4 Machine Learning Ways That will Help To Business Growth
- Businesses can use machine learning to develop software that understands natural human language.
- Machine learning can be used by businesses to increase the efficiency of their logistics and transport networks.
- Machine Learning allows businesses to take preventative maintenance steps to reduce downtime and increase profits.
- Machine learning allows businesses to leverage customer data to create user profiles and increase sales, as well as improve brand loyalty.
Machine learning is the key technology to the future of business. AI-driven software has already helped companies improve efficiency, customer relations, and increase sales.
Researchers estimate that machine learning has the potential to add $2.6 trillion in value to the marketing and sales industry by 2020, as well as another $2 trillion to manufacturing and logistics fields. According to the International Data Corporation, machine learning spending will amount to $77.6 Billion by 2022.
Companies of all sizes collaborate with Python development outsourcing companies to source data scientists and create custom data analytics software. Machine learning is a powerful tool for executives to increase their manufacturing and logistics efficiency, boost sales and provide a better customer experience.
What exactly is Machine Learning?
Machine learning, a new exciting discipline, combines key elements of mathematics and statistics to create a technology that is more than the sum of its parts.
Artificial intelligence and machine learning are based on the idea that engineers must be able to do more than just write programs to accomplish a task. Engineers should be able to write algorithms that teach computers how to create their own programs.
Importantly, the program must be intelligent enough to learn from past interactions and information. AI-driven software can write its programs, learn from past experiences and offer proactive solutions for the future.
Machine learning is being used by businesses to make actionable predictions and use the vast amount of data they have collected to help them invest in their business.
These are just four of the many ways that machine learning can help businesses grow.
1. Natural language
The tech industry’s greatest challenge since its inception is to create a program that understands natural language. The software has improved in recent years. Users can now type regular sentences into Google Search instead of using long search terms.
Computer programs are still unable to understand natural language or the speech used by humans every day. Machine learning is changing that.
AI-driven programs can learn from past mistakes and interactions. Applications like voice-activated assistants and search engines are now able to recognize regular human speech, allowing them to work confidently. These programs also improve their accuracy each day.
Executives and others can use voice-activated assistants such as Google Assistant or the Nuance Intelligent Virtual Assistant to increase efficiency and grow their businesses. They accomplish this in many ways.
First, AI-driven personal assistants are able to perform many of the same tasks that administrative assistants. These include booking flights, making appointments, and adding events to a schedule. They work 365 days a calendar, 24 hours a week.
These personal assistants also help employees save time during the day. In the past, professionals needed to manually search for historical data and other crucial information. Executives can ask their assistants to give them sales figures for a particular quarter or information about interest rates.
Retail and logistics are quickly becoming data analytics and machine-learning experts. Because their success is often tied to getting every penny out of every item,
Machine learning is a tool that companies can use to improve their logistics. It allows them to increase efficiency at every stage of the shipping, storage, and sales process. This technology allows forward-thinking companies to incorporate autonomous driving into their fleets.
Machine learning is being used by international shipping companies to increase their profits. These companies have installed thousands of components on long-haul trucks, cargo ships, and other smaller equipment. Managers can identify patterns of breakdown and create preventative maintenance plans that keep their trucks and ships in motion.
Machine learning is also being used by retailers like Amazon. Amazon, an online retailer giant, is using machine learning to improve its delivery network efficiency and anticipate customer needs.
Amazon, for example, has created an “anticipatory shipment” protocol that allows it to anticipate the geographic distribution and amount of orders for certain items. In anticipation of future purchases, the company now sends household and phone accessories to local distribution centers.
Already, the manufacturing industry is integrating machine learning technology at every stage of production.
AI-driven technology can save businesses money by streamlining inventory management, increasing production efficiency, and anticipating equipment breakdowns in advance.
The manufacturing industry enjoys a lot of data every day. Savvy companies like Seebo are using Python developers to create cutting-edge data analytics software. These programs make use of machine learning to predict seasonal manufacturing peak and lull periods and suggest process improvements. These programs also help companies avoid unexpected shutdowns by creating cost-saving maintenance plans.
McKinsey predicts that machine learning will help manufacturing businesses reduce material delivery times by 30% and achieve 12% fuel savings by optimizing their processes. Companies can also expect to increase their gross revenue by 13% when they integrate AI-driven technology into their businesses.
Deloitte estimates that machine learning could save businesses millions through preventative maintenance. Deloitte estimates AI-driven programs can help businesses reduce unplanned downtime by 15% to 30% and reduced maintenance costs by 20% to 30%.
4. Consumer data
Executives are excited to see the impact of increasing consumer data collection and analysis on profits and future growth. Over the last several decades, businesses have collected billions of data points about their customers. This includes information such as shopping habits, income, and demographic identifiers.
These companies can now use this data with AI-driven software. To build state-of-the-art data analytics software, executives are working with Python software developers to create useful and actionable forecasts.
Etsy, an online marketplace for retail, uses machine learning to enhance its customer experience. The company utilized the technology to create individualized customer profiles, improve search results, and improve user design.
The company’s unique use of data analytics is one of the reasons it has achieved annual revenues of $603 million despite stiff competition from larger retailers like Amazon and Target.
Another company that successfully uses AI-driven technology is Netflix. Machine learning is used to create extensive view profiles which accurately predict what movies and shows users will enjoy. Every time they scroll through new movies, customers interact with the program and give useful data.
Machine learning helps businesses plan for the future and increase sales. Companies of all sizes have started collaborating with Python web developers companies to hire data scientists and create software that encourages technology-driven growth.
AI-driven software has already been used in manufacturing and logistics to improve efficiency and increase sales. Retail companies have begun to work with Python development services in order to create custom software that analyzes customer data and improves sales.
The latest developments in natural language technology are expected to have a significant impact on both consumer devices and businesses. Artificial intelligence-driven personal assistants have already helped corporate employees save time while enhancing the quality of their work.