- 26 Mar 2023
- 1 Minute to read
- Updated on 26 Mar 2023
- 1 Minute to read
A group of signals with mutual domain and/or topic. Examples for data sources: Firmographics, Business web presence, Techographics.
The processed data Explorium is serving to the client. Examples for enrichments: Company insights, Companies financial report, Website traffic trends over 3 years
The data is organized in the context of the real-world entities they represent.
for example: ORGANIZATION is an entity we can ask multiple questions about, like what is the revenue of the organization? where its HQ are located? How many people are working in this organization?
The answers are represented in the data, all connected to the entity it's describing.
The basic enrichment unit. A signal can be a calculated attribute, based on one or more other attributes, raw or calculated. examples for signals: fraud score, unified revenue range.
Signals are used to extract features to help you make a decision. For example, maybe you are trying to score your lead. Any piece of information on the opportunity can be used to drive the actual decision, for example high likelihood to close.
|5 types of steps:
Enrich - Enable to pick and choose relevant signals and add them to your data
Transform - Enable to conduct data manipulation on the data to best serve the desire business outcome
Score - Enable to define rule sets creating a calculate metric based on the customer data and selected enrichments
Predict - Enable to build a predictive model based on historical data and then apply it on new data.
Filter - Enable to filter out the dataset based on rule sets
A recipe is compose from a sequence of steps, which are been executed every time a recipe is applied on a new data.
The percentage of queries that returned any information (not null) out of the total number of queries sent to the service.
Data Classifications (Ontology)
|The types of input value(s) required to run an enrichment. Examples of data classifications (ontologies): URL, company name, email address. Click here for more information about data classifications (Ontology).
Estimated Coverage (per signal)
|The percentage of queries that returned any information (not null) out of the total number of queries sent to the service.
Estimated Max Coverage (per dataset)
The maximum estimated coverage that was calculated from the list of signals in the dataset.