Plum connector

Use your Plum data for reporting, automation and AI.

Data Panda pulls your Plum match scores, role criteria, candidate profiles and assessment events into the same warehouse as your ATS, HRIS and revenue data. From one place we turn it into dashboards, automations, AI workflows and custom apps that hiring managers, talent leaders and finance use during the funnel, not only the week the requisition closes.

About Plum

The talent assessment platform that measures the soft skills the CV does not.

Plum was founded in 2012 in Canada by Caitlin and Neil MacGregor. The platform is built on industrial-organisational psychology and turns the assessment a candidate completes into a Plum Profile and a per-role Match Score, on top of Match Criteria a hiring team defines for each job. Plum is delivered in 20 languages across 176 countries and has been recognised in the HR Tech Awards (Best Talent Intelligence Solution, 2023).

The system is built to plug into the ATS and HCM the recruiter already lives in: native integrations exist for Greenhouse, Ashby, iCIMS, SAP SuccessFactors and Paylocity, with an open API for ATS and HCM platforms outside that list. The candidate record carries a Match Score per role, a Plum Status, a Match Criteria summary on the job and a profile with drivers and drainers, top talents and growth signals. Pulled into a warehouse next to the ATS pipeline, the HRIS worker record and the post-hire performance data, the Plum record finally answers the questions a Plum dashboard alone does not: which Match Score bands predicted on-target performance after twelve months, which roles attracted the candidate profile the offered package then lost to a competitor, and where the Match Criteria the hiring team set match the profile of the people who stayed.

What your Plum data is for

What you get once Plum is connected.

Hiring and assessment reporting

Match Score distribution, Plum funnel conversion and post-hire outcome on one page across every role and country.

Match Score distribution per role, BU and country, against the score band the policy expects
Plum funnel conversion (invited, in progress, complete) per role and recruiter, with the steps that stall named
Post-hire outcome (ramp time, twelve-month retention, performance review band) joined back to the Match Score the candidate carried

Process automation

Turn Plum invite, completion and Match Score events into the downstream work the rest of the stack expects, without a per-tool handoff.

Push a Slack or Teams ping to the hiring manager when a candidate completes a Plum assessment above the role's score band
Open a follow-up task in the recruiter tool when an invited candidate sits in Plum In-Progress past the SLA
Sync Plum Match Score and Plum Status onto the ATS application record the day the assessment closes

AI workflows

Put assessment, role criteria and post-hire outcomes behind AI that reads the full hiring picture.

Ramp-time prediction on Plum profile, role criteria and the first-90-days performance signal together
First-year retention scoring per Match Score band, joined to manager and team
Natural-language Q&A across Plum, the ATS pipeline and the post-hire HRIS record in one warehouse view

Custom apps on your data

Lightweight tools on Plum data for hiring managers and BU heads who should not need a Plum seat to read their own pipeline.

Hiring-manager workbench with the Plum scores, ATS stage and reference-check status per candidate on the open req
Talent-leader cockpit with Match Score distribution, time-to-complete and post-hire outcome per role family
Role-spec tracker that flags the jobs whose Match Criteria no longer match the profile of the people who stayed

Use cases

Use cases we deliver with Plum data.

A list of concrete reports, automations and AI features we have built on Plum data. Pick the one that matches your situation.

Match-versus-performanceMatch Score per hire plotted against the first-year performance review band the same person ended up in.

Match-versus-retentionMatch Score per hire joined to twelve-month retention, by role family and BU.

Plum funnel conversionInvited, in-progress and complete rates per role and recruiter, with the steps that stall named.

Time-to-complete trackingMedian time from Plum invite to completion per role and country, against the SLA the team set.

Match Criteria coverageOpen jobs without Match Criteria set, named per recruiter and BU before the requisition opens.

Score-band hit rate per roleShare of completed assessments that landed inside the role's target Match Score band, per role and quarter.

Hiring-manager Plum readoutPlum profile, score and reference status per candidate on the open req, on one page the hiring manager can scan.

Ramp-time predictionPlum profile and role criteria joined to first-90-days performance for a per-hire ramp-time estimate.

Role-spec driftRoles whose Match Criteria no longer match the profile of the people who stayed in the same role last year.

