10 Exciting Real-World Applications of AI in Retail
Overview
- The rise of artificial intelligence (AI) has disrupted many industries in recent years
- One of the most impacted industries – retail! Retail operations as we knew them have been revolutionized thanks to AI
- We look at 10 exciting real-world examples of how AI is transforming the retail industry
Introduction
Our core retail experience hasn’t changed much in recent years. We go into a store (or to an online portal), we browse through the available options, try them out and make the purchase.
The way we transition from one stage of this experience to the next? Well, that’s where the disruptions are happening thanks to artificial intelligence (AI). It has completely transformed the way we handle our retail experience – both from a customer’s perspective as well from a business standpoint.
Artificial Intelligence creates an opportunity for retailers to bridge the gap between virtual and physical sales channels. Brands are progressively using Artificial Intelligence to reduce cost, improve efficiency, achieve operational agility and increase the speed of decision making in the retail world.
According to IBM’s recent study, AI-driven intelligent automation in the retail and consumer products industries is projected to leap from 40 percent of companies today to more than 80 percent in the next three years.
That’s a significant leap and a major reason why retail businesses are jumping to create AI-driven strategies. It’s a great time to be a data scientist in retail – and in this article, we’ll see 10 exciting real-world applications of how AI is transforming the retail sector around the world.
If you’re new to AI and want to understand how it works, how it’s disrupting multiple industries and how it might impact your role, you should check out the below certified program:
Here are 10 Exciting Applications of AI in Retail:
- McDonald’s Drive Through Smart Voice Assistant
- H&M’s Assortment Planning using Artificial Intelligence
- Pepper Robots, The New Choice of Nestlé to Sell Coffee Machines
- Boch Automotive’s Artificial Intelligence Powered Sales Assistant
- Mango and Vodafone’s Smart Digital Dressing Room
- 53 Degrees North Applying Automated AI to the Process of Customer Segmentation
- Domino’s Pizza-Lovers Now Get Hot Piping Pizza Delivered By a Pizza Robot
- Nestlé’s AI Skill That Provides Voice Cooking Instructions As You Cook
- Walmart Deploys Robots To Scan Shelves
- Olay To Use AI To Personalize Skincare
Note: The numbers mentioned in this article were taken until November 2019. Given the rapid advancement in technology and businesses expanding accordingly, this will keep changing.
1. McDonald’s Drive-Through Smart Voice Assistant
One of the world’s favorite restaurants moved quickly to transition into the AI era. The top folks at McDonald’s have done impressively well to stay on top of the latest trends over the last few decades and their recent move indicates they are not relenting any time soon.
One of the things I’ve found a bit tedious is the drive-through line, especially in the evening. I’m sure most of you have gone through this and sketched out your own plan to improve the waiting time.
Well, artificial intelligence has solved that for us!
Our favorite burger chain has installed a voice-based platform for complex, multilingual, multi-accent and multi-item conversational ordering. It recently acquired an artificial intelligence company called Apprente, which has built this platform for them. Don’t you love the power of Natural Language Processing (NLP)?
Think about it – the need for a smart voice assistant in this drive-through was quite obvious considering the time each customer typically takes to place a single order. This makes the process of ordering faster and is cost-efficient as well – a win-win.
This technology is “sound-to-meaning,” in contrast to “speech-to-text.” Basically, the system does not transcribe what the customer says and then infer its meaning from that transcript. It goes directly from speech signals to result.
The company believes this provides a better approach for customer-experience related use cases, particularly in noisy environments such as restaurants and public areas or in cases where customers tend to use colloquial, poorly structured language, resulting in low-accuracy speech recognition.
Now we will be talking to a robot about McFlurries – how exciting!
How about building a speech-to-text-model in Python on your own machine? Then here is an exciting tutorial to get you started:
2. H&M’s Assortment Planning using Artificial Intelligence
The role of an apparel store owner is quite intensive. They have to plan for a new season and cautiously decide on what trends would the brand like to showcase to their customers. How in the world do you forecast fashion trends?
One approach is to track the past trends per season and then factor in the new styles (or fads). The brand can then make a decision based on these aspects.
