I've been evaluating Runway for the past quarter as a central dashboard for our engineering and marketing KPIs, and while the built-in views are excellent for infrastructure metrics, I found a notable gap in our ability to track marketing-driven data like SEO backlink profiles alongside technical performance. To address this, I built a custom view that correlates new backlink acquisition (from a data pipeline I'll describe) with organic traffic trends from Google Search Console.
The core of the setup is a data pipeline that ingests backlink data from Ahrefs' API into our data warehouse (BigQuery), which is then exposed via a lightweight GraphQL API. Runway's custom view feature, using React and the provided SDK, allows us to visualize this data in the context of our other operational dashboards. The primary components are:
* A time-series chart plotting new referring domains per day.
* A correlated line showing organic click-through rate for our target keyword cluster.
* A table listing the top new backlinks by domain authority, with metadata like follow/nofollow status and target URL.
Here is a simplified version of the main panel component that fetches and plots the correlated data:
```jsx
import { useQuery } from '@tanstack/react-query';
import { TimeSeriesChart } from '@runway/react-components';
const BacklinkTracker = ({ timeframe }) => {
const { data, isLoading } = useQuery({
queryKey: ['seo-backlinks', timeframe],
queryFn: () =>
fetch(`/api/seo-dashboard?range=${timeframe}`).then(res => res.json()),
});
const chartSeries = [
{
label: 'New Referring Domains',
data: data?.backlinks?.map(d => ({ timestamp: d.date, value: d.count })) || [],
},
{
label: 'Organic CTR (%)',
data: data?.ctr?.map(d => ({ timestamp: d.date, value: d.ctr })) || [],
yAxisId: 'secondary',
},
];
return (
);
};
```
The benefits of integrating this into Runway, rather than maintaining a separate Grafana or Looker Studio dashboard, are significant from an operational workflow perspective. Our on-call engineers can now immediately discern if a traffic fluctuation is potentially tied to a recent positive or negative SEO event, without context switching. Furthermore, we've added alerting rules within Runway that trigger if the backlink growth rate deviates significantly from the 30-day moving average, which could indicate either a successful campaign or a potential spam attack.
From a cost and performance perspective, this approach is efficient. The data is aggregated and cached at the API level, so the custom view places minimal additional load on Runway's rendering engine. The total latency from data warehouse to rendered view is under 2 seconds for a 90-day period. This integration has proven particularly useful for our sprint reviews, providing a unified view of engineering and growth marketing outcomes in a single pane of glass. I'm interested to hear if others have built similar marketing-tech correlation views or have optimized data pipelines for this type of hybrid dashboard.
Data over dogma