Recruiter assessment cadencePlum invites sent, completed and chased per recruiter, against the cadence the talent team agreed.

Adverse-impact monitoringScore-band pass rates per protected group where the policy and local law allow the cut, with the four-fifths rule charted.

Real business questions

Answers you will finally get.

Did the Match Score predict the people who performed and stayed?

Match Score per hire joined to first-year performance review band and twelve-month retention, broken out per role family and BU. Talent leaders see the role where the high-Match-Score hires consistently landed in the top performance band and stayed past year one, and the role where the band the team trusted did not move the outcome at all, before the next requisition opens with the same Match Criteria.

Where in the Plum funnel are candidates dropping off?

Invited, In-Progress and Complete rates per role, recruiter and country, with the median time between each step. The talent team sees the role where 40% of invited candidates never start the assessment, the recruiter whose chase cadence is half of the rest, and the country where the time-to-complete sits a week longer than the SLA, instead of opening Plum to count by hand.

Are the Match Criteria we set still matching the people who stay?

Match Criteria per role compared to the Plum profile of the workers who stayed past twelve months in the same role last year. Talent leaders see the role where the criteria still rank a trait the leavers also scored high on, and the role where the criteria miss the trait that consistently shows up in the people who stayed, so the next role-spec opens with the criteria the data already pointed at.

Value for everyone in the organisation

Where each function gets value.

For finance leaders

Cost-per-hire, mis-hire spend and the assessment subscription seen against the Match Score band that performed. The CFO sees the role family where a tighter Match Score band correlates with lower twelve-month attrition, and the budget conversation shifts from a flat assessment line to a return per role family the talent team can defend.

For sales leaders

Plum profile and Match Score per seller joined to ramp time, quota attainment and twelve-month retention. Sales leadership sees which Match Score band has the shortest ramp on the new-business role, and which territory the assessment has been flagging as risk before the resignation lands in the pipeline as orphaned opportunities.

For operations

Match Score and Plum profile per operations hire joined to throughput, error rate and retention on the floor. The COO sees the site where high-Match-Score operators consistently outperform on first-month productivity, and the role where the criteria the manager trusts no longer hold.

Ideas

What you can automate with Plum.

Pair with BambooHR

Read Plum Match Scores and profiles on the BambooHR worker record

Many SMB talent teams run BambooHR for the worker record and Plum for the assessment that decides who gets the offer. The two systems each hold half of the picture; the warehouse holds both. Plum Match Scores, Plum profiles and the role's Match Criteria land next to the BambooHR worker, manager, site and start-date record, so the people-analytics view answers questions BambooHR alone or Plum alone cannot: which Match Score band on the new-hire role correlates with the strongest first-year retention, which managers consistently hire from the band the policy expects, and where the role-spec the recruiter used drifted away from the profile of the people who stayed.

Pair with HiBob

Cross Plum assessment data with the HiBob employee record

Plum Match Scores and profiles per hire from Plum land next to the Bob worker record, manager, site, tenure and the post-hire performance record. People analytics sees the role family where a Match Score one band higher correlates with a measurable lift on twelve-month retention, the manager whose hires from the policy band consistently outperform their cohort, and the role where the criteria the recruiter set still rank a trait the leavers also scored high on. The Bob seat cost stops being a flat line and becomes a number per hire that hangs against the Match Score and outcome the same person carried.

Pair with Workday

Tie Plum Match Scores and Match Criteria to the Workday requisition record

Workday holds the requisition, the position, the comp band and the post-hire performance record; Plum holds the assessment outcome that decided who got the offer. In the warehouse, Plum Match Scores and Match Criteria land on the Workday requisition and the worker the offer turned into, so HR analytics sees which requisition opened with criteria that picked the score band that performed, which BU consistently approves hires below the policy score, and where Workday's twelve-month performance review reads against the Match Score the same person carried in. The talent-review meeting moves from a recruiter recap to a list of named roles whose criteria, scores and outcomes line up.

