With online shopping being the most popular activities worldwide, the eCommerce sector is expanding by leaps and bounds. In terms of revenue, the global B2C eCommerce sales are expected to reach 2.35 trillion U.S. dollars by 2018.
As the industry mushrooms and delivers to a whopping share of customers, globally; confronting challenges is obvious. From acknowledging trends to user requirements, managing customers to inventory, security to simplicity, the movers and shakers of e-commerce sector are continuously striving to gain a competitive edge.
Managing these operations and streamlining them manually bring roadblocks and this is when automation of some of the business-driving tasks come into picture, and eventually AI.
Artificial Intelligence (AI) indeed plays an imperative role in automating tasks in eCommerce applications. With some amazing AI technologies in forefront, it is leading way for various eCommerce solutions (web and mobile) to augment their functionality. Here, we take a look at how.
1. Recommendation and Personalization
“Smart personalization engines used to recognize customer intent will enable digital businesses to increase their profits by up to 15%.” Source: Gartner | Click to Tweet
“81% of consumers want brands to get to know them and understand when to approach them and when not to.” Source: Accenture | Click to Tweet
Personalized user experience let the businesses connect to the consumers. In eCommerce applications, personalization is generally rendered through recommendations.
“Products you may like” is one of the finest examples of how eCommerce industry tries to read the consumers and customers. With a history of products browsed, purchased in past, or added to wishlist; an estimation is generated for user’s interest, which can therefore helps to hold them for longer.
Previously (and still in some cases), recommendations were hard-coded by developers, on the basis of product categories, brands, age, gender, and other related factors. However, that process is time consuming and doesn’t hit the dead center.
Personalization and recommendations can be simplified using AI technology, called Machine Learning (ML). The technology uses cognitive learning to understand user behaviour within the application, to identify their interest.
Using ML, the recommendation algorithms are categorized as: Collaborative Filtering and Content-Based Filtering.
- Algorithms for collaborative-filtering works on the basis of what other customers (of same age, gender, browsing-patterns, brand preference) have chosen, lately. Such recommendations work when the user browse for products after a long time-span.
- Algorithms for content-based filtering considers customer profile for recommendation. The data collected may include gender, brands, age, or product related content (like brand, price range, color, category etc.).
2. Inventory Management and Forecasts
“70% of shoppers would shop for an item at a competitor if it was unavailable, rather than waiting any length of time for back-ordered inventory.” Source: SymphonyIRI group | Click to Tweet
Keeping the shelves stocked, responding to dynamic requirements, and updating the inventory is an exasperating job manually (especially during sales).
Artificial Intelligence can simplify this job through predictive analytics, which involves data mining, machine learning, and predictive modelling to keep a track of current and past facts, in order to make futuristic predictions. This way, AI keep the admins informed about product quantity, sales, returns, demand etc., without human intervention.
3. Faster, Smarter Search
Amazon.com had 598 million products (Stats for: October 10th, 2017). With so many products available under one roof, it is certainly imperative to simplify the search criteria for users. No matter how many or how great products an eCommerce portal hosts, until a user is able to find it, it is impossible for sellers to generate sales.
To make search convenient and powerful for users, AI can help. Following are some of the ways AI can assist to simplify search in eCommerce.
Search Ranking: This ensures that users get the search results, according to relevance. For this, the machine learning algorithms keep an account for the frequency of search terms and accordingly define ranking of particular search term. For relevance estimation, the user profile can be analyzed, which may include data like gender, age, past search terms, browsing or shopping habits etc. Such search algorithms are great at predicting the customer requirement, trying to cater to their needs.
Query Expansion: This is another way of helping user to search for the product they must be hunting for. In query expansion, the user is offered with suggestions to complete their search term, while they are still typing. Very similar to what you experience in Google search.
Image-Based Search: This type of search allow users to add an image in the search box and get related image results. For this, image recognition and classification (subset of AI) is being used.
4. Customer Relationship Management
“By 2019, half of major commerce companies and retailers with online stores will have redesigned their commerce sites to accommodate voice searches and voice navigation.” Source: Gartner | Click to Tweet
One of the finest examples for how AI can assist eCommerce industry in customer relationship management is through chatbots. A chatbot is basically a service, trained with data set, powered by AI (machine learning, text analytics and natural language processing), and wrapped in a chat interface.
Taking customer service to another level, eCommerce applications are integrating voice searches and voice navigation system using speech recognition and natural language processing at the backend.
5. Detecting Frauds and Inconsistencies
With eCommerce portals expanding at a high pace, tracking inconsistencies become a little difficult. Minor or major, inconsistencies are a roadblock to seamless user experience, and ultimately the business.
Fraud Detection: The most popular application of AI in eCommerce is fraud detection, wherein, continuously analyzed user behaviour saves buyers and sellers from abusive customers. High payments received through stolen cards, excessive orders, payment retraction are some of the cases that can be resolved through AI.
Anomaly Detection: Incomplete product information (like missing image, description, title, incorrect brand or category etc.) are huge set back for the users. If such anomalies are detected automatically and on time, it can certainly save sellers from pervading losses.
For automated detection of such activities, machine learning algorithms are trained with data sets of situations and keep learning through pattern of data. An alert is passed, when an abnormal behaviour in user activities or application performance is traced. While creating a robust algorithm is a challenging job, but with ever expanding data sets, the algorithms keep updating them with the anomalies.
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