Firmnav

Firmnav is a no-code AI platform for revenue teams that enables businesses to build and activate AI models on top of their own data. According to the vendor, it is designed for businesses of all sizes, from small startups to large enterprises. The target audience for Firmnav includes Chief Commercial Officers (CCOs), Chief Revenue Officers (CROs), Chief Sales Officers (CSOs), advisors, banking professionals, sales professionals, business development managers, marketing professionals, and sales operations managers. It caters to a wide range of industries including banking, manufacturing, telco, energy, corporate finance, insurance, private equity, SaaS & Tech, and services.

Key Features

Data integration: According to the vendor, users can connect their preferred commercial tools such as CRM, marketing automation, support, and warehouse. They can easily train AI models to power their commercial efforts with predictive analytics and activate their predictions directly into their existing stack.

No-code engine: The vendor claims that users can train AI models and make predictions without coding. They can leverage off-the-shelf AI models or build custom metric models without the need for data scientists or written code. This feature enables various use cases such as product recommendation, targeting, upselling, personalization, and resource allocation.

Data ingestion: The vendor states that users can connect and unify their own customer data into Firmnav AI Studio. They can utilize their own customer data to build AI models for various metrics such as customer lifetime value, win probability, deal size, sales cycle, and upsell.

AI modeling: According to the vendor, users can train AI models across different types, including binary classification, multi-class classification, multi-label classification, and quantitative predictions. They can refine their models with feedback using reinforcement learning and adaptive learning techniques.

AI predictions: The vendor claims that users can predict key customer metrics such as likelihood to buy a product, win probability, deal size, and sales cycle. They can gain insights into the factors driving these predictions and understand the reasons behind specific predictions through explainability features.

Data enrichment: According to the vendor, users can enrich their own customer data with external attributes such as financials, website text, social media, news, job posts, and funding. This external company data can enhance the accuracy and effectiveness of their AI models.

Activation channels: The vendor states that users can integrate and activate predictions directly into their preferred sales and marketing stack, including CRM, MAP, ads, and support. They can visualize and analyze predictions using custom metrics and visualization tools. Additionally, they can connect with various data sources, including core company data, CRM, data connectors, and data warehouse.

Web experience: According to the vendor, users can customize the web experience with features like semantic search, machine learning, webscraping, and pre-trained models. They can search for companies based on specific criteria and identify the most similar publicly-traded peers to any public or private company.

Custom metrics (e.g. kWh energy): The vendor claims that users can predict energy consumption for each company by leveraging internal data, smart data, and deep learning models. They can identify and select the right customers based on Ideal Customer Profile (ICP) profiles, segmentation, AI-powered customer analysis, and sales plays. Additionally, they can use AI customer predictions to improve win rates and understand potential future customers.

Win probability prediction: According to the vendor, users can utilize AI to predict the win probability for each deal in their pipeline. They can identify the top positive and negative predictors driving these win probability predictions and improve sales strategies accordingly.