Overview
Running campaigns across multiple platforms produces data across multiple platforms. Google Ads performance lives in Google Ads. Meta campaign results live in Meta Business Suite. LinkedIn data lives in LinkedIn Campaign Manager. Email performance lives in the ESP. SEO data lives in Search Console and whatever rank tracking tool is in use. Each platform presents its own metrics, in its own format, against its own date ranges, with its own attribution logic. Getting a complete picture of campaign performance requires logging into each platform separately, exporting or noting the numbers, and assembling them manually — a process that is time-consuming, error-prone, and produces a snapshot that is already out of date by the time it is complete.
A campaign dashboard consolidates this data into a single operational view — pulling metrics from every connected platform automatically, normalising them into a consistent format, and presenting the performance picture that campaign managers and marketing leadership need to make informed decisions. Not a snapshot assembled manually once a week, but a live view that is current whenever it is opened.
We build custom campaign dashboards for marketing agencies, in-house performance marketing teams, and any organisation running campaigns across multiple platforms that needs operational visibility into campaign performance without the manual data assembly that individual platform views require.
What Campaign Dashboards Surface
Cross-platform spend and performance. The fundamental view that campaign management requires — total spend, impressions, clicks, conversions, cost per conversion, and return on ad spend — aggregated across every platform the campaign portfolio spans. Seeing Google Ads and Meta side by side, with LinkedIn and email in the same view, enables the performance comparison and budget allocation decisions that per-platform views cannot support.
Spend pacing against budget — how much of the allocated budget has been spent, how much remains, and whether the current spend rate will exhaust the budget before the campaign period ends or leave budget unspent — gives campaign managers the visibility to adjust bids, budgets, and targeting before pace problems become budget problems.
Channel and campaign breakdown. The aggregate performance broken down by channel, by campaign, by ad group, by ad — the hierarchical drill-down that identifies where performance is strong and where it is weak. A campaign with a good aggregate ROAS may contain individual ad groups that are significantly under- or over-performing; the drill-down view surfaces these differences without requiring manual navigation through each platform's interface.
Conversion and funnel metrics. Campaign performance is ultimately measured by the outcomes it produces — leads generated, sales completed, revenue attributed, customer acquisitions. Conversion metrics pulled from the platforms that measure them — Google Ads conversions, Meta pixel events, CRM lead records, e-commerce transaction data — give campaign managers the outcome data alongside the top-of-funnel metrics.
Attribution model visibility — the different conversion counts that first-click, last-click, linear, and data-driven attribution models produce for the same underlying conversions — helps campaign managers understand the attribution picture rather than relying on the default model that each platform applies independently.
Audience and targeting performance. Performance by audience segment, by demographic, by geographic location, by device type, and by placement — the targeting breakdown that identifies where the campaign is reaching its intended audience effectively and where it is not. Audience overlap analysis between campaigns that are targeting similar audiences on the same platform surfaces the duplication that can inflate spend without proportionally increasing reach.
Creative performance. Ad-level performance for creative testing — the headline variants, the image variants, the video variants — showing which creative is driving performance and which is underperforming. Creative fatigue indicators — the frequency metrics and engagement rate trends that signal when an audience has seen the same creative too many times — trigger creative refresh decisions before performance degrades significantly.
SEO and organic performance. For campaigns where paid and organic are both contributing to performance, organic metrics from Google Search Console — impressions, clicks, average position, click-through rate — alongside paid metrics gives the full search performance picture. Keyword-level visibility across paid and organic shows where the campaign is competing with its own organic rankings and where paid is complementing organic coverage.
Email campaign metrics. Send volume, delivery rate, open rate, click rate, conversion rate, and unsubscribe rate from the ESP integration — giving email performance visibility in the same dashboard as paid media performance rather than requiring a separate platform login for email metrics.
Anomaly detection and alerting. Campaign metrics that deviate significantly from recent performance — a sudden drop in impressions that may indicate a policy issue, a conversion rate spike that warrants investigation, a spend rate that is tracking off-pace — are surfaced through automated anomaly detection rather than requiring manual monitoring. Alerts notify campaign managers when metrics require attention, reducing the risk of performance problems going undetected between manual checks.
Data Source Integrations
Campaign dashboards are only as comprehensive as the platforms they connect to. We integrate with the full range of platforms that marketing campaigns run across:
Paid search and shopping. Google Ads — campaign, ad group, keyword, and ad performance via the Google Ads API. Microsoft Advertising (Bing Ads) — search and shopping performance via the Microsoft Advertising API. Google Merchant Center — shopping feed health and performance data.
Paid social. Meta Ads (Facebook and Instagram) — campaign, ad set, and ad performance via the Meta Marketing API. LinkedIn Campaign Manager — B2B campaign performance via the LinkedIn Marketing API. TikTok Ads — performance via the TikTok for Business API. Twitter/X Ads — campaign performance via the Twitter Ads API.
Organic and SEO. Google Search Console — organic search impressions, clicks, and position data via the Search Console API. Google Analytics 4 — session, user, and conversion data via the GA4 Data API. Website analytics platforms where the organisation uses alternatives to GA4.
Email marketing. Mailchimp, ActiveCampaign, Klaviyo, HubSpot Email — campaign performance metrics via each platform's API. Deliverability and engagement metrics at campaign and list level.
CRM and conversion data. Salesforce, HubSpot CRM — lead and opportunity data for connecting ad spend to pipeline and revenue outcomes. Custom conversion tracking for e-commerce platforms — Shopify, WooCommerce — order and revenue data that enables ROAS calculation from actual transaction data rather than platform-reported conversions.
