Ashby Analytics: Complete Guide to Recruiting Data in 2026

Recruiting teams generate massive amounts of data — candidate pipelines, offer acceptance rates, time-to-hire, source effectiveness. But for marketing analysts, that data often stays trapped inside the ATS, disconnected from the campaigns that drive awareness and the attribution models that measure ROI.

Ashby Analytics is built to solve this. It's a recruiting analytics platform embedded directly into Ashby's all-in-one recruiting software, designed to turn candidate data into strategic insights. Unlike traditional ATS platforms where analytics is an afterthought, Ashby treats Analytics as a first-class product area — meaning dedicated product teams, continuous development, and a unified data model that connects ATS, scheduling, CRM, and analytics layers.

For marketing analysts, this matters because recruitment marketing is increasingly measured like any other funnel. You need to track how campaigns translate into applications, how paid channels compare to organic referrals, and where budget should shift to improve hiring outcomes. This guide shows you how to use Ashby Analytics to answer those questions — and how to connect recruiting data to your broader marketing stack when Ashby's native capabilities aren't enough.

Key Takeaways

✓ Ashby Analytics is embedded directly into Ashby's recruiting platform, with a unified data model that connects ATS, scheduling, CRM, and reporting layers

✓ Analytics is treated as a first-class product area at Ashby, with dedicated teams and continuous feature development rather than an add-on module

✓ The platform is designed for high-volume, high-complexity recruiting environments — typically companies from 100 to 1,000+ employees

✓ AI features in Ashby act as a conversational layer over recruiting data, allowing you to ask natural-language questions and generate insights without SQL

✓ Marketing analysts often need to connect Ashby data to broader attribution models, which requires an integration layer that normalizes ATS data alongside campaign metrics

✓ Improvado provides 1,000+ pre-built connectors including Ashby, unifying recruiting data with paid media, CRM, and web analytics in a single governed warehouse

What Is Ashby Analytics

Ashby Analytics is the reporting and data layer built into Ashby's recruiting platform. Unlike most ATS platforms where analytics is a separate module or third-party add-on, everything in Ashby shares one data layer. That means candidate records, interview schedules, sourcing activity, and CRM interactions all flow into the same unified model.

This matters because it eliminates the data silos that plague most recruiting stacks. You don't need to export CSV files, join tables manually, or wait for nightly syncs. When a candidate moves stages, that change is immediately available in your reports. When a recruiter updates a source tag, your attribution metrics reflect it in real time.

The platform is designed for high-volume, high-complexity environments, typically targeting companies from around 100 to 1,000+ employees. It's not built for small teams running a handful of roles per quarter — it's built for organizations where recruiting is a core operational function, where hiring velocity directly impacts revenue, and where data quality determines whether you hit headcount targets.

Why Ashby Analytics Matters for Marketing Analysts

Marketing analysts typically own attribution models, campaign ROI, and channel effectiveness. But most attribution stops at the lead or MQL stage. Recruiting is one of the few areas where marketing spend directly funds a measurable business outcome — a hired employee — yet that data rarely makes it into the marketing stack.

Ashby Analytics gives you visibility into the full candidate journey. You can track which job boards drive the most qualified applicants, which content campaigns lead to referrals, and which paid channels produce candidates who actually accept offers. That's the data marketing teams need to optimize employer brand spend, allocate recruitment marketing budgets, and prove ROI to finance.

The challenge is integration. Ashby data lives in Ashby. Your paid media data lives in Google Ads and Meta. Your web analytics live in GA4. Your CRM data lives in Salesforce or HubSpot. To build a unified attribution model, you need all of that data in one place, normalized to a common schema, and refreshed frequently enough to make decisions.

That's where platforms like Improvado come in. Improvado connects to Ashby alongside 1,000+ other marketing and sales data sources, automatically normalizing field names, handling schema changes, and loading everything into your data warehouse or BI tool. For marketing analysts, that means you can join Ashby candidate data to campaign spend, calculate cost-per-hire by channel, and build recruitment attribution dashboards that update daily.

Step 1: Understand Your Data Model

Before you build reports, you need to understand how Ashby structures data. The platform uses a unified data model where candidates, jobs, applications, interviews, and feedback all connect through shared IDs. This is different from platforms where each module maintains its own schema.

