To understand customers better and satisfy their expectations, many eCommerce brands today use artificial intelligence. The technology’s most popular subset – machine learning – can make sense of all data that online shops collect, drive insights that improve customer experience, boost internal business processes, and fight fraud. In this article by altexsoft, we will describe these opportunities – each of which you can start implementing in your business right now.
Personalised shopping experience
Personalisation is real-time customisation of a customer’s journey. Likely as not, your platform already collects and stores a ton of information about your users. Google Analytics alone provides you with information about user location, their device, browser, and the operating system they use, not to mention how much time they spend on each page and where they came from.
Called metadata, this technical information’s easily available and reliable, though not always very useful. Additionally, you probably store other types of data, such as behaviour data about users’ clicks and actions or information from your CRM.
Often, the more there is for a user to interact with, the greater the accuracy of the personalised experience. Machine learning is able to use the smallest piece of data about every hover or followed link to personalise on a deeper level. It almost feels like the system can read a user’s mind.
In practice, this personalisation results in timely messages, alerts, visuals that should be specifically interesting to each person, and dynamic content that changes according to demand and supply.
Fighting fake reviews
Customer reviews have played and still play an important role in helping people make purchasing decisions. Ninety-seven per cent of people read online reviews regularly and 87 per cent trust them just as much as personal connections. However, people are getting more sceptical.
These days artificial intelligence is capable of analysing large volumes of user-generated content. Let’s take an example from research firm Aspectiva. They used machine learning algorithms to analyse reviews for Bellagio, the Las Vegas hotel, across the whole web and gathered about 25,000 opinions. The overwhelming numbers allow customers to see the truth since there are always more real than fake reviews. This engine can be used by online shops to leverage the public opinion that was already expressed on the web instead of inviting fake reviews on their platform.
Another way to battle malicious reviews is used by Amazon. In 2015 they changed their rating system that used to simply calculate an average of all reviews. This allowed a single negative review to influence the whole rating of a product and vice versa. Now, their self-improving, machine learning-based system picks one review it thinks to be true according to different factors such as upvotes, recency, and whether it was written by a verified user. Then, it brings these reliable reviews to the top.
Inventory management and sales forecasting
Sales forecasting is one of the biggest disruption opportunities in commerce. Being able to tell how much of a given product you will sell by a certain date allows shops to stock inventories more efficiently and eliminate large sums of unwanted costs. It’s especially valuable working with perishable products, which include not only groceries but also concert and transportation tickets – anything that makes you lose money when it’s unsold.
Today, AI-powered software can gather historical data about past purchases and help sales departments drive conclusions for easier decision-making. Such conclusions include:
- Identifying the most and least popular products during an exact period of time
- Suggesting products that can be successfully promoted on a given date
- Predicting how upcoming sports and cultural events impact sales
- Calculating the probability of a purchase to give short-term views on turnover
Sales predictions also have a vast influence on inventory management. Being aware of slumps and spikes in demand helps prevent the ordering of out-of-stock goods or piling up an inventory of stock that will not be sold. To reduce product loss or spoilage, minimise waiting costs and storage, companies analyse customer behaviour via machine learning.
Automated customer service and chatbots
Today, you won’t find a big online store that doesn’t offer some form of customer support channel. However, it costs businesses millions to run a customer service centre. This includes keeping support agents busy 24/7 and wasting human resources that can be otherwise directed towards more creative and intelligent tasks. Here’s when chatbots come into the picture.
AI-enabled personal assistants are capable of answering simple questions. They can give a customer the status of an order and perform mundane tasks like finding a specific item just by a customer’s description.
Chatbots elevate the online shopping experience by:
- Reducing response time and giving instant answers in comparison to human assistants
- Increasing user retention by sending notifications and reminders
- Providing upselling opportunities through the personalized approach
AI-powered Customer Relationship Management (CRM) systems
Sales automation has become a trend in 2017. The amount of data businesses can gather from customers and the number of channels through which we can interact with them has grown exponentially during recent years and our marketing and sales programs need to reflect that. Today, with many businesses still running their marketing activities in Excel spreadsheets, the CRM systems market is ready to adopt changes. The main challenges businesses are faced with right now are:
- Manual data input and the risk of errors
- Viewing historical reports instead of tracking KPIs
- Lack of connectivity between CRM data and data from other company systems
Today major CRM vendors including Salesforce, SAP, Oracle, Microsoft, and Adobe have all invested in AI-powered startups to introduce AI capabilities in their systems. Salesforce, for instance, offers Einstein – a tool aimed at making AI applications more accessible for all businesses. The tool can prioritise leads most likely to convert, automatically add data to a CRM, provide personalised recommendations to shoppers, and even analyse what your customers are sharing about you on social media.
Visual search
One of the biggest openings for AI technology in eCommerce is helping clients find products faster. It can be achieved using chatbots or making textual search more semantic, but one of the most promising technologies is visual search through image recognition. The technology benefits all: Customers instantly find exactly what they want allowing businesses to majorly shorten the user journey culminating in checkout.
Voice search and smart homes integration
Connected home applications are the biggest part of all consumer IoT markets today and it inevitably changes the way people make purchases. Just last year, a six-year-old accidentally ordered a dollhouse when asking Amazon Alexa to play with her. This new ability to ask an assistant to order products makes it easier for eCommerce brands to reach customers.
The basic list of features you might want to provide in your voice assistant includes:
- Adding/removing an item from the cart
- Checking cart
- Check out with cart items
- Reorder previous order
- Review shipping status
- Check availability
Combating counterfeit products
Amazon, along with many other big retail brands, has a counterfeit problem. The number of counterfeit items sold via Amazon’s marketplace prompted Apple to file a lawsuit against vendors selling fake products, and even convinced brands like Swatch and Birkenstock to leave the service altogether. Amazon fights the problem by marking legitimate brands in their Brand Registry. However, low prices still attract customers to buy from unauthorized sources.
Several opportunities have arisen that use deep learning and image recognition technologies to detect fake products. Thus, DataWeave tackles the problem of image theft by training neural networks on millions of catalogue items from different categories. Alibaba, trying to fight its reputation as a platform for cheap fakes, introduced a bot scanning system that analyses each product added to their listings every day. The tool also reviews user behaviour data, payments, and logistics to detect suspected goods and merchants.
