E-Commerce is, without a doubt, the child of the internet. The growth of e-commerce over the last decade has been nothing but unprecedented.
In just five years, the e-commerce industry has grown to 5.4 trillion US dollars from 4.28 trillion US dollars. It is also forecasted that the industry would top 6 trillion US dollars in 2024.
Of course, the internet has spawned the evolution of e-commerce. It is the support of subsequent technological advances that happened, still happening, in the field that makes all the difference.
One of such technologies is machine learning. Machine learning in e-commerce has been extensively used by companies to drive impeccable and hard-to-find customer insights.
The learning thus acquired is applied to businesses to sell more, bolster bottom lines, and drive home more satisfied consumers.
Most businesses now use machine learning to create business strategies and expand them.
Like everything else, businesses were hesitant to leverage machine learning for business in the beginning. However, the advantages of machine learning in helping them do better business and retaining their customers have encouraged businesses to think otherwise.
The fact that there are multiple machine learning development services in the market makes it all the more effortless to leverage.
If you are an entrepreneur that wants to improve your business and customer experience on your website, machine learning is the best way to achieve your goals.
Here is a report that suggests machine learning and AI can bolster the effectiveness of customer care of a business.
Machine learning has come a long way over the decade. Hence, integrating the same into your e-commerce business will help your business in more ways than done.
Let’s see how!
Applications of Machine Learning in E-Commerce Business
It is safe to assume that the e-commerce industry and technology have a close relationship, which is symbiotic in every sense of the word. Hence, the role of machine learning in eCommerce is also multifaceted.
Let’s take a look at how machine learning helps an e-commerce business:
User-Centric E-Commerce Search
In-site search is one of the primary means to find apt products by most visitors on any e-commerce website. The chances of a customer buying a product are 40% high when he/she is searching for a product on the site.
Machine learning can help businesses accurately return the products to customers and thereby reduce the time they spend on searching.
The shorter the search span of a customer, the higher the chances of conversion.
Product Demand Analysis and Sensing
Machine learning can be used to identify the demand for products and encourage the supply-chain team to stock adequate products. Such strategies can reflect considerably on the bottom lines of a business during festive periods and sale days.
Diverse machine learning algorithms have the power to analyze past data with macro-economic data to predict products’ demands.
Deployment of Responsive Chatbots
The number of e-commerce customers is increasing steadily. This creates a huge requirement for adequate customer service. However, it is impossible to support every customer with an agent or customer service executive.
This is where intelligent, machine learning-capable Chatbots come in handy.
ML Chatbots are smart and they learn to deal with issues on their own when they are used more with more customer scenarios.
Dynamic Price Setting
It is impossible to scale the price of products according to dynamic trends, demand, and supply with humans. However, machine learning can do that pretty effectively, too.
They can carry out multiple A/B price tests for diverse products simultaneously and arrive at the most ideal price for the products.
This will help businesses meet their business and customer satisfaction goals.
Customer Segmentation and Targeting
Customer segmentation and accurate targeting have become imperative for better performance of push notifications, email campaigns, and promo codes.
Machine learning algorithms are perfect to predict user behavior, demographics, and inclination to follow trends.
Therefore, the performance of such campaigns and their ROIs can be improved significantly with machine learning.
Personal Product Recommendations
Cross-selling and up-selling products on e-commerce platforms are two important ways to increase revenue. This is often done by suggesting products similar to the ones in customer’s in-site search and choice.
The better the recommendations are, the higher the chances of conversion. Machine learning algorithms can do that with great effectiveness.
These techniques are already used by businesses with great success.
Consumer Sentiment Analysis
E-commerce brands would always love to know what their customers and followers think about their services and offerings.
Machine learning algorithms can assess the sentiment people have regarding brands from analyzing social media chatter.
That’s often the most accurate representation they can get.
Effective and Timely Fraud Detection
E-commerce fraud is real and alarming. But with machine learning, businesses can easily pick up frauds with e-mail ID patterns, history of purchase, buying behavior, and IP addresses. Such responses can improve the image of the business further.
Visual Search Assistance and Help
Visual search has been getting more attention lately. It is easier for users to take pictures of products and search for them on an e-commerce site.
ML algorithms can identify these images and recommend similar images from the inventory.
Prevention of Spam
All customers would look at a few feedback and reviews of the products from real customers available on the site.
In fact, the star rating can become a buying metric. However, spammers can review-bomb products with fake reviews to put the products down.
However, with ML algorithms, this can be tackled rather easily in real-time.
How is E-Commerce Changing, Now?
