Overview
Lead scoring is a strategic approach to prospecting where you assign points to each of your prospects. When you use scores based on the attributes that define your ideal customers, you can more easily identify the prospects likely to convert into buyers.
Scores on Apollo allow you to take a data-driven approach to prospecting without a lot of manual setup. Use auto-scores for out-of-the-box ratings designed to help you find the contacts that are the best fit for your team's engagement.
Apollo generates auto-scores based on your company website and prospecting activity. These scores are based on signals that contribute to a rating for a prospect. Let's break that down further.
Score filters and features like auto-scoring are available on certain Apollo plans. If you need access to scores, upgrade your plan. If you have questions about upgrading, reach out to the Apollo sales team.
Auto-scores
Apollo uses auto-scores by default when you search for people or companies. Apollo automatically generates scores based on your account and prospecting activity, helping identify and rank the most important business scenarios affecting your ideal customers. When you save contacts and accounts, Apollo learns more about the ideal customer profile for your organization, and over time, auto-scores become more refined to match your team's outreach.
Apollo generates each person or company score based on how well they match signals within a score. A signal is a business trend that signifies an important barometer for outreach, like office expansion or headcount growth. With auto-scores, Apollo automatically selects signals based on your organization and its prospecting activity. However, if you create a custom score, you can choose signals from a signal library or create custom signals to define your score.
If you create a score, you assign a value to each signal in the score:
- Excellent
- Good
- Fair
Apollo then assigns people and companies individual ratings based on how well they match signals in the score. Prospects earn a rating based on the highest performing signal the prospect matches. For example, if a person matches 2 good signals and 1 excellent signal, they would receive an excellent score rating.
Score Ratings
Score ratings help you visualize a prospect's fit. Apollo assigns one of the following ratings to each person or company that you prospect based on how well they fit your score:
- ⭐ Excellent
- 😄 Good
- 🙂 Fair
- ✖ Not a fit
You can filter your search results by a minimum score rating to hone in on the most likely buyers. Then, hover over an individual score for more details about the prospect fit.
Get Started
You can use scores without any setup. Apollo enables auto-score by default when you prospect for people and companies in Apollo.
Search for prospects, then click a person or company for insights on the score. Apollo shares the score rating and the signals that contributed to the rating.
To create a score unique to your business, check out create a custom score.
FAQs
Frequently asked questions
| How are auto-scores created? | Can I create a custom score? |
| Where can I view scores for people and companies? | Can I access scores on my plan? |
| Can I sync scores with my CRM? | How does the new score model compare to the legacy model? |
How are auto-scores created?
Apollo generates auto-scores for your team based on 2 main sources of information:
- Company details shared in onboarding, like your company name or website.
- Account and usage data, like team prospecting activity when you save contacts or accounts. The more contacts and accounts you save, the more accurate your score becomes.
Apollo assigns people and companies individual score ratings based on how well they match your score. Prospects earn a rating based on the highest performing signal the prospect matches. For example, if a company matches 2 good signals and 1 excellent signal, the company would receive an excellent score rating.
Can I create a custom score?
Auto-scores are easy to use from the get-go, but you can also create custom scores. However, you can only view the results of 1 scoring model at a time. If you're an admin on Apollo and you create custom scores, ensure you set 1 primary people score and 1 primary company score for your team.
Where can I view scores for people and companies?
Can I access scores on my plan?
Access to scores depends on your Apollo plan. To use scores when prospecting, upgrade your plan. If you have questions about upgrading, reach out to the Apollo sales team.
Can I sync scores with my CRM?
Yes, you can! If you use a CRM like Salesforce or HubSpot, Apollo syncs scores for contacts and accounts with your CRM:
Learn more about field mapping with the Salesforce or HubSpot integrations.
How does the new score model compare to the legacy model?
The legacy scoring model was based on points, with each prospect earning points for matching signals in a score. A prospect's score was measured by the total points earned, divided by the total possible points.
The new scoring model is based on each prospect earning a score for the highest performing signal the prospect matches. This approach streamlines the scoring model, providing clearer, more straightforward metrics.
Imagine you’re ranking restaurants in town, and at one stop, there are multiple courses served. The legacy model would rate the restaurant based on the average quality of all the dishes served. But the new model focuses only on the best meal, making it much easier to find the restaurants that stand out.
Let's break that down further. Imagine a prospect matches 3 signals that are fair, good, and excellent:
| Legacy model | |
|---|---|
|
A prospect matches 3 signals:
|
In the legacy model, each signal gets a point value. The prospect earns 6 points. If the total possible points was 12, the prospect has 6 of 12 points. Based on the signal values, the prospect earns a fair score. |
| New model | |
|---|---|
|
A prospect matches 3 signals:
|
In the new model, the prospect earns an excellent score because that's the highest performing signal they match. |
In contrast to the legacy model, which averaged the results of all signals, the new scoring model emphasizes the highest quality signal, leading to more straightforward prospect weighting.