What bad CRM data costs you
- AI answers from what is in the record. If sector is blank, the last contact date is wrong and the same person exists three times, the answers will be wrong with complete confidence.
- Automations misfire or never trigger when the fields they run on are empty, inconsistent or duplicated. Consultants stop trusting them.
- Campaigns go to the wrong people. Bad contact data means bounced emails, wrong names and campaigns sent to the wrong people.
- The right accounts, leads and contacts never get identified or engaged.
What you learn from the analysis
Which contacts are worth acting on first
Each contact record carries a score, a fit rating and a recommended next action, so your desks start with the right ones.
What your best clients have in common
A profile built from the clients you have actually converted, not generic firmographics.
Which dormant contacts, and the accounts they sit at, match that profile
The contacts that have gone quiet but sit at companies that look like your best clients.
Where your contact data is strong, where it is thin and what can be recovered
Not every gap can be filled. The analysis shows which can and which cannot.
What you receive for every contact record
A data quality score, an ideal client fit rating and a recommended next action for every contact record, plus a summary of the findings that matter.
- Contact
- Sam Example, Example Engineering Group
- Fields assessed
- Job title present. Email present. Sector present. Company size present. Location present. Seniority blank. Source blank. Last contact 14 months ago.
- Data quality score
- 68 out of 100
- Ideal client fit
- Strong. Matches the sector and size profile built from your converted clients.
- Recoverable
- Seniority and source likely recoverable.
- Recommended next action
- Re-engage this week.
- Why
- Dormant account in a sector and size band that matches your converted clients. Job title suggests a decision maker.
Company details on the contact's account are used to judge fit. Company records are not scored themselves.
How the analysis works
Every contact record is scored against the fields a recruitment desk relies on. It is also scored against a profile built from the clients your business has actually converted, using the company records for those clients. Scores come from a model we build with you, combining key information from your business with our own expertise on which signals matter, so the score reflects the value of each contact to your agency.
How much can be recovered
On average, key information can be found or resolved on about 70% of contact records. Not every gap can be filled, and the analysis shows which. Identifying the records that cannot be recovered is part of the value, because it shows you where not to spend time.
Two steps. Analysis first.
Step 1
CRM Data and AI Readiness Analysis
£3,000 plus VAT
Scope: contact records, the people at your clients and prospects. Company records are used to build your ideal client profile and to judge fit, but are not scored. Candidate records are not included.
Fixed scope. One-off.
- A data quality score for every contact record
- An ideal client fit rating for every contact record
- A recommended next action for every contact record
- A summary of the findings that matter
Step 2
Enrichment
Separate project
Scoped and priced from what the analysis shows, because every database is different. Not included in the £3,000. One-off work, with the habits and automations that keep the data in shape handed over at the end.
Enrichment is not included in the analysis fee.
Is your recruitment CRM data ready for AI and automation?
The usual symptoms:
- Duplicate candidates and contacts
- Contacts who have moved on
- Blank sector, seniority and location fields
- Tags and statuses that mean different things on different desks
- No record of where a record came from
- Useful detail buried in notes and CVs that never reached a field
If you are moving to a new CRM, fix this before you migrate, not after. Our CRM implementation service covers the migration itself.
What good data makes possible
Better data is not the goal. It makes three things possible.
Put AI on top of your own data
Agencies want to ask questions of their CRM, find candidates faster and spot clients going quiet. AI can only work with what is in the record. There is more than one way to add the AI layer, and all of them need the data ready first. If you need help choosing the route, our RecTech Consultancy service covers that.
Automate inside the CRM
Workflows, reminders, status changes and follow-up tasks all run off fields. Where those fields are empty, inconsistent or duplicated, automation either misfires or never triggers, and consultants stop trusting it.
Segment properly and market to the right people
Segmenting by sector, seniority, location, placement history or last contact only works when those fields are filled in and coded the same way across the database. Marketing automation and everyday campaigns sit on top of that segmentation. Bad contact data means bounced emails, wrong names and campaigns sent to the wrong people.
