Over the past several months, many retailers have been struggling with keeping their physical stores open, leading some to bankruptcy. In addition, share prices have been gradually decreasing, and sales have not been as impressive as they once were. As a result, some are turning to progressive technology and investing in data; that way, they can improve their current relationships with customers. Indeed, today, those who have started to embrace the digital world are seeing much more success than those still holding onto the past. Annually, online sales are more than $2.4 trillion, as this platform is much easier and more convenient to shop from for consumers. After all, as a consumer yourself, wouldn’t you prefer to shop from the comfort of your bedroom rather than having to drive from one location to another in order to make a purchase? Take this into consideration when trying to determine the next step for your firm; if you really want to remain a player in the retail game, you are going to have to make some changes.
Once you have made your decision to dive into the digital world, how and to whom you will advertise to should be your top priority. By targeting the right people at the right time, you will be able to make sure that you will see the most optimal results. This is best accomplished by combining consumer information with intelligent technology; that way, you can pinpoint the habits of buyers. After all, today, when a customer shops online, their journey can consist of anything from websites online on their desktop to applications found on their mobile phones, combined with the information they obtain at physical shops. While this may make it difficult to monitor their exact movements, today, it is possible to get information in real-time about how shoppers behave using machine learning in retail; it collects copious amounts of data and then analyses all of it to give you just that. With the help of Reinforcement Learning (RL), which is a part of machine learning, companies can figure out more about shoppers and even make predictions regarding how they will react to various advertisements by getting access to information about past consumer actions. In other words, RL is able to figure out what the best action to take is in particular situations. This is accomplished by training those RL algorithms to become proficient in a certain kind of problem-solving. As a result, artificial agents have to evaluate the options available and the pros and cons of what they have to offer to figure out the best response to a specific situation. So, how exactly can RL help retailers?
At the core of RL lies its flexibility. For instance, agents in ML have to act based on a certain selection of predetermined rules for every situation. In contrast, the rewards in RL don’t come as quickly since the surroundings are always changing after each activity. Since maze boundaries are not locked, the agents have to figure out how they should act on their own with the help of rewards; through them, they can figure out what’s the best way to get to the goal. As a result, RL is the best base for ad targeting in real-time as the retail world is ever-changing today.
Now, when you combine RL processing capabilities with information about shoppers, which includes first-party information regarding site visits, previous purchases, the location, ad exposure, among many others, retailers will be able to figure out the exact order of actions. As a result, they can create ads that produce the most significant results. For instance, an analysis could show how a customer often shares video advertisements on social media platforms and how they have made purchases of featured items in the past, which proves that this kind of plan yields the best results for that particular kind of customer. In addition, you can teach these RL algorithms to accurately reach various KPIs whenever they are going after certain kinds of customers. Indeed, they will only get even better with time with the help of positive rewards as they will clarify your strategy, the recurrence of ad serves, as well as the selection of formats. As a result, you can use the data collected to figure out broader trends throughout the audience that you are going after.
As you can see, RL is able to make advertisements that are more captivating, benefitting both the firm and the customer. By figuring out what shoppers prefer and what they don’t, retailers are able to pinpoint the best way to present their advertisements, including the correct place and amount, rather than merely putting everything everywhere, which wastes more money and yields less successful results. Through it, they will be able to strengthen their relationship with customers while simultaneously improving the perception of their firm in the eyes of shoppers, thus enhancing their loyalty. At the same time, firms will be able to ensure to their customers that they can provide them with the customization that they want on their terms.
