Future E-Commerce Developments Using Cutting-Edge Technologies

E-commerce is growing rapidly in all forms of industries like clothing, shoes, electronics, hardware, tools, etc. You name it, it will be on the e-commerce platform. The major advantage of e-commerce is that people can choose the products from their personal places and do not need to go anywhere. So there is also a need for marketing for the e-commerce platforms, and in the future, these convenient technologies will be adopted in the shopping industry. Those are the major points mentioned in this blog.

Recommendations on products for the customers

A product recommendation is basically a filtering system that seeks to predict and show the items that a user would like to purchase. It may not be entirely accurate, but if it shows you what you like, then it is doing its job right.

The suggestion of the particular product to the customer is very important while they are watching the product.

For example, in the fashion industry, if a customer is seeing a pair of jeans for him, the best recommendation will be a T-shirt and a shirt with double-layered fashion, so the customer will be attracted to this combination and purchase it.

The recommendation system is more popular, and it will be a powerful method to reach the desired products for the customer with better suggestions. In recent times, the pharmaceutical industry is also looking forward to building its platform on e-commerce with faster delivery options for customers. In the skin, products are also recommended while the customer is willing to purchase them.

The cloud giant AWS also has a separate service for the recommendation system. The service is named AWS Personalize.

The features offered by Amazon Personalize are:

● Combine customer and contextual data to generate high-quality recommendations.

● Automated machine learning.

●Continuous learning to improve performance.

Google Cloud also has a service for this called Recommendation AI

Key Features of Recommendation AI:

● Custom models. Each model is trained specifically for your data, based on machine learning, sequence, and transformer techniques.

● Personalized results. Leverage personalization algorithms without any machine learning expertise. Recommendations are based on user behavior and activities like views, clicks, and in-store purchases as well as online activity, so that every prediction result is personalized.

● Omnichannel recommendations. With the API model, go beyond website recommendations to personalize your entire shopper journey to recommendations on mobile apps, personalized email recommendations, store kiosks, or call center applications.

Boost conversions with Chat Support for the customer.

Customer Chat Support is the best way to get in touch with customers and clear up any questions or problems they may have with their order. Sometimes rule-based chatbots work well, but they should be designed so that the customer feels as if he is texting with someone who can help him. Another point of view is that the company should keep phone support even if the problem can be solved through chat support since only a small number of problems can be solved over the phone.

Chatbots are amongst the most accessible examples of machine learning in e-commerce. Lots of sites feature a chatbot offering you assistance. For online stores, the tools help with common queries and direct visitors to specific products.

Where machine learning comes in is when it comes to improving the responses of chatbots. An AI-enabled bot can use the interactions it has to learn and tweak its future replies. The more a chatbot is used, the more human it seems and the better the information it provides.

AWS has a chat service that is called AWS Amazon Lex. AWS Connect is the voice service.

AWS Connect and AWS Lex use cases:

AWS Connect

Telephony: Amazon Connect manages a network of phone service providers from all over the world. This means you don’t have to deal with multiple vendors, negotiate complicated multi-year contracts, or commit to peak call volumes.

High-Quality Audio: When your customers can’t hear you clearly, it can lead to wasted time and frustration. With Amazon Connect, calls are made over the internet from a computing device like a PC, using the Amazon Connect softphone.

Omnichannel outbound campaigns: It helps you communicate across voice, SMS, and email to serve your customers quickly and improve agent productivity while supporting compliance with local regulations.

Amazon Lex AWS

High-quality speech recognition and natural language understanding: technologies to create a Speech Language Understanding system. Amazon Lex uses the same proven technology that powers Alexa. Based on a few examples given by the developer, Amazon Lex can figure out how different users can say what they want.

Context management: As the conversation develops, being able to classify utterances accurately requires managing context across multi-turn conversations. Amazon Lex has built-in support for context management, so you can manage the context without having to write any code.

One-click deployment to multiple platforms: this allows you to easily publish your bot to chat services directly from the Amazon Lex console, reducing multi-platform development efforts. Rich formatting options give chat platforms like Facebook Messenger, Slack, and Twilio SMS a simple, easy-to-use interface.

Google Cloud Platform also has a service for chatbots called Dialogflow.

Voice bots for customer service

Chatbots for B2C conversations

Visual flow builder: Reduce development time with interactive flow visualizations that allow builders to quickly see, understand, edit, and share their work. It also allows for easy collaboration across teams.

State-based data models: Reuse intents, define transitions and data conditions intuitively and handle extra questions. This lets customers deviate from the main topic and return to it in a natural way.

End-to-end management: Take care of all your agent management needs, including CI/CD, analytics, experiments, and bot evaluation, inside Dialogflow — you don’t need any other custom software.

Predictions based on previous data

If the company has real visitors to your site for the purchase of the required products, then, using their history of purchases, the site can suggest products for their current situation that are suitable for them at the best price.

Usually, fashion trends change, but by using previous purchases, we can predict the future and which month and what type of products will be sold based on location. For example, in the summer season, summer wear will sell a lot in some locations because of the heat in the location where it goes on. If we have the previous data, we can recommend the customer purchase these products during this period. School products will move fast during the opening of the school using the data. Based on the customer’s travel history, travel products can also be suggested to them as things to buy. For example, if the customer traveled last year in the same region, suggestions can be given to the customer.

Using the recommendation engine and analyzing the future can boost the company’s sales as well. It can be used for all types of industries like fashion, electronics, travel, etc.

In AWS, we can build a real-time prediction system using the AWS service called Amazon Machine Learning.

Key features Machine Learning on Amazon

Data sources contain metadata associated with data inputs to Amazon ML: A data source is an object that contains metadata about your input data. Amazon ML reads your input data, figures out descriptive statistics about its attributes, and stores the statistics, along with a schema and other information, as part of the data source object.

ML models generate predictions using the patterns extracted from the input data: An ML model is a mathematical model that generates predictions by finding patterns in your data. Amazon ML supports three types of ML models: binary classification, multiclass classification, and regression.

Evaluations measure the quality of ML models: An evaluation checks the quality of your machine learning model and sees how well it works.

Batch Predictions asynchronously generate predictions for multiple input data observations: Batch predictions are for a set of observations that can be run all at once. This is ideal for predictive analyses that do not have a real-time requirement.

Real-time Predictions synchronously generate predictions for individual data observations: Real-time predictions are for applications with a low latency requirement, such as interactive web, mobile, or desktop applications. Any ML model can be queried for predictions by using the low latency real-time prediction API.

Amazon SageMaker: Is based on the company’s 20 years of experience building real-world ML applications, such as product recommendations, personalization, smart shopping, robotics, and devices that respond to your voice.

Google Cloud Platform also has a service for real-time prediction systems called the AI Platform .

As the diagram indicates, you can use AI Platform to manage the following stages in the ML workflow:

● Train an ML model on your data:

○ Model accuracy is evaluated by training the model.

○ Tune hyperparameters

● Deploy your trained model.

● Send prediction requests to your model:

○ Online prediction.

○ Batch prediction (for TensorFlow only).

● Monitor the predictions on an ongoing basis.

● Manage your models and model versions.

Conclusion:

We have assisted clients in resolving issues in the e-commerce industry sectors such as ChatBot creation using AWS, Amazon Lex, and others.

AWS Connect is used for customer support. We took the text from prescriptions for pharmaceutical e-commerce platforms and used AWS Personalize and GCP Document AI to do it.

Using Amazon Machine Learning, a service provided by AWS, to forecast upcoming trends in the e-commerce industry.

Thank you for reading this blog.

To know more about this topic, write to us at info@ankercloud.com and we will be happy to help you!