Business

5 Applications of Machine Learning in Business

Published

4 years ago

December 19, 2020

Machine learning is a form of artificial intelligence. It allows systems to learn and improve from experience without the need for explicit programming. This process also automates analytical model building. From financial services to healthcare, it can deliver a plethora of benefits, such as reduced cost and improved efficiency. Across several industries, it has a wide array of applications, including those we’ll be talking about below.

Before we start, if you want to learn more about how to successfully integrate machine learning in business, consider studying online! With a machine learning course, you will know how to make the most out of such technology to improve business processes.

1. Dynamic Pricing

Also known as demand pricing, dynamic pricing uses real-time supply and demand to dictate price. The actions of a customer, such as engaging with a marketing campaign, will also provide the basis for pricing. It requires processing massive amounts of information, and this is one area where machine learning can be helpful. It mines data without programming. This will use advanced software that learns more as it is fed with more information.

2. Spam Detection

In the past, emails were filtered using a rule-based system. It relies on built-in knowledge. Machine learning offers a more sophisticated alternative. It does not need direct programming to mine data and makes sense of available information. It uses brain-like neural networks, which will be more intelligent in filtering spams. It recognizes junk mail and phishing messages to make a business less vulnerable to data breaches.

3. Fraud Detection

Beyond spams, machine learning also has a significant role in improving cybersecurity by detecting fraud. It can understand patterns in an instant, making it quick to spot potential anomalies. This explains why the finance sector is one of the biggest users of machine learning today. An example of its application would be in credit card usage. Machine learning stores data about usage, such as location. So, when it detects that a card is used in another country, it can automatically flag a transaction to prevent fraudulent activity.

4. Churn Modeling

From credit card companies to cable service providers, customer churn is one of the most important concepts to understand. It is the percentage of customers that stopped using a product or service within a period. Churn modeling aims to understand customer behaviors and will motivate businesses to elevate their strategies to improve customer retention. Machine learning uses data like demographics and sales for churn modeling.

5. Customer Segmentation

Separating customers into distinct groups requires a data-intensive approach and not just relying on intuition. With the help of machine learning, it is easier to cluster and classify your customers depending on factors like demographics or buyer personas. This will make it easier to understand the feelings, needs, motivations, and characteristics of customers, which will be crucial in creating more effective marketing campaigns, as well as products and services.

From dynamic pricing to customer segmentation, machine learning has a wide array of business applications. Regardless of the size and nature of your business, it can make processes more intelligent and efficient!

Business

Private Listings by Harold X. Clarke: A New Approach to Fine Real Estate

Published

4 days ago

December 16, 2024

Matthew Wiles

Photo credit: Private Listings by Harold X. Clarke.

Byline: Andi Stark

Private Listings by Harold X. Clarke, a real estate platform operating across Hawaii, is rewriting how properties are bought and sold in the region. Unlike larger firms reliant on public listings and mass marketing, Private Listings’ strategy prioritizes personalization, privacy, and meticulous curation of ultra-high-end, off-market properties, including oceanfront estates, gated community residences, and architectural masterpieces.

Harold Clarke, founder of Private Listings, describes their method as one that rejects “cookie-cutter solutions in favor of understanding the nuances of both buyers and sellers.” This approach has resonated with ultra-high-net-worth individuals (UHNWIs) seeking refined and discreet real estate transactions.

The Hawaiian real estate market remains a hub for global investors, with the median price for a single-family home in the state reaching $900,000 in 2024, according to the Hawaii Association of Realtors. Within this competitive landscape, Private Listings is building up to be a trusted name for properties that extend beyond luxury into generational investments.

Challenging the Industry Norms

Private Listings deliberately avoids the conventions of large-scale real estate firms. By focusing on fewer, higher-value properties, the company ensures that each transaction is treated with the same level of care and confidentiality.

Public listing platforms, while effective for broader markets, often expose sellers to unnecessary attention or unqualified inquiries. For Clarke, this model is misaligned with the needs of UHNWIs. “Privacy isn’t a luxury for our clients—it’s a necessity,” Clarke explains.

This philosophy has led Private Listings to handle some of Hawaii’s most significant real estate transactions, including off-market properties valued at over $40 million. Its success is not measured by the volume of listings but by the depth of trust built with clients, many of whom return for subsequent transactions.

Adapting to Changing Client Demands

While Private Listings maintains a foundation of traditional practices, the firm also recognizes the evolving needs of its clientele. The global real estate market is increasingly influenced by concerns over digital security, with a 15% rise in data breaches targeting high-net-worth individuals in the past three years, according to cybersecurity firm NortonLifeLock.

To address these risks, Private Listings employs rigorous screening for potential buyers and uses secure platforms for communication and transactions. The firm’s “by invitation only” model ensures that clients remain protected from the pitfalls of public exposure. Clarke notes, “Our goal is not just to sell homes but to create an environment where clients feel safe and confident during every step of the process.”

The Human Element in Real Estate Transactions

Despite advancements in technology, Private Listings firmly believes that real estate transactions cannot be reduced to algorithms or automation. Unlike firms that depend heavily on online data aggregation, Private Listings emphasizes human connection and insight.

The company’s sales strategy integrates personalized client interactions, in-depth market analysis, and years of experience navigating Hawaii’s unique real estate ecosystem. Clarke’s background in managing family assets and his global perspective is significant in shaping this essence.

Future Directions for Private Listings by Harold X. Clarke

As Hawaii continues to attract global attention, Private Listings aims to expand its influence within the state while maintaining its core principles. The company is currently developing a new platform to streamline services for UHNWIs, blending their demand for discretion with seamless access to Hawaii’s finest off-market properties.

Additionally, Private Listings is strengthening its ties with local communities, recognizing that sustainable growth benefits both the company and the islands’ ecosystems.

Private Listings by Harold X. Clarke has set itself apart in Hawaii’s real estate scene by moving away from the typical mass-market approach. Through a mix of traditional values and modern sensibilities, the firm continues to define what it means to transact ultra-high-value properties with integrity and care.