Can you name one industry that you know is immune to the disruptive technological advancements of machine learning?
No wonder. Because, from manufacturing to life science, from financial services to healthcare, from retail to entertainment, machine learning is revolutionizing every sector you could think of.
But particularly susceptible to machine learning innovations is eCommerce. Being in one of the most competitive spaces, any company that sells online is compelled to examine what machine learning might mean to its brand in the future.
With legions of online brands digitally transforming their complex business operations with machine learning applications, the eCommerce category is underway, a never-seen-before technological disruption.
From personalizing shopping experiences and optimizing prices to detecting fraud and cutting international air freight courier costs, machine learning is already infiltrating the eCommerce space.
So, let’s talk about the use cases of machine learning technology and understand how its synergy with eCommerce is rewriting the narratives of how we buy and sell online.
How Does Machine Learning Make eCommerce Experiences Smoother for All?
Machine Learning (ML) is essentially an offshoot of Artificial Intelligence (AI) which relies on self-learning computer algorithms that can be trained to process large datasets at high speed and detect insightful patterns.
The best part is that these algorithms’ performance improves autonomously as they query more and more data. It allows them to spot recurring patterns and anomalies and offers valuable insights. Something that is proving to be tremendously valuable in analyzing problems, predicting future trends, and making data-driven decisions.
Here’s how machine learning is enabling eCommerce companies to achieve a competitive edge:
Product Recommendation Engines
Too much variety can be confusing. Even more so for online shopping – sifting through a ton of irrelevant items before finding what you really need can be frustrating for shoppers. So, how can online stores save their visitors from overwhelmingly large product lists? The answer is- Product recommendation engines.
It is a powerful data filtering software that uses machine learning algorithms. It taps into the site visitors’ demographic data and past shopping tendencies and suggests the most relevant products they might be interested in. With its insane data processing capabilities, ML algorithms enable brands to analyze the shopping behavior of hundreds of millions of users and predict the products that might grab specific users’ attention.
While it delivers a positive customer shopping experience, the business can attain higher click-through and conversion rates, better customer retention, and more repeat purchases.
Personalized Shopping Experience
McKinsey’s Next in Personalization 2021 Report reveals that 71% of consumers expect personalized interactions, and 76% get frustrated when this doesn’t happen.
Thankfully, with the power of ML, eCommerce brands are finally able to personalize shopping experiences to their customers’ tastes.
Ecommerce personalisation makes each customer feel special using product recommendations, dynamic content delivery, and customized offers through real-time tracking of their onsite behaviors, purchase history, demographics, data points, etc.
Retailers can make customer journeys super engaging by personalizing everything from onsite searches, product recommendations, email marketing, feedback requests, push notifications, and even pricing.
And what does effective personalisation mean to your eCommerce business?
- Better conversions by helping the customers find what exactly they need.
- A wealth of insights into which product lines work or do not work.
- Increased customer loyalty.
You must be wondering if smart chatbots are any different from simple chatbots.
Yes, not just different, they are a lot more advanced than the conventional ones. Designed to simulate near-human interactions with customers, smart chatbots can strike free-flowing conversations by understanding the language, intent, and sentiments.
The ability of smart bots or virtual assistants to greatly enhance the quality of interactions between the visitor and an online shopping platform is credited to natural language processing and deep learning.
These most powerful descendants of ML in conjunction with AI, allow chatbots to:
- Solve simple queries just like a rule-based bot
- Understand the context, slang, and unique pronunciation
- Learn from previous interactions with other users
- Answer complex questions
As a busy eCommerce business owner, you can achieve significant task automation by deploying smart chatbots for the following functions:
- Understanding user queries and conferring them with useful information
- Acting as a 24/7 customer support
- Nurturing qualified leads
- Gathering feedback on customer experience
- Recommending relevant products to boost sales
Optimizing prices that both maximize profits and minimize customer churn rate has always been the bane of every eCommerce brand’s existence. The fair price is insanely difficult to determine because so many underlying factors are constantly changing. It’s been further complicated by the sheer volume of data led by the massive explosion of online activities.
That’s why eCommerce businesses like Zara, Boohoo, Shein, etc., are now capitalizing on ML-based pricing tools to adjust their pricing strategies. ML algorithms analyze countless factors, detect the patterns within the given data, and come up with the right prices for thousands of products within minutes.
ML-based price optimisation tools help retailers tailor discounts, reduce prices for price-sensitive customers without compromising on profitability, and, most importantly, better serve their customers.
With online frauds and cybercrimes turning into the prevalent reality of the digitized world, eCommerce platforms have become easy targets for scammers.
Identity theft, email account phishing, chargeback fraud, and fraudulent electronic payments are only a few of the most common frauds they have to thwart to protect their merchants and customers.
Machine learning can significantly reduce the scope of such frauds by rapidly processing exhaustive data to detect any anomaly that may signify fraudulent attempts.
Last-Mile Delivery Optimisation
In addition to being costly, last-mile delivery is constantly growing to be very complex to manage, given the booming trends of same-day and next-day delivery of eCommerce orders. But with the advent of ML, it is now possible to optimize delivery processes in real-time to cut shipping costs and expedite delivery times.
Following delivery operations can be optimized with ML:
- Route optimisation and scheduling to help couriers find the best route for shipment in real-time and avoid congested areas.
- Delivery time prediction by analyzing factors such as driver schedules, distance, and traffic, based on data gathered from GPS, weather forecasts, social media, etc.
- Predicting failed delivery before it happens so that delivery schedules can be adjusted during delivery planning.
The Final Word
Given the inevitability of data-driven decision-making, there can be no question that machine learning is here to stay. And if there is one industry that is embracing this powerful technology to make operations efficient, it is eCommerce. Once implemented wisely, ML is sure to be an invaluable tool that would give a competitive edge to brands during disruptive market changes.