Published on
January 14, 2021
The COVID-19 pandemic has altered how retailers function and engage with clients.
Many developments, including contactless purchases, personalization, and the importance of great loyalty and rewards schemes, appear to be accelerating in the pandemic.
So, let's take a closer look at what is going on behind the curtain and the technologies that boosts organizational performance and productivity.
Artificial Intelligence (AI) stands out as the technology that helps retailers become much more efficient.
Although AI for retail has been present for a while, the past year’s extraordinary situation has encouraged its adoption, making it an essential part of every fast-growing retail strategy.
Research states that the pandemic won’t stop companies from investing in AI.
The Driving ROI Through AI Report discovered that businesses expanded AI investments by an average of 4.6% over the past year and are planning to spend 8.3% each year over the next three years.
What is more, by the year 2025, the AI industry will hit $191 billion, which is quite a leap from the $40 billion it is currently estimated at.
The retail industry will also join these trends. According to Mordor Intelligence, the Artificial Intelligence in retail market is expected to reach $10.90 billion by 2025, growing at a CAGR of 35% over the forecast period 2020 - 2025.
The retail sector is rising at an insane rate, and consumers are spoiled continuously with thousands of options.
This makes brand loyalty very fragile. Customers turn towards interactions with products that communicate with them and brands that deliver customized experiences and speak directly to them.
This drives the need for more sophisticated processes and retail automation in every step of the way.
So, how can brands use AI for retail automation that will result in increased customer satisfaction?
Let’s start from the beginning.
Artificial Intelligence is a buzzword tossed around in various industries.
When we say AI, we refer to various technologies that can learn from vast datasets, like machine learning and predictive analytics, and use that knowledge to anticipate, forecast, advise, and help retailers make targeted, data-driven business decisions.
Using advanced tools to turn raw data into actionable observations, these technologies can even work independently, without human intervention.
Artificial Intelligence in retail also uses behavioral analytics and consumer analysis to get valuable market insights and enhance several different touchpoints in the company customer support field.
These innovative approaches have made Artificial Intelligence an essential part of digital transformation in the retail industry.
Artificial Intelligence in retail businesses can be used both online and in physical stores.
AI-driven chatbots or fully-automated personal assistants on a website, for example, offer shoppers a customized suggestion or adaptive pricing based on their site behavior, shopping background, and other related details.
In physical stores, AI-based services are also widespread, using data sources such as consumer in-store experiences on mobile devices and sensors.
Through educating an algorithm with sales data and other related material, retail store managers may also use AI in process optimization for window displays.
This helps to forecast results, such as the probability of a person purchasing two products together if they are seen next to each other.
In retail, physical stores remain mainstream.
However, the online shopping experience is becoming more and more popular, which puts retailers in a position where they have to fight competitors that are just a click away.
In order to transform customer experiences, AI in retail can help sellers obtain the disruptive advantage they need to remain significant.
For example, utilizing AI-driven demand forecasting to maximize inventory, AI in retail can power personalization and help keep retailers flexible.
Suggested read: How Is AI Going to Affect the Fashion Industry in 2022
The latest wave of consumers, the Millennials and Gen Zers, demand seamless and efficient shopping experiences.
Brands need to know them as their neighborhood grocery seller does.
They don’t care about irrelevant offerings. They want you to give them exactly the product they imagined.
And they want it here and now.
This young generation was raised in a digital environment where they can get to the products they want with just a few clicks.
They are used to simple and straight-to-the-point interfaces. To provide this, retailers can implement a powerful technology like AI.
Process automation in retail is helping sellers reduce waiting times, check product availability, complete orders quickly, personalize product recommendations, and provide a seamless checkout experience.
According to BCG research, customers stated that they were 110% more likely to add more items to their shopping cart when the shopping experience is highly-personalized.
Furthermore, they indicated that they were 40% more likely to spend more than they had planned.
This means that personalization significantly improves customer satisfaction.
