Understand and improve offer quality
On the offer detail page, Flino shows a badge below the title that tells you whether the offer has all the essential data, and if not, what’s missing. The badge is small but saves later effort: complete offers lead to better AI suggestions, a correct acceptance link, and reliable pipeline data.
The three levels
Section titled “The three levels”| Badge | Meaning |
|---|---|
| Offer well prepared | All checked details are there (see below) |
| Review offer | Some optional details are missing that would improve clarity and tracking |
| Offer incomplete | Important required details are missing that the offer needs to be complete |
The first time you notice the badge, a short explainer popover “New: offer quality” appears:
“Flino shows whether your offer is fully prepared — based on key details like company, contact, title and amount. This helps you systematically improve close rates.”
Click Got it to dismiss the popover. The badge stays visible.
What fields are checked
Section titled “What fields are checked”Flino distinguishes two classes.
Critical: missing any of these and the badge is “Offer incomplete”:
- Contact assigned
- Title set
- Amount set
Important: missing any of these and the badge is “Review offer”:
- Offer number
- Valid until date
- Contact email (if a contact is assigned)
- Salutation on the contact (if the contact has an email address)
If every field in both classes is filled, the badge reads “Offer well prepared”. The explainer text mentions the company, but a missing company does not change the level.
When you hover over the badge, Flino shows a short explanation and the items that are still missing. At “Well prepared” there is nothing to do. You see the same rating in the pipeline as a coloured dot on the offer.
Why this matters
Section titled “Why this matters”Data quality is not an end in itself. It has three concrete effects on what Flino does for you:
1. Better AI suggestions
Section titled “1. Better AI suggestions”The AI builds email texts and recommendations from your AI context. Without the scope of work, the amount, or the salutation, it generates generic suggestions. With complete data, the suggestion becomes concrete and personal, and you have less to rewrite.
2. A working acceptance link
Section titled “2. A working acceptance link”The acceptance link is only useful when the recipient sees title, amount, and especially the valid-until date. Without the valid-until date, the acceptance page shows no expiry hint, and the Request extension button cannot kick in if the offer does expire.
3. Reliable pipeline data
Section titled “3. Reliable pipeline data”In the pipeline and in Analytics, Flino calculates with amounts, times, and statuses. If a won or lost offer is saved without an amount, the detail page shows the hint “This offer has no amount. Revenue and success metrics are incomplete.”, because it skews your analytics.
How to fill in fields
Section titled “How to fill in fields”On the detail page click Edit and fill in the missing fields. In particular:
- Assign contact: via the contact picker in the Edit dialog.
- Set salutation: directly on the contact under Contacts, or when sending in the Salutation missing dialog.
- Add contact email: either from the send dialog (Flino asks you automatically) or directly on the contact.
- Title, amount, offer number, valid-until date: in the Edit dialog. If the offer has versions, these fields are locked there; you then change them on the version in the Offer versions card.
- Create or assign company: via the company picker, or under Companies.
Once you save, the badge updates automatically.
Tip: keep data quality clean from the start
Section titled “Tip: keep data quality clean from the start”In the PDF import, the review dialog shows for each extracted field whether the AI Detected it in the PDF or Not detected; for your first offer during onboarding, Flino rates the fields as “Confident”, “Review”, or “Uncertain”. Take the two minutes there to check critical fields, see Create offer. Effort up front saves chasing later.
Cross-links
Section titled “Cross-links”- Create offer: clean data from day one
- AI context: how the badge connects to AI quality
- Acceptance link: why the valid-until date matters in particular
- Pipeline: where the data-quality indicators show up in the list