AER – Earth provides research & studies on everything interconnected with our planet through physical processes that links the atmosphere, oceans, and Earth. They study new predictors of seasonal weather trends, estimating increase in flood risk in the future, and innovations in modeling the Earth’s energy cycle in global climate models.
A store’s location directly impacts supply, profits, marketing, and almost every other aspect of store performance. Much can be learned from the performance of different types of retail stores in different neighborhoods, countries, or even climate areas. Therefore, retailers use geospatial data machine learning models to plan store locations and predict profits.
Insurance claims management is the process of managing a claim from reception to settlement. The insurance claim process is particularly suited to machine learning solutions as much can be done to cut time and costs, leading to speedier resolution of claims to the satisfaction of both insurer and insured.
An online recommendation engine is software that analyzes available user data to generate suggestions for something the user may also be interested in. Such engines are used in an advertising capacity to promote products or services or for the purpose of recommending similar content to the content the user has consumed in the past, such as in streaming services and online shopping services.
Product performance forecasting predicts future sales volumes—at what price, during which time, and in which market. It enables businesses to make informed decisions in both the short term and the long term. Nowadays, companies rely on machine learning software for product performance forecasting as these programs continuously improve, providing businesses with faster and more reliable estimates all the time.
Planet OS Datahub offers weather, geospatial, & other earth data, both historical & forecasted. Users can even choose image resolution
AER – Earth provides research & studies on everything interconnected with our planet through physical processes that links the atmosphere, oceans, and Earth. They study new predictors of seasonal weather trends, estimating increase in flood risk in the future, and innovations in modeling the Earth’s energy cycle in global climate models.
A store’s location directly impacts supply, profits, marketing, and almost every other aspect of store performance. Much can be learned from the performance of different types of retail stores in different neighborhoods, countries, or even climate areas. Therefore, retailers use geospatial data machine learning models to plan store locations and predict profits.
Insurance claims management is the process of managing a claim from reception to settlement. The insurance claim process is particularly suited to machine learning solutions as much can be done to cut time and costs, leading to speedier resolution of claims to the satisfaction of both insurer and insured.
An online recommendation engine is software that analyzes available user data to generate suggestions for something the user may also be interested in. Such engines are used in an advertising capacity to promote products or services or for the purpose of recommending similar content to the content the user has consumed in the past, such as in streaming services and online shopping services.
Price optimization is the mathematical analysis by a company determines the response of potential buyers to different prices for its products and services. The aim is to meet a company’s objectives of maximizing profits and growing and retaining a customer base.
Product performance forecasting predicts future sales volumes—at what price, during which time, and in which market. It enables businesses to make informed decisions in both the short term and the long term. Nowadays, companies rely on machine learning software for product performance forecasting as these programs continuously improve, providing businesses with faster and more reliable estimates all the time.
Promotional planning is the process of optimizing marketing tools, strategies, and resources to promote a product or service to generate demand and meet set objectives. Artificial intelligence (AI) can be used to effectively plan promotional events, measuring their outcomes, and adjusting as necessary to achieve growth.
The shelf is a dynamic environment, where shoppers select items purposefully and on impulse, where store owners showcase products to entice customers in the store and online.
Inventory management does not just entail having the right stock but also ensuring said stock was effectively sourced, stored, and sold at the right price, at the right time.
Exante Global Flow Analytics supports alpha generation and risk management by extracting comprehensive price signals from detailed capital flow analysis. Exante complements hard data and raw model outputs with timely, narrative-based content, focusing on key global thematics and risk scenarios. Additionally, Exante maintains dialogues with their clients, providing bespoke coverage and service.
Stirista offers data that politicians and advisors can use to reach out to constituents and donors. Stirista has (so far) 150 million registered voter details sorted into 360 data points, including donation history. With this contact, demographic, web, and behavioral information, Stirista Political Data enables targeted advertising and outreach.
Stirista can also provide historical campaign data at all levels to help politicians plan effective political strategies.
Acxiom Infobase provides customer insight data for targeted marketing campaigns in a wide variety of industries
Acxiom Personix offers people and customer lifetime value data as well as consumer behavior profiles
AnalyticsIQ’s BusinessCore Database provides B2B marketing data on 18 millions businesses and 60 million business professionals.
The BusinessCore Database collects company data and people data. The company data includes contact information, purchase drivers (price, for example), and transaction history. The people data includes personnel contact data, role in the company, and purchase transaction history.