Anivo lead scores: read fit, data quality and opportunity signals.
Understand Anivo’s rank, fit and data-quality scores before choosing a prospect. Read company signals, check contact details and turn your review into a clear CRM note and next step.
Anivo’s scores help you decide which companies to review first. Rank supports review order, fit relates to the approved research target, and data quality describes the available information. None confirms a current need, buying intent or a sale. Read the separate indicators in company details, verify the firm and decide whether your offer is relevant.
Use the score to start a review, then check the company.
A high score can put a firm near the top of your review list. It does not establish that the company needs your service, has a budget or wants to buy. A percentage shown beside a rank score is not a probability of a reply or sale.
Check the company’s activity, location and relationship to your offer. Treat an opportunity signal as an observation to investigate. Learn the actual need through a conversation.
This guide explains how to interpret results. Use the Anivo user guide for research and CRM steps, and the ideal customer profile guide to define the companies you want.
Read the three scores separately in company details.
| Indicator | Meaning | Limit | Your check |
|---|---|---|---|
| Rank score | Combines fit, contact information and data quality to support review order. | Not a sales or reply probability. | Read fit and quality separately rather than relying on the largest number. |
| Fit | Assesses available company information against the approved target segment and region. | Does not confirm budget, a current need or buying authority. | Check activity, location and the person’s role where available. |
| Data quality | Assesses completeness, format validity and available reliability and freshness indicators. | Complete information can describe a company that is unsuitable for your offer. | Compare company identity, phone, email and website for consistency. |
Indicators appear when the record contains the relevant information. Research details and CRM details can show different information. A score missing from the detail view is not a measured zero.
Separate an observed signal from the conclusion you draw.
| Indicator | What it describes | What it does not prove | Your check |
|---|---|---|---|
| Segment | The target group associated with the firm in this research. | Every activity or need of the company. | Does the group match your target customer? |
| Star rating and review count | The business rating and review volume held in the record. | Anivo fit or buying intent. | Inspect review content and dates as well as the average. |
| Website summary | A short description from readable company website content. | A complete or current picture of the business. | Compare it with the company’s own service and about pages. |
| Detected tools | Technology indicators found on inspected website pages. | How extensively a tool is used, satisfaction or an intention to replace it. | Explain why the observation relates to your offer. |
| Contact-field availability | Whether a phone, email or website field is available; locked records may show availability. | That you will reach the right person or get a reply. | After unlocking, check that the details belong to the firm. |
| Predicted email | A guessed address rather than an address found for the business. | That the mailbox exists. | Verify the address before considering it for outreach. |
| Confidence, MX and domain match | Additional contact indicators in CRM details, when available. | Guaranteed delivery or the existence of a particular mailbox. | Read the discovery explanation, prediction state and company-domain relationship together. |
| Previously seen | The firm appeared in an earlier research. | A new need or sales opportunity. | Check previous contact and CRM notes to avoid repeated messages. |
MX records describe domain-level mail routing; they do not verify a particular recipient’s mailbox. The SMTP specification distinguishes domain routing from recipient handling. A confidence indicator is also not a delivery guarantee.
No visible signal can mean that readable information was unavailable. It does not show that the firm has no opportunity. A detected tool can support a question; it does not prove dissatisfaction.
Check the target before interpreting a high fit score.
The research plan shows your business, offer, target customer types and region. Segments inferred from the offer may be marked separately. Review these targets before approval.
Fit belongs to that approved research context. The same company can receive different assessments for different targets. A high score against the wrong target still leaves you with the wrong company for your work.
Describing your service does not establish that each firm’s need was individually verified. Keep your offer, the observed company information and the unanswered need separate.
Treat uncertainty as a check to complete.
- Missing fields can reduce data quality and the evidence supporting fit. Do not replace unknown information with a guess.
- If the category and website description conflict, review the company identity before acting.
- Check similar names, branches and locations. A matching name alone is insufficient.
- A newly collected record can contain old website information. Collection date alone does not establish freshness.
- Do not assume every contradiction is detected automatically or every score refreshes as time passes.
When an important condition is unclear, write it as a question for the next review. Pause contact if the company identity cannot be resolved.
Use filters to narrow the review list, not to certify a lead.
| Filter | Selection | Interpretation |
|---|---|---|
| Strong fit (70+) | Fit of 70 or more. | A review threshold, not a 70% chance of a sale. |
| Qualified (55+) | Fit of 55 or more. | A fit threshold, not a confirmed budget, need or decision-maker. |
| No fit score in details | The research has not supplied this measurement. | Not a measured zero; a fit filter may exclude the record. |
Read thresholds against the approved target. If you need to inspect unmeasured candidates, remove the fit filter and review their company information.
