Obsidian is a financial analytics platform for SaaS companies that assigns cost to each user action and helps you build P&L at a customer level. Obsidian is the first unit economics intelligence platform, which help you measure metrics at a customer level. This will enable you to optimise your sales, marketing, and operations.
With Obsidian you can track customer P&L, find expensive features and vendors, and look at real-time customer profiles.
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SaaS companies looking to optimise their sales, marketing, and operations by understanding their customer economics. This includes decision-makers in finance, sales, marketing, and product teams.
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Obsidian Analytics is the first platform to offer unit economics intelligence specifically for SaaS companies, allowing customer-level profitability analysis and cost assignment to individual user actions.
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We are the first to create unit economics intelligence, giving you
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Obsidian Analytics was born as many SaaS companies struggle to accurately measure and optimise their unit economics (a problem we were struggling with before). So, we created a comprehensive, customer-centric view of profitability, enabling businesses to make informed decisions about their product, pricing, and growth strategies. By leveraging advanced analytics and integrating with existing data pipelines, Obsidian Analytics aims to empower SaaS companies with the insights they need to drive sustainable growth and profitability.
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Vue for client side, databases (PostgreSQL, MongoDB), and cloud infrastructure (GCP).
Based on our record, Plotly seems to be more popular. It has been mentiond 30 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / 7 days ago
For dashboards: - https://plotly.com/ is probably my favourite, but there are others like streamlit, voila and others... Source: 7 months ago
If your CEO wants you to solo build an alternative to Tableau, PowerBi, or even Plotly then consider him/her delusional. Source: about 1 year ago
Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: about 1 year ago
I use plotly and like it a lot. It is slower though. Noticeable if you want to batch-generate a bunch of images and dump them into a folder. But that probably isn't the case most times. Source: about 1 year ago
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