---
updatedAt: 2026-10-15T22:57:26.000Z
agentTools:
projectIndex: https://data.crunchbase.com/llms.txt
---
# Anticipate what’s next for your ICP-fit accounts
Using Crunchbase’s Predictions and Insights - Funding Predicition
Goal: Prioritize accounts predicted to raise funding though a list of highly targeted companies in Crunchbase that meet your ICP. Uncover their latest Funding Prediction so your team can reach out early to establish the relationship before your competitors do. This means that when the company does receive its round of funding, your company is already their vendor of choice.
Persona: GTM
Use Case: Prioritize ICP accounts predicted to raise funding
Primary API(s): Search (Organizations) & Lookup a Funding Prediction
# Step 1:
## Search for a Funding Prediction
In this step, we’ll request the latest Funding Prediction for a specific company to uncover its likelihood of future funding.
## Endpoint
****
Search funding predictions
Using search APIs
## Query
For the first part of this exercise, we’re going to search for the latest Funding Prediction for the company Writer via (UUID: a3674669-6d9e-4f70-895f-3c8c99bcb7ad):
#### Key Fields for this Query
| field\_id | What this field does | operator\_id (these are the compatible operators per field) |
| :----------- | :---------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------- |
| organization | Filter by the organization’s UUID. For this example, we’re using the company Writer, with the UUID: a3674669-6d9e-4f70-895f-3c8c99bcb7ad) | blank;contains;eq;includes;not\_contains;not\_eq;not\_includes;starts |
Now that we have the field\_id defined, here is the sample query we can run that will search for the available Funding Predictions for the company Writer:
```json
curl --request POST \
--url https://api.crunchbase.com/v4/data/searches/funding_predictions \
--header 'X-cb-user-key: dac9eef1292e432a8a94XXXXXXXXXXXX' \
--header 'accept: application/json' \
--header 'content-type: application/json' \
--data '
{
"field_ids": [
"organization",
"generated_on"
],
"query": [
{
"field_id": "organization",
"operator_id": "includes",
"type": "predicate",
"values": [
"a3674669-6d9e-4f70-895f-3c8c99bcb7ad"
]
}
]
}
'
```
Read `generated_on` to identify the latest prediction. This request does not specify a sort order. Paginate through all matching records, preserve the organization filter, and select the record with the greatest `generated_on` value. Use that record’s UUID in Step 2; do not assume the first returned record is the latest.
## Response
The response contains 10 Funding Prediction entities, with the date of each prediction and their unique identifier (UUID). This UUID can be used to get more information about that specific Funding Prediction.
The response below is condensed to show 1 of the 10 funding predictions, and does not contain the entire response from the API
```json
{
"count": 10,
"entities": [
{
"uuid": "3f62c086-138a-43cf-87ba-3a9e7f3f6c9c",
"properties": {
"identifier": {
"entity_def_id": "funding_prediction",
"uuid": "3f62c086-138a-43cf-87ba-3a9e7f3f6c9c",
"value": "Funding Prediction 2025-09-22",
"image_id": "81cfc38feb1a47878d8b160476c4144a"
},
"organization": {
"permalink": "writer",
"image_id": "jqgtyt2qclr1ikl5mpjv",
"uuid": "a3674669-6d9e-4f70-895f-3c8c99bcb7ad",
"entity_def_id": "organization",
"value": "Writer"
}
}
},
```
## Outcome
By using the Search FundingPrediction Entities endpoint, we’re able to query Crunchbase for a list of all the funding predictions for specific companies within our ICP. From here, we can take the latest one and retrieve all the information about it (more on that below).
# Step 2:
## Retrieve the data for the latest Funding Prediction for Writer
Goal: Now that we have a list of the Funding Predictions for the company Writer, we’re now going to query Crunchbase for the latest one, and retrieve all the information we have for that prediction - including projected timing and probability score — to determine the likelihood and expected timeline of the funding event. These fields can be integrated into scoring models, CRM updates, or notification systems to trigger downstream workflows based on funding likelihood.
## Endpoint
****
Get funding prediction
Using entity lookup APIs
## Query
For the second part of this exercise, we’re going to retrieve the data behind the latest funding prediction that we found in the previous query (above) using its unique identifier (UUID). Replace `{prediction_uuid}` below with the UUID of the latest record selected in Step 1:
#### Key Fields for this Query
| field\_id | What this field means |
| :----------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------- |
| created\_at | The date when this prediction was created and added to our graph |
| probability\_score | The predicted probability that this Organization will receive funding |
| probability\_score\_timeseries | Provides a timeframe for when we expect this prediction to come true |
| probability\_tier | The predicted probability of this organization raising funds is divided into distinct tiers that quantify the likelihood of this event. |
Now that we have the field\_ids defined, here is the sample query we can run that will retrieve the underlying data for the Funding Prediction and the fields in the table above
```
curl --request GET \
--url 'https://api.crunchbase.com/v4/data/entities/funding_predictions/{prediction_uuid}?field_ids=created_at,probability_score,probability_score_timeseries,organization,probability_tier&card_ids=' \
--header 'X-cb-user-key: dac9eef1292e432a8a94XXXXXXXXXXXX' \
--header 'accept: application/json'
```
## Response
The following illustrative response shows the shape of a funding prediction lookup. Your response depends on the prediction UUID selected in Step 1.
```
{
"properties": {
"organization": {
"uuid": "a3674669-6d9e-4f70-895f-3c8c99bcb7ad",
"value": "Writer",
"image_id": "jqgtyt2qclr1ikl5mpjv",
"permalink": "writer",
"entity_def_id": "organization"
},
"identifier": {
"uuid": "81bb80ba-9bf9-4fb7-8d43-0800a09657c4",
"value": "Funding Prediction 2025-10-25",
"image_id": "35429ad1989b4996a19407e018d6460a",
"entity_def_id": "funding_prediction"
},
"probability_score": 0.9385,
"created_at": "2025-10-25T16:58:36Z",
"probability_tier": "p200_positive_low",
"probability_score_timeseries": {
"months_24_plus": 0.000007017093258889777,
"months_00_to_05": 0.9999927168195302,
"months_06_to_11": 1.136322591185592e-7,
"months_12_to_24": 1.52454951763353e-7
}
}
}
```
## Outcome
From this response, we’re able to conclude:
In this illustrative response, `probability_score` is 0.9385 (about 94%). Separately, `probability_score_timeseries.months_00_to_05` is approximately 0.999993. The overall score and timing values are distinct outputs; 94% is not the value reported for the 0–5-month interval.
Having this level of detail on what’s coming around the corner allows your teams to know which companies to target before your competitors do