A data-driven advertising dashboard that makes complex campaign data instantly actionable.
Overview
AiQEM Tech is an Ethiopian AI and blockchain company providing advertising analytics services to businesses. As their in-house UX Designer, I designed an end-to-end advertising analytics dashboard that gave marketing teams and their clients a single place to track, analyse, and act on campaign performance data.
The product surfaced five core data types — impressions, click-through rates, campaign spend, audience segments, and conversion funnels — across a modular dashboard interface. The challenge: presenting this breadth of data without overwhelming users who needed to make fast, confident decisions.
I was the sole UX Designer on the project, working across the full design process from initial research through to the high-fidelity Figma handoff delivered to AiQEM's development team.
5
Core data modules
2
User types served
1
Design system built
↓
Complexity, not data
The Problem
AiQEM's campaign managers spent significant time each week manually assembling data from separate tools to create client reports. The dashboard needed to eliminate that entirely — while serving two fundamentally different users at the same time.
AiQEM's internal team needed to move fast: scanning across campaigns, spotting anomalies, and adjusting targeting in real time. External clients needed confidence that their budget was being spent well — without needing to understand the underlying data complexity.
“The hardest design problem wasn't finding the right chart type. It was deciding what not to show — and when.”
Understanding the Users
The dashboard served two distinct groups. Designing for both simultaneously without fragmenting the experience was one of the core UX challenges.
Campaign managers
Needed to move fast — scanning across campaigns, spotting anomalies, and adjusting targeting or spend in real time.
View-only access
Not deep analytics users — they needed confidence that their budget was being spent well and their ads were reaching the right people.
Design Process
Interviewed AiQEM's campaign managers to understand their daily workflow — how they moved between tools, what decisions they needed to make quickly, and where the biggest frustrations were. Key finding: they spent significant time each week manually assembling data from separate sources. The dashboard needed to eliminate that entirely.
Audited Google Ads, Meta Ads Manager, and HubSpot's analytics dashboards — mapping how each handled data density, filtering, and dual-user scenarios. Identified that the best-in-class tools used progressive disclosure and persistent global filters — two patterns I carried directly into the design.
Defined the module structure and navigation model before touching any UI. Key decision: a left-rail nav with five fixed modules, each containing its own filters and sub-views. A persistent global header with date range and campaign selectors applies context across all modules simultaneously.
Lo-fi wireframes tested with AiQEM's internal team across 3 rounds. Most significant feedback: the initial design surfaced too many chart types simultaneously. Added a view-toggle pattern (table vs. chart vs. summary card) to all modules as a result.
Built the full high-fidelity dashboard in Figma. Designed a complete component library covering charts, filter components, data tables, KPI cards, and modal patterns, with full developer handoff annotations.
Design Decisions
Data hierarchy
Every module opens with a summary card showing the single most important number — total impressions, overall CTR, total spend. Detail is one click away, not immediately visible. Users could scan the entire dashboard in under 10 seconds to get a health check, then drill down where needed.
Filtering system
A global campaign selector and date range picker persists in the top navigation — any filter applied there applies to all modules simultaneously. This solved the navigation confusion problem: users always know that what they're looking at is consistent across views.
Chart language
Defined a consistent chart grammar: time-series data always uses area charts, breakdowns always use horizontal bar charts, funnels always use the same step-down visual. Users only had to learn the visual language once — after that, pattern recognition made navigating between modules fast and intuitive.
Dual-user design
Rather than building separate interfaces for the internal team and clients, the same dashboard adapts based on permission level. Internal users see all campaigns; clients see only theirs. The underlying UI is identical — reducing design and development complexity.
Outcome
The dashboard was delivered as a complete Figma handoff covering all five data modules with a fully documented design system, component library, and annotated specifications.
AiQEM needed to ship the product before a competitor entered the market. This meant making fast, well-reasoned design decisions rather than over-deliberating. The discipline of progressive disclosure and persistent filtering solved the data-density problem cleanly — and was also the most buildable solution.
The dashboard consolidated what previously required multiple separate tools into a single, coherent experience — giving AiQEM's team a platform they could confidently demo to clients as a differentiator.