In the age of technology and standardization, AI’s contribution to driving business decisions has been pivotal and exemplary compared to traditional methods, which struggle to cope with the sheer volume of information and analysis. Artificial Intelligence has revolutionized market analytics, unlocking unprecedented insights into consumer behavior and market trends in the AI-driven market.
AI driven market analytics tools use advanced algorithms and machine learning techniques to excerpt actionable insights from huge datasets in real-time. According to research from Markets and Markets, the AI industry is projected to reach $1.4 billion by 2025, growing at a CAGR of 16.5% from 2020 to 2025. This astounding rise underlines the growing adoption of AI technologies to gain a competitive edge in the market.
AI Predictive Analytics – The Futuristic Research Method
AI algorithms can forecast future trends, demands, and consumer preferences by analyzing historical data and identifying patterns. According to research, more than 80% of global marketers use AI for their online marketing campaigns. There are three types of AI marketing solutions:
- Machine Learning
- Big Data and Analytics
- AI Marketing Platforms & Tools
AI market analytics have improved in recent times, courtesy of companies like Google, IBM, OpenAI, Microsoft, etc. Metso, a Finland-based firm, specializing in sustainable mineral-processing technology infused AI driven asset optimization and predictive maintenance. The solution was a time-saver; problems, such as poor energy efficiency, minimal use of fixed assets, and high downtime were addressed meticulously. Concurrently, advanced AI algorithms were programmed to detect failure patterns and provide potential solutions.
Application of AI Predictive Analytics
Following are some pertinent aspects managed by predictive analytics:
- Fraud Detection
- Forecasting and Predicting Purchasing Behaviors
- Maintenance of Equipment
- Healthcare Diagnosis
- Risk Modeling
- Customer Segregation
- Cross-Selling & Upselling
- Content Recommendations
- Minimizing Customer Churn
Traditional location-based segmentation methods are limited in their capacity to manage the complexities of consumer preferences. On the other hand, AI algorithms can examine various data sources, including SM interactions, and browsing and purchase history to create proportionate consumer segments.
Netflix, for instance, uses AI algorithms to examine viewers’ watching habits for personalized content recommendations. This approach propels user experience and drives engagement and brand loyalty. According to research by McKinsey, firms that personalize client experiences with AI observe a 6-10% increase in returns.
Besides data segmentation, AI is used to handle competitive intelligence and market monitoring. Modern analytical tools and platforms can amass and depict outcomes based on vast amounts of data from different sources, including social media mentions, news articles, and customer reviews to render real-time insights.
Companies like SAS and IBM provide AI driven market intelligence platforms that use NLP (natural language processing) and sentiment analysis to manage online conversations. By standing out of the competition, firms can adapt their strategies in response to altering market conditions to gain an advantage.
According to Jeff Howell, Director of Growth at Alayacare, “We work with Element AI to produce algorithms that predict negative health events in the elderly (at homes). Seniors would take different vitals each day with AI analyzing the pattern to provide a comprehensive diagnosis of the client’s age, gender, and other aspects. We reduced ER visits and hospitalizations by 73% and 64% amongst a chronically ill patient set.”
Would you want artificial intelligence systems to alter your working process and make it more efficient? Propel your business today!
