Machine Learning & Artificial Intelligence Helping Drive Retail Sales

access_time September 10, 2019
by   TechmavenitAdmin
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It is becoming evidently clear that the shopping experience is becoming more and more integrated and that customers tend to switch across multitude of sales channels – whether shopping online from a desktop or mobile to buying from a bricks and mortar store. Retailers are aware that almost 81% of customers conduct an independent research before entering a bricks and mortar store to purchase an item. The tendency of customers to patronize several sales channels before making a purchase, offers the twin benefit of allowing customers to get the best out of their shopping experience as also provide retailers with an enormous amount of valuable data. The increasing competition and the growing number of touch points have made it clear that providing a good customer experience is the most important element in concluding a successful sale. In order to offer that enriching customer experience, businesses are making use of tools and applications provided by machine learning to analyze and put to productive use the vast amount of data at its command. Machine learning uses algorithms that learn from data to build predictive models that choose where to look for insights and this provides for a great deal of opportunity for businesses across all sectors. Everyone is aware that Amazon, the retail giant, derives fifty five percent of its sales via the recommendations made by machine learning algorithms. Amazon wanted to enhance their services and personalize consumer shopping experience and to achieve that, Amazon ventured in to customer data collection and analysis and succeeded in that. Their data collection and analysis was so successful that they could rightly predict what you would purchase today even before you even make up your mind. A survey by Accenture Institute for high performance has revealed that nearly 40% of companies surveyed by it already use machine learning to improve their sales and marketing. Let us now look at the manner in which machine learning will help retailers improve their sales performance. Search Analysis By implementing machine-learning processes, businesses are able to analyze customer queries coming to their site. The data analysis modules help them analyze customer search history, product preference, click through, purchase history etc. After a careful analysis of customer search history, businesses are able to better target customers with targeted ads that suit the personal preferences of customers. This will help businesses improve sales performance. Personalized Product Recommendations Implementing artificial intelligence will also help businesses with predictive analysis that will result in providing customers with personalized product recommendations. True Religion, the American clothing retailer, used the AI tool Einstein from Salesforce, to provide personalized product recommendations. It has been reported that forty five brands that used Einstein powered tools saw 7- 16% revenue growth per user. This AI implementation resulted in successfully analyzing customer search history and their purchase preferences and provided customers with personalized product recommendations. Customer Sentiment Analysis With machine learning processes, businesses are better able to analyze customer sentiments and review the customer feedback. Product reviews provided by customers help businesses gauge the opinion of customers with regard to the product price, product quality and the level of service received by customers. This feedback and analysis will help businesses rectify shortcomings in areas where customers have expressed negative sentiments. Fraud Detection & prevention Machine learning helps businesses detect and prevent online frauds with its predictive analysis ability. Machine learning is able to analyze suspicious actions that pertain to fake accounts, unauthorized login entries and unauthorized money withdrawal attempts. It is able to detect fraudulent activities and predict possibility of frauds being committed.

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