Pair with Slack

Drive Plum funnel and assessment Slack moments from your warehouse

Plum invites, completions and stalled assessments post in the right Slack channels without a recruiter chasing them by hand. A new completion above the role's policy band pings the hiring manager the same hour with the candidate, the score and the open req. A candidate stuck in Plum In-Progress past the SLA pings the recruiter on day three with the chase template attached. A weekly readout in the talent-team channel summarises the funnel per role and recruiter against the cadence the team agreed, instead of one person opening Plum to compile the list every Friday.

Pair with HubSpot

Match HubSpot revenue-team retention with Plum Match Scores

Plum Match Scores and profiles per seller hire land next to the HubSpot owner record, pipeline coverage, ramp date and closed-won numbers per rep. Revenue leadership sees the new-business role where the Match Score band the talent team trusted predicted the strongest first-year quota attainment, the rep whose Plum profile flagged a fit gap that the ramp data then confirmed, and the territory where the assessment has been flagging early signal before the resignation report ties the loss to a hiring miss. The recruiter and revenue-leader conversation stops being one anecdote and starts being a list of named roles whose Match Scores and quota outcomes track together.

Pair with Salesforce

Cross Salesforce sales-team performance with Plum assessment outcomes

Plum Match Scores and profiles per seller hire land next to closed-won ARR, ramp date and quota attainment per rep from Salesforce, on the same employee key. Sales leadership sees the territory where the assessment band the talent team set predicted the rep who hit ramp on time and the territory where the same band missed, and the role where the Match Criteria the recruiter used still rank a trait that did not separate the top reps from the rest. The QBR slide moves from a hiring-quality anecdote to a list of named roles whose Plum and Salesforce records line up.

Your existing tools

Your data lands in a warehouse. Your BI tools read from it.

You keep the reporting tool you already have. We connect it to the warehouse where your Plum data lives.

Power BI Microsoft

Fabric Microsoft

Snowflake Data warehouse

BigQuery Google

Tableau Visualisation

Excel Sheets & pivots

Three steps

From Plum to answers in three steps.

Connect securely

OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.

Land in your warehouse

Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.

Reporting, automation, AI

We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.

Two ways to work with us

Pick the track that fits how you work.

Track 01

Self-serve

We set up the foundation. Your team builds on top.

Plum connector configured and running
Warehouse set up in your cloud account
Clean access for your Power BI, Fabric or Tableau team
Documentation on what's in the data model
Sync monitoring so you're warned before reports break

Best fit Teams that already have a BI analyst or data engineer and want to own the build.

Track 02

Done for you

We build the whole thing, end to end.

Everything in Self-serve
Dashboards built to the questions your team actually asks
Automations between your systems
AI workflows scoped to real tasks your team runs
Custom apps where a dashboard does not cut it
Ongoing delivery at a pace that fits your team

Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.

Before you book

Frequently asked questions.

Who owns the data?

You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.

How fresh is the data?

Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.

Do I need a warehouse already?

No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.

Which Plum tables land in the warehouse?

The connector pulls the Candidates register with assessment metadata, the per-role Match Score and Plum Status, the Plum Profile (drivers and drainers, top talents, growth signals), the Jobs register with the Match Criteria the hiring team set, the Assessment events (invited, in-progress, complete) and the Plum Link references that point back into the Plum platform. Authentication runs through a Plum API token scoped to your account.

How is the assessment data handled for fair-hiring monitoring?

Match Score and score-band records can be joined to the protected-class fields the HRIS holds, where the policy and local law allow the cut, so the talent team can chart adverse-impact ratios and the four-fifths rule per role and quarter. The per-respondent profile content stays in restricted schemas the talent and DEI roles reach, while the dashboards the rest of the business uses see the role-level outcome. Plum's own NYC Local Law 144 audit covers the assessment itself; the warehouse extends the same monitoring to the funnel that surrounds it.

Can Match Criteria per role be tracked over time?

Yes. The Match Criteria a hiring team sets on a Plum job land in the warehouse with the role, the recruiter and the date stamp, so the talent team can see how the criteria changed across requisitions for the same role family, where a criteria revision moved the score-band hit rate, and which roles still open with the criteria template the data has already moved past.

GDPR-compliant

Data stays in the EU

You own the warehouse

A first deliverable live in four to six weeks.

We review your Plum setup and the systems around it. Together we pick the first thing worth building.

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