4 Stunning Applications of Machine Learning
If you’ve been keeping up to date with technology in the online retail landscape, you may have come across machine learning as an up-and-coming technology in e-commerce.
In fact, Gartner predicts that by 2020 (just a year away!), over 80% of all customer interactions will be handled by AI. At the same time, a significant chunk of retailers plan to invest in AI and IoT technologies by 2021.
Clearly, machine learning is one of the leading buzzwords when it comes to online retail technologies.
But what exactly is machine learning? And how can it be applied to online retail? In this article, we’ll give you an overview of machine learning, what it can do for the online retail industry and some of machine learning’s applications for online retailers.
What is Machine Learning?
According to Expert System, machine learning is the application of artificial intelligence (AI) technology to provide systems the capability to automatically learn and improve from experience without explicit programming.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
How does Machine Learning differ from other related terms like AI and deep learning?
You may have seen these three terms mentioned in the same breath. Plus, they are often related to each other. What are the differences between the three terms?
Some definitions to lay out what each of them means:
Artificial intelligence: A technique that enables computers to imitate human behavior.
Machine learning: A subset of AI technology, machine learning gives computers the ability to automatically learn on their own as they are exposed to more data over time.
Deep learning: A subset of machine learning, here, multilayered neural networks learn from vast amounts of data. This mimics the human brain and how we learn new things.
Contrary to popular belief, machine learning isn’t solely reserved for high-tech scientific research and other obscure uses. Did you know several initiatives for online retailers are based on machine learning and predictive analysis technology? Here’s how machine learning can be applied for your e-commerce business for both the customer experience side and post-purchase supply chain angles. You may have already seen examples of this from leading retailers.
Application 1: Product recommendation engines
Ever seen an ad for a pillow pop up on your screen as you’re scrolling Amazon.com – just days before you were searching for pillows? You may have seen their product recommender engine at work.
Recommender engines use machine learning to predict prospective customer’s interests and recommend product items that may resonate with them. Naturally, these personalized recommendations are powerful to drive sales for online stores. Recommendations typically speed up searches, pipe user’s interests by sending out personalized email offers and drive customer retention through targeted content.
But you may be wondering, where do they get this data from? It can stem from explicit user ratings after watching a movie, from search engine queries typed in by the user or by analyzing previous purchase histories to get a sense of the user’s preferences and desires. Sites like Spotify, Netflix, and YouTube use that data to suggest and recommend playlists, new artists or make video recommendations.
Application 2: Visual Search
With visual search, you can take a picture of something you like and it’s instantly matched with visually similar products in an online store’s inventory.
But did you know that technologies like these will not be possible without machine learning? Customers can upload an image to help narrow down search results to more specific items. Combined with machine learning technology, algorithms can help reduce customer’s time-to-search while producing more personalized search results.
This is not a new application of machine learning. Companies like Microsoft, Google and eBay presented their own visual search solutions Bing Visual Search, Google Lens, and eBay Image Search respectively in 2017.
Even smaller retailers have also got on the bandwagon.
Take Singapore online furniture retailer HipVan for instance.
They use machine learning technology to power their Search By Image function for their mobile app – allowing shoppers to upload a photo of an item in order to search for exact or similar products on the platform. Doing so improved their shopper engagement rate by 121%. In addition, shoppers who searched their catalog using an image were 2.7x more likely to convert via the app.
Application 3: Predicting customer behavior for marketing
This application of machine learning algorithms is to estimate how buyers will behave in the future based on existing behavioral data.
Imagine the potential of this ability – to understand what consumers want and need ideally before they even do. These systems allow retailers to segment customers based on behavioral data and personalize marketing strategies to a customer’s specific wants and needs.
Such actions typically perform better than general, non-personalised approaches. According to McKinsey, retail personalization can boost sales by 15-20% while enhancing brand loyalty and trust.
Efforts at personalization are also in line with customer expectations. According to Salesforce’s fourth annual State of Marketing report, 52% of B2C customers said they would switch brands if they didn’t feel they were receiving a personalized experience.
With machine learning, such applications are possible. For example, you could find out which customers are likely to make purchases in the next 7 days or predict the date/time of significant life events like marriage or pregnancies.
Other uses of predictive analytics in marketing include:
Segmentation of user and customer
Improving customer satisfaction
Application 4: Predicting estimated delivery dates of your parcels
We’ve established that customers want proactive updates on their deliveries. On top of that, accurate predictions of delivery times and dates can also help with resource planning, manpower allocation and enhancing supply chain processes.
Here is where machine learning technology can come into play by helping to predict estimated delivery dates of parcels. Based on the distance and the route parcels has to take, this value is an aggregation of the time it usually takes a given logistics carrier to bring a parcel to its destination.
With machine learning, adding estimations on delivery times can assist in:
Enhance customer experience with timely, hyper-targeted delivery notifications
Insights for customers on the delivery experience
Additional analytics and research on logistics carriers practices.
Getting started with machine learning for online retailers
The possibilities of machine learning are vast. After seeing these examples, you might be wondering: ‘how do I get started with this technology for my business?’
We recommend starting to think of a few criteria to begin some form of implementation while working with your IT team or partner to consider the technical details.
A database to access to get the information the application needs about your brand
A full description of your needs and requirements; including the issues you want to be addressed and what you’ll like to achieve out of this exercise
Examples of the functionality you’ll like to emulate. For example, enjoy Amazon’s predictive recommendations engine and would like to see something similar for your business? Provide this information to your developer as a means of reference while scoping out requirements.
Assess if there are any APIs or built-in software packages that will help you achieve the results you want.
Machine learning will not be something you can achieve overnight, but it’s worth exploring it’s potential. Otherwise, why not work with partners that can help you achieve similar results at a much lower time or cost?
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