Data is key to creating a hyper-personalized customer service. The main problem with retail is the shortage of reliable data.
Also, the available data ends up being processed ineffectively or in a manner that does not provide the brand with a single perspective across all channels.
The thing is, we already have the data brands need.
We just have to use it smartly. In fact, research states that we’ve generated more data during the past two years than during the entire human history before that.
A powerful technology like AI can help retailers process this data and use it to obtain more knowledge about their customers and improve their experiences.
AI has the potential to bring process automation in retail for each step in a way that will improve the precision, performance, and scalability of operations.
It’s changing retail through an integrated retail automation approach, from customer engagement and acquisition using accurate data to catalog and product tracking to the post-shopping experience.
Retailers can construct data models using Artificial Intelligence to extract insights and develop their own decision-making processes.
This will assist marketers with challenges like market forecasting and creating smarter, data-driven choices.
Moreover, it will help them meet customer expectations more closely.
Read more about customer experience
Enormous Excel sheets, inaccurate vendor data, manual data entry, and poor insights on inventory data are only some of the problems fashion brands face regarding product tagging.
Automatic tagging is an essential part of the retail automation process.
It represents an AI-based method that automates the entire product tagging process, creating rich metadata for each item of the product catalog and eliminating the possibility for human errors.
Computer Vision algorithms can process an image, detect different attributes of the products, and tag them accordingly.
AI systems need to process many photographs to learn how clothes and clothing look and what characteristics can be used to identify a fashion object present in an image.
This is precisely what automatic tagging is. A qualified AI system that has been trained with thousands of fashion photos.
Suggested read: The Complete Guide for Automatic Product Tagging in E-Commerce
Every photo gets multiple attribute labels added with automatic tagging.
These attribute marks are far more than ordinary tags, offering deep, detailed product insights.
The AI-based algorithms also have the ability to create titles and product details that result in SEO-ready rich metadata that is easily discoverable.
Accurate and SEO-ready identifiers, titles, and descriptions will dramatically improve product discoverability for search engines like Google.
They boost the catalog's metadata, increasing the accuracy of the details regarding the products. Search engines can now index the metadata, and, in exchange, the product rating in the SERP will be improved.
Retail businesses can benefit a lot from automatic tagging.
For example, accurate tags result in more precise textual search on their e-commerce store. Next, the product filtering will be significantly more specific.
Products will also have a better visibility as they will be described more precisely.
Most importantly, these smart tags provide analytics, helping retailers make better decisions.
A retailer business can also use visual search to enable their website visitors to find a product from an image.
They can simply take a picture of a product they want in the real world and then use the image to find a store offering it on the internet.
Thanks to AI in retail, visual search can improve user engagement, increase product discoverability, and boost conversion rates.
Machine learning retail technology has the ability to identify millions of items within the retail ecosystem and categorize them within seconds for consumers, making it simpler than ever to browse and compare products.
Searching by images is a natural process for our brains, designed to analyze visual information.
Big data stats suggest that people capture approximately 80% of photographs with their smartphones.
Considering that over 1.4 billion devices will be delivered worldwide this year alone, we can only anticipate this percentage to rise.
This means that people take photos of everything, including things they want to buy. If you allow them to use these photos to find the exact product they want, you’ll be making the shopper’s journey much more convenient.
Suggested read: The Essential Guide to Visual Search in Fashion Ecommerce
AI in retail is generating improved forecasting of demand.
An AI-based inventory management system predicts industry trends and allows strategic adjustments to the production, merchandising, and business strategy of an organization by collecting insights from market, customer, and competitor data.
Along with pricing and advertising preparation, this often concerns supply chain planning.
Retailers and distributors will no longer have to estimate demand for products by guesswork.
Instead, with AI, they will now combine data sets to make detailed projections about the future, allowing them to make well-informed business decisions.
A perfect way to leverage AI technology when enhancing customer experiences and interaction in the retail business sector is to develop digital chat programs called chatbots.
