medium.com
Open in
urlscan Pro
162.159.153.4
Public Scan
Submitted URL: https://tailskewemail.mxficus.com/6554e657633cbd725ebb3fa1/l/DnEEJCP00QsGfhRxk?rn=gI1FmbyVmVgMXZsJXYoNkI&re=gIt92YuMncl5GdyFGcoR3d...
Effective URL: https://medium.com/@carriere.maxime93/similarity-company-analysis-a-free-model-for-comprehensive-competitor-identif...
Submission: On February 07 via api from ES — Scanned from ES
Effective URL: https://medium.com/@carriere.maxime93/similarity-company-analysis-a-free-model-for-comprehensive-competitor-identif...
Submission: On February 07 via api from ES — Scanned from ES
Form analysis
0 forms found in the DOMText Content
Open in app Sign up Sign in Write Sign up Sign in SIMILARITY COMPANY ANALYSIS: A FREE MODEL FOR COMPREHENSIVE COMPETITOR IDENTIFICATION Carriere Maxime · Follow 2 min read · Nov 30, 2023 35 Listen Share Investing or starting a business in a specific industry can make it challenging to identify competitors. While platforms like Refinitiv or Bloomberg offer this service, their average cost exceeds 20k per year. To address this issue, I’ve created a free model that helps find similar companies based on publicly available online information. How does the model work? Model breakdown The model has three parts: (1) a search model, (2) a parse model, and (3) a matching model. Using just the company name, website, and a brief description, the search model retrieves pages from Google and other sources. Next, the parse model eliminates common words, focuses on important information using word frequency and semantic relevance tools like KeyBert and Rake. Lastly, the matching model compares these extracted words with our database, which includes information on over 70,000 companies worldwide. Similary Matrix The matching model generates a matrix showing how semantically similar a company’s information is to our database (left). By applying straightforward Hierarchical Clustering, you can see distinct clusters in darker colors, each representing various fields (right). For example, the large cluster in the center is linked to tech companies, further divided into smaller sectors. What output? The model provides the top 10 matches for both public and private companies. It includes their countries of operation, websites, and a confidence index (green: high; orange: medium; red: low). Why choose this over Bloomberg? This model’s advantage lies in its reliance on up-to-date online research. It ensures the information on companies is current. Additionally, the flexibility of the short description allows for a detailed breakdown of a company. For example, Tesla engages in various areas: cars, batteries, and autonomous vehicles. If the description focuses on a specific sub-sector, the model identifies competitors within that sector while maintaining an overall understanding of the industry. Where to try? How to help? The model is entirely free to use at https://tailskew.com/, and no information is needed. Feel free to use it and provide as much feedback as possible to help enhance the model and share what you’d like to see in the future! Maxime Carrière SIGN UP TO DISCOVER HUMAN STORIES THAT DEEPEN YOUR UNDERSTANDING OF THE WORLD. FREE Distraction-free reading. No ads. Organize your knowledge with lists and highlights. Tell your story. Find your audience. Sign up for free MEMBERSHIP Access the best member-only stories. Support independent authors. Listen to audio narrations. Read offline. Join the Partner Program and earn for your writing. Try for $5/month Investing AI Finance Machine Learning Economics 35 35 Follow WRITTEN BY CARRIERE MAXIME 1 Follower Follow RECOMMENDED FROM MEDIUM Cassie Kozyrkov WHAT ARE: EMBEDDINGS? VECTOR DATABASES? VECTOR SEARCH? K-NN? ANN? A SIMPLE EXPLAINER TO DEBUZZ THESE AI BUZZWORDS ·6 min read·4 days ago 1.1K 14 Ryan O'Sullivan USING CAUSAL GRAPHS TO ANSWER CAUSAL QUESTIONS THIS ARTICLE GIVES A PRACTICAL INTRODUCTION TO THE POTENTIAL OF CAUSAL GRAPHS. 9 min read·Jan 31 35 LISTS PREDICTIVE MODELING W/ PYTHON 20 stories·874 saves THE NEW CHATBOTS: CHATGPT, BARD, AND BEYOND 12 stories·296 saves PRACTICAL GUIDES TO MACHINE LEARNING 10 stories·1017 saves NATURAL LANGUAGE PROCESSING 1162 stories·639 saves Valerian Ccn in softplus-publication EXPLORING RECOMMENDATION SYSTEM ALGORITHMS: FROM CLASSIC TO CUTTING-EDGE THIS ARTICLE PROVIDES A COMPREHENSIVE EXPLORATION OF RECOMMENDATION SYSTEM ALGORITHMS, RANGING FROM TRADITIONAL TO STATE-OF-THE-ART. 7 min read·Jan 20 200 1 Samuele Mazzanti in Towards Data Science ARE OUTLIERS HARDER TO PREDICT? AN EMPIRICAL ANALYSIS ABOUT WHETHER ML MODELS MAKE MORE MISTAKES WHEN MAKING PREDICTIONS ON OUTLIERS ·8 min read·2 days ago 318 13 Austin Starks in Artificial Intelligence in Plain English REINFORCEMENT LEARNING IS DEAD. LONG LIVE THE TRANSFORMER! LARGE LANGUAGE MODELS ARE MORE POWERFUL THAN YOU IMAGINE 8 min read·Jan 13 852 24 Nikhil Adithyan in Level Up Coding STOCK MARKET SENTIMENT PREDICTION WITH OPENAI AND PYTHON AN INTERESTING EXPLORATION OF THE POWER OF LLMS IN STOCK ANALYSIS 11 min read·2 days ago 216 1 See more recommendations Help Status About Careers Blog Privacy Terms Text to speech Teams To make Medium work, we log user data. By using Medium, you agree to our Privacy Policy, including cookie policy.