The core entities you'll work with:

• Candidates — individual people in your pipeline, with demographic data, source tags, and activity history

• Applications — a candidate's submission to a specific job, with stage progression and timestamps

• Jobs — open roles, with metadata like department, location, hiring manager, and priority

• Interviews — scheduled events, with participant lists, feedback forms, and outcomes

• Sources — where candidates come from, with hierarchical tagging (e.g. Job Board → LinkedIn → Paid Ad)

• Custom fields — any additional data your team tracks, like referral source details, campaign UTM parameters, or assessment scores

The power of this model is that you can slice candidate data by any combination of these dimensions. Want to see average time-to-hire by department and source? That's a straightforward query. Want to compare offer acceptance rates for candidates from paid ads versus organic referrals? The data structure supports it natively.

For marketing analysts, the most important fields are source tags and custom UTM parameters. Make sure your team has a consistent tagging taxonomy. If one recruiter tags LinkedIn ads as "LinkedIn Paid" and another tags them as "Social - Sponsored," your attribution reports will be fragmented and unreliable.

Mapping Ashby Fields to Marketing Metrics

To connect Ashby data to your marketing attribution model, you need to map recruiting fields to marketing dimensions:

If you're building reports that span both marketing campaigns and recruiting outcomes, this mapping becomes your join key. You'll need to ensure that the source taxonomy in Ashby matches the channel taxonomy in your marketing data warehouse.

Step 2: Build Your First Report

Ashby Analytics provides a report builder interface where you can create custom dashboards without writing SQL. The builder uses a drag-and-drop model: select dimensions (what you want to group by), metrics (what you want to measure), and filters (what you want to include or exclude).

Start with a simple question: Which job boards produce the most hires?

Here's how to structure that report:

• Dimension: Candidate Source (Level 1)

• Metric: Count of Candidates (Status = Hired)

• Filter: Hired Date = Last 90 Days

• Visualization: Bar chart, sorted descending by count

This gives you a ranked list of sources by hiring volume. But volume alone doesn't tell you efficiency. To calculate cost-per-hire by source, you need to join this data with your paid media spend — which requires exporting Ashby data or connecting it to an external BI tool.

For more complex questions, use segmentation. Ashby allows you to create candidate segments based on any combination of fields. For example, you might create a segment called "High-Intent Applicants" defined as candidates who:

• Applied within 48 hours of job posting

• Completed a take-home assessment

• Were referred by a current employee

Once defined, this segment becomes a reusable filter. You can compare high-intent applicants across sources, track their conversion rates, and identify which channels produce the best-qualified candidates.

Using AI to Query Your Data

Ashby includes AI features that act as a conversational layer over recruiting data. Instead of building a report manually, you can ask natural-language questions like:

• "What's the average time-to-hire for engineering roles in Q1 2026?"

• "Which sources have the highest offer acceptance rate?"

• "Show me candidates who moved from phone screen to onsite in under 7 days."

The AI interprets your question, generates the appropriate query, and returns a formatted answer. This is useful for ad-hoc analysis when you need a quick insight but don't want to build a full dashboard.

However, AI-generated queries have limitations. They work best for straightforward questions about data that already exists in Ashby. If you're trying to join recruiting data with external marketing data — for example, calculating ROI by comparing Ashby hires to Google Ads spend — the AI can't access data outside the Ashby platform.

Step 3: Connect Ashby to Your Marketing Stack

Ashby Analytics is powerful within the recruiting workflow, but marketing analysts need to connect it to the rest of their data infrastructure. That means integrating Ashby with your data warehouse, BI tool, or marketing analytics platform.

There are three common approaches:

• CSV Export: Ashby allows you to export report data as CSV. This works for one-off analysis but doesn't scale. You have to manually re-export every time you need fresh data, and there's no way to automate the join with other data sources.

• API Integration: Ashby provides a REST API for programmatic access to candidate and job data. If you have engineering resources, you can build custom scripts to pull data into your warehouse. The downside is ongoing maintenance — you have to handle schema changes, rate limits, and error handling yourself.

• Third-Party ETL Platform: Tools like Improvado, Fivetran, or Stitch provide pre-built connectors for Ashby. They handle authentication, schema mapping, incremental syncs, and error recovery automatically. This is the approach most marketing teams use when they need reliable, automated data pipelines.

Improvado is particularly well-suited for marketing analysts because it specializes in marketing and sales data. The platform connects to 1,000+ data sources — including Ashby, Google Ads, Meta, LinkedIn, Salesforce, HubSpot, and GA4 — and normalizes everything into a unified schema. That means you can join Ashby candidate data with paid media spend, web analytics, and CRM activity in a single query.

Example Integration Workflow

Here's how a typical Ashby → Improvado → BigQuery → Looker integration works:

• Step 1: Connect Ashby to Improvado using OAuth authentication. Grant read access to candidate, application, and job data.

