In March 2018 Eagle Alpha highlighted lack of consumer interest in the launch of Samsung’s flagship Galaxy S9 handset using data from its Web Queries tool and Google Trends.
This data can provide an insight into long-term trends, as well as the most recent trading performance, and is based on metrics such as average selling price (ASP) and share of “bestselling” products in a category, as ranked by the website provider.
Eagle Alpha correctly predicted TJX results using consumer transaction and online search data.
With these insights, analysts can benchmark performance against competitors, reveal competitors' online strategies, discover new opportunities, identify emerging trends and understand consumer intent and journey.
The vendor tracks every company operating in the U.S. and their locations, accounting for over 95% of U.S. employment.
This company delivers data and analytics based on purchase behaviour from a panel of 2.5 million+ US shoppers.
This dataset is generated by the XYME framework using a machine learning model ensemble containing more than 200,000 models (approximately 500 models for each ticker).
The purpose of this report is to provide asset managers with examples of how alternative data can be used. This report is the fourth in a series of reports published by Eagle Alpha.
This data can provide insight into sales and sales by square foot estimates (including traffic by individual location), separate store and parking lot traffic, dwell times, and allows for forecasting.
The company develops geospatial data analytics to help its clients unlock societal and economic trends at a global scale.
Using web crawled and online search data, Eagle Alpha correctly predicted the GoPro results.
Using online search data, Eagle Alpha correctly predicted the UAA results.
