How to Create a Recruitment Dashboard in Looker with AI

A great recruitment dashboard doesn't just show you metrics, it tells you a story about your entire hiring process, from the first application to the final offer. This article will show you how to build a powerful recruitment dashboard in Looker, highlighting the key metrics you need to track and explaining how AI can dramatically simplify the entire process.

Why a Recruitment Dashboard is a Game-Changer

In a competitive job market, hiring decisions based on gut feelings and tangled spreadsheets can lead to slow hiring cycles, high costs, and missed opportunities. A well-designed recruitment dashboard centralizes your hiring data, moving you from guesswork to a data-driven strategy. It transforms raw numbers from your Applicant Tracking System (ATS) into clear, actionable insights.

With a centralized dashboard, you can:

• Make Faster, Smarter Decisions: Instantly see which channels are sourcing the best candidates and where your top applicants are coming from.

• Identify Bottlenecks Instantly: Is one hiring manager taking twice as long to review applications? Is a specific role stuck in the interview stage for weeks? A dashboard makes these problems impossible to ignore.

• Optimize Your Hiring Funnel: See exactly where candidates are dropping off. This helps you improve everything from your job descriptions to your interview process, creating a better candidate experience.

• Communicate Performance Clearly: Easily share progress with stakeholders and leadership, demonstrating the value and efficiency of your talent acquisition team.

The Most Important Recruitment Metrics to Track

Before you build anything, you need to know what to measure. A great dashboard organizes key metrics logically, giving you a full 360-degree view of your hiring health. Below are the essential metrics, grouped into categories that tell a complete story.

Pipeline Health Metrics

These metrics give you a high-level overview of the speed and efficiency of your hiring funnel.

• Time to Fill: The total number of days from when a job requisition is opened to when a candidate accepts the offer. Why it matters: A long Time to Fill can lead to lost productivity and signal inefficiencies in your process.

• Time to Hire: The total number of days from when a candidate applies to when they accept the offer. Why it matters: This metric reflects the candidate's journey. A lengthy Time to Hire can frustrate top talent, who may accept another offer while you're still scheduling interviews.

• Active Candidates by Stage: A snapshot of how many candidates are currently in each stage of your hiring pipeline (e.g., Applied, Phone Screen, Interview, Offer). Why it matters: It helps you visualize your pipeline's health and spot potential roadblocks where candidates get stuck.

• Offer Acceptance Rate: The percentage of candidates who accept a formal job offer. Why it matters: A low rate can indicate that your offers aren't competitive or that something in your final interview stages is turning candidates away.

Source Effectiveness Metrics

This category helps you understand where your best candidates come from so you can double down on what works and cut what doesn't.

• Source of Hire: The percentage of hires that come from each channel (e.g., LinkedIn, employee referrals, job boards, career site). Why it matters: This tells you where to invest your recruitment budget and effort for the best return.

• Cost Per Hire: The total recruiting cost divided by the number of hires. This includes recruiter salaries, agency fees, job board subscriptions, and advertising spend. Why it matters: It directly measures the financial efficiency of your recruitment efforts.

• Applicants Per Hire by Source: The number of applicants it takes from a specific channel to result in one successful hire. Why it matters: Some sources may deliver a high volume of applicants but a low volume of qualified hires. This metric reveals the quality of each source, not just the quantity.

Team Performance & Workload Metrics

Use these metrics to balance workloads and improve your team’s internal processes.

• Openings per Recruiter: The number of open job requisitions assigned to each recruiter. Why it matters: This helps ensure workloads are distributed fairly and helps you identify when a recruiter might be overloaded, leading to burnout and a drop in performance.

• Funnel Conversion Rates by Recruiter: Compare the percentage of candidates who move from one stage to the next for each team member. Why it matters: It can highlight top performers who excel at screening or closing and identify recruiters who might need extra training in specific areas.

Setting Up Your Data for Looker

Before you can build dynamic charts, your data needs to be accessible and organized. Looker is a powerful tool, but it works best when it can connect to a single, structured data source.

