The problem01
Influencer campaign reporting is usually a spreadsheet assembled by hand. Someone opens each creator's analytics, copies reach and engagement into cells, and by the time the report is ready the campaign has already moved on.
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— Case study 02
Bee Dash
Influencer marketing analytics without manual reporting
project overview
A dashboard for marketing teams that pulls numbers from social media accounts automatically and shows whether a campaign is actually working.
shipped outcome
Campaign performance becomes visible in near real time, with creators compared on equivalent metrics instead of platform-specific vanity numbers.
- Role
- Full-stack product engineering
- Timeline
- Ongoing
- Year
- 2024
- Team
- Small delivery team
The approach02
Bee Dash ingests metrics through social platform APIs, maps incompatible fields into one canonical schema, and renders comparable decision-focused views. The API, client dashboard, and internal admin live in one monorepo with shared types so metric definitions cannot drift silently between applications.
The result03
Campaign performance becomes visible in near real time, with creators compared on equivalent metrics instead of platform-specific vanity numbers.
— Interface evidence
The product, not a placeholder
These are the verified interface captures from the project build. Scroll inside taller frames to inspect each complete page.
— Delivery process
From operating constraint to shipped system
The work is sequenced around risk. Domain rules and failure states come before interface polish; automation arrives before handoff.
Define canonical metrics
The team agreed what reach, engagement, and campaign performance mean across platforms before writing platform-specific adapters.
Build ingestion adapters
Each social source handles its own authentication, pagination, and rate limits while producing the same normalized output.
Design around decisions
Every chart answers a practical question: campaign pacing, creator comparison, renewal value, or best-performing content format.
Share contracts, not copies
API, dashboard, and admin consume shared domain types and UI foundations from one workspace.
— System architecture
Clear boundaries, explicit responsibilities
Each layer has one job and a narrow contract. That keeps external services replaceable and product behavior testable.
Next.jsNestJSTypeScriptPostgreSQLPrismashadcn/uipnpm workspacesSocial APIs
bee_dash.sys● online
reach
2.8m
engagement
8.4%
active
24
Client
Next.js dashboard
Campaign views, creator comparison, filters, and interactive data visualization.
Internal
Operations admin
Account, campaign, access, and source management kept separate from the client product.
API
NestJS service
Modular ingestion, aggregation, scheduling, and access control.
Data
PostgreSQL + Prisma
Normalized time-series metrics with typed queries and versioned migrations.
— What shipped
Product capabilities
Multi-platform ingestion
Scheduled adapters pull social data and map it into one stable reporting model.
Equivalent creator comparison
Creators can be compared on normalized metrics instead of unrelated platform-specific fields.
Interactive campaign views
Teams filter and drill into live reporting rather than receiving another static export.
— Engineering pressure
Challenges resolved
Aggressive rate limits
Ingestion is batched, scheduled, and backed off deliberately rather than retrying blindly.
Platform schema drift
Adapters isolate API churn so a platform change does not spread into every chart and query.
Growing historical volume
The schema supports trend queries without turning every dashboard request into a full-history scan.
verified project destinations
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