Retail and Machine Learning: Innovations Abound
AI (Artificial Intelligence) and ML (machine learning) are top trends of technology in the marketing world. They have a prodigious influence on the tech industry and e-commerce businesses that depend on online sales. The utilization of artificial intelligence technology is ubiquitous.
First movers, such as Alibaba, Amazon, or eBay, have successfully integrated artificial intelligence technologies across the whole cycle of sales, including post-sale client service and storage logistics.
You certainly don’t have to have a massive company or sell online to benefit from the power of machine learning in retail. This guide will show you how both brick and mortar stores and online retailers can incorporate machine learning technology to decrease cost and increase sales.
From groceries to clothes and household items, the potential in the retail sector shows excellent promise. The use cases and applications presented in the guide are a segment of the practical machine learning (ML) projects and illustrate what may be completed today in a retail company. It’s important to note that retail companies are beginning to push the envelope with machine learning, so if you have a unique situation, think how about how these practical machine learning examples could be tweaked to make an impact at your company.
You will notice that each use case of machine learning is defined as a symbol and a growth chart or both signs simultaneously. These special symbols specify whether a use case relates to a project of machine learning that:
primarily decreases charges through automation.
predominantly turns profits.
+ impacts both costs and revenues.
Applications in Online Retail
Browsing Through a Digital Catalog
Clients enjoy online catalog browsing because they display products attractively and provide sufficient information about these products. However, creators of automatic digital catalogs typically offer several decent solutions; the utilization of custom technologies of machine learning may meaningfully improve customers’ experience to impact their conversion rates and increase engagement.
Many retail and e-commerce companies leverage the benefits and power of boosting sales and data by executing recommender systems on the websites. Companies depending on recommend structures concentrate on increasing sales with personalized offers and a better customer experience.
Suggestions typically increase searches’ speed and make it convenient for users to evaluate and access the content they are fascinated in and astonish them with several offers they might not have looked for.
Companies can retain and gain more customers by sending several emails with promotional links to amazing offers that satisfy recipients’ interest and recommendations of TV shows and films suitable for their customer profile.
It can increase the comfort level of users, and they feel understood. Moreover, they are likely to purchase more products and consume additional content. Understanding users’ nature and desires and showing that understanding immediately can decrease the probability of customers leaving a platform. Machine learning can translate it into more purchases and reduce the risk of losing customers to competitors.
Recommendations in the system can offer extra value to customers, increasing the appeal of products. Moreover, machine learning can help businesses to get the best product placement in the market, according to their goals. This helps to boost their earnings while squeezing out the competition.
Pricing is an essential predictor of productivity and revenue. According to econometric science, machine learning algorithms may take critical pricing variables into account and define a pricing strategy (automatic) with dynamic and real-time prices.
Customers’ behavior may give an idea to predict price and understand their willingness to pay for a product. You can find out items that customers purchase instantly and find out the time they usually spend on every web page.
In this process, an algorithm may continuously crawl each website and search for the critical details on competitors’ prices for similar or the same products, identifying hot deals and collecting essential data about the history of cost on the last weeks or days.
Machine learning can make sure to include seasonality, supply, external events relevant to business (a match, a festival, a concert), offer, and market demand. Imagine a system that automatically prices which can effectively optimize and adjust prices for the retail organization. It is possible with machine learning.
Having an AI solution like this can help you understand the different trends and events of the market. It enables you to get the best insights compared to your competitors allowing for meaningful business decisions.
Clients try to look for different visual content before making any purchase. Though, in a few cases, they may not find the best keywords to describe their needs. The visual search can make things easy for customers to search for precisely what they are looking for.
There is no need to type a query like a cordless kit with a durable soft case that can return with numerous general results. Customers are capable of uploading images to narrow down search results to get specific items. Increasing and a considerable amount of sharing and snapping pictures will help machine learning algorithms to achieve the best outcomes.
For online content, these use cases are trending. Top companies, such as eBay, Google, or Microsoft, have successfully presented Visual Bing Search, Image Search, and Google Lens in 2017.
In the e-commerce market, Lens Your Look is essential to Pinterest. It is a visual search engine to find the best outfit ideas after getting inspiration from your wardrobe. If you want new methods to wear the best blazer or jeans, you simply add your photos of these dresses in the search and search outfit ideas that you can purchase.