Call tracking. Invoca, CallRail, and other call tracking platforms — call conversion data for campaigns where telephone calls are a significant conversion path.
Dashboard Architecture
Automated data retrieval. Data is pulled from each connected platform on a configurable schedule — hourly for platforms with high-frequency data and intraday decision-making requirements, daily for platforms where data is only updated once per day or where historical accuracy is more important than real-time currency. API rate limits, authentication token management, and the retry logic that handles temporary API unavailability are managed by the data retrieval layer rather than requiring manual intervention when a platform API has issues.
Data normalisation. Each platform uses different metric names, different attribution windows, different conversion definitions, and different date granularities. The normalisation layer maps platform-specific metrics to a consistent metric taxonomy — what Google calls "conversions" and what Meta calls "results" are different things, and the dashboard needs to represent them in a way that is accurate without being confusing. Currency conversion for multi-currency account portfolios ensures that spend and revenue metrics are comparable across accounts regardless of the currency each account operates in.
Metric calculation layer. Derived metrics that the dashboard needs but that no individual platform provides directly — blended ROAS across all platforms, cost per acquisition combining all spend against all conversions, share of wallet across channels — are calculated from the normalised platform data in the metric calculation layer. Custom metric definitions for metrics that are specific to the organisation's performance framework are configured once and applied consistently across all dashboard views.
Historical data storage. Platform APIs often provide limited historical data access — Google Ads provides campaign data for a defined historical window, Meta provides data within a defined lookback period. The dashboard's historical data store retains platform data as it is retrieved, building the historical record that trend analysis and period-over-period comparison requires beyond the API's native window.
Real-time and near-real-time updates. For campaign managers making intraday optimisation decisions — bid adjustments, budget shifts, creative swaps — dashboard data currency matters. Platforms that support real-time or near-real-time API access are queried at the frequency that intraday decision-making requires. The dashboard displays the data currency for each metric so that campaign managers understand when each data point was last updated.
Customisation and Configuration
Flexible metric selection. The metrics displayed are configured to the organisation's performance framework — the metrics that matter for the specific campaign types, channels, and business objectives in use. An e-commerce campaign dashboard emphasises ROAS, revenue, and transaction metrics. A lead generation dashboard emphasises CPL, lead volume, and conversion rate. A brand awareness dashboard emphasises reach, frequency, and brand search lift. The same underlying data infrastructure supports different metric configurations for different campaign types and client contexts.
Custom date ranges and comparison periods. Campaign performance is measured against the periods that are relevant — the campaign flight period, the financial month, the week-over-week comparison, the year-over-year comparison. Date range flexibility allows the campaign manager to view performance for any period and compare it against any reference period without being constrained by the fixed date ranges that platform native reporting provides.
Audience and segment filtering. Filtering the dashboard view by campaign tag, by audience type, by geographic region, by product category, or by any other dimension that the campaign portfolio is structured around allows campaign managers to view performance for a specific subset of the portfolio without the noise of the full dataset. Saved filter combinations that correspond to frequently-used views — performance for a specific client, performance for a specific product line — reduce the configuration overhead of accessing commonly-needed views.
Multi-client and multi-brand views. For agencies managing multiple clients, and for businesses managing multiple brands, the dashboard architecture supports both the individual client or brand view and the aggregate view across the full portfolio. Agency-level metrics — total managed spend, total conversions across all clients, share of budget by channel across the portfolio — give agency leadership the aggregate visibility that individual client views do not.
Technologies Used
- React / Next.js — campaign dashboard frontend, chart and visualisation components, filter and date range controls, alert management
- TypeScript — type-safe frontend and API code throughout
- Rust / Axum — high-performance data ingestion from multiple platform APIs, metric calculation engine, anomaly detection
- C# / ASP.NET Core — complex platform API integrations, data normalisation logic, historical data management
- SQL (PostgreSQL, MySQL) — normalised campaign data storage, historical metrics, alert configuration, dashboard configuration
- Redis — API response caching, real-time metric state, alert processing queue
- Google Ads API — paid search and shopping data
- Meta Marketing API — Facebook and Instagram campaign data
- LinkedIn Marketing API — B2B paid social data
- Google Analytics 4 Data API — web analytics and conversion data
- Google Search Console API — organic search performance data
- Mailchimp / Klaviyo / ActiveCampaign APIs — email campaign performance data
- Salesforce / HubSpot APIs — CRM lead and revenue data
- Shopify / WooCommerce APIs — e-commerce transaction data for ROAS calculation
- SMTP / Slack / push notifications — anomaly alerts and performance notifications
The Manual Assembly Alternative
The alternative to a campaign dashboard is manual data assembly — logging into each platform, noting or exporting the metrics, and constructing the performance view in a spreadsheet or presentation. This process has a cost that is paid in time every time it is done. For an agency managing multiple clients with multiple active campaigns each, the manual assembly time accumulates into a significant operational overhead that limits how frequently meaningful performance analysis can be done and how quickly the team can respond to performance changes.
Custom campaign dashboards eliminate this overhead — not by approximating the manual process in software, but by replacing it entirely with automated data retrieval that produces the current performance view whenever it is needed rather than whenever someone has time to assemble it.
One Dashboard, All Your Campaigns
Campaign performance visibility should not depend on how many platforms you are running campaigns on or how much time is available to assemble data manually. A dashboard that consolidates every platform automatically gives the campaign team the operational view they need to manage performance, not the operational overhead of assembling the view before they can see it.