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POST
/
v1
/
businesses
/
bombora_intent
/
bulk_enrich
Bombora Intent
curl --request POST \
  --url https://api.explorium.ai/v1/businesses/bombora_intent/bulk_enrich \
  --header 'Content-Type: application/json' \
  --header 'api_key: <api-key>' \
  --data '{
  "request_context": null,
  "parameters": {
    "topics": [
      "<string>"
    ],
    "min_score": 123
  },
  "business_ids": [
    "<string>"
  ]
}'
{
  "response_context": {
    "correlation_id": "<string>",
    "request_status": "success",
    "time_took_in_seconds": 123
  },
  "data": [
    {
      "business_id": "<string>",
      "data": {
        "business_id": "<string>",
        "company_name": "<string>",
        "company_website": "<string>",
        "date_stamp": "<string>",
        "intent_topics": "<string>",
        "level_of_intent": "<string>",
        "topic_count": "<string>"
      }
    }
  ],
  "entity_id": "<string>",
  "total_results": 123
}

​
Description

The Business Intent Topics (Bombora) — Bulk enrichment enables you to retrieve intent topic insights for multiple businesses in a single request.
It reveals which topics each company is actively researching online, offering comprehensive visibility into collective business interests and buying intent across a portfolio of accounts. Powered by Bombora’s weekly intent dataset, the enrichment analyzes topic-level content consumption patterns to detect surging interest signals and engagement intensity at the company level. For each provided company (business_id), the enrichment returns a ranked list of intent topics with a composite score above 60, grouped by category and timestamped by week.
The results highlight which topics each business is researching most intensely and classify them by intent level (In-Depth, Active, Early). This enrichment is particularly useful for:
  • Account prioritization: Identify which companies across your target list are currently researching relevant topics.
  • Intent-driven segmentation: Group accounts by research activity and level of engagement.
  • ABM automation: Scale intent-based personalization to entire account lists.
  • Pipeline acceleration: Detect surging interest signals across multiple prospects simultaneously.
Refresh cadence: Weekly Raw intent data powered by: Image(1) Pn

​
Input

Provide a list of business_id values obtained from the Match Businesses API.Optional parameters:
  • topic_parameters — semicolon-separated list of “category: topic” filters (e.g., technology: ios;other: vendor profiling).
    • To build this list, use the Autocomplete endpoint with business_intent_topics — and preferably semantic_search=true — to retrieve a broader, smarter set of intent topics. This ensures your topic list is accurate, relevant, and aligned with Bombora’s taxonomy.
  • min_score — optional integer > 60. If omitted, the default threshold is 60.
💡 Recommendation:
To achieve a meaningful and accurate measurement of intent, it is strongly recommended to include at least 12 topics that reflect the company’s genuine areas of interest.
Providing too few topics — or omitting the list entirely — will return all available topics for the company, which may include a wide variety of unrelated subjects and lead to misleading or noisy intent insights.

​
Processing

The enrichment process queries Bombora’s weekly dataset to identify surging topics for each company in the provided list.
Only topics with composite_score >= threshold are returned, where:
  • threshold = min_score (if provided and >60)
  • otherwise threshold = 60 (default)

​
Output

Returns, for each input business_id, the company’s top intent topics, including the composite score, engagement level, and weekly timestamp.
curl -X 'POST' \
  'https://api.explorium.ai/v1/businesses/bombora_intent/enrich' \
  -H 'accept: application/json' \
  -H 'api_key: "your api_key" \
  -H 'Content-Type: application/json' \
  -d '{
  "request_context": {},
  "parameters": {
    "topics": [
      "training & development: corporate universities;training & development: career management;training & development: group coaching;training & development: classroom management;training & development: experiential learning;training & development: gmat test preparation;training & development: seminars and educational programs;training & development: sat preparation;training & development: team building;training & development: gmat test preparation;training & development: essential skills;training & development: gre test preparation"
    ],
    "min_score": 76
  },
  "business_id": "8adce3ca1cef0c986b22310e369a0793"
}

{
  "response_context": {
    "correlation_id": "34e96a94f55d40a18994f55b2f36ecb8",
    "request_status": "success",
    "time_took_in_seconds": 43.515
  },
  "data": {
    "business_id": "8adce3ca1cef0c986b22310e369a0793",
    "company_name": "apple",
    "company_website": "apple.com",
    "date_stamp": "20251102",
    "intent_topics": "[{\"topic\":\"training & development: gmat test preparation\",\"composite_score\":76},{\"topic\":\"training & development: seminars and educational programs\",\"composite_score\":82},{\"topic\":\"training & development: classroom management\",\"composite_score\":77},{\"topic\":\"training & development: experiential learning\",\"composite_score\":76}]",
    "level_of_intent": "In Depth Research",
    "topic_count": "4 / 12"
  },
  "entity_id": "8adce3ca1cef0c986b22310e369a0793"
}
  • Always provide valid business_id inputs retrieved from the Match Businesses API.
  • Specify topic_parameters to focus enrichment results on relevant industries or technologies.
  • Use min_score only if you wish to raise the threshold above 60 — values ≤60 are not valid.
  • Check for null values — if Bombora has no data for a given domain, all topic-related fields will return null.
  • Combine intent data with firmographic and technographic enrichments for full go-to-market insights.
  • Use the Autocomplete endpoint with business_intent_topics — and preferably semantic_search=true — to build accurate and relevant topic_parameters before running enrichment.
SignalAPI NameDescriptionData Type
level_of_intentLevel of IntentIndicates how actively a company engaged with selected topics during the past week.

Calculation: Number of topics with a composite score above 25% divided by the total number of input topics.

Ranges:
• >25% → In-Depth Research
• 5–25% → Active Research
• <5% → Early Research

Recommendation: To ensure a meaningful and accurate reflection of intent, it is recommended to input at least 12 topicsthat are relevant to the areas of interest.

Returns null when no input topics are available.		CATEGORY
topic_countTopic CountShows the number of topics with a composite score above 60, relative to all topics evaluated (e.g., 5/5). When no input topics are provided, all topics are returned.TEXT
intent_topicsIntent TopicsContains an array of objects summarizing the company’s active interest in specific Bombora topics over a given week. Each object includes the topic name, category, composite score, and date.TEXT
company_websiteCompany WebsiteThe company’s primary website or domain URL.URL
company_nameCompany NameThe official or recognized name of the company.TEXT
date_stampdateThe date of the most recent Bombora data file (weekly), in format YYYYMMDDTEXT
📌 _For additional enrichment options, explore related API endpoints below.

Authorizations

​
api_key
string
header
required

Body

application/json
​
business_ids
string[]
required
Required array length: 1 - 50 elements
​
request_context
object | null
Example:

null

​
parameters
object

Response

Successful Response

This is base response model for all responses in partner service.

​
response_context
object
required
​
total_results
integer
required
​
data
BusinessesBulkEnrichRow[BomboraIntentOutputSchema] · object[]
​
entity_id