What do I need to start?
The above-described solutions describe that machine learning models are wholly trained with data about purchases, customers, and products. Extra information, including feedback and reviews, may be helpful.
|Recommendation Engines||· Information from consumers (searches, purchases, and navigational data)|
· Information from products (prices, descriptions, and images)
|Pricing Strategy||· Information from consumers (navigational data, searches, and purchases)|
· Information from competitors (social media, websites, and brands)
· Information from products (prices and descriptions)
· Information from outside events (as per availability)
|Visual Search||· Information from products (descriptions and images)|
In the event this data is not made available to you can web crawl for the information. You will need to check with your legal department if what you want to crawl is aloud. Furthermore, the Millennial Survey Deloitte 2016 states that 58 percent of millennials and ½ of shoppers might agree to share their information if retailers are ready to offer something special, such as offers or services.
It is practical for these benefits to create the mentioned solutions if you have an app or online site for shopping. Related to the volume of required data; however, extra data is useful to get better results when creating an AI solution.
Predicting Customer Behavior
The actual reason to predict the customer’s behavior is to estimate buyers’ behavior in the future according to the data of previous behavior. These systems make it easy for retailers to perform modified marketing actions and segment consumers efficiently compared to general approaches. Furthermore, it proves helpful to take measures according to the needs of customers. These predictions can increase retention and loyalty – not to mention added profit.
Typically, it proves helpful to predict buying behavior and purchases. For instance, it helps you to understand the purchasing behavior of customers in the coming seven days. Complex predictions can prove helpful with significant events in the lives of people. For instance, if you can predict pregnancy or marriage and send special custom offers, you have a leg up on the competition.
Forecasting customers’ changes may be challenging, and machine learning algorithms can help you in this process.
What do I need to start?
The models of prediction need behavior data of the consumer. For example, buying trends or purchasing history might include knowledge specific to the domain and social media activities.
|Information from consumers (navigational data, searches, and purchases)|
How frequently your consumers make transactions? Are they habitual to purchase during the time of sale or before birthdays? The number of items they typically buy? Do you know about their current shopping habits? Or what essential topics are they discussing on different social media platforms? A model to forecast the future behavior of the customer uses this type of information. Moreover, the retailer’s experience is vital in selecting specific business criteria and fine-tunes this model.
Social Media: Brand and Customer Monitoring
Nowadays, social media are networking platforms, and consumers use it as an essential marketplace to purchase services and products. Evaluating social media on a considerable scale and obtaining precious insights is probable with machine learning power. Retailers may thus get data about factors driving revenue, traffic, and engagement.
By evaluating and tracking the information flow, retailers may augment the channel, target timing, content, and audience according to their marketing campaigns and social media posts.
Observing mentions of retailers to understand and get insights is famous. Credit goes to recognizing an image because retailers may now see it portraying through videos and pictures shared daily. Simultaneously, the technology may be utilized to evaluate and act on the generation of content by their respective competitors.
What do I need to start?
It is a critical use case where the development is completed from scratch, and retailers understand customer interest and essential competitors. Data about accounts of social media proves helpful but not obligatory. Working with LogicPlum as your AI solution provider would accelerate your results, guiding you along the way.
+ Virtual Assistants and Chatbots
Chatbots play an essential role. They can stimulate conversations of a human and motivated people to shop that purchaser usually get in physical stores. This type of interaction proves helpful at different stages. For instance, chatbots may be utilized to inspire extra consumer purchases, boost our catalog’s searching capabilities, personalize the customer’s experience, or manage a considerable segment of customer service.
+ Smart Assistant
Smart assistants’ primary objective is to outdo a salesperson (human) to help you search for what you need. An excellent illustration of intelligent assistants is GWYN (Gifts When You Need), Flowers.com, gourmet foods, and a floral gift retailer. A natural and friendly conversation helps retailers to provide the right information to customers about the recipient of gifts. Moreover, the assistant can tailor offerings according to the purchased gifts for the same recipients.
A smart assistant may have a tremendous influence. A company can experience a 70% increase in orders with the use of an intelligent assistant.
