insights due to the volume and diversity of available information. The absence of an intuitive structure in the interface resulted in slower decision-making and frustration during data exploration. To improve comprehension and lessen mental strain, users required a clear, meaningful method of interacting with complex datasets.

Lack of personalisation

Users had different goals, roles, and responsibilities, yet the system delivered the same default overview to everyone. This uniform approach overloaded users with irrelevant information and prevented them from customising their dashboards to align with their specific preferences, priorities, and contexts at every stage of their needs. The absence of personalisation discouraged engagement and limited the tool’s overall effectiveness.

Data complexity

Users frequently experience difficulty in interpreting insights due to the volume and diversity of available information. The absence of an intuitive structure in the interface resulted in slower decision-making and frustration during data exploration. To improve comprehension and lessen mental strain, users required a clear, meaningful method of interacting with complex datasets.

Lack of personalisation

Users had different goals, roles, and responsibilities, yet the system delivered the same default overview to everyone. This uniform approach overloaded users with irrelevant information and prevented them from customising their dashboards to align with their specific preferences, priorities, and contexts at every stage of their needs. The absence of personalisation discouraged engagement and limited the tool’s overall effectiveness.

Data complexity

Users frequently experience difficulty in interpreting insights due to the volume and diversity of available information. The absence of an intuitive structure in the interface resulted in slower decision-making and frustration during data exploration. To improve comprehension and lessen mental strain, users required a clear, meaningful method of interacting with complex datasets.

Lack of personalisation

Users had different goals, roles, and responsibilities, yet the system delivered the same default overview to everyone. This uniform approach overloaded users with irrelevant information and prevented them from customising their dashboards to align with their specific preferences, priorities, and contexts at every stage of their needs. The absence of personalisation discouraged engagement and limited the tool’s overall effectiveness.

02

Challenge

The project involved designing a data-driven visual strategy supported by artificial intelligence (AI) and machine learning (ML), analysing data to create user-focused visualisations that clearly communicate insights. It also included developing adaptable interface environments for retail, banking, and healthcare sectors, ensuring flexibility to accommodate market trends and content diversity, while delivering a seamless digital experience through high-fidelity mock-ups, user flows, and a cohesive design system.

03

Research

Understanding the target users and context

To build genuine empathy for our target users, I developed three primary personas grounded in insights from stakeholders and retail clients. These personas helped bring user needs into focus, allowing me to step into their shoes, follow their daily routines, and understand their challenges. The result was a more grounded, user-centred design process shaped by real behaviours rather than assumptions.

User personas

To build genuine empathy for our target users, I developed three primary personas grounded in insights from stakeholders and retail clients. These personas helped bring user needs into focus, allowing me to step into their shoes, follow their daily routines, and understand their challenges. The result was a more grounded, user-centred design process shaped by real behaviours rather than assumptions.

Understanding the target users and context

To build genuine empathy for our target users, I developed three primary personas grounded in insights from stakeholders and retail clients. These personas helped bring user needs into focus, allowing me to step into their shoes, follow their daily routines, and understand their challenges. The result was a more grounded, user-centred design process shaped by real behaviours rather than assumptions.

User personas

To build genuine empathy for our target users, I developed three primary personas grounded in insights from stakeholders and retail clients. These personas helped bring user needs into focus, allowing me to step into their shoes, follow their daily routines, and understand their challenges. The result was a more grounded, user-centred design process shaped by real behaviours rather than assumptions.

Understanding the target users and context

To build genuine empathy for our target users, I developed three primary personas grounded in insights from stakeholders and retail clients. These personas helped bring user needs into focus, allowing me to step into their shoes, follow their daily routines, and understand their challenges. The result was a more grounded, user-centred design process shaped by real behaviours rather than assumptions.

To build genuine empathy for our target users, I developed three primary personas grounded in insights from stakeholders and retail clients. These personas helped bring user needs into focus, allowing me to step into their shoes, follow their daily routines, and understand their challenges. The result was a more grounded, user-centred design process shaped by real behaviours rather than assumptions.

