My Process for Ripe Exits
Understanding and discovery played pivotal roles in tailoring our design to precisely meet user needs.
Given Ripe Exits' internal focus and testing with select clients, our primary emphasis was on prediction accuracy and the advantages of being a 'first mover' when assets ripen.
Applying AI in the Private Equity industry
Sourcing valuable and accessible data
Conducting extensive research
Leveraging cluster analysis to comprehend PE firms' behavior patterns
Crafting an ML pipeline for ripe exit predictions
Ripe Exits: Predictive ML for PE Asset Sales
Can Machine Learning forecast Private Equity asset sell-offs?
This journey began in parallel with data collection from the engineering team and interviews with sales experts in the Fin-tech industry's vertical. The Mergers and Acquisitions market is time-sensitive, and Ripe Exits aims to predict the optimal moment to sell assets.
Intralinks, the company testing Ripe Exits, possesses years of market data, making it an ideal training ground for a Machine Learning algorithm to predict the ripest assets.
Part of the ecosystem
Recognizing Ripe Exits as part of the broader Intralinks ecosystem is crucial. It allows us to define goals and guide users to the next phases of the deal: Due Diligence and Deal Closure, areas for which Intralinks offers specific products.
Project: Ripe Exits
Senior UX/UI Designer
FinTech, Investment Banking, Web App, Dashboard, Analytics
Data visualisation to Ripe prediction
With all data, swift decision-making is essential. Ripe Exits steps in to analyze vast datasets, and as Principal Product Designer, I proposed data visualizations to facilitate decision-making and highlight areas requiring immediate attention.
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How do buyers define right now when an asset is "ripe"? What are their cues?
Who is the one that makes the analysis of the market and highlights potential "ripe" assets?
In the competitive Mergers and Acquisitions market, timing is critical. Ripe Exits, true to its name, seeks to predict the ideal moment to sell assets. Intralinks, already possessing years of market data, forms the foundation for training a Machine Learning algorithm to pinpoint the ripest assets.