There has been a huge advancement in the incorporation of Artificial Intelligence (AI) technology by various industries and institutions. Sectors such as health, construction, agriculture, and business are the frontier institutions embracing the new technology. The awareness of how this technology augments productivity and reduces the cost of operation is the major reason behind its acceptance globally. Nowadays, AI is easily integrated into different tech devices to enhance their functionality. Even human communication has become more effective with the assistance of AI. According to a 2021 Truelist statistic, have ongoing investment in AI. 91.5% of leading global businesses The desire of many businesses to grow rapidly, increase their level of productivity and make more profits brought about the embracement of machine learning (ML), which is a subset of AI. What’s Machine Learning? Machine learning (ML) technology emerged to produce humans with precise information, analysis, and prediction of outcomes of any task with the help of data. ML is a unique form of technology that allows computer systems – AI software – to learn, adapt, improve, and produce results without following explicit instructions. In other words, ML can be described as a kind of AI technology capable of functioning and making decisions on its own without taking human instructions. ML makes use of algorithms to process and analyze data and draw inferences. There are two major types of algorithms used by ML, these are and . Supervised Learning Unsupervised Learning Supervised machine learning is when a data scientist or developer has to teach and guide the algorithm on how to read data and derive an accurate conclusion. This could be linked to how a toddler learns from his/her parents. Supervised ML cannot make decisions/predictions out of what has been taught through the algorithm. It only functions with labeled data. Unsupervised machine learning is the new phenomenon changing the narrative of AI technology. This gives computers (software) the ability to process, analyze complex data, and make predictions without human supervision. Unsupervised ML can run through unlabelled data and make accurate predictions. Many industries are working on incorporating this new technology into their operations.  According to a 2020 Mckinsey survey, of the 2,000+ industries and companies that participated are exploring or planning on using ML. over 50% So many companies are keen on leveraging ML in their operation. Since their major focus has always been on how to make more profit, create more connections, attract more consumers, and stay above their competitors. Fortunately, these areas of business are best executed by ML. Starting with how to gain customers, ML is being used by many businesses to analyze and predict the behaviors of their consumers; observing what they would like to buy, what will interest them most, and their financial strength. What’s Consumer Behavior? is simply the observation of methods or processes consumers use in choosing the products they would like to purchase and the brands they would like to patronize. There are three notable factors that influence consumers' behavior: physical, psychological, and social factors. Consumer behavior Physical factor: Consumers get influenced to buy or patronize a product based on their age, gender, culture, and demographic location. Psychological factor: Consumers' behavior toward a particular product or service can also be a result of reactionary responses to marketing stimuli. How a product or service is advertised can trigger how consumers react. Social factor: Some consumers get to patronize a brand due to their social relationships. Family members, friends, level of education, and media literacy can contribute to how consumers behave over a product or service. Knowing how these factors influence the behavior of consumers is significant for any business that is ready to make sales. Another important focus of any business should also be fixed on understanding its . consumer buying power Sales & Consumer Buying Power The agenda of every business is to sell its products to the targeted audience and ensure the retention of such consumers for future patronage. To make sales, businesses focus on how they can present their products to the audience by hitting on their pain point. A sure way of achieving this goal is through observation of . consumer buying power is a significant aspect of business that enhances the level of sales. It has to do with the awareness of the financial ability of prospective customers in making purchases. A company/business needs to carefully observe the financial strength of its targeted audience before placing a tag price on its products. Consumer buying power Consumer buying power can be assessed through socio-economic factors like inflation and recession in the consumers’ lives. Sales & marketing agents of many businesses fail a lot to observe the buying power of their consumers, and that affects their business sales and patronage. How Machine Learning Helps Businesses & Organizations Predict Consumer Behavior According to Statista, the market value of AI/ML applications in enterprises between 2016-2025 is to hit 31 billion U.S. dollars. This shows how important and valuable technology has become. A lot of big businesses now make use of ML assistance in analyzing consumers’ data to know exactly what the consumers want. predicted Below are ways ML helps businesses and organizations grow and predict their consumer behavior: Customer Churn Model Occasionally, businesses experience short patronage from their regular customers. The abrupt withdrawal of customers from purchasing a product or patronizing a service can be because of the hike in the price of the product, or maybe the customers are tired of the brand and want something else. ML can help your company/business analyze the data of your customers and give insight into why some of them may stop patronizing your products and what you can do to retain those customers. If the issue is about the price of a product, the company can roll out discount offers to boost the interest of those customers from walking away. ML also helps companies and businesses to examine the dynamic nature of the market and make suggestions on how your brand/products can be improved — by having flexible prices that consumers can afford — to defeat your competitors in the business. Customer Segmentation It’s mostly hard for many businesses to determine what could interest a targeted audience to patronize their brand at first approach. That’s because careful observation of each consumer to learn about their preferences is needed. But ML enables data scientists to utilize classification algorithms to divide the consumers into personas-based specifications. ML algorithms help in regrouping consumers based on their demographics, browsing history, and affinity, which enables businesses to tailor-out products & services that best suit the interests of consumers. Model Customer Lifetime Value Machine learning also helps businesses to analyze the lifetime value of customers through the history of purchases they have made in the past. ML can help a business or company predict the amount of money a consumer can spend patronizing their brand. This gives the business or company an overview of what it should be expecting from each consumer, and at the same time, supplies them with an insight on how they can maximize the value of their consumers. How Machine Learning Helps in Marketing Strategy Marketing is an important channel companies/businesses use to publicize their products and services to the audience. A good marketing strategy will help a company generate a lot of sales and profits. The incorporation of ML into the marketing process has enabled marketers to implement good marketing strategies that project what the consumers would find appealing and worth buying. With the assessment of the consumers’ data, ML gets to predict which products should interest each consumer and how marketers can advertise them. ML assists marketers with its . prediction power ML can project the outcome of each decision a business/company is trying to make, and this helps a company/business to look out for risks and avoid wrong decisions. Different Organizations That Utilize ML for a Smooth Operation There are tons of institutions/organizations that are currently incorporating machine learning into their operation for effectiveness and productivity. In social media services, companies like Facebook and Twitter have succeeded in utilizing machine learning to provide their users with the best service. Facebook has succeeded in embedding AI technology, into its Messenger App, which assists businesses and consumers to have hassle-free interactions on the social App. chatbots, Twitter, on the other hand, makes use of ML algorithms to judge each tweet based on several metrics. ML algorithms also analyze the history of each tweet and recommend similar tweets users may like to see. In the construction industry, companies like Plooto and Sitekick are changing the construction narrative. (U.K) is leveraging ML to help builders maximize the space and reduce the cost of development. ML in this field is developed for construction layout optimization. Plooto (U.S) helps builders in managing the amount of time and effort needed to complete construction through ML. ML algorithms are used to analyze pictures and videos of the ongoing construction and make predictions about unforeseen risks or damage. SiteKick In the healthcare sector, ML is being used in companies like Ciox Health and PathAI to assist medical practitioners with the treatment of patients. uses ML to power its Datavant Switchboard which projects the data of each patient to healthcare practitioners. uses ML to aid pathologists in making fast and precise diagnoses. Ciox Health PathAI is also an ML software that predicts an illness and the treatment a patient needs in real time for doctors to intervene quickly. KenSci Conclusion With the market value of machine learning set to reach in 2025, the only future expectation of this type of artificial Intelligence is its gross acceptance and incorporation by more industries, organizations, and institutions. 126 billion US dollars The realization of the value ML adds to the growth of businesses will also inspire more business owners and companies to embrace the technology to enhance their ecosystem.

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