That is proving to be a problem in today’s world as customers have a variety of tastes. Social media has changed the meaning of fashion – and apparel outlets, even the biggest brands in the world, are struggling to keep up.
So taking into account historical data to make a decision on the current scenario may be an obsolete approach.
This, as you might have guessed already, is where AI comes in.
AI algorithms can predict the most relevant items to add to a retailer’s inventory by analyzing the product assortments of competing brands and comparing those products to the demographics and shopping history of that customer.
Big brands like H&M have realized the importance of using AI in their assortment planning. H&M aims to forecast trends months in advance. The retail giant is employing over 200 data scientists, analysts and engineers to use AI to review purchasing patterns of every item in each store.
The data incorporates all the information from five billion footfalls from last year to its stores and traction on its websites. It also considers data from external sources. H&M is breaking the stereotype with a one-size-fits-all merchandising approach to its 4,958 stores all over the world.
Here are its benefits:
- The localization of inventory will suit the needs of its clients in every geographic area
- Sharp data insights will help the brand eliminate bad product cycles
- With RFID tech to its stores, there will be an improvement in its supply chain process
We know the industry is undergoing a huge shift – the catalyst for this transformation is technology. It’s not just one technology, but a set that includes artificial intelligence (AI), augmented reality (AR), robotics and more.” – CEO Karl-Johan Persson, H&M
Soon, you and I will have the latest-in collection with the convenience of fast delivery by our most favorite apparel brand! I am sure this will multiply your visits to the store and tempt you to shop more and more.
A similar concept called Market Basket Analysis is a must-know if you’re a data scientist in the retail world. Check out how it works in Excel:
3. Pepper Robot – Nestlé’s Solution for Selling to sell coffee Machines.
I was raised in the southern part of India and coffee is literally my ‘hot’ favorite. I get spoilt for choice when I visit specialty coffee stores but often get disappointed when I find no one around to assist me in buying the best machine to brew my coffee.
Don’t you wish you had a robot coffee buddy who could guide you to buy the best coffee machine by understanding your needs? Here’s the good news – this is now happening thanks to AI! Take a look at the concept in the below video:
Nestlé Japan is using a humanoid robot to sell its coffee machines built by SoftBank Robotics. It’s one of the first robots in the world that can sense and respond by feeling human emotions.
Its equipped with the latest voice and emotion recognition technology. And the best part is it can respond by understanding human facial expressions!
Pepper will be able to explain Nescafé products and services and engage in conversation with consumers.” – Kohzoh Takaoka, President, and CEO of Nestlé Japan
Starting your day with the perfect aromatic blend of your favorite coffee brewed just right is the most heavenly feeling in the world. I am looking forward to talking with a humanoid coffee robot soon!
As mentioned above, this robot can read facial expressions and as a data science professional if you want to learn Facial Expression Recognition hands-on then here is an article for it.
4. Boch Automotive’s Artificial Intelligence-Powered Sales Assistant
Every car dealership enhances its bottom-line via after-sales service. Given the current dire state, the automotive industry finds itself in right now, a dealership’s very existence might depend on this.
So how do they make sure that customers are engaged successfully to drive this revenue? Not only is the service revenue important, but this engagement will also increase the chances that their customers will make the next car purchase with the same dealership (or recommend it to others).
But anyone who has worked in the sales field knows that engaging customers is a tricky challenge task. It takes time, effort and close monitoring and that’s a massive task with a service team when there are thousands of customers to work on.
Boch Automotive, an England based car dealership company, has adopted a unique AI software that streamlines its sales funnel and establishes an automated sales assistant to increase service revenue via engagements.
Conversica “sales assistant” software is designed to automate and enhance sales processes by identifying and conversing with internet leads. The sales lead and management company claims that authentic-sounding messages result in an average engagement rate of 35%.
That is quite a big number!
“This assistant is specifically trained in skills to generate new interest, engage demand, and drive outreach pre-event. AI Assistants communicate back-and-forth promptly, professionally and persistently, identifying the hot leads and handing them off to sales people to generate more sales opportunities and increase revenue.” – Conversica
This attributed to an average 60 percent increase in sales per month at one Toyota dealership.