• Step 2: Improvado pulls Ashby data on a scheduled cadence (hourly, daily, or real-time depending on your plan). The connector handles pagination, rate limiting, and incremental updates automatically.

• Step 3: Improvado normalizes Ashby field names to match your existing marketing schema. For example, candidate_source in Ashby becomes utm_source in your warehouse, so you can join it with campaign data from Google Ads.

• Step 4: Data loads into your warehouse (BigQuery, Snowflake, Redshift, or Databricks). All Ashby tables live alongside your marketing tables in the same database.

• Step 5: You build a Looker dashboard that joins Ashby hires with Google Ads spend, calculates cost-per-hire by campaign, and visualizes ROI by channel.

The entire process runs automatically. When a candidate is hired in Ashby, that record appears in your warehouse within hours, and your Looker dashboard updates accordingly.

Step 4: Build Recruitment Attribution Reports

Once Ashby data is flowing into your warehouse, you can build attribution models that connect marketing spend to hiring outcomes. This is the same logic you use for lead attribution, but the final conversion event is a hire instead of an MQL or closed deal.

The core metric is cost-per-hire by source. To calculate it:

Join Ashby hires to campaign spend by matching the source field in Ashby to the campaign name in your ad platform. Group by source, sum total spend, count total hires, and divide:

Cost per Hire = Total Spend / Total Hires

This gives you a simple efficiency metric. But attribution gets more complex when candidates interact with multiple touchpoints. A candidate might see a LinkedIn ad, visit your careers page organically, get referred by an employee, and then apply. Which source gets credit?

Use the same multi-touch attribution models you apply to lead gen:

• First-touch: Credit the first known interaction (e.g. the LinkedIn ad)

• Last-touch: Credit the final interaction before applying (e.g. the employee referral)

• Linear: Split credit equally across all touchpoints

• Time-decay: Give more credit to interactions closer to the application date

Ashby doesn't track multi-touch journeys natively — it only records the source that directly led to the application. To build a full multi-touch model, you need to enrich Ashby data with web analytics (GA4), CRM activity (Salesforce), and email engagement (HubSpot). That's where a unified data platform becomes essential.

Example Dashboard Metrics

Here are the key metrics marketing analysts should track when combining Ashby data with campaign data:

• Applications by Source: Volume of candidates from each channel

• Application Rate: Applications / Impressions (for paid channels)

• Pass-Through Rate: Candidates who reach onsite stage / Total applicants

• Offer Acceptance Rate: Accepted offers / Total offers (by source)

• Cost per Application: Spend / Applications

• Cost per Hire: Spend / Hires

• Time to Hire: Days from application to offer acceptance

• Source ROI: (Average salary × tenure) / Total source spend

The last metric — Source ROI — is advanced, but it's the most strategic. It measures the lifetime value of hires from each channel. A source might have a higher cost-per-hire but produce employees who stay longer and perform better. That's a higher-ROI investment than a cheap source that produces high turnover.

Common Mistakes to Avoid

Marketing analysts new to recruiting data often make predictable errors. Here's what to watch for:

Inconsistent Source Tagging

If your recruiting team doesn't follow a strict tagging taxonomy, your attribution reports will be unreliable. One recruiter might tag a LinkedIn ad as "LinkedIn - Paid," another as "Social Media," and a third as "Sponsored Post." When you aggregate by source, you'll see three separate rows instead of one consolidated metric.

Solution: Define a canonical source taxonomy and enforce it with dropdown menus or validation rules. Map that taxonomy to your marketing UTM structure so data from both systems aligns.

Ignoring Data Latency

Ashby updates in real time, but your data pipeline might not. If you're syncing data once a day, your dashboard will always be 24 hours behind. That's fine for strategic reporting, but it creates confusion when recruiters see different numbers in Ashby than analysts see in Looker.

Solution: Set sync frequency based on how you use the data. For executive dashboards reviewed weekly, daily syncs are sufficient. For operational dashboards that guide daily decisions, use hourly or real-time syncs.

Not Accounting for Time Lag

A candidate who applies today might not get hired for 60–90 days. If you're measuring cost-per-hire by looking at this month's spend and this month's hires, you're comparing unrelated cohorts. You're crediting hires to the wrong campaigns.

Solution: Use cohort-based reporting. Group hires by application date, not hire date. Then join that cohort to the spend data from the same time period. This gives you an accurate picture of which campaigns produced which hires.