What can follow the analysis
These are separate projects, scoped after the analysis.
Know who to target: target lists built from who you've actually converted
You're fishing in the same pond as everyone else, your consultants lose hours each week to manual research, or you're entering a sector with no contacts. The prospects you want often sit in industry databases, public records and specialist sources your current tools can't reach.
- Company intelligence. Target lists built from what has actually converted, not generic firmographics.
- Ideal client modelling. Four signals from your converted clients (industry, company size, relevant job titles and engagement history) turned into a working model.
- Lookalike expansion. AI-powered identification of companies matching your best client profile.
- Bespoke research. Custom workflows to answer specific questions about your target market.
Ask questions of your own data: AI data warehouses for recruitment agencies
Most CRMs hold more useful data than anyone is using, and less than most people assume. Before any AI layer goes on top, the real question is what the CRM contains, what it's missing and where the missing data lives instead.
We build data warehouses that connect your CRM and other business data sources to an AI reasoning layer. Non-technical staff ask plain English questions and get answers grounded in your own data. For example: which clients have gone quiet in the last year, or which sectors are producing your best margins.
Access is built with proper governance from day one, not bolted on afterwards: tiered permissions, multi-factor authentication and audit trails.
The work doesn't stop at a chatbot. Done properly, it's a foundation you can keep building on, from using AI outputs in your own client-facing work through to running your own agents on the same data.
Who this is for
Agencies whose team is doing manual research instead of billing, whose CRM data has gone quietly out of date, or who want AI working on their own data and not a generic chatbot.
Frequently asked questions
What is a CRM data audit for a recruitment agency?
It is a review of every contact record in your CRM against the fields your desks rely on. Ours is called the CRM Data and AI Readiness Analysis. Each contact record gets a data quality score, an ideal client fit rating and a recommended next action, and you get a summary of the findings that matter.
What does AI ready CRM data mean?
Records complete and consistent enough for an AI tool, an automation or a campaign to act on without guessing.
How much does the analysis cost?
£3,000 plus VAT. It is a fixed scope, one-off piece of work. It covers contact records only.
Is enrichment included?
No. Enrichment is a separate project, scoped and priced from what the analysis shows, because every database is different.
How many contact records can be resolved?
On average, key information can be found or resolved on about 70% of contact records. Not every gap can be filled, and the analysis shows which.
What is an AI data warehouse for a recruitment agency?
A central store that connects your CRM and other business data to an AI layer, so your team can query it in plain English with proper access controls.
What is the difference between the analysis and enrichment?
The analysis scores every record and shows what can be recovered. Enrichment is a separate project that fills the gaps, scoped and priced from what the analysis shows. Enrichment is not included in the analysis fee.
What data should a recruitment agency track?
Track the data your desks act on every day: contact details and role, sector and seniority, where each record came from, last contact and outcome, placement and fee history, and consent status. If a field is never used to make a decision, run a report or trigger an action, question whether it needs to be there.
How can better data improve recruitment decisions?
Decisions about who to call, which clients to pursue and where to spend consultant time are only as good as the records behind them. Complete, consistently coded data shows you which sectors and clients convert, which have gone quiet and where your database is thin. Poor data hides all of that, so you decide on instinct or on whoever is easiest to find.
Can we add AI to a CRM with poor data?
Technically yes, but the results will not be reliable. AI answers from whatever is in the record, so duplicates, blanks and inconsistent coding produce confident answers that are wrong. Getting the data ready first is what makes an AI layer worth building, whether that is AI inside your CRM, a layer over it or a data warehouse.
Do you delete records from our CRM?
No. We flag duplicates and records that may warrant removal, and your team reviews, merges or removes them. Nothing is deleted or merged by script, so every decision about your data stays with you and you can see what is changing before it happens.
Is this ongoing work?
No. The analysis and any enrichment are one-off projects. At the end we hand over the habits and CRM automations that keep the data in shape.