Commerce Chatbots are Revolutionizing the World of Retail and eCommerce
Conversational Commerce is the most essential tool for retail eCommerce brands to reach their customers. In a recent survey, it was discovered that 75% of customers prefer to receive messages from brands, highlighting the need for conversations rather than promotional messages.
Whatsapp messages and SMS have emerged to be the fastest-growing channel to engage with customers in the most effective manner but yet another emerging way to do that is through chatbots.
Imagine this, you want to purchase your favorite shoe at midnight hour, you can either select from the thousands of options available on the eCommerce website or simply chat with a bot, tell about your preference, and let the magic of recommendation surprise you.
Retail and eCommerce brands have been largely benefited from the adoption of chatbots. In this blog, let’s explore a few such brands and understand how we can train data to build such smart and efficient bots.
American Eagle brand for Lingerie – Aerie’s Chatbot Adoption has Won Millions of Hearts!
Aerie has appealed to the hearts of thousands of GenZ Millennials with their Chatbot. The brand has a young and appealing sect of customers who adapt to new technology really fast. Hence, the success of their chatbot adaption has been skyrocketing.
After a few selections, the bot identifies the style of lingerie the customer likes, and the customizations are fine tunned further to offer the perfect product.
Within weeks their chatbot acquired more than double the average numbers of users the brand added monthly across all social channels combined!
Want to try your hands on this amazing chatbot? Scan and get started!
Whole Foods Conversational Bot Enables Customers to Search for Recipies Using Emoji’s
Want to cook but have no patience to run helter-skelter to accumulate all the ingredients? Well, worry no more. Whole Food’s conversational chatbot is here to help.
The U.S-based healthy food supermarket chain has introduced a chatbot that can do the grocery shopping and also help you with the recipe.
But what’s more interesting is that it is clearly more than just a chatbot. It’s an A.I agent who knows how to cook. Just pick out any food emoji from your keypad and get started. If you like a banana, then just by sharing the banana emoji you can see recipes that involve that product.
Customers can also mix and match words and ingredients to create the dish of their choice. Mix the words Mexican+Banana+Sweet = your customized recipe. If you like Tabouli then just ask for the recipe and you will get it.
Jeff Jenkins, global executive of digital strategy and marketing at Whole Foods Market said “We are living in the ‘expectation economy,’ where consumers expect to have information at their fingertips, and we want to keep innovating to meet our customers where they are,”
This app also helps the brand in enhancing its planogram compliance. It does intelligent tracking of products requested by customers before visiting the store.
So when the customer is in-store he/she can receive suggestions or information about complementary products to increase the in-store sale of groceries.
- Read more on – The Next Big Thing in Retail Planogram (POG) Compliance with Computer Vision Data Annotation
Lidl’s Conversational Bot is Here to Save the Day by Suggesting Best Wine with Relevant Parings!
Lidl realized that they were extremely successful with their wine section, they won many awards and their sales were topping the charts! Yet, none of their employees or their contact store personals knew anything about wines.
The knowledge was sitting with their full-time on-staff sommelier! Lidl was not interested in sharing this information as static content on their website in the form of an FAQ, they wanted to engage with their customers.
So, Margot was introduced it is an intelligent conversational chatbot that answers queries and gives suggestions instantly.
Margot is an exciting bot, it helps customers find wines by the country, region, grape, color, and price. It also shares tips on food pairings and engages customers in a fun quiz to test their knowledge.
Ask her, she will surely help you with the wine that blends with your taste pallet!
But at this point a more intriguing question arises, how can these bots be trained? What quality of data do you need to feed in to help them become super-efficient and most importantly – is it an expensive investment? Well, let’s figure out answers to all of these in the section below, read on!
How to Prepare Training Data for Your Conversational Chatbot?
NLP or Text annotation is used to create the training data for chatbots. The idea is to make human and machine interaction intelligible.
Data scientists or Machine Learning engineers acquire the data from historic customer interactions and feed the same into the machine learning model to help the machine understand the human text tone and respond accordingly.
Before the data is fed in it needs to be accurately labeled with text annotation and NLP annotation highlighting the keywords with metadata enabling the machine to understand sentences easily.
When conversations are annotated with data labeling it becomes easier for machines to respond to human queries. Data annotation can make or break an AI-powered bot, it needs to be accurate and reliable. Ultimately, chatbots are only as good as training data that is fed into them.
Is Creating Chatbot Training Data An Expensive Affair?
Unstructured data is also called unlabeled data and cannot be used for training a certain kind of Ai-oriented models. The training data contains the communication within the humans on a particular topic and when they are annotated with Data Labeling the communications with humans becomes much more easier.
There are multiple ways to get your training data annotated:
- Manual Data Labeling
- Crowdsourced Data Labeling
- Automated (Ai-powered) Data Labeling
Manual Data Annotation is not recommended because of the following reasons:
- Managing and maintaining the quality of data labeling
- Workforce management
- Keeping a track of the cost incurred
- Compliance with data privacy requirements
- The task to ensure data security
Crowdsourced Data Annotation has these challenges:
- Managing data quality is difficult, most data labelers want to just finish the task and get their pay which does not negate the fact that your data might be mislabeled most of the time.
- Freelancers doing this task are typically unvetted people sitting at home and performing the task.
- Project Management gets daunting.
The best way forward is adopting an Ai-powered data annotation tool that can eliminate all the challenges of the above two methods.
Moreover ai-powered data annotation tools are not expensive at all, they are simple, reliable and most efficient way to annotate your data.
Advantages of Adopting Industry Specialized Automated Data Annotator with Machine Learning
We have gained a fair understanding of why manual and crowdsourced data labeling is not a good choice to opt for if companies are looking to get an accurate, secured, and reliable data labeling task done.
The way-forward is adopting automation, that too industry-specific ones. Labellerr is a retail and CPG focused, highly accurate data annotation platform with machine learning capabilities. The advantages of using such a platform are:
- Retailers can achieve data labeling at scale and they can be free from the hassle of slow manual data labeling tasks. Especially with more and more retailers adopting computer vision technology to maintain planogram compliance, autonomous checkouts, inventory stock detection, customer footfall detection, customer sentiment analysis, and many more such use-cases.
- Retailers can get high-quality, reliable data labeling done. Unlike crowdsourcing platforms data labeling companies hire long-term employees to oversee the task of data annotation. Thereby your data is in secure hands and the quality of output will be outstanding. Here are the key measures taken by Labellerr to ensure your data is safe and secure:
- NDA signed with all the employees working on the data
- Prohibition of pen drives
- Video surveillance for monitoring
- Biometric systems to monitor the movement of employees
- ISO certification 27001:2013 and 9001:2015
- Labellerr’s platform is deployed on the most secure and resilient platform like GCP, AWS and Azure
- The transfer learning methods of labeling can be used to increase speed. Labellerr has pre-built use-case based machine learning models that are Retail & Consumer Packaged Goods (CPG) specific. A similar annotated dataset has already been tested on the model hence the accuracy is relatively higher and the output will be as desired.
- Well, here is another feature I believe you would love to explore, Labellerr is a one-stop-shop for all your data annotation tasks. It has a ML-powered data annotator platform and a marketplace. You can get data annotation tasks done by selecting a vendor.
- Their data annotation platform is so robust and agile that anyone can annotate data without any support from the technical team.
It’s easy-breezy and the most comforting tool to get the task done – Check it out for yourself – Labellerr, an automated platform for image annotations and data labeling for AI and machine learning with the best quality and accuracy at flexible pricing.
For human-assisted ai-powered data annotation end-to-end solution check out their one-stop-shop marketplace and get-set-label!