E-commerce has come a long way from what it was two decades ago. It is now one of the biggest industries in the world and growing.
Amazon has seen its share prices skyrocket when the pandemic hit the world and brought the planet to its knee.
When all other businesses took a direct hit from Covid-19, it was the e-commerce industry that sailed effortlessly in the thunderstorm of reduced consumption, income, and heightened pessimism.
As consumers around the world went digital, the market share of the e-commerce industry in the global retail market rallied to 17% in 2020 from 14% in 2019.
The signals are showing how the trend is going to continue.
Like every other sector of business, e-commerce, too, has been riding on the waves of change. Let’s take a look at some of the chief ways how e-commerce industry is changing:
Technologies: As more advanced and nuanced technologies are on the rise, they get incorporated into eCommerce businesses. These technologies have a direct impact on both the growth and profitability of businesses. One such example is machine learning.
Shipping became important: Consumers are veering towards e-commerce brands that offer sustainable, quick, and effective shipping solutions.
Competition: With more favorable means to make e-commerce profitable, the number of e-commerce businesses has been on the rise. It changed the landscape of competition in the industry pushing brands to innovate and adapt.
Consumer behavior: When consumers are provided with diverse options to choose from, they begin looking for value for their time, better satisfaction, and quick service delivery. This is the reason more businesses started getting rid of delivery charges and ensuring quicker delivery.
Retention, a priority: It is extremely difficult to acquire new consumers. Hence, brands began looking for ways to retain their existing customers. The rate of acquisition is often significantly higher than that of retention.
For a business, adopting technologies like machine learning and artificial intelligence is the best way to go forth in their e-commerce business journey.
How Can the E-Commerce Industry Benefit From ML?
As you have already seen before, the application of ML in the e-commerce industry is tenfold. Naturally, the benefits, too, are high.
But what makes the real deal is how to apply them intelligently to your business. You may know a machine learning development company or two, but you need to realize what you want.
It is when you are aware of the limitations of your business that you can overcome by effectively leveraging machine learning and AI that you can take your business to the next level.
Following are the major ways that the industry can benefit from the intelligent incorporation of machine learning into its operations:
It helps bring better and more conversions for businesses. This will directly impact the profitability and sustainability of a business.
More effective marketing campaigns that can capture the imaginations of the users for better CTRs and ROIs. This will impact diverse campaigns run on a range of platforms including social media and search engines.
Ensure higher in-house efficiency with all types of operations from inventory management, supply chain, better marketing and promotions, and to more value-adding, intelligent management business decisions.
Machine learning can help businesses make more informed and intelligent decisions on a range of matters. That can range from marketing, strategy, inventory, supply chain, and customer service. Important decisions about business expansions can also be taken with the insights developed from machine learning and AI.
Another important benefit of Machine learning is personalization via intelligent analytics. With machine learning, an eCommerce business can understand what kind of products and services that a person needs from analyzing past browsing and purchase history. This can be leveraged when the customer is online to show products and services that a person may buy. As the experience is going to be truly personal, users will find the eCommerce site more user-friendly and responsive with its suggestions.
Machine Learning and Different E-Commerce Models
The ubiquitous nature of machine learning ensures that it can be used with all types of e-commerce business models.
No matter where you are trying to apply machine learning capabilities, it can help a business pick up the right consumer behavior and shopping patterns.
These insights will surely help the brand do better business.
B2C—Business to Customers: The most common eCommerce model where businesses bring products and services for the end-user to purchase and use. This is where machine learning has been applied the most due to the dynamics of customer behavior and purchase patterns.
B2B—Business to Business: This is where businesses sell their products and services to other businesses. With a larger scale, higher-order value, and more repeat business, B2B e-commerce models also leverage machine learning for higher ROI and better decision-making.
C2B-Consumers to Business: There are platforms that allow consumers to sell products and services to businesses in need. Here, consumers publish the work they do and businesses would pay them to acquire their services.
C2C-Consumer to Consumer: Here consumers come together on a common platform to exchange goods and services that they need. These types of businesses thrive on the interest of consumers who come to avail the services.
All these types of e-commerce business models can take advantage of machine learning and the benefits it brings.
Some Key Usage Statistics on AI and ML in Retail Business
As machine learning has become a hot commodity in the technology industry for businesses, the trend to adopt the same for better efficiency and customer management has been upward, as well.
Most businesses that already had implemented the same to their business have expanded the application of the same comprehensively to other departments as well.
The sentiment of businesses towards machine learning has remained the same.
