With the boom of big data, one of the most essential challenges for modern organizations is the development of data-driven infrastructures. Advancements in machine learning have opened the gateway to a broad range of applications, which can transform data into real business value.
One area that is heavily affected by this advancement is e-commerce. In the US market, about 190 million consumers - more than half of the population – made online purchases in 2016 according to Forrester Research. Aside from the revenue benefits, the ecommerce culture has enabled companies to collect data on a large scale and offers companies details on the whole customer journey - what they are looking at, what they are looking for, how they rate and view products, etc. With Machine Learning, this type of data can be used to improve the user experience, making it accessible, more efficient, more personalized, and more adaptive.
1. Communication made autonomous
In the modern context of online purchasing, communication should be instantaneous. The second an e-commerce site cannot provide the information their customer wants is the second you lose him and his money. As well as providing customers with product information, consider installing a modern chatbot as well.
Today’s technologically advanced chatbot offerings are more than just text generating tool based on presets and rules, they are self-learning systems capable of providing meaningful and relevant information by combining a websites’ database with customers’ personalized patterns. A chatbot can handle multiple languages, can respond anytime, anywhere, with or without human supervision. Even though chatbots can’t cover all topics, they can at least provide prompt customer service and reduce the need of costly call centers and other human resource heavy solutions.
The application of machine learning is not only limited to the chatbot however. By using machine learning engines, businesses can automate any communication channel they have. Improvements in voice recognition allow companies to use phone answering robots instead of humans thus providing a real round-the-clock service.
2. Fraud detection
Fraud is a billion-dollar business and it harms online transactions more than any other false practices in the field. Traditional approaches to identifying fraud have been rule-based. This means you have to manually flag a transaction as fraudulent in advance. However this method isn’t flexible and inevitably results in an intensive battle between the seller and the fraud.
With recent advances in Big Data, the modern approach is to leverage the vast amounts of patterns collected from online transactions and build a model that allows the computer to flag or predict fraud in future transactions for us. Data Science and Machine Learning techniques such as Deep Neural Networks (DNNs) are the obvious solutions in this situation.
The main machine learning techniques used for fraud management include:
- Data mining to segment the data, then different data analysis models will be applied so patterns in fraudulent transaction will be detected automatically
- Rule-based system could be used as a leverage with implementation of supervised machine learning to detect fraud
- Combining different methods, both supervised and unsupervised into one system and using ongoing monitoring and adjustment
- Neural networks that are able to detect suspicious patterns from a big sample size and can detect fraudulent activity later.
3. Personalization, personalization, and personalization
You cannot talk about e-commerce without talking about personalization. When there are literally thousands of e-commerce sites out there, the ones that make you come back are those which engage you on a personal level.
That means whenever you log into your favorite e-commerce website, you already have everything your heart desires, even items you hadn’t realized your heart desired! The beauty of product recommendation and other personalized functionalities is found in the automated process called “Recommendation Engines”. And this process is usually powered by Machine Learning which can handle a huge amount of data every day.
According to a study published by Evergage in 2016, 88% of customers expect a personalized website experience. And this personalized experience is not only on the website. It refers to everything within the digital environment: e-mail, social networks, google search engine, mobile apps, etc. The more a customer interacts with products on an e-commerce website, the more likely she will be exposed to the information of those products on other platforms connected to that website. This exposure will greatly affect her decision to buy certain products.
For this reason, personalization has become the buzzword in e-commerce circles of late. But because manually personalizing a potential million customers would be an incredible waste of human resource, e-commerce giants, Sitecore and Episerver are adding more automation to the personalization process they offer. And if you don’t want to use built-in functionalities due to lacking of technical capabilities, there are other user-friendly tools that can be integrated into your e-commerce website such as Marketo or Hubspot.
If you’re yet to invest in machine learning, we’d urge you to do so. Our developers know the technologies you’ll need to power your e-commerce ambition. To learn more, get in touch.