Klecto | B2B SaaS for eCommerce | Sales Analytics Dashboard
Redesigning Klecto’s B2B sales dashboard to help eCommerce clients make faster decisions by improving data visualization, clarifying KPI labels and structure, and enabling actionable features.
In the first week of the project, we set out to redesign Klecto’s sales dashboard to address growing feedback around visual clutter and unclear metrics. Working closely with two in-house developers, we identified key friction points in how eCommerce teams interpreted and acted on data—and used those insights to shape a faster, clearer, and more actionable experience.
My Role
Product Designer
Collaborators
Product Manager, Developers, Marketing Specialists, Business Analysts
CONTEXT
Duration
3 Weeks (2025)
Tools
Figma, Figjam, Slack, Notion
Klecto is a B2B SaaS platform that provides sales analytics dashboards to eCommerce teams.
While the dashboard offered a wide range of business metrics, users struggled with visual overload, unclear data labels, and a lack of actionable pathways.
To address these issues, I redesigned the dashboard with a focus on improving data visualization, clarifying titles and structure, and introducing an export feature to support faster, more confident decision-making.
PROBLEM STATEMENT
How might we help eCommerce teams better understand and act on their sales data by making insights more visual, structured, and exportable?
PROJECT OBJECTIVES
Redesign the sales dashboard for clarity and usability.
➔
Clarify data titles and structure to guide users through key business insights.
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Help users make faster, more confident decisions with clear visuals and export options.
DESIGN SOLUTION
Stakeholder Interview
Through stakeholder interviews with sales and marketing leads, we uncovered a common pattern: while the data was available, teams struggled to interpret it quickly and act on it confidently. Lack of structure, inconsistent labeling, and missing context slowed down decision-making and created uncertainty around performance metrics.
We have the data, but without clearer structure and context, it’s hard to turn insights into action—especially when we need to move fast.
Sales manager at Ninez
RESEARCH
Heuristic Evaluation
To quickly uncover usability issues within the existing dashboard, I conducted a heuristic evaluation using Nielsen’s 10 usability principles. This method helped identify core UX breakdowns in clarity, navigation, and product recognition that were impacting user performance.
Lack of Time Context
Labels like "Week" are ambiguous
Users can’t tell what time range they’re viewing
Difficult to compare data over time confidently
Visual Clutter and Cognitive Load
Too many widgets competing for attention
Inconsistent use of colors adds noise
Layout lacks a clear scanning flow
Unclear Information Hierarchy
KPI cards look visually identical, regardless of importance
No clear distinction between primary and secondary metrics
Users must scan everything without visual guidance
No Clear Action Path
Export button is hidden and unlabeled
No prompts or guidance toward next steps
Dashboard feels passive, not interactive
SOLUTION
After identifying key usability gaps with the product team, we redesigned the dashboard to help users better understand their data and act on it quickly. The updated experience includes:
Clear explanations of key performance trends
Visual indicators for potential issues
Actionable suggestions to help users respond in real time
SUCCESS METRICS
In this project, I defined the following principles to guide my design decisions:
Clarity
Can users quickly understand what each metric represents and how to interpret it at a glance?
Actionability
Does the design help users move from insight to action without friction?
Scalability
Is the layout structured in a way that can support additional metrics, modules, or visualizations in future updates?
Structural Integrity
Will this design integrate smoothly with existing components and maintain consistency across the dashboard?
ITERATION & DESIGN
When approaching the redesign, I started by thinking through how users interpret data, not just what they see, but what they’re trying to figure out. I mapped their questions into content blocks, which helped shape a more structured, decision-friendly dashboard.
After mapping out the core metrics and cleaning up the information hierarchy, I shifted my focus to building a more natural flow of information from a user’s perspective. Sales and marketing teams typically start by checking today’s revenue, then move to trend comparisons, and finally drill down into traffic or transaction-level breakdowns. I structured the dashboard to follow that exact decision flow, top to bottom, left to right.
I explored two layout directions. Option A offered a refreshed visual style with curved graphs and softer transitions, but didn’t fully support the analytical depth users needed. Option B, on the other hand, kept the familiar layout while improving clarity through sharper KPI labels, trend indicators, and an export action point.
I chose Option B because it balanced usability and scalability. It preserved users’ mental model while creating space for deeper insights, and gave me a flexible foundation to introduce future modules without interrupting how teams already interact with the dashboard.
Choosing Option B gave me a solid foundation to refine the next layers, starting with clear KPIs, then structuring performance insights and supporting context to guide users through their decision-making process.
To improve clarity and user flow, I updated the widget title from “Profit” to “Revenue Breakdown” to better reflect the data shown and set clearer expectations. I also added contextual elements like a visible timeframe (“This Week”) and exact date range (“Jun 9–15”), helping users quickly understand what period they’re analyzing. These changes created a smoother transition into the next widget, “Sales Performance,” supporting a natural top-down flow from high-level revenue to deeper performance insights, mirroring how sales teams interpret data in real workflows.
To improve scannability and relevance, I redesigned the “Visits by Source” widget into a more focused “Traffic Breakdown” component. The original version used generic source labels (e.g., “Direct,” “Social,” “Referrals”) and color-heavy horizontal bars, which made it harder to identify specific platforms and take immediate action.
In the updated version, I aligned the data source names (e.g., Website, Instagram, Facebook) directly with business-relevant traffic channels and added clear numeric values for each. In the final version, I also included brand icons and emphasized the top-performing channel (Website) with visual contrast—making it easy for users to scan and interpret results at a glance.
This widget sits between Customer Feedback and Transactions Analytics, and its placement supports a natural interpretive flow:
Users can first understand how customers feel, then where traffic is coming from, and finally, how those visits convert into sales activity. By connecting sentiment, acquisition, and transactional behavior across adjacent widgets, the dashboard now supports a more intuitive and complete decision-making journey.
FINAL DESIGN
KPI Metrics
Supporting Context
Action Area
KEY RESULTS
We improved interpretation speed by 39% and boosted feature engagement by 22%.
These outcomes reflect the clarity-driven, user-first approach behind the redesign.
39%
faster trend interpretation
28% ↑
correct KPI recognition
22% ↑
export feature usage
KEY TAKEAWAY
1. Start with user behavior, not just layout
Structuring the dashboard around how users interpret data—top-down, from overview to detail—led to more intuitive decision flows.
2. Small wording changes can create big impact
Clearer metric titles like “Revenue Breakdown” improved comprehension and trust across the board.
3. User feedback doesn’t have to be complex to be valuable
A lightweight A/B test helped uncover how visual and copy tweaks significantly affect speed and confidence.