+ Intelligent Search
When you understand different search terms, search engines can work better than you in finding data. Although, searchers need extra sophisticated information, including items of a particular color or shape. Since retail brands have massive catalogs and may be complicated to navigate, an event may be available with filters through a faceted search engine. Interactive chatbots may deal efficiently with diffuse semantic and ambiguous requests, in a similar method a shop assistant of a human might do.
Chatbots may be programmed explicitly with individual responses to recurrently asked questions, relieving a customer’s service from frequent interactions. They may include vibrant business-oriented objectives, including answer questions relevant to delivery and tracking, for improvement in shipping procedure post-purchase.
A suitable chatbot may decrease costs significantly without data loss because it may detect when one complicated question requires human intervention and redirect the consumer to one live customer agent for interaction.
What do I need to start?
The bot may address a sample dataset of conversations, and it is user input, but not mandatory. For contact centers, a FAQ may be sufficient input for the development of a chatbot. An intelligent system of search or smart assistants needs more information related to the catalog will become necessary. Again one chatbot may be designed through an incremental method; therefore, it may handle according to your services and products.
It is essential to recognize and understand your business and easily imagine the requirements of your retail organization. Furthermore, some pertained models are available for some type of interactions that may be adapted to use case by presenting particular business knowledge.
|Smart Assistant||· Information from products (descriptions and images)|
· Conversations sample (if available) or one set of unique use cases
|Intelligent Search||· Information from products (descriptions and images)|
|Contact Center||· FAQ|
· Information from products (descriptions and images)
Applications in Brick and Mortar Retail
Retail Stocking and Inventory
Augmenting predictive maintenance and inventory planning is a critical concern and an essential logistic apprehension for retailers.
Predicting Inventory Needs
Algorithms of machine learning may use purchase data for the prediction of inventory requires in actual time. According to the season, day of weeks, nearby occasions, past behavior of customers, and social media data, these algorithms may offer a regular dashboard of recommended orders to purchase managers.
+ The Power of Computer Vision
Retailers from brick and mortar stores may take benefits from several impressive current outcomes on a computer vision. Several new methods in this field might generate real-time, accurate estimates of essential products in an available store. Undoubtedly, this information helps the machine learning algorithm notify managers of the store about unexpected configurations of portfolio data that might be because of theft or a sudden boost in the product demand.
Another use is the utilization of images to analyze specific shelf space utilization and recognizes sub-optimal configurations. LoweBot is an excellent example of technology. It is a retail service autonomous robot that helps customers monitor and shop inventory consistently and give actual feedback to their employees.
What do I need to start?
The model of inventory planning requires data related to the behavior of customers. For instance, buying trends or purchasing history might include social media activities and knowledge specific to a domain.
Algorithms for computer vision require images for processing. This information is available from installed security cameras in stores, or employees may take it.
|Predicting Inventory Needs||· Information from clients (social media, searches, and purchases)|
· Information from outside events (if possible)
|Computer Vision||· Store videos and images|
· Information from products (images and description)
Behavioral Tracking via Video Analytics
An essential thing about several physical stores is the interaction and behavior of different humans with different products may generate valued insights in other ways that are complicated for online retailers. Algorithms of computer vision may recognize people’s faces and characteristics, such as age range and gender, generate valuable exploitable data.
Analyzing Navigational Routes
The placement of items is an essential subject for several physical retailers, who continuously look for different methods to recognize customers’ paths to make purchases. Algorithms of computer vision may track customers’ journey in stores to identify how they will interact. These specific algorithms may detect the particular walking patterns and direction of the stare of the consumers. Retailers may utilize the data to restructure the layout of stores or evaluate the products’ interest. They may discover different locations that get maximum visual attention and traffic.
Do aged people buy more on weekdays? Do teenagers want to cover different parts of the store, for instance, the front section? Is a store got more customers in winter? Additional variables, including age, season, or weekdays, could be utilized to create insights that help modify products’ placement and develop efficient promotions dramatically.
In retail, theft prevention is a fundamental problem with a strong return on investment. Machine learning technologies can assist in the particular utilization of video cameras for the detection of shoplifting.
