It can be used to analyze data and share insights across your organization or embed them in your app or website. It provides interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. Power BI is a business analytics service by Microsoft.
Pycaret to build the machine learning model, predict the lead scores and probabilities What is Power BI? PostgreSQL to store the new lead dataset with the lead scores and probabilities predicted by the machine learning model Power BI to visualize the results and build the dashboard with the data from the PostgreSQL database
Jupyter Notebook to build the machine learning model with pycaret PowerBI dashboard (image by author) Technologies Used Machine learning can be used to automate the process and make it more efficient. However, it is a time-consuming process and can be error-prone. Usually lead scoring is done manually by sales teams. Lead scoring is a common practice in the marketing industry and is used to prioritize leads for sales teams. The score is used to determine the likelihood of a lead converting to a customer. Lead scoring is a process of assigning a score to a lead based on their behavior and profile. Basic knowledge of machine learning classification models What is Lead Scoring? You can find the code for this tutorial on my GitHub here. We will then use the model to score the leads, store the new lead dataset in a PostgreSQL database and visualize the results in Power BI. We will use a dataset of 1000 leads and build a machine learning model with pycaret to predict the likelihood of a lead converting to a customer. This tutorial will walk you through the process of building a lead scoring dashboard with Power BI. Photo by Markus Winkler on Unsplash Introduction
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