The ‘ai’ in ‘Retail’ now stands for Artificial Intelligence, and for good reason too!
The digital transformation of industries is not a new phenomenon by any means. In fact, it is a process that had already been in motion for several years leading up to the pandemic, with the process just being accelerated by the global COVID-19 pandemic. Corporations globally have been on the transformation curve for a while now, looking to adapt to the challenges the process requires, especially with regard to their digital-first operations.
An industry that has made significant advances in this digital transformation process has undoubtedly been retail. Retail has seen transformation intimately over the past 70 years or so – from the departmental store to the rise of the supermarket to more mass production models – and now into its fourth chapter. The fourth industrial revolution is nigh – driven by e-commerce giants such as Alibaba and Amazon – into an era where technology is the major driver of the industry. Not only is it better anticipating and meeting the needs of consumers, technology is also making retail supply chains more efficient. Data today is managing all three major components of retail: the store, the merchandise and the consumers. The key to driving the process, however, is artificial intelligence.
Artificial Intelligence is used to uncover patterns and insights with precisive effectiveness – much more than what humans can do, and over a much broader data spectrum than what a human can offer. AI has the unique ability of quickly analysing not only complex numeric and text data, but also ‘sensory’ data such as visual cues from customers as they purchase products.
The AI cheat-sheet of Digital Marketing
A major point of retail where AI finds widespread usage is in digital marketing, through the collection of vast swathes of data. For retail marketers, acquisition is crucial – especially in identifying the right people with intent to interact with the product. AI carries this task out through the aggregation and analysis of large volumes of online and offline data for a holistic view of the customer.
Next comes engagement. “Successful engagement means sending the right user the right information at the right time, on the right channel and via the right device. This is an extremely daunting task for humans to take on, not least because they would only have the resources to reach very few groups of users through a limited set of channels. AI can automate user engagement by analysing all available information to pinpoint the right time to reach out.”
The next crucial step in the process is conversion. AI carries out real-time analyses in consumer behaviour to “determine hesitant customers so that retailers can provide incentives only to people who are most likely to convert with a coupon or discount.” This streamlines the entire process, as firms can now concentrate mainly on their potential customer base, whilst saving on valuable marketing costs. Retailers also use AI to look at previously active consumers who have currently become dormant – thereby livening up to the possibility of offering personalised incentives to win the customer back.
Welcome to Smart Retail
It is not just in Digital Marketing that AI is making waves, however. AI has also managed to streamline the entire retail chain – especially the production process. It not only analyses data relating to quality control, it helps automate robots in the warehousing, increasing efficiency in various aspects while cutting down on manpower costs. Additionally, by analysing user shopping patterns, it aids in resource planning and enhancement of the customer experience through the use of tools like AI chatbots as well.
“A few examples include fashion chain Zara, which has used robots for self-checkout, reducing queues at customer service desks; and furniture seller West Elm and apparel retailer Uniqlo which have used AI to suggest the most relevant products to customers using data including facial expressions.”
Another major noteworthy facet where AI has taken rapid strides is in improving human-like replies for algorithmic systems. Natural Language Processing has allowed for major upgrades in automated customer service across several languages, thereby allowing retailers and brands to solve more customers quicker, leading to higher customer satisfaction as well.
Reinforcement Learning in AI has also been a breakthrough for firms in terms of resource allocation – in aspects such as inventory, logistics, storage and manpower. According to Dr. Min Sun, Chief AI Scientist at Chinese AI Marketing firm Appier, “Companies that have a logistics component such as delivering or transporting goods (e-commerce companies, food delivery providers, etc), have typically made resource allocation decisions by looking at current patterns and past experience. However, by adding reinforcement learning, these businesses can now predict future resource allocation, ensuring the best action for an anticipated future situation (i.e. more drivers in times of heavy traffic, high demand for certain inventory, etc.).”