The retail industry has always been at the forefront of innovation. It started with the massive pivot of the customers from the brick and mortar to online shopping. Covid has further exacerbated this shift as is evident in the change in consumer preferences and buying behavior. This change calls for agility in supply chains to serve the consumer's demands in a more effective and efficient way.
The eCommerce brands and businesses generate large volumes of data with the increasing interactions of the users with online websites. Such interactions aka data catch every data scientist’s fancy as they use machine learning algorithms on such large-scale data to identify the patterns and generate business insights.
AI use-cases in Retail
In this article, we will discuss some of the use cases of AI/ML in the retail industry.
Let's start with an analogy - the support staff in a retail store is always available to help you find the relevant product, the closest variant match (in case the exact product is not available), a similar product with better pricing, and a lot more. Note that their job (read search space) is still limited to all the products available within the store.
Now enters the AI that finds the relevant product for your search query in the universe of the entire catalog of the eCommerce brand. Notably, the search space has increased manifold as the AI algorithm needs to find the closest matching product with the best competitive pricing to facilitate the conversion.
Chatbots, virtual assistants, and voice-command assistants do the same job in the digital world. They ease online shopping by helping the users by scanning through the vast catalog in a fraction of time and finding the relevant product.
AI is not just limited to the text query but is also capable of analyzing the huge corpus of images to identify the relevant products on the basis of the uploaded image of the product by the user. This saves the user the hassle of typing the characteristics of the product and leads to the better matching product identification.
Lead generation and retention
Smart AI systems analyze the logs of the user search e.g. the amount of time spent across categories, products saved for later, products in the cart, etc, and keep the customer engaged by sending special deals and offers to improve the chances of conversion. This search history and customers’ touchpoints on digital platforms make an excellent repository of insights for the businesses as they can send personalized recommendations with discounted prices.
These deals also serve as a great extrapolation of what once used to be a static discount rule. Dynamic pricing helps businesses devise a more agile way to understand supply and demand dynamics in real-time and drive their customers through conversion by serving customized discounts.
The user search history and clickstream journey not only help in retaining their interests by providing better discounts and deals, but AI also has the potential to identify the subset of other users with similar interests in products as the targeted user. It then recommends those products by predicting what each user might need next.
While this is driving great conversions for the businesses, pushing such personalized recommendations certainly gives customers a seamless experience by notifying them of their potential interests.
The buck doesn’t stop here - personalization is the integrated view across multiple channels like mobile web app, website and email campaigns that is bolstering the customer experience.
Demand forecasting is at the core of the fulfillment process. It involves predicting what products need to be at what location and in what quantity that the customer is likely to buy in a given time frame. The goal is to position the products that the customers will likely buy in the forecast duration closer to their geography. It involves a multitude of attributes like historical purchase patterns, promotions, discounts, holidays, etc.
Inventory Management and Last-Mile Delivery
Maintaining the right amount of inventory to appropriately serve the customers’ demand is one tough job. Consumer behavior and patterns change sporadically. A lot of exogenous variables come to play. One such recent development is godspeed grocery delivery time. Now, users do not need to plan their grocery needs in advance. Such factors add up to how users’ demands change and so is the need for a smart way of optimizing inventory. Not to forget the profound impact on the last-mile delivery process.
Challenges in Adoption of AI in Retail
AI has a lot of potential to improve the user experience and retail industry as a whole in the years to come. Having said that, building AI-enabled applications is no mean feat and has its own challenges.
It requires highly skilled data scientists along with cutting-edge software and hardware. Undoubtedly, such large-scale transformational projects call for huge business investments and need a thorough understanding of high-value problem statements. The critical question is to identify those high business impact projects. While there is no golden rule, the pragmatic way would be to identify a couple of the use-cases that have a direct impact on the company’s top and bottom line.
Another way to onboard such high-visibility projects is to study the market and see how competitors or other players in the market are using AI to accelerate the digital transformation journey. The key is to show the impact first, which would open the door for the rest of the AI-driven projects and act as an enabler in optimizing the entire supply chain.
In nutshell, AI technology is a big enabler in digital transformation and requires agility and timely adoption of AI to accelerate this journey.