--- title: "Introduction to HG Insights Data Studio" slug: "introduction-to-madkudu-data-studio" updated: 2025-11-05T15:57:37Z published: 2025-11-05T15:57:37Z canonical: "help.madkudu.com/introduction-to-madkudu-data-studio" --- > ## Documentation Index > Fetch the complete documentation index at: https://help.madkudu.com/llms.txt > Use this file to discover all available pages before exploring further. # Introduction to HG Insights Data Studio HG Insights Data Studio allows you to easily edit your scoring configuration and much more. The Studio allows you to - understand who is your Ideal Customer Profile**(**ICP**)** - create models during your onboarding and edit them: adding rules based on your Sales team feedback, surfacing more information in your CRM, adjusting the volume of MQLs ... - create Computations and Aggregations to export directly to your CRM - analyze the DNA of a list of leads - create predictive models or simple rule-based segmentations based on Firmographic data, Demographic data and Technographic data - preview samples of scored leads before updating your model in production - ... and much more! ### **Where do I see my model(s)?** In the HG Insights Admin App, click Data Studio and then Open Data studio ![](https://cdn.document360.io/a55e7ea5-b8ac-4456-874d-10cc92097370/Images/Documentation/image(700).png) Alternatively, you can also go to studio.madkudu.com and authenticate. You will arrive on the home page where you can - View the list of live models, if any - Click on the model name to **Open** it - **Rename** model name - **Duplicate** the model to start from a copy instead of modifying the model live in your system - **Archive** models - **Create** new models ![](https://cdn.document360.io/a55e7ea5-b8ac-4456-874d-10cc92097370/Images/Documentation/image(551).png) ### What are the different models? - [Customer Fit](/v1/docs/customer-fit-scoring) - PQL = [Likelihood to buy at the Person level](/v1/docs/lead-engagement-scoring) (which includes the [Lead Grade](/v1/docs/lead-grade-scoring)) - MQA = [Likelihood to buy at the Account level](/v1/docs/account-engagement-scoring) ### What is the difference between Realtime and Batch? The Customer Fit model can be used to score your leads in Real-time, to send your leads straight to Sales as soon as they request a demo for example or as soon as they are created in your CRM, while a **batch** scoring would make sure their score is regularly updated with any additional information collected. [Learn more about realtime and batch scoring.](/v1/docs/batch-versus-real-time-scoring-in-your-integrations) The real-time model can only use the data available... in real time, while the batch model can use all the data you have in your CRM, from your Enrichment provider or Sales input. Therefore the 2 models do not have access to the same information and therefore need to be built separately. [Learn more about how HG Insights uses your CRM data in the customer fit model.](/v1/docs/combined-computations-leveraging-enrichment-from-madkudu-and-your-crm) --- ### FAQ #### Who has access to the Data Studio? The platform is accessible - in read-only mode for users with the role "**user**" -> they can see how the model is configured, but cannot modify it. - in **edit mode** for users with the role **Architect**, and **Admin** [Learn more about user roles](/v1/docs/user-roles-permissions) #### Do I need a Data Science background to use the platform? Nop! We'll point out a few core concepts on the way but the goal is to remove any complexity or need for prior Data Science knowledge. However, if you are a Data Scientist, there would be advanced parameters you would be comfortable changing. #### Do I need to know any code to configure HG Insights scoring? No! You may run into some SQL code but on advanced features only allowing more advanced customization. If you are a SQL ninja on the other hand, then you'll be able to play around with these advanced configurations. MadKudu's platform to create scoring models, computations and segmentations. Ideal Customer Profile - your definition of what demographic, firmographic, and technographic traits make up an ideal customer enrichment traits used in Fit models or segmentations (e.g. industry, company_size, tag_is_b2b, has_dbms_tech, is_hiring...). Behavioral aggregation used to account for event frequency (e.g. number of active users in x days, number of pageviews in x days). data points on a Company (industry, company size, country ...) data points on a Person (title, role, seniority, Twitter handle ...) data points on a Company tech stack (before or behind the firewall) model predicting the Likelihood to Buy of a Person. It shows the level of engagement of the lead/contact, based on behavioral data. model predicting the Likelihood to Buy of an Account. It shows the level of engagement of the account, based on behavioral and intent data. model predicting the Fit of a Person, based on firmographic and technographic data processing and scoring of records in realtime, as opposed to batch scoring. firmographic, demographic, technographic data points collected on users and account from your systems or MadKudu's 3rd party providers (Clearbit, PredictLeads).