Onboarding a new HR and payroll customer
1Goal description
New customers operational faster, with less work for them and for us, and working independently afterwards without much explanation.
Buddee is een HR- en salarisplatform. Klanten nemen onze HR-software, onze salarisadministratie of beide af; bij salarisadministratie verzorgen wij de verloning. In deze case gaat het om een nieuwe klant die beide afneemt.
Een klant kan zelf een account aanmaken, of we zetten dat klaar tijdens het salesproces. Daarna moeten bestaande bedrijfs- en personeelsgegevens worden overgezet en moet de omgeving worden ingericht, voor HR én salaris. Nu krijgt een klant hulp bij de inrichting en een onboardingscall. Daarna moet de klant grotendeels zelf de weg vinden, en krijgen we nog veel vragen. We willen dat nieuwe klanten sneller operationeel zijn, met minder werk voor hen en voor ons, en dat ze daarna zelfstandig met Buddee werken zonder veel uitleg nodig te hebben.
2KPIs
The goal is translated into a few measurable KPIs, each tied to a part of it. Product does not set these alone: they are agreed with the rest of the management team, with a baseline and a target.
3The current flow
Map every step from contract to first correct payslip: who does it, in which tool, the hand-offs and the waiting. Based on the call with customer success, the Valuecase portal and my own signup.
HR onboarding
| # | Step | Who | Where |
|---|---|---|---|
| 1 | Contract signed, portal sent | Sales | Buddee, Valuecase |
| 2 | Company details filled in | Customer | Valuecase |
| 3 | Employee template uploaded | Customer | Valuecase, Excel |
| 4 | Template checked and imported | Customer success | Buddee |
| 5 | Leave, roles and workflows set | Customer, customer success | Buddee, call |
| 6 | Employees invited (go-live) | Customer | Buddee |
Payroll onboarding
| # | Step | Who | Where |
|---|---|---|---|
| 1 | Contract signed | Sales | – |
| 2 | Payroll data delivered | Customer | – |
| 3 | Payroll rebuilt in Nmbrs | Payroll specialist | Nmbrs |
| 4 | CAO questions and checks done | Customer, payroll specialist | Call |
| 5 | First payroll run | Payroll team | Nmbrs |
| 6 | First correct payslip (end of onboarding) | Customer | Buddee |
4Funnel data
Put numbers on the flow from step 3, starting at contract signing: how many customers reach each step, how fast, and at what effort. Not finished within 3 months counts as lost.
Key measurements
HR onboarding
| Step | Reached | Median days | Lost here | Buddee hours |
|---|---|---|---|---|
| Contract signed, portal sent | 100% | 0 | 0% | 0.5 h |
| Company details filled in | 99% | 3 +3 | 1% | 0 h |
| Employee template uploaded | 97% | 16 +13 | 2% | 0.5 h |
| Template checked and imported | 97% | 24 +8 | 0% | 1.5 h |
| Leave, roles and workflows set | 96% | 33 +9 | 1% | 1.5 h |
| Employees invited (go-live) | 96% | 36 +3 | 0% | 0 h |
| Back in Valuecase after go-live (within 6 months) | 12% | – | – | – |
Payroll onboarding
| Step | Reached | Median days | Lost here | Buddee hours |
|---|---|---|---|---|
| Contract signed | 100% | 0 | 0% | 0 h |
| Payroll data delivered | 98% | 14 +14 | 2% | 1 h |
| Payroll rebuilt in Nmbrs | 98% | 22 +8 | 0% | 1.5 h |
| CAO questions and checks done | 97% | 26 +4 | 1% | 0.5 h |
| First payroll run | 97% | 36 +10 | 0% | 0.5 h |
| First correct payslip (end of onboarding) | 96% | 39 +3 | 1% | 0.5 h |
5Internal input
Talk to the people who run onboarding every day, customer success and payroll, and read the meeting transcripts and internally reported tickets in Jira. AI clusters the questions and pain points per step.
Key findings
- The real onboarding happens outside the product: portal, email and people.
- Biggest hurdles: employee data, then leave balances, then roles, permissions and workflows.
