The right view
for each question
Making sales, targets, price lists and margins easier to explore and act on.
- Design lead
- Lorenzo Rogante
- Design scope
- Head of Design / Design Lead
- Collaboration
- Management and development team
- Designed for
- Commercial teams and account managers
Commercial teams need different views of the same customer base. Geography locates relationships; comparison reveals differences; outreach provides a route from an observation to a prepared response. The design keeps these tasks connected.
or explore the workspace
Key decisions
A map with room to work
The main workspace prioritises the map while keeping the conversation alongside it.
Continuity across views
Customer identity and the selected context remain stable as geography becomes a comparison.
Analysis into outreach
Audience, offer and source quality stay connected to the investigation that prompted the campaign.
Read comparison as a table
| Customer | Sales | Forecast CLV | Churn risk |
|---|
Geography: Natural Earth · Public domain. Fictional locations, not business addresses.
Read geography and commercial signals together
The map occupies the main workspace, with the conversation alongside it. Customer locations and commercial signals can be inspected together before the team narrows the investigation to a region or relationship.
Let the question determine the view
A map explains where customers are; a ranked chart supports comparison. The interaction uses the question to select an appropriate representation instead of requiring a report choice before the investigation begins.
Preserve identity through the transition
In this reconstruction, geographic markers become the anchors of a ranked sales chart. Selection, filters and period remain shared, making it possible to follow the same customer as the representation changes.
Distinguish past performance from future estimates
Historical sales, estimated future value and churn risk answer different questions. Separate labels and measures prevent strong past sales from obscuring a relationship that may need attention.
Sales and potential
are different measures
€368,200 historical sales · €463,900 forecast CLV
Connect the finding to a response
A regional decline in potential customer value can lead into outreach. Audience selection, a relevant offer and message preparation form a connected workflow, retaining the context of the original investigation.
Select recipients
and review the message
Customer Map → Campaigns → Templates → Products → Data Sources
Review the audience and the underlying data
Recipients, templates, products and data sources remain available during preparation. Missing information is surfaced before the next step. The interactive reconstruction supports reviewing a local draft; it does not send a campaign.
Select recipients
and review the message
Customer Map → Campaigns → Templates → Products → Data Sources
A new view, the same customer context
This case study presents professional product design work. Interfaces have been recreated for the portfolio using synthetic records and generic names. Interactive sequences illustrate design decisions rather than record the original application. The figures are examples, not measured project outcomes.
Turning customer signals into commercial action
A changing customer portfolio creates a practical question: where should the commercial team focus its attention, and what should it do next? My work on Customer Intelligence connected customer analysis, AI interaction and commercial workflows within a shared product experience.
The challenge
Customer Intelligence brings together three areas: understanding an existing customer portfolio, discovering new customers, and finding relevant tender opportunities. I worked across all three. This case study focuses on portfolio management and the transition from a customer signal to a commercial action.
Different roles use the product for different decisions. A sales agent needs to prioritise customers; an area manager needs to understand regional performance; company leadership needs a broader view of risks and opportunities. The experience had to support these different perspectives while keeping the underlying information understandable.
My role
As Head of Design / Design Lead at BackwellTech, I helped shape the product direction and defined its user experience and visual language. My responsibilities covered information architecture, interaction flows, interface design, reusable components, prototyping and collaboration with developers.
The work was informed by management input and discussions with engineering. I translated commercial requirements and AI capabilities into an experience that connects a user’s question, the relevant analytical view and a possible next action.
A representative workflow
An area manager opens the product and notices that the predicted lifetime value of customers in a region is declining. The signal concerns future customer value; it is not a measurement of revenue already lost.
The manager uses that insight to launch an automated, personalised offer intended to limit the potential loss. This scenario connects three steps: identify a regional signal, understand which customers it concerns, and initiate a targeted commercial action.
The offer’s effectiveness would need to be evaluated separately. The design work focuses on making the path from analysis to action understandable.
Three design decisions
1. Keep analysis in one adaptable workspace
I gave the main visual workspace as much space as possible, with the map central to geographic exploration. The AI interaction is connected to that workspace: a question can change the main view to match the information being requested.
This supports a conversational approach to analysis. Users can ask about customer performance and have the visual experience respond to the analytical task. Regional patterns suit a map; a comparison of customer sales calls for a comparative chart. The interaction model lets the representation follow the question.
Keeping conversation and analysis linked is intended to help users explore related questions while maintaining the business context of their investigation.
2. Connect predictive signals to commercial workflows
The experience needed a clear continuation after an insight had been identified. In the regional customer-value scenario, the next step is a personalised offer aimed at limiting potential losses.
I organised the experience around the relationship between the signal, the affected customers and the action the user chooses to initiate. This gives the analytical information an operational purpose and keeps the user’s commercial goal visible throughout the flow.
3. Make incomplete data explicit and recoverable
Missing information is part of the operating context of the product. When data is incomplete, the AI brings the gap to the user’s attention. If the user has the required information, they can supply it.
I treated this as part of the experience: users need to understand what information is missing and where their input can help. Making that handoff visible supports a clearer relationship between the system’s analysis and the person’s business knowledge.
Outcome and next step
Customer Intelligence is used by customers. My design work defined a UX and visual foundation across portfolio analysis, customer discovery and tender-related experiences, with this case focusing on portfolio management.
The interaction direction connects conversation, visual analysis and commercial action. No measured retention or revenue improvement is attributed to the interface in this case study.
I would next evaluate whether users can distinguish a prediction from an observed result, understand how their question changes the analytical view, and identify the appropriate next action. I would also examine how clearly the experience explains missing information and requests user input.