Treating All Hires Equally

Not all hires have the same business impact. A senior engineer who stays three years is far more valuable than a junior marketer who leaves in six months. If you optimize purely for cost-per-hire, you might shift budget toward cheaper sources that produce lower-quality candidates.

Solution: Weight hires by role seniority, salary band, or expected tenure. Calculate a "quality-adjusted cost-per-hire" that accounts for the value of each role. This requires enriching Ashby data with HR data (salary, tenure, performance reviews), which means connecting to your HRIS platform alongside your ATS.

Tools That Help with Ashby Analytics

Ashby Analytics is powerful on its own, but most marketing analysts need to connect it to other tools to build comprehensive attribution models. Here's how the ecosystem typically works:

Improvado is the most marketing-analyst-friendly option because it's purpose-built for marketing data. The platform connects to Ashby alongside your entire paid media stack, CRM, web analytics, and email platforms. Everything flows into a unified schema where field names are already normalized — you don't have to manually map candidate_source to utm_source or reconcile date formats across 15 different APIs.

The platform also handles schema changes automatically. When Ashby updates a field name or adds a new data type, Improvado detects the change and updates your pipeline without breaking existing queries. That's critical for analysts who don't want to spend hours debugging broken dashboards every time an API changes.

One limitation: Improvado uses custom pricing based on data volume and connector count, so it's typically adopted by mid-market and enterprise teams rather than startups. If you're a small team with a limited budget, Stitch or custom API scripts may be more cost-effective — but you'll trade cost savings for engineering time.

Advanced Use Cases

Once you've built basic recruiting attribution dashboards, here are more sophisticated analyses you can run by combining Ashby data with your broader marketing stack:

Employer Brand Impact Analysis

Measure how content marketing and brand campaigns affect candidate quality. Join Ashby application data with GA4 session data to track:

• Which blog posts drive the most qualified applicants

• How many sessions a candidate visits before applying

• Which content topics correlate with higher offer acceptance rates

This tells you whether your employer brand content is actually moving the needle on hiring, or whether it's just generating vanity traffic.

Referral Program ROI

Employee referrals are often the highest-quality source, but they're not free. You pay referral bonuses, and you invest in internal marketing to keep the program top-of-mind. Calculate the true ROI by:

• Summing all referral bonus payouts (from HRIS or finance data)

• Adding internal campaign costs (email, Slack, events)

• Dividing by the number of referral hires

Compare that cost-per-hire to paid sources. If referrals cost $5,000 per hire and LinkedIn ads cost $4,000, but referral hires stay twice as long, referrals are still the better investment.

Pipeline Velocity by Source

Some sources produce candidates who move through the funnel faster. Track median days-in-stage by source to identify which channels produce "ready-to-hire" candidates versus those who need more nurturing.

If LinkedIn ads produce candidates who reach offer stage in 30 days, but Indeed applicants take 60 days, that time difference has real cost implications — recruiter hours, opportunity cost, and the risk of losing candidates to competing offers.

Diversity Hiring Metrics

If your company tracks demographic data (with candidate consent), you can analyze which sources produce the most diverse candidate pools. This is sensitive data, so handle it carefully — anonymize where possible, and ensure compliance with employment law.

Join Ashby demographic data with source data to calculate:

• Representation by source (% of applicants from underrepresented groups)

• Pass-through rates by demographic and source (are certain groups advancing at different rates?)

• Offer acceptance rates by demographic (are you losing diverse candidates at the final stage?)

This analysis helps you identify whether your recruiting channels are reaching diverse talent pools, and whether your interview process is equitable across demographics.

How Improvado Simplifies Ashby Analytics

For marketing analysts who need to connect Ashby data to the rest of their stack, Improvado provides a purpose-built solution. Here's what makes it different from general ETL tools:

• Marketing-specific data model: Improvado's schema is designed for marketing use cases. Field names are pre-mapped to common marketing dimensions (utm_source, campaign_id, conversion_date), so you don't have to write custom transformations.

• 1,000+ connectors: Connect Ashby alongside Google Ads, Meta, LinkedIn, TikTok, Salesforce, HubSpot, GA4, and hundreds of other sources. All data flows into a single warehouse.

• Automatic schema normalization: When different platforms use different field names for the same concept (e.g. "source" vs "utm_source" vs "traffic_source"), Improvado reconciles them automatically.

• No-code setup: Analysts can configure connectors, schedule syncs, and map fields through a web UI. No Python scripts, no API keys to manage, no infrastructure to maintain.

• Marketing Data Governance: Built-in validation rules catch common errors — missing UTM parameters, duplicate campaign names, budget overruns — before they corrupt your reports.