Sales Assistants in the form of chatbots can be built very easily. If you are a Data Scientist then you should try your hands on making one and this article will help you build it from scratch!
5. Mango and Vodafone’s Smart Digital Dressing Room
I love this application of AI in retail! How many times have you gone into the trial room and emerged disappointed that the apparel you picked out just didn’t work out?
It’s a constant struggle! And then the process begins again – we exit the room and hunt for the perfect size. This is now changing drastically thanks to artificial intelligence.
We now have our own digital mirror that helps us scan the code on your clothes in the fitting room and directly contact the floor staff for any assistance required.
Shop assistants receive your requests in real-time on digital watches. We will also be able to use our own smartwatches to save the details of any outfits we like. Super cool, right?
This concept of a digital fitting room is using an Internet of Things (IoT) digital mirror that was designed by Mango and developed by Vodafone in collaboration with Jogotech.
“This is a really exciting project for Mango. We see the future of retailing as a blend of the online and the offline. These new fitting rooms are another step in the digital transformation of our stores to create a whole new experience for our customers.” – Guillermo Corominas, Mango’s Chief Client Officer
Mango will soon setup digital fitting rooms in its top stores, from Barcelona to New York. It will surely solve the most important drill of coming out of the fitting room often and parading around looking for alternatives. Quite a relief, isn’t it?
If you want to get started in IoT then here is a great blog to understand how to manipulate IoT database.
6. 53 Degrees North – Automated AI for Customer Segmentation
Customer segmentation is one of the most widely adopted use cases in the industry. Any B2C organization you come across – they will be heavily relying on customer segments to prop up their bottom line.
But given the unprecedented rise in the data we’re generating these days, manually creating these segments? Not a good idea. There are actually multiple ways to parse through your customer data and create customized segments.
Data science and AI have transformed this landscape as well. Using techniques like market basket analysis, association rules, clustering and so on, businesses are able to create granular segments to enhance their marketing efforts.
53 Degrees North (53DN), an Irish lifestyle retail chain, partnered with Brandyfloss for using their automated customer segmentation software. This solution will solve the segmentation problem and propel its marketing campaigns to the corrected targeted population.
Using the Brandyfloss algorithm, a group of 3,612 target customers was created. For comparison, a B Group of 3,612 (identical in size) was selected at random from the remaining customer list. 53DN ran an email marketing campaign to both groups, promoting an offer on hiking boots for the ten days.
At the end of the campaign, the sales data was studied. A massive 95 percent of the total sales came from the group of customers that were created using Brandyfloss. A stunning 93% of the total revenue came from the group of customers that were created using Brandyfloss.
You need to get familiar with this if you’re in a B2C company, regardless of whether you’re in the marketing and sales function. And if you want to learn more about the algorithm behind segmentation, here are a couple of articles on clustering:
- An Introduction to Clustering and its Different Types
- The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need
7. Domino’s Pizza – Now Get Hot Piping Pizza Delivered By a Pizza Robot
Are you craving pizza after reading the heading? Think about the bubbling cheese on the top and perfectly stuffed crust. I am so tempted to order one right away!
Unfortunately, the scenario of bubbling cheese fades by the time the pizza reaches our doorstep. It’s quite rare to get a pizza that feels it just came out of the oven.
Here’s the good news – our favorite pizza brand has solved this problem using AI.
Domino’s has launched its Domino’s Robotic Unit (DRU). They have built an artificial intelligence-based robot that will deliver hot piping pizza at your doorstep!
“DRU is for us everything robotic, everything machine-learned, and everything AI.” – Don Meij, Domino’s Group CEO and Managing Director
This innovation marvel is like a self-driving car with a mini oven and a fridge on wheels. In fact, its a much cooler (or hotter?) version of a self-driving car! The vehicle itself is a collaboration between Domino’s Pizza and a Sydney-based robotic company Marathon Targets. Here’s a quick demo of the robot:
Amazing, right? As the famous saying goes – A pizza makes everything possible!
