Contextual and Intelligent commerce goes beyond leveraging impulse purchasing and with this, commerce platforms can now understand contextual information and human senses which includes Visual, Voice, Touch, Sentiments etc. Although, the technology is still emerging online, applications of this concept are already being seen in real-world examples.
Some of the most important elements are –
- Voice Activated : Alternative to using a keyboard and mouse to browse, order and purchase products online. All the customer needs to search and buy something online using voice commands.
Some of the voice Activated products are - Apple’s Siri, Google’s Assistant, Microsoft’s Cortana, Amazon’s Alexa. Open-source tool recently been introduced and can be used for integrating eCommerce with voice-enable assistants is VoiceCommerce.js , Open Assistant.
Technologies Behind : Natural Language Processing(NLP), Natural Language generation (NLG),Speech Recognition(ASR, TTS), Machine learning.
- Visual/Image Search- eCommerce brands of all verticals are leveraging photos to drive higher online conversion, increase average order value. The level of adoption of this technology is the highest in e-commerce including search and advertising. Content tagging from social media, following fashion trends from celebrities are major use cases on the rise.
Some of the visual search enabled products are - Google Cloud Vision, Amazon Rekognition, Google lens, Pinterest lens, IBM Visual Recognition. Available Open Source tools are Zappen, Luminoth (v. 0.1) for object detection. If you’re interested for what’s in picture - YOLO.
Technologies Behind : Search Engine, Image Extractor, deep learning , Neural Network.
- Facial Recognition/ Finger Print Recognition –We can now use our face to unlock our smartphone, order food by smiling at a kiosk monitor(CaliBurger), and even blink at a smartphone camera to confirm an online purchase(MasterCard Identity Check app), pay with a smile(Alipay app). Sentiment analysis is also taken care here.
Some of the Facial and fingerprint recognition enabled products are Alibaba, Amazon, Facebook, Apple’s fingerprint reader. OpenFace is the first open source tool capable of facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation. VeriFinger is a fingerprint identification technology designed for biometric systems developers and integrators.
Technologies Behind: Biometric, Sentiment Analyzer, Fingerprint Validator, artificial intelligence, Blockchain
- Conversational Bots – As it stands today, bots are and will remain the most prominent channel of conversational commerce for years to come. Advancements in artificial intelligence allow conversation bots to track website visitors and run their behavior through machine learning algorithms, then pop up at the right time and place to turn browsers into buyers. 2 key elements to this - AI makes it possible for bots to parse human language, understand intent, and compose replies, Bots communicate in human language through a variety of interfaces—IM, email, and voice are the platforms.
Some of the available chatbots platform are Astute Bot, Engati, NanoRep, Botsify.
Technologies Behind: AI and natural language processing, Machine Learning, Big Data Technology, Bot Analytic.
- Cognitive Commerce - Emerging technology that uses cognitive computing (computers that learn) to automatically observe customer behaviors and use those observations to deliver personalized solutions.
Below are some of the advances in cognitive computing that are shaping the e-commerce sector -
i. IBM Watson - The program uses Insights Assistant, an application with cognitive capabilities that helps merchandisers to identify abnormal conditions in their marketplaces and recommends appropriate actions.
ii. Predictive Analytics - These analytical tools look at the various variables that can help in generating the desired engagement from the client such as clicking on an online product promotion of subscribing to a newsletter.
Some of the available Predictive Analytics Tools are Rapid Miner Studio, IBM predictive analysis, SAP predictive analysis. APIs available are Google cloud prediction API, APIgee. open source tools are Rapid Miner, HP Haven Predictive Analytics, Apache Mahout.
iii. Direct Purchase through Social Media - When you come across a product suggestion on your favorite social networking platform, once you click on the product’s picture, an application coupled with cognitive capabilities will direct you to the product page. This application helps to drive traffic to an e-commerce website and increase the consumers’ chances of buying a particular product.
Some of the examples are Instagram shop, Google Express
iv. Personalized recommendation - They help retailers to gain complex insights into their product and customer bases.
Some of the available recommendation engines are Recolize(free for the first 300 clicks on the recommendations generated by it), Yuspify, Barilliance, Nosto
AI in to eCommerce
Most of the elements mentioned above, make use of AI along with the machine learning. Machine learning is often considered a subset of artificial intelligence. Typically, developers will create an algorithm for a robot to follow (AI) and then develop machine learning capabilities for the robot to make its own rules.
In a short-term, A.I. can aid us to do our job efficiently and smarter. Below are some of the other functional areas where AI tools can help you-
- User Experience by AI – Shoppers have a high bar on user experience. Machine Intelligence can influence them with a best-in-class user experience.
- Predictive pricing and incentives – A.I. tools can add a dynamic pricing layer to your store. They use machine learning and data science to understand your user behavior and change the product price in real-time.
- Marketing and Analytics - Today many subsections of marketing uses machine learning like user data enrichment, user identification across devices, intelligent Ads personalization, predictive analytics and so on.
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