Your recruitment data likely lives in an Applicant Tracking System (ATS) like Greenhouse, Lever, or Workday. To use it in Looker, the ideal approach is to first bring that data into a central data warehouse, like Google BigQuery, Snowflake, or Amazon Redshift. Think of the warehouse as a clean, organized central hub for all your hiring information.

You can use an ELT (Extract, Load, Transform) tool to automatically pull data from your ATS and load it into your warehouse. This keeps your data fresh and ensures that your Looker dashboard always reflects real-time activity.

Building Your Recruitment Dashboard in Looker: The Manual Way

With your data in place, you can start building in Looker. The traditional process involves a series of technical steps that give you precise control over your dashboard's final output.

Step 1: Connect Looker to Your Data Source

The first step is establishing a connection between Looker and your data warehouse where your ATS data is stored. This is done in the Admin section of Looker and requires database credentials.

Step 2: Define Your Data Model with LookML

This is where Looker's true power - and complexity - lies. Looker uses a language called LookML (Looker Modeling Language) to define your data. In simple terms, you create a model that tells Looker how your database tables relate to each other and how to calculate your metrics.

You’ll define Dimensions (the things you want to group by, like Job Department or Source of Hire) and Measures (the things you want to calculate, like Average Time to Hire or Total Candidates). This requires someone with technical skills to write and maintain the LookML code, which serves as the "source of truth" for all reporting.

Step 3: Create Individual Visualizations (Looks)

Once your LookML model is set up, you can start building individual charts and reports, which Looker calls "Looks." You navigate to the "Explore" interface, select the dimensions and measures you defined in your model, apply any filters, and choose a visualization type (e.g., bar chart, line graph, number tile). For instance, to create a look for Time to Fill, you might select the "Job Title" dimension and the "Average Time to Fill" measure.

Step 4: Assemble Your Looks into a Dashboard

With several Looks saved, you can add them to a new dashboard. You can drag, drop, and resize these Looks on a grid-based canvas. You should arrange them logically, perhaps with high-level KPI tiles at the top, a pipeline overview in the middle, and more granular charts for source effectiveness at the bottom.

Step 5: Add Interactive Filters

To make the dashboard truly useful, add filters. For example, add a date range filter, a department filter, and a recruiter filter directly to the dashboard. This allows any user to slice and dice the data to answer their specific questions without having to build a new report from scratch.

The AI Shortcut: Building Your Dashboard with Natural Language

The manual process above is powerful but requires significant technical expertise and time. The learning curve for LookML is steep, and most HR and recruiting professionals don't have the bandwidth to become data modelers. This is where AI changes the game entirely.

Instead of manually writing LookML and clicking through menus to build each chart, AI-powered analytics tools allow you to describe what you want to see in plain English. Looker has been integrating generative AI features to help simplify report creation, and dedicated AI platforms build on this concept to eliminate the technical barriers completely.

Imagine being able to build your dashboard with simple prompts like:

• "Show me our average time to fill by department for the last 6 months as a bar chart."

• "Create a pie chart showing the percentage of hires from each source this year."

• "What is my current offer acceptance rate for engineering roles versus sales roles?"

• "Build a table with a list of all open positions, the recruiter assigned, and how many days each has been open."

The AI interprets your request, automatically queries the data, and generates the correct visualization in seconds. This turns what was once a multi-hour technical process into a 30-second task. It unlocks the ability for anyone on your team - from the Head of Talent to individual recruiters - to ask questions directly of the data and get immediate answers. This is an incredible way to move more quickly, as your curiosity is no longer blocked by a technical person's to-do list.

Final Thoughts

Building a recruitment dashboard in Looker gives you a powerful command center for your entire hiring operation, enabling you to optimize your funnel and make data-backed decisions. By focusing on key metrics around pipeline health and source effectiveness, you can build a tool that drives real business impact.

While the traditional setup in Looker is robust, it often requires significant technical resources. If the idea of writing LookML and manually configuring each chart and report sounds like a major hurdle, we get it. That's why we built Graphed to be the simplest path to getting these same powerful insights. You can connect your ATS and other data sources in a few clicks, then just describe the charts and dashboards you need using plain English. All your key metrics update in real-time without the steep learning curve, empowering your entire team to hire smarter, not harder.