Algorithms of facial recognition may be trained for spot famous for shoplifters as they enter a store. In 2015, Walmart evaluated technology as a critical anti-theft mechanism. Similarly, computer vision may detect if a person picks an item, and it might see if a person hides an item in the jacket or backpack. Furthermore, a similar approach may be utilized for detection as checkout clerks avoid scanning items, either on purpose or inadvertently.
A machine learning-based system may alert actual managers or security personnel and send them specific video excerpts so they may review by themselves before tormenting the people in each store.
Product Tracking and Gesture Recognition
Retailers of brick and mortar stores may not have information about different items that consumers pick up, look at, and then put back on shelves. They may not have any data either about the next choices of customers.
The computer vision algorithm may monitor hand gestures and facial shoppers to estimate the success of an item. This application can generate valuable data about the number of items picked up from shelves and then put back on the racks or shopping carts and purchased.
What do I need to start?
If a store has security cameras with certain image quality, you may have all things needed to implement the solutions mentioned previously.
+ Virtual Mirrors
Interactive or virtual mirrors bring a varied experience for shoppers between digital interactions and physical stores. It is a technology incredibly precious for make-up boutiques and fashion retailers. There is no need to try out clothes because virtual mirrors permit customers to navigate via virtual models of clothes to select and find the best clothes. Additionally, available virtual mirrors might recommend accessories along with appropriate clothing. Furthermore, a “look book” might be developed by fusing outfits; therefore, shoppers may decide on suitable clothes for them.
Cosmetic boutiques may improve the experience of customers by allowing them to try products virtually. A unique virtual mirror may act as beauty consultants, evaluating someone’s skin, looking for dark spots, clogged pores, and wrinkles, and creating a report with tangible actions for execution.
A smart mirror or virtual mirror acts as a device to display photos on one screen. Some versions may feature augmented reality along with video display and use a virtual graphical avatar of users. Virtual mirrors act as mobile phone applications and allow users to adjust their appearances, such as accessories, make-up, or hairstyles.
This technology is useful for online shopping, and shopping in-store allows people to evaluate their final looks, such as clothing, accessory, handbag, and make-up. Numerous color contact websites feature virtual environments for simulating the look of a user after wearing contact lenses. Virtual mirrors use face detection, face tracking, and computer vision technologies to evaluate visual patterns. The technology utilizes the algorithm for collection and evaluation from multiple images and data. A mirror moves in actual time for a 360 view. The mirror has numerous options, such as change the lighting of the room. The users will get an opportunity to check his/her looks in different lights.
What do I need to start?
It is a hot market. Numerous turnkey solutions are available in the marketplace, including HiMirror, a consultant for health and beauty, or SenseMi, a unique mirror for different fashion stores. Outside the current solutions, a retailer will need custom developments as per the company’s requirements and the products’ characteristics.
+ In-store Assistant
Some proposed solutions for different online retailing may be included in the mobile apps to get an in-store intelligent assistant. These are the most famous use cases of artificial intelligence in retail. These assistants could inform consumers about where a family of different products or products are available in the stores and help them locate what is best for the requirements or propose the best expedition to them in various stores to choose different products.
Mobile apps might recommend items that may be missing probably from a shopping list. A phone or tablet could be utilized as a crucial pointing device that permits consumers instant insights about items in their shopping cart. Speech recognition proves helpful for interaction with the assistant because it gives the feeling of humans.
Macy’s On Call is an actual example of this technology, and the application may help shoppers get data while they are navigating in the stores of the company. Another exciting example or use case is the in-store Digital Tire Journey web app, and Sears Automotive has proposed it to help shoppers search what they require among different types of tires.
Several in-store helpers create a dual advantage. Moreover, they offer actual value to clients that enhance retention and loyalty. Furthermore, they allow retailers to gather maximum data and be utilized as important input by machine learning solutions.
What do I need to start?
For the development of in-store assistants, data about products, customers, and inventory is required typically. Moreover, it is necessary to design an outline of probable interaction to start development. Remember that a virtual assistant may be created in incremental methods.
|· Information from consumers (interaction history and purchases)|
· Information from products (description and images)
|In-store Assistant||· Information from inventory (products and store)|
· Interaction samples (if possible) or use cases set