- Payroll is a separate track with its own specialist and a rebuild in Nmbrs.
- His one wish: "fewer choices and more explanation in the system".
Examples of input
6Customer interactions
Read the emails, chats and calls with customers during onboarding and their first 90 days. Each contact is tagged with its step, so AI can count topics per step and per week.
Examples of input
7Observations and assumptions
Go and look for myself. I signed up as a new customer and went through the Valuecase onboarding portal, in a test space set up by customer success. What I found are observations and assumptions to test, not a list of fixes.
8Competitor analysis
How the closest competitors in the Dutch market take a new customer from signup to the first payslip, based on their public websites and help pages.
9Key problems and opportunities
Every input from steps 3 to 8 is distilled into problems and opportunities, each with its sources, and grouped by the KPI it affects. I then use it for follow-up questions, to challenge each finding and to bring up input we missed. This is not a process product does in isolation: it is a discussion and a collaboration with all stakeholders involved. Nor is it a manual task: AI distils the essential information from all of it.
Buddee hours per onboarding 8 h → 4 h
Customer hours per onboarding 31 h → 15 h
Support questions in the first 90 days 9 → 4
10Focus
From eight findings, we pick three to start with, compared on three criteria.
Focus issues
11Solutions
For each focus issue, a few possible solutions, from small changes to bigger ideas. Quick to sketch, cheap to throw away.
Data import is labour-intensive
Onboarding is overwhelming and split across tools
Users still have questions after go-live
12Solution validation
Before building, test whether a solution solves the problem. Prefer real customers and real data; mockups and pitches are cheaper but weaker evidence.
13Prioritization
Here we decide which solutions to build first, scoring each on effort and expected impact. In practice the scores are not hard facts, as neither can be measured exactly in advance. They are a sanity check: what makes sense, where to invest, and what feels right for everyone involved.
| Solution | Effort | Expected impact | Score |
|---|---|---|---|
| Data import is labour-intensive | |||
| 1AI-guided data setup | High | Very high | 13 |
| 2Start from your last payslips | Medium | High | 13 |
| 3AI scan of your current system | High | High | 10 |
| Onboarding is overwhelming and split across tools | |||
| 4Personal to-do list inside Buddee | Medium | Very high | 17 |
| 5Proactive AI onboarding assistant | High | Very high | 13 |
| Users still have questions after go-live | |||
| 6Onboarding content stays available in Buddee | Low | High | 20 |
| 7Ask Buddee about your company | High | High | 10 |
First 3 months
- 6Onboarding content stays available in Buddee. Low effort, high impact: the quickest win.
- 2A proof of concept for starting from the last payslips. Mainly to test how viable this use case is: how much can we reliably read from real payslips?
- 4Personal to-do list inside Buddee. Something we can build up step by step, one onboarding step at a time.
What we deliberately do not do
- No fully self-service payroll setup from day one: payroll specialists stay involved for CAO questions and the first run.
- No custom integration per old system up front: start with the AI scan and the payslips, and build integrations only where volume justifies it.
- No new, separate onboarding module: reuse existing Buddee screens and bring in the content that already exists in Valuecase.
- No big-bang switch away from Valuecase: content moves into Buddee step by step, using the Valuecase API where possible, and the portal stays as long as it is needed.
14Build
15Release
- Release gradually: first a group of new customers, then everyone.
- Tell customers and the team what changed, customer success and payroll first.
- Update the help content in Buddee at the same time.
- Follow adoption, so the new way of working actually lands.
16Results analysis
- Compare the KPIs with the baseline: Buddee hours, customer hours and support questions.
- Set up dynamic dashboards, for example with Claude's dashboard feature, so the numbers stay current without manual work.
- Use AI to find correlations in behaviour we were not tracking but that turn out to matter.
- Watch for side effects: a change can perform well and still raise more support questions, or create work elsewhere in the company or the funnel.
- Use decision points set in advance: scale up, adjust or stop.
17Iteration
- Feed what we learn back into the solutions.
- Pick the next problem from the list.
- Run the cycle again against a new baseline.