Competitive analysis

Competitive research revealed that tools like Salesforce and SAP dominate the enterprise analytics space, but their greatest strengths typically double as their biggest UX challenges.

Salesforce stands out for its AI-powered analytics, automation, and flexible forecasting, yet users frequently describe it as “intimidating”, less because of what it can’t do, and more because of how much it can do, all at once.
SAP, meanwhile, delivers impressive scalability and advanced reporting, but its complexity and inconsistent visual hierarchy can turn even simple data exploration into a bit of a navigation exercise.

In short, both platforms are highly capable of generating in-depth insights; they just don’t always make those insights easy to find. For retail professionals who need clarity and speed (not a guided tour), this creates a gap between data availability and actual usability.

Competitive analysis

Competitive research revealed that tools like Salesforce and SAP dominate the enterprise analytics space, but their greatest strengths typically double as their biggest UX challenges.

Salesforce stands out for its AI-powered analytics, automation, and flexible forecasting, yet users frequently describe it as “intimidating”, less because of what it can’t do, and more because of how much it can do, all at once.
SAP, meanwhile, delivers impressive scalability and advanced reporting, but its complexity and inconsistent visual hierarchy can turn even simple data exploration into a bit of a navigation exercise.

In short, both platforms are highly capable of generating in-depth insights; they just don’t always make those insights easy to find. For retail professionals who need clarity and speed (not a guided tour), this creates a gap between data availability and actual usability.

04

Ideation

Simplicity

Simplify the interface by adopting a minimalist visual language that emphasises clean structure, generous white space, and balanced composition for intuitive navigation. Use straightforward charts and a restrained colour palette to reduce visual noise, ensuring that key data and insights stand out clearly.

Prototyping

As the engineering team began transforming collected data into technical components, developing chart functionalities and data visualisation logic, I worked in parallel to build the first interactive prototype in Figma. This prototype served as a tool to validate our initial concepts, align cross-functional discussions about key features, and help the team prioritise development efforts and resources.

Simplicity

Simplify the interface by adopting a minimalist visual language that emphasises clean structure, generous white space, and balanced composition for intuitive navigation. Use straightforward charts and a restrained colour palette to reduce visual noise, ensuring that key data and insights stand out clearly.

Prototyping

As the engineering team began transforming collected data into technical components, developing chart functionalities and data visualisation logic, I worked in parallel to build the first interactive prototype in Figma. This prototype served as a tool to validate our initial concepts, align cross-functional discussions about key features, and help the team prioritise development efforts and resources.

Simplicity

Simplify the interface by adopting a minimalist visual language that emphasises clean structure, generous white space, and balanced composition for intuitive navigation. Use straightforward charts and a restrained colour palette to reduce visual noise, ensuring that key data and insights stand out clearly.

As the engineering team began transforming collected data into technical components, developing chart functionalities and data visualisation logic, I worked in parallel to build the first interactive prototype in Figma. This prototype served as a tool to validate our initial concepts, align cross-functional discussions about key features, and help the team prioritise development efforts and resources.

05

Solution

Geographic performance

Tracking location to understand and identify sales patterns within the area.

Sales forecast

Predict sales/revenue forecast through statistical modelling techniques to analyse historical data and identify complex relationships that can be used to predict future sales.

Next best action (NBA)

Combines data analytics and artificial intelligence to enable companies to better understand their customers and anticipate their needs.  It provides pertinent and specialised interactions with customers/clients across all channels, delivering highly personalised experiences that culminate in improved experiences, productivity, and a higher customer lifetime value.

External factors

There are external elements that can influence its operations, performance, or outcomes during a certain time frame.  This information is crucial for both sales and marketing tactics, as well as for managing stocks and distribution.

Sentiment analysis

Understand customers' emotions towards products, brands, and services. Analyse customer behaviour towards sales and customer service. Gather these insights to refine services, venture into a new market when releasing new products, or while upgrading existing products. Get powerful insights to boost marketing strategy by keeping an eye on industry trends by analysing sentiment towards new features or products on social media.



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