Review the firm before choosing a CRM or contact action.
Check the research target.
Confirm the company type, region and service. A score against the wrong target cannot settle your decision.
Read each score for its own purpose.
Use rank for review order, fit for target matching and quality for information usability. Keep missing scores distinct from zero.
Verify the company identity.
Compare name, category or industry, location and website. Review role, company size and specialities where available.
Record gaps and contradictions.
Compare available facts. Check inconsistent descriptions and old content. Decide which unanswered condition matters to your offer.
Override a high score when needed.
Exclude the wrong region, company type, competitors or other groups you chose to avoid. Pause if identity is unresolved or the business has stopped operating.
Write the reason for adding to CRM.
Record why the firm is worth following, which information is missing and what to check next. CRM entry does not confirm need.
Base the first message on verified information.
Check the contact channel, draft and recipient. Use a real observation and ask about the need instead of claiming an unverified problem.
No usable email address does not necessarily disqualify the whole company. Assess another appropriate channel when available. Contact availability and suitability for your offer are separate questions.
Turn an unknown need into a question.
| Review area | Example |
|---|---|
| Available information | Address in the target region, dental clinic category, website and published phone number. The website summary describes clinic services. |
| Unknown information | How appointments are managed, whether an online solution already exists and who makes that decision. No published email is found. |
| Company check | The specialist reviews the website and confirms the identity. |
| CRM note and next step | “Appointment process unknown.” Use the published phone to identify the appropriate person. |
The first question is “How do you manage appointments?” The specialist does not claim “Your appointment system is inadequate.” The observed facts support a conversation, not a conclusion about need.
Keep the reason for your decision with the firm.
| Review result | Decision | Note to keep |
|---|---|---|
| Identity and target match; need unknown | Consider CRM tracking and an appropriate first conversation. | Verified fit, unanswered need and next contact check. |
| Important information is missing or contradictory | Review further before contacting. | The conflict and the evidence needed to resolve it. |
| Confirmed exclusion or wrong target | Do not pursue this offer for the firm. | The region, activity or other exclusion behind the decision. |
These are review outcomes, not additional Anivo score labels or automated actions.
For a short list, manual website review and written notes may be sufficient. Anivo helps you prioritise and keep research, CRM and communication together as the list grows. Keep human review in either workflow.
Use how Anivo works to connect research with CRM and email. The next useful step is a verified company, a clear question and a recorded reason to follow up.
Questions about Anivo company scores
Interpret fit, data quality and contact indicators before outreach.
Is the rank percentage my chance of making a sale?
No. Rank supports review order using fit, contact information and data quality. Its percentage display does not express a probability of a reply or sale.
When should I reject a high-scoring company?
Do not pursue the offer when the company is in the wrong region or target group, is a competitor or meets another exclusion you chose. Pause contact if identity remains unresolved or the company has stopped operating.
Does high fit mean the company needs my service?
No. It supports matching against the approved research target. Confirm the current need through a conversation.
Should I send immediately when data quality is high?
First check company fit and contact details. Complete information and a relevant offer are separate matters.
Does a missing fit score mean the company is unsuitable?
No. A missing score is not a measured zero. Fit filters can exclude candidates whose fit was not measured in that research. Remove the filter to review available company information.
What does the Strong fit filter mean?
In the current interface, Strong fit selects fit of 70 or more; Qualified selects 55 or more. These are review thresholds, not probabilities or confirmation of buying intent.
Can I rely on a predicted address with valid MX?
MX relates to the domain’s mail infrastructure. It does not confirm that the particular mailbox exists or belongs to the appropriate recipient. Check the address before considering it for outreach.
Does a missing company signal mean there is no opportunity?
No. Readable information may have been unavailable. A missing signal does not establish that there is no need; a visible signal does not confirm a need either.
Product references and email documentation
Adapted from Anivo’s company-score document and checked against the current application code. The clinic example is fictional; no score or customer outcome is reported.
- Anivo user guide
Research approval, company review, CRM records and communication steps.
- How Anivo works
Research, opportunity signals and the connected workflow.
- RFC 5321: SMTP address resolution
Primary technical reference for domain-level mail routing, distinct from verification of a recipient mailbox.