What would you still have if the advertising platform or social network that brings you your customers changed its rules tomorrow? The question is uncomfortable, because the answer is often: very little. The followers of a page, the audiences of an ad network and the buyers on a marketplace belong first to those intermediaries.
First-party data, meaning the information you collect yourself from your customers and prospects through your own channels, is by contrast an asset you control. Like any capital, it is built, organised, grows and needs upkeep.
Build: start with what you already own
An SME holds more data than it thinks. It sits in the invoicing software, the sales team’s spreadsheets, email inboxes, website forms, the cash register, messaging conversations and business cards brought back from a trade show. The first task is to list these sources one by one.
Then sort what you find into four families:
- identity and contact details;
- transactions: quotes, orders, invoices, payments;
- interactions: emails, calls, visits, support requests;
- declared preferences: consents, areas of interest, preferred channel.
A customer relationship management (CRM) tool then gives this information a single home. The AROVA CRM fills this role. Without a common point of reference, each department keeps its own version of the customer.
Collect: fewer fields, more value
A piece of data is obtained in exchange for a service. The customer agrees to share something when they understand what they gain: a more accurate quote, a useful reminder, content adapted to their business. This exchange principle should guide every collection point.
- Ask for little at first contact, then complete the profile over time. This practice is called progressive profiling.
- Explain the purpose next to the field: “your sector helps us send you examples close to your activity”.
- Obtain a separate consent for each channel (email, SMS, messaging), and record its date and source.
- Remove fields that nobody has used for a year.
This restraint also serves you. A short form deters fewer people than a long questionnaire, and a database limited to what is useful is cheaper to secure.
Organise: one record per customer, one reference for everyone
Organisation determines the value of the capital. Three decisions are enough to set the framework.
- Choose a common identifier: usually the email address for a private customer, and the company-contact pair in B2B. Merge duplicates based on this identifier.
- Set a short list of mandatory fields and standardised values for sector, contact source and status.
- Appoint someone responsible for the database. This person arbitrates the data entry rules and checks quality every month.
Finally, connect the CRM to your management software. When orders and payments from the ERP, your management system, feed the customer record, marketing works from real sales rather than clicks.
Grow: what AI draws from your data
A clean database opens the door to uses that manual sorting cannot offer.
- Segmentation: a model groups your customers by recency, frequency and value of purchases, and finds profiles you had not thought of.
- Prediction: it estimates the churn risk, that is the probability that a customer stops buying, and flags the accounts to call back.
- Request sorting: an assistant classifies incoming messages by subject and urgency, and summarises an account’s history before a meeting.
- Assisted drafting: it prepares message variants per segment, which your team corrects.
The machine flags, the team decides. Choosing which segments to serve first, the offer, the tone and the moment to pick up the phone remain commercial decisions. A churn score does not replace the knowledge a salesperson has of their customer.
Maintain: the mistakes that erode the capital
- Buying contact lists. You do not know where the consent came from, and your messages risk ending up in spam folders.
- Letting each salesperson keep their own spreadsheet. The day they leave, part of the capital leaves with them.
- Never purging. Contacts inactive for a long time weaken your statistics and your sender reputation.
- Confusing volume with value. A small base of active, consenting contacts is worth more than a large silent file.
- Forgetting people’s rights. Everyone must be able to access, correct or delete their data without obstacles.
Four indicators are enough to track the health of the database: the share of complete records, the share of contacts with a dated consent, the duplicate rate, and the share of contacts active in the last twelve months. For retention periods and legal notices, rely on a lawyer.
What changes by sector
- B2B wholesale and distribution: the order history per account reveals customers who are spacing out their purchases. The salesperson receives the alert before the account is lost.
- Private clinics: strictly separate the medical file from contact data. Marketing only handles the latter, with explicit consent for each use.
- Real estate development: the buying cycle is long. The budget, area and property type the prospect states allow you to offer the right project at the right time.
AISYSNEXT and your customer reference
AISYSNEXT publishes the AROVA CRM, integrated with AROVA ERP, which centralises sales, purchasing, inventory and finance. This continuity between management and customer relationship avoids rebuilding an account’s history from several files.
Our teams also work upstream: inventory of sources, collection rules, migration of existing data. To discuss this based on your situation, contact our team.
Frequently asked questions
What is the difference between first-party and third-party data?
You collect first-party data directly, through your own channels, from people who know you. Third-party data comes from outside players who aggregate and resell it. You control the origin and quality of the first kind, rarely of the second.
Where should you start when everything is in spreadsheets?
Start with the inventory and the choice of a common identifier. Then import a first scope, for example active customers, rather than the whole history. You will correct the rules before generalising them.
Is a small database enough to use AI?
Yes, for request sorting and assisted drafting, which do not depend on volume. Prediction models need more history. Simple segmentation by purchase recency and frequency is already useful in the meantime.
