How can machine learning algorithms augment e-commerce offerings?

It is no brainer that the e-commerce market has transformed the Indian market like never before. Thanks to factors like rising smartphone penetration, the launch of 4G network, and increasing consumer wealth, analytics-driven customer engagement, and digital payment, the e-commerce sector is on an upward trajectory. It is projected that this industry will surpass the US to become the second-largest E-commerce market in the world by 2034. According to the PWC survey, with Internet penetration expected to almost double to 60% by 2022, the nation is arguably the world's most promising Internet economy, with a rapidly increasing 'netizen' population. Further owing to improving data affordability, consumption growth, and newer financial products, the e-commerce market is set to grow, be it across e-tail, travel, consumer services or online financial services. This also implies e-commerce will not only boost the Indian economy but it will also have a cascading effect on other industrial verticals too. Meanwhile, the niche leaders are now planning to invest in machine learning to enhance the offerings of this sector.

Machine learning is known for its ability to self-learn and retrain itself by studying a given data set. In the e-commerce space, this AI application can learn what customers prefer and how they want to see information to get them to make a purchase. Then it can test and adapt, using new options and information to slowly refine the best way to reach your customers.

Better Search and Display

When a user wants a product on an e-commerce store, he needs to search for it using the preferred keywords. So, the site owner must ensure that they have attributed those keywords to products that users are searching for. Machine learning looks for synonyms in the nouns provided by a user as well as similar phrases people use for the same type of problem. It does this by analyzing the site and its metrics, thus, allowing e-commerce sites to prioritize click rates and existing conversions while putting high-rated products on the top of the page.

Fraud Detection

Even e-commerce sites are prone to fraud. The problems like customers buying with stolen credit cards, or retracting payments after the item has been delivered, are common. Machine learning can be leveraged to identify patterns in data, identify what's 'normal' behavior and notify admins when something is not 'normal'. For. E.g during payment, it can analyze customer data and can pinpoint the hallmarks of an actual purchase. In case anything seems suspicious it'll get flagged up as potentially fraudulent. This is why we often get alerts about sign in from another location, or unrecognized device.

Pricing Optimization

Online pricing is very crucial. Machine learning models can not only collect information regarding pricing trends, the competitors' prices and demand for various items, but it can combine this information with customer behavior to determine the best price for each of the products. This data can also help in planning for opportunities like discount coupons to drive down prices to a certain point or for savings ahead of a specific holiday or holiday season where the items are purchased in order to encourage increased spending this year.

Trends Prediction

Unlike retail stores, predicting customer behavior, preferences and trends can be a mammoth task. Machine learning helps in studying customer behavior and predicting the wave of sales of products, i.e. which products will be more in demand during a particular time or how the shoppers' preferences change with time. Besides, trend analysis impacts procurement, be it from an external vendor or internal fulfillment. Hence, it is critical to analyze the trend of a product, as it can reduce the catalog's overall size, the maintenance cost of products, and improve space utilization at the warehouse.

Segmentation and Retargeting

In a brick-and-mortar store, one can talk to salesperson to find out what they want or need. Salesperson often pays attention to what a customer is saying, his body language, behavior, and many other factors to help the customer. But when it comes to online, things can get quite baffling to make sense of vast amount of data present. So, customer segmentation becomes extremely important for e-commerce, as it allows companies to adapt their communication strategies for every customer. Machine learning enables e-commerce owners to better retarget users by looking at data to find out what had worked to convert similar profiles in the past through retargeting. This also helps in creating a personalized experience for customers.

Some sites also use chatbots to help customers for the same. These chatbots can offer basic information like shipping options, colors, size charts, and other formulaic options or sometimes update about returns and replace items and more.