Acuris Attention Advertising

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.

Hedge fund investments use several different strategies to achieve returns in both domestic and international markets. They are often aggressively managed and trade in land, real estate, stocks, derivatives, currencies, etc. markets.

The most lucrative hedge funds also have a diverse portfolio—which means investors need to analyze large amounts of data to make investment decisions.

Everyone wants more leads, but the more we are able to generate, the harder it becomes to identify which of them are actually worth the time and effort spent in order to try to convert them. Lead scoring models let you automatically rank your leads in order of the perceived value each lead represents to your company. Resources for marketing and sales can then be distributed by the priority determined by lead scoring.

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. 

The abundance of job seekers nowadays leads many companies to use machine learning throughout their recruitment process. Some examples of this can be found in the fields of talent management, talent attraction, and candidate selection.

Talent management entails understanding the best fields, positions, and backgrounds to set talent up for success. Talent attraction selects the top outlets and channels for attracting the most suitable employees. Finally, candidate selection use neuro-linguistic programming (NLP) tools on CV’s to find suitable candidates.

Apartment rental prices are influenced by various factors. The aim of a house or apartment rent prediction model is to analyze the different features of an apartment and its surroundings to generate the most suitable price for rent.

Organizations must satisfy the unique demands of their customers. The good old days of mass marketing and predictions on limited mixture of products in one location are gone. Conversely, the use of artificial intelligence in inventory generates huge dividends to companies willing to reshape their supply chain orders.

This technological development results from the availability of massive amounts of real-time data now routinely generated by enterprise software systems and smart products. By collecting this data, organizing it, and interpreting it, artificial intelligence and machine learning have introduced an entirely new level of data processing leading to deeper business insights.

AI has been used to improve supply chain and demand forecasting for a couple of decades. Demand planning applications use data driven algorithms to take historical data and use it to forecast. Demand forecasting can include promotion planning or stock and sales forecasting. The machine looks at the forecast, compares it to actual shipments, and suggests alternatives and optimization options. Over time, many companies started doing more specific forecasting for specific regions, products or stores or for more granular points in time. Both retailers and their suppliers (CPG companies or 3rd party shipping and supply chain companies) use their data to help conduct automated decision making within the supply chain.

Targeted marketing is a marketing strategy which identifies an audience that is most likely to buy a product or service and subsequently creates a marketing campaign designed specifically to advertise said product or service to the target audience, using advertisements and promotional messages. This allows different companies to hone in on certain market segments, creating a “specialty” and possibly lowering competition with similar companies.

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.”

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.