Geoblink Data Sources

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.

Customer lifetime value (CLTV) is the expected profit that a single customer brings to a company over the course of their lifetime. CLTV represents a shift in emphasis from quarterly or annual profits to the long-term relationships with customers.

Most if not all companies and organizations seek to establish themselves in social media platforms such as Facebook, Twitter, and Instagram in addition to their official website. Online and social media performance tracking is meant to analyze the effectiveness of a company’s social media presence. Are they generating new leads? Are they promoting brand awareness? These questions are important to assess the efficiency of the use of these social media platforms.

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.

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.

Property value and mortgage value assessment is the estimation of the future value of a particular asset for purchase, insurance, and more.

Unlike most other purchases, real estate tends to rise in value over time. This rise is influenced by economic and social trends, environmental conditions, and on governmental controls and regulations.  These large-scale trends directly affect the general demand for property ownership, the scarcity of property for purchase, the utility of the property for potential owners, and the ease at which property rights can be transferred. 

Remarketing campaigns show ads to people who have visited a business’s website or downloaded one of its apps. Remarketing identifies people who have shown interest in a company in order to prompt them to recall the business, increasing the odds of them converting.

Nowadays, the abundance of data and the advances in Machine Learning and big data applications reduce the need for top-down segmentation of customers. Smart customer clustering based on many commonalities help companies better address customer needs to provide the right experience and divide resources efficiently.

Algorithmic stock trading—aka “algo-trading”—uses machine learning algorithms to make stock market trades faster than a human could, calculating the best time, price, and amount to trade in an instant.

Brand awareness is the degree to which a customer is able to recall and recognize your brand. Brand awareness is critical for promotion and marketing, especially for a young company. High brand awareness can result in a company becoming very popular or “trending.”

B2C fraud includes insurance fraud, payment fraud, identity theft, etc. and reviewing claims is so time-consuming and difficult that many insurers do little more than a cursory review on small claims. Fraudsters know this and will file—and win—small claims for losses that didn’t really occur.  

Credit scoring is a statistical analysis performed by lenders and financial institutions to assess a person’s creditworthiness for mortgages, credit cards, and private loans. Credit scoring is used by lenders to decide whether to extend or deny credit.
Traditionally, a person’s credit score determined by credit bureaus is a number between 300 and 850 with 850 being the highest credit rating possible. As new types of lenders and insurers emerge, however, the traditional credit score becomes just one parameter joined with a large variety of alternative data that helps determine a person’s creditworthiness. 

Fighting money laundering is a complicated task with substantial costs and risks, including—but not limited to—regulatory, reputational, and financial crime risks. Money laundering can be difficult to track, with many false alerts making detection even more challenging. But new technology, such as artificial intelligence (AI) and big data, can increase detection rates and keep your firm safe.

Portfolio management is the management of investments to meet long-term financial objectives.

Today, machine learning models and external data are used in order to help companies and individuals better manage, diversify, and maintain their assets and take on less risk for higher reward.
Today, machine learning models and external data are used in order to help companies and individuals better manage, diversify, and maintain their assets and take on less risk for higher reward. Portfolio management models and use cases include any collection of investment instruments like shares, mutual funds, bonds, FDs and other cash equivalents, etc.

Many companies supply goods, loans, and services based on business and trade credit, either invoicing customers for payment at a later date or providing B2B loans. Business credit risk management assists companies with lending decisions based on a client’s financial health as well as other parameters that may indicate how likely they are to pay on time. Providing the right amount of credit will reduce the risk of late payments or defaults, which expose the vendor to financial risk.