• AI Agent: Ask natural-language questions across all connected data sources, including Ashby. For example: "Which paid campaigns produced the most engineering hires in Q1?" The AI generates the query, joins Ashby and Google Ads data, and returns the answer.

One limitation: Improvado is not ideal for small startups with limited budgets. The platform uses custom pricing and typically makes sense for teams spending $50k+ per month on paid media, where recruiting attribution directly impacts budget allocation decisions. If you're a 10-person company hiring one or two people per quarter, a simpler tool (or manual CSV exports) may be sufficient.

Conclusion

Ashby Analytics gives recruiting teams a powerful data layer for tracking candidate pipelines, interview performance, and hiring outcomes. For marketing analysts, the real value comes when you connect that data to your broader attribution models — joining Ashby hires with campaign spend, web analytics, and CRM activity to measure the full ROI of recruitment marketing.

The platform's unified data model and AI-powered query layer make it easy to answer questions within the recruiting workflow. But to build strategic dashboards that span marketing and recruiting, you need an integration layer that normalizes data across platforms and automates the ETL process.

That's where tools like Improvado become essential. By connecting Ashby to your entire marketing stack — Google Ads, Meta, LinkedIn, Salesforce, GA4, and hundreds of other sources — you can finally measure cost-per-hire by campaign, calculate recruitment attribution, and prove the ROI of employer brand investments.

Start with the basics: build a source-effectiveness report in Ashby, export it to a spreadsheet, and manually join it with your paid media spend. Once you've proven the value of that analysis, invest in automation. Connect Ashby to your warehouse, build real-time dashboards, and turn recruiting data into a strategic asset that informs budget allocation, channel strategy, and hiring decisions.

FAQ

What is Ashby Analytics used for?

Ashby Analytics is used to track and analyze recruiting data — candidate pipelines, source effectiveness, time-to-hire, interview performance, and offer acceptance rates. It's embedded directly into Ashby's ATS platform, providing real-time reporting on hiring metrics. Marketing analysts use it to connect recruiting outcomes to campaign spend, calculate cost-per-hire by source, and measure the ROI of recruitment marketing efforts.

How does Ashby analytics differ from traditional ATS reporting?

Unlike traditional ATS platforms where analytics is an add-on module, Ashby treats analytics as a first-class product area with dedicated development teams and a unified data model. Everything shares one data layer, so candidate records, interview schedules, and sourcing activity all connect natively without exports or manual joins. The platform also includes AI-powered conversational queries, allowing you to ask natural-language questions instead of building reports manually.

Can I connect Ashby analytics to my data warehouse?

Yes. Ashby provides a REST API for programmatic data access, and several ETL platforms offer pre-built connectors. Tools like Improvado, Fivetran, and Stitch can sync Ashby data to warehouses like BigQuery, Snowflake, Redshift, or Databricks. This allows you to join recruiting data with marketing campaign data, CRM activity, and web analytics to build comprehensive attribution models.

What metrics should marketing analysts track in Ashby?

Key metrics include: applications by source, application rate (applications per impression for paid channels), pass-through rate (candidates reaching onsite stage divided by total applicants), offer acceptance rate by source, cost per application, cost per hire, time to hire, and source ROI (calculated as average salary times tenure divided by total source spend). These metrics help you measure which channels produce the highest-quality candidates and where to allocate recruitment marketing budget.

How do I calculate cost-per-hire by channel?

To calculate cost-per-hire by channel, join Ashby hire data with your paid media spend data. Match the candidate source field in Ashby to the campaign name in your ad platform (Google Ads, Meta, LinkedIn). Group by source, sum total spend over a time period, count total hires from that source, and divide: Cost per Hire equals Total Spend divided by Total Hires. This metric tells you the efficiency of each channel in producing actual hires, not just applicants.

Does Ashby support multi-touch attribution?

No. Ashby records the direct source that led to an application, but it doesn't natively track multi-touch journeys where candidates interact with multiple channels before applying. To build a full multi-touch attribution model, you need to enrich Ashby data with web analytics from GA4, CRM activity from Salesforce, and email engagement from HubSpot. That requires a unified data platform that connects all these sources and maps candidate IDs across systems.

How often does Ashby data sync to external tools?

Sync frequency depends on the ETL tool you use. Most platforms offer hourly, daily, or real-time sync options. For strategic reporting reviewed weekly, daily syncs are sufficient. For operational dashboards that guide daily recruiting decisions, use hourly or real-time syncs. Keep in mind that more frequent syncs consume more API calls and may increase costs depending on your ETL platform's pricing model.