The following statistics show how machine learning and artificial intelligence have seen a positive trend in recent years in the retail industry:
1. Over 28% of retailers use machine learning and artificial intelligence to enhance their service offerings and customer satisfaction. That is an unprecedented growth from 4% in 2016—which is 700%, Capgemini reports.
2. In 2019, machine learning was funded with 42 billion US dollars for research. This is more than all other AI-related research funding combined.
3. 24% of retail businesses have plans to implement machine learning and AI-chatbots on their websites in 2020.
4. In 2020, it has been reported that 48% of retail businesses use AI-chatbots for customer care while 47% use them for quality control and inventory management.
5. Businesses also use the help of AI and machine learning to bolster their online catalogs, says a survey. 54% of the respondents of the survey used AI for competition monitoring.
6. Google published a report that said the application of ML/AI technologies can add approximately 280-650 billion US dollars to the value chain.
Machine Learning Techniques Used By Leading E-Commerce Firms
Machine learning and AI have become an integral combination for e-commerce businesses to drive home more sales and delighted customers.
In doing so, e-commerce businesses have used a range of machine learning techniques.
These techniques may be unique to their business model.
However, if you are trying to adopt machine learning and want to bolster your business with the help of it, here is a list for you.
The following are a few popular machine learning techniques that you can use:
Regression
One of the most common machine learning techniques, regression is typically used to analyze and review past data to define a future figure. It could be the price of a product based on the historical pricing details.
Classification
For businesses that want to know if a customer is going to purchase a product or not, the classification technique is often used. The technique is limited to give either of the two answers—yes or no. This is also extensively used to classify a range of products on the basis of assigned values.
Clustering
As the name would give it away, this technique groups elements based on their shared characteristics. Some of the most used clustering algorithms are:
K-Means
Mean-Shift
Expectation-Maximization
Steps for Adopting ML in Your E-Commerce Business
While implementing machine learning for your business can be extremely value-adding, it is important for you to adopt the same correctly. There are many machine learning development services that can help you with the same.
Here are a few steps that you can adopt to ensure that you are getting the best help for adopting machine learning in your e-commerce business:
Understand the Requirements: Try to understand what type of machine learning support that you need. It is based on your business’s specific needs that machine learning is developed and integrated.
Address the Scalability: You need to define the scale of your operation for the machine learning algorithm to work perfectly for you. This will help the machine learning development company effectively develop the right model for you.
Tune the Model for Performance: The next thing you can do is to work with the development team to tune the same for its performance. As a lot of your efforts and investments are to make significant business growth, an underperforming model is not what you want.
Automate the Model: The final step is to ensure that the model runs repeatedly without heavy interventions from the user. Most businesses would need a model that will work with minimal intervention from the user for a considerable future.
Challenges When Deploying ML and Strategies to Overcome Them
There is no doubt that businesses find it challenging to deploy machine learning models in their business. The problem lies in considering the entire thing a technical practice instead of a business move.
In fact, the deployment of machine learning models on an eCommerce website must be considered a strategic and business initiative that can change the way business is done.
This will help a business alleviate some of the challenges.
Another important aspect is the lack of collaboration between the teams that run the business now and the team that is responsible for the deployment of machine learning.
Comprehensive interactions between the machine learning teams and business management teams are what will set the right path for the journey that the business is about to take.
The more open and effective the interactions are, the better the development of the model will be. This will surely create the most advantageous situation for the implementation of the model, and the business.
Another aspect to make the machine learning model work perfectly as expected is to train the model in historical data. If you seek the support of an experienced machine learning development company, they would tell you how it needs to work.
If your business can take care of these needs with the help of an established team for a machine learning model that understands the role of machine learning in the eCommerce business, you will surely sail the challenging waters trouble-free.
Conclusion
There is no doubt that machine learning and AI have become an integral part of any successful e-commerce business.
Choosing the right model and apt company that offers machine learning development developers is key to success.
As technologies have been changing and transforming the landscape of the e-commerce business, adopting more advanced technologies to not only serve customers better but also to bolster the bottom lines is really important. But delighting customers is the best strategy to ensure sustained business
Even though there will be roadblocks and challenges, they are destined to be overcome. It is with such a persevering attitude that a business can change, adapt, and grow in these challenging times.
As the competition is fiercer than ever before, survival of the fittest with the help of technology is imminent. And delighting the customers and creating a strong band of loyal consumers is the way to become the fittest.
We can help you with that by deploying intelligent and effective machine learning models for your eCommerce business with our machine learning development services.
You can contact us here to get more details, as well.
