Machine learning is an emerging technology of many fields in a day to day life. Online shopping is a highly beneficiary field in grabbing customer, increase sales, high return on investment and in turns increase the reputation of the brand. Thus implementing artificial intelligence in retail industry enhances the business and takes it to the next level.
Mobile Application Development Company engages artificial intelligence to grab the customer and to find a good place in the market. There are two primary advantages to implementing Machine learning in online shopping. One is to increase sales and earn the profit and another one is to avoid the consequences in decision making. E-commerce business needs millions of decision to be taken in which Machine learning plays a significant role.
Machine learning encounters shopping behavior
Machine learning identifies the underlying pattern behavior of the customer based on the history of products they buy through online. For instance, people purchasing rice from the supermarket on a particular brand, particular price, and particular flavor. These details are recorded and used for other purposes like product suggestion or likelihood of customer and stock checking (inventory management) etc.
Large scale analysis is done to identify the likelihood products of a particular location. A large scale of analysis is done in Australia that a number of family cooks risotto every week. This information helps the business in a high manner by deciding the amount of stock should be managed in a warehouse in the week. This information would then share to all friendly organization enabling more effective inventory management.
Machine learning in pricing dynamics
Pricing a product in the retail industry is not easy work. Lots of information is necessary information is required to develop a pricing tag of a product. In a large retail organization, it is not possible of assigning pricing for each product based on demand and availability.
Machine learning gives the way to enhance the process of pricing by using details such as purchase histories, product preferences, and other data to develop deep insights and pricing tailored to maximize revenue and profit.
Machine learning in Customer feedback
Traditionally feedbacks are generated using feedback cards in the retail industry in which customer fill out and placed in a suggestion box. This feedback is read by an organizational personal and necessary remedy is taken. Social Medias are considered as a platform for collecting feedback in order to respond, resolve and engage the customer in conversation.
Machine learning and Artificial intelligence will enable for the first time bulk analysis of multiple sources of messy, unstructured data such as customer recorded verbal comments or video data. There is no need of asking the customer to fill the feedback form in the website or mobile app. Machine learning automatically grabs the information from the customer by the purchase history.
Recent changes in E-commerce
Numerous strategies and technologies are changed over the last few decades and machine learning promises to change things even more. Despite the fact that technology plays an important role in interacting with retailers today, e-commerce has actually been around for almost 40 years.
According to the statistics, 2017 retail sales in e-commerce is reached $2.29 trillion and had grown up to $2.774 trillion by the end of 2018. These statistics show that e-commerce will grow 20 percent every year. This statistics shows the drastic changes in the retail industry and online shopping after the introduction of Machine learning.
Role of Machine Learning in E-Commerce
Generally, machine learning allows e-commerce businesses to create a better customer experience. In the present era, most of them prefer for personalization and to communicate with their favorite brands too. A recent report exclaims that about seventy-three percent of the customers are not satisfied with the content. This gigantic concept offers retailers its best ability to personalize overall interaction with customers. The real fact is that car abandonment rates should be lower and the sales should be at its peak.
What about its search results?
Enhancing search result offers good payoffs for the retailers and also the e-commerce search result is improved for each and every time the customer shops on a concerned website. Important details such as account personal preferences and purchase history are taken into account.
Machine learning is better than traditional search methods such as keyword matching. For example in eBay, along with eight hundred million listed items, the retailer is making use of artificial intelligence and big data to display relevant search results.
According to various patterns in the categorized shopping behavior, machine learning is used to recommend e-commerce products and also helps to increase conversion rates. There is a list of machine learning algorithms are available which helps to collect data regarding the factor of pricing trends, competitor’s price including demands for a list of items.
Customer segmentation is more important for e-commerce as it allows organizations to involve in the communication strategies as it is included for each and every customer. This gigantic concept is used to get a deeper knowledge about customer’s needs and at the same to create for a better shopping experience.
From the above-discussed points, you come to know that more and more opportunities are available for machine learning in e-commerce. Yes, it is going to become a vital part of efficient online retail.
Conclusion
Artificial Intelligence and machine learning is the key for today’s retailers in enhancing customer segmentation using factors like metadata, semantic analysis, collaborative filtering and predictive recommendation to increase conversions and grow their platform. Trade and supply chain of the retail market is drastically increased in implementing machine learning to the organization. Machine learning replaces high skilled labors in maintaining very crucial activities of the organization.
Organization online market also gets clear insights into the marketing position of updates which helps them to take the important real-time decision. This helps the organization to implement the marketing strategies the right time.