I suggest you read this article to gain a broad understanding of the buzzwords associated with AI, its applications, the careers & opportunities it has and its future.
8. Nestlé’s AI Skill Provides Voice Cooking Instructions as you Cook
It’s a constant struggle to keep referring to the recipe manual on my gadget and cooking simultaneously. How I wish there was someone to just tell me what to do and make things simpler.
Well, it just got real! Now, I can have my personal cooking assistant who would tell me what to do and I just have to follow and the world’s largest food company Nestlé has made this a reality. The company is set out to train Amazon’s Alexa and create a custom skill to deliver voice-first hands-free cooking assistance.
Nestlé’s GoodNes skill for Alexa inspires people to cook, connects them with the perfect recipe, and provides step-by-step help along the way. It is enabled with a goodness visual guide that offers a visual voice browsing experience.
To develop the skill, Nestlé teamed up with Mobiquity Inc., a third-party digital engagement provider with deep experience in building for voice. Mobiquity’s Global Alexa Lab, with expertise in voice design, was able to help the SVIO team design, build, and submit the skill quickly.
It’s simple, if you have guests at home and want to cook something special for them then all you have to do is ask Alexa. It will browse the recipe for you, send the shopping list to your mail and also play some amazing music in the background. Alexa is a perfect multi-tasker we need for our kitchen!
Just like Alexa do you wish to have your own virtual digital assistant? Then here is an article that will help you build one right away!
9. Walmart Deploys Robots To Scan Shelves
Physical stock counting is a thing of the past! Today, we have AI-powered scanners that can scan a shelf on the go and account for the quantity of each product displayed.
Tampa Bay Walmarts has introduced a robot on wheels that is doing this task very effectively. The mega-chain prefers to call them “autonomous scanners”. Check it out!
The robot travels along the aisle, stretches its arm till the top of the shelves and automatically captures the required data. It takes in prices and the number of items available. Its a job usually done by managers with handheld scanners.
That’s quite a lot of time and effort saved and the company will also cut down on operational costs with this adoption. The company aims to increase its customer interaction rather than spending its customer’s time on shelf alignment.
Can you guess the AI technology that powers this system? Yes, it’s Computer Vision! The system uses deep neural networks to scan barcodes, analyze the items on each shelf, and so on.
Honestly, computer vision is among the hottest fields to get into right now. Amazon Go is a classic example of computer vision in the real-world. Check out the below course to get started with this:
10. Olay – Using AI to Personalize Skincare
I love this! This is a concept taken from my imagination purview – a makeup and skincare business disrupting the way in which the skincare industry functions.
Olay’s AI-powered skin advisor, an online service that relies on artificial intelligence and proprietary deep learning, is analyzing a user’s skincare needs at a very granular level.
Olay outlines a four-step process:
- A simple no-makeup (yes, #nomakeup) selfie
- A questionnaire about the user’s current regimen and requirements (similar to an in-store consultation with a beautician)
- Analysis of Olay’s pre-determined “aging zones” (the forehead, cheek, mouth, crow’s feet, and under-eye), and
- Product recommendations
Platform users will get recommendations specifically for the areas that could be “improved” but also for the areas that are already doing well and are considered “best”.
A beta version of the web-based platform is already available and there are more than a whopping 1 million subscribers already on the platform.
Want to know how this technology took shape and what are the various techniques used? This case study will help you understand how Parc built and enabled smarter skincare with Machine Learning for Olay
End Notes
The global retail sector’s technology spending will grow 3.6 percent to reach almost $203.6 billion in 2020, with similar growth rates for the next two years, according to the latest forecast from Gartner, Inc.
Retail brands are investing huge sums of money to ensure we get the best experience.
The Retail Industry today is more fragmented and competitive than ever before. Knowing about all the above developments I am sure you can gauge the speed of advancements ahead. AI is revolutionizing the retail industry by making it cost-effective to deliver a personalized, immersive and optimized experience for every customer.
If you know about any other advancements in the field, then do share them in the comments section below. I would love to read them!
Very Insightful topics covered! Thanks for sharing.
An article with every aspect covered. Great reading