The rise of AI technology is playing a huge role in the shift in consumer expectations. By bringing brands closer to their customers and making automated, personalized customer service a reality, machine learning has the potential to transform e-Commerce. Conversely, a deficient AI application can just as quickly frustrate a customer and erode a brand’s image. Training is one area that defines how well an AI application will perform in the real world.
The increasing range of AI use-cases in e-Commerce suggests that the ability to utilize mountains of previously unmanageable data is having far-reaching effects. In sales, AI is making it possible to accurately target valuable prospects, while virtual assistants can increasingly ask questions that improve individual recommendations and drive a higher purchase rate. Machine-learning models (the underlying algorithms that power AI) can also make use of data collected from the customer, revealing unseen patterns in their behavior to ensure that the most popular products are always in stock.
1. Virtual assistants and chatbots. AI chatbots and virtual assistants can help retailers create a truly personalized customer experience. Advances in natural language processing (NLP) have made it possible to resolve customer questions without the help of a human, while also making individual recommendations based on knowing the customer’s journey, location, purchase history and more. Chatbots also can simplify the order placement process, either through asking questions or listening to voice commands. By simultaneously removing barriers to purchase and improving customer experience, the use of AI on the front lines of customer interaction has the potential to boost customer loyalty.
2. Search relevance. An intelligent search function can make or break an e-Commerce site. Through NLP, AI is helping to ensure that search results remain consistent, regardless of what the customer types. By contextualizing search queries and comparing them to data about previous purchases, algorithms can learn to prioritize products that have high conversion rates. Developments in image tagging also may expand search possibilities, allowing AI programs to further hone a reliable, self-improving search engine.
3. Sales enablement. Used correctly, customer data can be turned into a sales powerhouse. Developments in AI mean that it’s now possible to automatically retarget customers who have looked at specific products, or predict which customers represent the best business opportunities. By detecting and surfacing data patterns, algorithms can help e-Commerce sites predict which products will sell at certain times. It may even be possible to bring this technology into the real world through image or video tagging, providing the same insights by tracking how much time a customer spends in a store.
Training Is The Foundation For A Killer Chatbot
The goal of any chatbot is to maintain natural conversation fluidity and provide consistent engagement. This means your machine-learning model must master a wide range of languages, dialects, accents and tones of voice if you want to engage your target audience. Successful ‘human-like’ chatbots rely on two things:
- Context Understanding. This is the ability to remember and track different aspects of a conversation — location, time, preferences of people — and combine all of the inputs to ‘paint a picture’ of the conversation. Just like humans use surrounding context to inform their interactions, chatbots also need information that keeps the conversation going.
- Intent Recognition. This is the ability to extract relevant information from each sentence, word and verb, and understand the intention and the meaning behind it. This allows the use of long complex sentences by users, because the chatbot is able to understand and extract multiple intents.
Training is an essential step towards any successful machine-learning model, yet people who are new to AI often don’t give it the attention it deserves. The fact is, the more data you feed a chatbot, the better it can adapt to human speech and all its idiosyncrasies, and the better it will be at achieving that ‘human-like’ conversation level. An effective chatbot requires massive amounts of high-quality data in order for it to reliably respond effectively to different human interactions. Once the right set of data is introduced into the training process, your machine-learning algorithm will make massive gains — which will translate into improved customer experiences and a more effective deployment.
What Is High-Quality Training Data?
Essentially, training data is what you use to teach your algorithm to perform its designed function. When it’s incorporated into your chatbot’s underlying model, training data becomes a set of examples that your algorithm can return to for help when making predictions about new data. Each data point usually consists of an input and a label, where the label provides the answer to the ‘question’ that you want your model to handle.
Great training data is a rare commodity that takes time to source or create. As more and more companies begin to dabble in AI to power their e-Commerce applications, high-quality human-annotated training data is providing the competitive edge that separates the industry leaders from the pack. It’s worth taking the time to build a solid plan around your training data, and identify trustworthy data providers that understand your sector and share your vision.
When you’re sure that your training data is absolutely aligned with the business goals you’ve set for your chatbot (which might include call-center deflection, support triage, or pre-sales qualification), your AI-powered application will deliver a better ROI. Or, in simple English: your shiny new chatbot will perform better, be more useful to your customers, and add value to your brand’s image.
Charly Walther is VP of product and growth at Gengo.ai, a global, people-powered translation platform optimized for developers of multilingual ML/AI applications. With 10+ years of know-how in providing AI training data, Gengo has an impressive track record of successful projects with the world’s top technology companies. Walther joined Gengo from Uber, where he was a product manager in Uber’s Advanced Technologies Group. Contact him at firstname.lastname@example.org and @gengoit.