Such bots use AI and machine learning to communicate with clients, answer popular requests, and guide them to relevant answers and results.
Chatbots, in essence, obtain useful customer knowledge that can be used to guide potential business decisions.
Using AI and computer vision through image recognition can allow the chatbot, as a virtual assistant, to see the problem.
The effects of this for the retail environment are profound.
For example, Levi’s AI-based visual chatbot helps online shoppers find the pair of jeans they would like best.
If marketers can "see" and recognize their customers on an individual basis through computer vision, they can really improve their abilities to personalize sales, promotion, and service.
When consumers are still thinking about a buying decision, through selecting items focused on the needs, desires, and behavior of shoppers, smart product recommendations may help narrow down the number of options.
A solution for product recommendations is considered a necessity for most online retailers.
Even though online stores use them extensively, product recommendations may be challenging to implement with the needed flexibility due to sluggish data collection, limitations on massive databases, and the inability of specific technologies to provide recommendations across all customer touchpoints.
AI helps to address these challenges.
Thanks to the opportunity to collect real-time behavioral information from all channels and use it to guide decisions, marketers can provide more detailed and concrete tips to the customer.
These retail solutions can bring many benefits, including higher AOV, increased conversions, and more delightful customer experiences.
One concrete example is showcasing similar products to those the customers have already liked or purchased.
This personalizes their view and offers items they are more likely to buy.
Suggested read: The What, Why, and How of Product Recommendation Engines
Neiman Marcus' luxury retail store uses AI to make it easy for buyers to locate a certain type of product.
Their app allows users to take photographs of things they encounter when out and about and then check for the same or a related object in the Neiman Marcus inventory.
Instead of using unclear search words to locate an object, a very close match can easily be found with the help of images.
The future of shopping definitely includes visual search because it improves the search experience by reducing irrelevant outcomes.
The pink skirt keywords can return thousands of irrelevant results, while none represents what the searcher really wants.
To eliminate the keyword challenge, visual search utilizes a type of computer vision, attracting users to shop at Neiman Marcus instead of going to a competitor's website.
The Goody Boxes, which contain a range of second-hand apparel pieces customized to fit the style of each buyer, have recently been launched by ThredUp's online consignment shop.
Customers keep the things they want to compensate for and redeem the items they do not want.
ThredUp's AI-based algorithm remembers each customer's tastes so that potential boxes will best suit their preference.
For users, the non-subscription packages are better than looking for separate items.
H&M's famous apparel store depends on keeping on top of trends to be competitive.
In order to evaluate purchase orders and returns and determine sales at each location, the store uses powerful AI.
The algorithm allows managers to recognize which products are more popular in specific areas and get ready for their demand.
For example, the data can show that in big cities, sparkly dresses sell well.
This allows the retail giant to adjust the inventory to reflect what consumers want.
AI for the retail industry can help fashion teams from all over the world to maximize efficiencies through their workflows.
Implementing automation processes assist marketers with reliable data generation to deliver insights about the desires and behaviors of shoppers.
AI has the ability to change every element of online retail. It substitutes instincts and offers retailers a reliable road-map for the future.
When introducing AI, retail sector leaders need to be realistic in their strategy.
They need to recognize that it is a technology that requires a lot of commitment to demonstrate long-term results.
To achieve success, a business needs to establish a long-term AI strategy.
Starting with small initiatives and then aligning them with long-term actions is the safest way forward.
The AI-based product suite from Pixyle will assist you in personalizing the whole shopping experience.
By incorporating this toolkit, you can improve the visibility of your products, increase the relevance of your offering, create better customer service processes, and skyrocket sales.
Harness the power of Visual AI
Harness the power of Visual AI
Harness the power of Visual AI
Harness the power of Visual AI
Harness the power of Visual AI
Harness the power of Visual AI
Pixyle AI generates e-commerce product data that enables brands, retailers and marketplaces to deliver exceptional product discovery experiences.
