How Can AI Help Retailers Discover Why Behind the Buy?
How Can AI Help Retailers Discover Why Behind the Buy?
You might not be aware, but artificial intelligence advancements have found their way to the retail sector as well. There are quite a few reasons why AI is the perfect technology for retail.
First, let us say a word on how AI analytics works.AI uses machine learning and neural networks to process large datasets through its algorithms and make appropriate decisions. The data needs to be precise and linked to the actual problem, and data management needs to be top-notch. AI tries to learn from the data and predict outcomes in the future.
In retail, data can be customer profiles and purchases in a recent time frame. AI can get to know their buying preferences and predict their behavior in the future. Vendors can use that data along with AI technologies to adjust their discounts, purchase suggestions, and their stock.
Uses of AI in Retail
Product Recommendations and Personalization
The best example of this is Amazon. In case you were wondering, yes, they use AI and data science for their product suggestions. AI algorithms analyze the unstructured data of users who have purchased the same product. Based on the result, they bundle or recommend other products for you to purchase. AI helps Amazon increase revenue and drive customer loyalty.
In other cases, online retailers will personalize their websites based on your recent queries and viewed items. The next time you visit their website, you will see similar products, personalized for you. Everything clicks together just to make you spend more money!
Pricing and Discounts
Big sellers don’t have time to run advanced analytics on all item purchases. Big data companies like Amazon employ their AI algorithms to comb through their unstructured data and determine the overall demand for a product. Algorithms determine the price or discount to maximize the profit.
Automated Inventory Management
Being out-of-stock for a highly-demanded item means missing out on huge profits. Predicting when an item will run out is tough unless you turn towards AI technologies as a retailer. AI-managed inventory requires more data for a specific item. It is generally something that works properly only for big data companies.
Even though AI in retail is often mentioned in the context of online shopping, there are cases where it applies to physical stores. You have probably heard how big supermarket chains arrange their products in a way that will make us spend more money than intended.
Well, the product layout formula is not defined – it is, in fact, the outcome of machine learning algorithms. The market's customers form the unstructured data each time they visit. It can help them arrange items to make sure they spot a nice discount for a product they like.
Future of AI Technologies in Retail
Data scientists around the world are working around the clock to implement current technologies and develop new ones. Here are some fascinating data science technologies related to retail:
Emotion recognition is a hot topic in AI development. Recognizing human emotions can help vendors understand their shoppers' feelings about their products. They can then use big data analytics to leverage sales. Store owners install small cameras and sensors in locations of high traffic. Areas of special interests include shelves, entrances, and exits.
The emotion analytics can help with the following:
Product pricing: negative emotions in the aisles show customers are not happy with the prices they see.
Inventory management: visible positive emotions indicate that the product is a must-have. Shops should make sure to have it in stock all the time.
Product and aisle layout: It includes advanced analytics of the customer’s in-store path and emotions. Emotion recognition makes it easier for retailers to optimize their product layout in a way that will make customers happier.
Precise facial/emotion recognition depends on a comprehensive dataset that is varied enough in terms of different genders and races. AI bias in facial recognition is real, and it can present a problem.
Real-Time Behaviour Analysis
Acting fast means a lot in today's retail industry. The long-term predictive analysis will bring profit in the long run, but sometimes, quick and prompt decisions can be just as important. For instance, camera positioning in your store can recognize a customer walking in. It can pull his/her data from your database and brief your staff with name and purchase preferences. Such preparation enables better customer experience and interaction. Behavior analytics also work for online shopping. In this scenario, a customer visits your website, and AI algorithms try their best to adapt to the customer's needs in real-time.
Also, reacting to events in time can increase your profits in the short term. For example, if a heat-wave is about to strike an area, specific keywords like “fan“ or “air conditioning“ might exhibit an increase in volume. Real-time machine learning algorithms can analyze this increase and adjust your store's marketing and prices.
IBM Cognitive Computing
Following the ever-growing increase in customer expectations and demands, IBM, as one of the biggest data science companies, has pursued cognitive computing. It provides real-time learning and decision making, and it tries to emulate how customers think. Cognitive computing is designed to make the engagement between businesses and customers natural and personalized.
Will COVID-19 Speed up AI Adoption in Retail?
COVID-19 lockdowns had a massive effect on retail businesses around the world and many stores have been forced to modernize their operations through technology. Most customers turned to online shopping, and businesses had to accommodate that switch. Before the pandemic, AI had started to enter into retail, but it was mostly used by big data companies who would make the most profit out of it. However, more and more companies are willing to invest in some types of automation in the future, as seen in the figure below:
Credits: Will the Pandemic Accelerate Adoption of Artificial Intelligence?
There are several reasons why companies should adopt AI technologies as soon as possible:
COVID-19 makes field research next to impossible
The more technology advances, the more data there is, which makes AI technologies more precise
There are plenty of examples of businesses increasing their profits after AI adoptions
The coronavirus pandemic was a proper way to test already existent AI models that faced unusual consumer behavior. To illustrate, here are the top 10 Amazon search terms from April 12-18: toilet paper, face mask, hand sanitizer, paper towels, Lysol spray, Clorox wipes, mask, Lysol, masks for germ protection, and N95 masks. The pandemic resulted in a sharp increase of coronavirus-related search terms, as seen on the figure below:
Credits:Our weird behavior during the pandemic is messing with AI models
The short answer to the question in the subtitle is yes. COVID-19 will increase AI adoption in retail. A new market research report published by data scientists in Meticulous Research indicated that the AI industry in the retail market is expected to grow at a CAGR of 34.4 from 2020 to 2027. The essence of AI is achieving more by doing less; just let the algorithm do its thing, and it should bring better decisions than any marketing manager.
The AI tools in the retail industry will vary: from purchase history and browsing habits analytics to emotion recognition and natural language processing, all to make sure you buy a product and come back again.
Artificial intelligence has fitted into the retail narrative perfectly. There are numerous ways in which big data analytics can lay the ground for retailers to make more money by essentially making the customer happier. Data science companies that offer their services to retailers exist, and hiring them helps businesses increase revenue. It is a win-win situation for as long as business owners decide to invest in these technologies.
The future of retail will bring more technologies that bother with things you see in science fiction movies. Think about cameras everywhere, recognizing faces and human emotion or AI making eerily precise predictions you have never talked about or shared online. All information will be used for data mining. However, contrary to the movies, it is not about world domination, love, or revenge. It is just about making money.