AI in Retail Marketing and eCommerce
AI was just a philosophical concept until the late 20th century when researchers introduced methodologies to make it real. AI has been around in retail marketing, mostly to reduce the tedious work of remembering all the finances. Traditional shops used to maintain a list of loyal customers too.
Shops were on their way to going digital long before the pandemic, but the sudden shift to online buying and increased digital transactions caught many by surprise. The physical retail stores experienced most of the trauma. The pandemic disrupted the supply chains from all over the world. To cope up with the changes, many are resorting to e-commerce solutions. Brands that started in brick-and-mortar are coming up with innovative automation solutions, both in the physical and virtual worlds.
How would this year look like?
A study conducted by a leading market research firm states that global eCommerce sales would almost reach the $5 trillion mark, projected to grow at a 14.3% rate in 2021. Everything has settled down and customers have switched their method of shopping. Now it’s time to make use of AI optimally instead of coming up with temporary solutions. AI in Retail and eCommerce does not use too complex technologies, and many B2B services are offering easy modern solutions. E-commerce is getting more saturated day by day.
Introducing AI into at least one of the following sections of the business will help you stay smarter and act faster:
Inventory Management – Prediction of demand close to the supply
The virtual world has made things easier for customers and saved a lot of costs for shops. Nowadays, there’s even a trend of sustainable fashion, and brands are looking forward to reducing waste and avoid overstocking. Gaining insights from consumers helps businesses to manufacture products with high demands more than the rest. The inverse is also possible; if consumers are looking for a certain product, the insight engines can direct them towards the best product.
Chatbots are getting famous among users, who do the work of prediction quite flawlessly. They inform about the discounts and optimize the price according to the customer record. In fact, a study reveals that 47% of users would buy via a chatbot. The section also includes warehouse management, where the employees have to pay attention to each object. AI software would keep the track of shipments and update the inventory accordingly, even if those records are in two different places in your system.
Operation Optimization – Increased efficiency and effectiveness
AI can be helpful even in back-end operations, where you don’t need to invest human time anymore. The site automatically updates out-of-stock or new-in-stock items according to your inventory. Moreover, the employees of various departments can maintain a clear flow of communication. Managers can track employee performance and monitor the machines to ensure minimal error. Optimizing the business digitally also helps in compliance with legal rules and regulations, and you don’t have to worry about constantly updating the system with new rules. This is an apt example of an Edge AI application, which gathers data from local hardware devices without uploading it to the Cloud, and provides the related statistics.
Customer Management – Building a long-lasting trust
Now that the customers cannot physically verify the things they are buying, Many brands are using AR (augmented reality) solutions. Customers can virtually try out clothes, accessories, make-up, etc to get an idea of how it looks on them. Nike launched a Custom Shoes facility for the customers; the devices scan the dimensions of the customer’s feet and then the customers can make their own shoes. Certain outlets of the clothing brand Zara introduced cashier-free stores; customers can check the products out on their own. The smart store updates when an object is picked up from a place, or when it is put back.
There are obviously some drawbacks, and not everyone can be an honest customer, but in the future, this might be a thing. eCommerce websites recommend products based on the consumer history, intelligently predicting the needs. This is possible due to previous action trends of other customers who bought the same thing along with something else related to it.
All this requires profound applications of deep learning and ML.
Knowledge And Insight Management – Managing and processing information
This section concerns the business development aspects such as service enhancement and customer satisfaction tracking. A business constantly has to check whether a service or product they are delivering is in the best state. AI brings all the data together and gives valuable statistics as an output. It reduces the time needed to invest as well as speeds up the improvement in problematic areas.
If an eCommerce server is receiving too much negative feedback about a certain product/dealer/service, AI reads through the data and recommends solutions to eliminate the issue. It becomes easier to track even the behavior of customers in a physical store. AI will quickly recognize if a recent review about the shop was published by a customer who just stepped out of the store, by linking both the pieces of information together. It determines whether a review was fake or not, using similar algorithms.