AI Chatbots are the future of enterprise customer service, automating up to 85% of customer service interactions. Don’t believe us? Facebook Messenger had no bots in February 2016. 14 months later, there were about 100,000 of them.
Changing trends in customer engagement have led to many innovative ways where chatbots can be used. Chatbots improve customer experience and increase customer satisfaction.
Chatbots have become a very popular medium for enterprises
To ask questions, access data, and share information using Natural Language technologies, we have turned to chatbots. Most of us have had experiences with chatbots in our daily lives to perform simple tasks quickly and in our own time. However, the adoption of AI chatbots in the enterprise is still very slow.
A lot of enterprise data, e.g., customer details, employee details, order details etc., are stored in structured formats in RDBMS, Excel files, ERPS or other such proprietary structured formats. Most HR chatbots operate on text data, so processing structured data becomes a task.
Let’s say an HR personnel wants to find out how many employees joined in the previous month. Or the names and contact numbers of employees who stay in a specific location. Further, the names of the managers of those employees who have not recognized their team members and so on. How would the HR personnel get access to this data?
They would have to log in to an HRIS system and then look through many reports or formulate search queries to get to the data. This takes a lot of time and in every case the exact data and relationships of data may not be determined from predefined reports.
How chatbots are used in enterprise customer service?
Chatbots, also known as virtual agents, must have the ability to understand what a customer is typing, or saying. They have to understand the customer’s intent, and take action for the customer. These conversations between customers and chatbots must take place in a secure environment. In a place where the chatbot can hand off the interaction to a live agent.
Using a chatbot for enterprise customer service has many benefits:
Why does your enterprise need an on-premise software?
On-premise software means that data is available on site, within your premises, as opposed to hosting it on a server, or on a data cloud. The company downloads the data behind its firewall.
So, what are the features of an on-premise solution? Why should you think about implementing it into your data system?
Other business software do a fantastic job of implementing third-party applications into your software. But they don’t give you the flexibility you need for your solutions. Flexibility and customization are as per your vendor, not you. On-premise systems allow customizations for you to work your software into your current workflows.
Because data is available on the premises, the possibilities for customization are endless.
Vendors have the freedom to make changes that your software is subject to. If there are issues on your vendor’s side, the changes will be reflected in your software. This lack of autonomy can be problematic as it makes you dependent on your vendor.
Having your software on-premises eliminates this problem. Your data is yours to control. You can alter it whenever you want to.
Your numbers are fluctuating, and you need a solution that keeps up with that. The scalability of on-premise systems gives you this privilege. Scaling not only vertically, but also horizontally allows you to remain optimized while keeping your data infrastructure agile, giving you a fast response time. Scalability also means that you will be provided with continuous maintenance and service. Here's an article about horizontal vs vertical scaling that can answer all your queries about scalability.
Your data is connected locally, so there’s no need for an internet connection when the bot is used for internal purposes. Your data is fully available, even when your internet connection is not. This makes processes more efficient and increases productivity, so now you’re no longer dependent on WiFi to access your data.
Because your software is not connected to an external network, your data stays secure. That’s reassuring. This allows you to put more sensitive data, like customer details, employee details, logistics, accounts, etc., into your platform.
With the help of an on-premise chatbot, you will have control over all security measures used for physical access control. That means you have a limit on who has access to your data at all times. Only the people who have access hold the power to configure, code, and manage your data.
And there is also no need for third-party security audits. So, no additional costs and you keep your information protected. So no cyber attacks, no security breaches.
The control of your data lies in your hands.
What does it take to make chatbots enterprise ready?
Human - Machine interaction has never been easy. Humans want to interact with machines in the same way they interact with other humans and that is through language. The need for humanisation of machines has led to the growth of AI, machine learning and deep learning.
Chatbots are an early and novel way to begin man - machine interaction.
In chatbot terminology, interactions between humans and website chatbots are called as dialogues.
Almost every enterprise customer would like to customise the dialogues to make them more tuned, relevant and crisp for their customers, prospects and stakeholders. They may also choose different conversation flows to improve the user experience among different business needs.
Some enterprises may want to label the chatbots, which means they want to personalise the chatbots according to their specific requirements. Certain enterprises may request visual element changes to make the chatbots more user-friendly.
Additionally, chatbot integration with internal systems will have to be customised if enterprises do not provide standardised integration interfaces. The chatbot platform would have to provide the above features to build customised chatbots. Building every bot from scratch would be time-consuming. So, providing bot templates and bot platforms like Engati for various use cases or industry segments will make the bot building process faster.
Do we need NLU for enterprise chatbots or is NLP sufficient? This is debatable and depends on the use cases and how much sophistication and accuracy is required from the bots.
We will all agree that we would like chatbots to interpret tone, sentiment, emotion, analogy, simile, metaphor, perception, abstraction and experience. Most chatbots of today retrieve the responses from a database based on the provided input.
Generative based model is the future and this model will enable the generation of responses in real time. This means that we have to pre-define the responses. Generative models based on deep learning techniques are most promising.
Enterprises will use chatbots to enhance decision making and in many cases the chatbots will be performing critical functions. Therefore, it is expected that they understand conversations and respond with a high degree of accuracy.
Most of the use cases handled by chatbots are related to understanding the queries of the business. Thus, intricate knowledge of the business will be a prerequisite for employing chatbots in business. For example, if we talk of a customer support chatbots for eCommerce, the chatbot should understand business terminology and processes to answer questions related to a customer’s order.
Training will impart a significant part of this domain knowledge. In many cases, we have to augment machine knowledge to provide better service.
To make the process even better, we will need specific knowledge rather than general knowledge. SMEs on the domains have to design and train chatbots for specific domains. In the future, we will have B2B chatbots that will conduct business transactions. We will need high degree of domain knowledge for such a thing to happen.
Every enterprise will have regulatory requirements for auditing business processes and transactions. Chatbots will have to keep a track on all events and interactions between users and the enterprise. This will create a path to check traceability, reconciliation and resolve conflicts, if any.
As chatbots handle more and more functions, providing a foolproof audit mechanism will become necessary. Chatbots capture this information and use it to detect fraud and find irregularities to enforce ethical business practices. Customer support and service calls are recorded for analysis and training purposes. Similarly, the recording of events and interactions of chatbots can be used to train the chatbots for better performance. Chatbots that are capable of superior auditing will have an advantage over the others.
Intelligent Chatbots are a global phenomenon. Thus, most global enterprises will require smart chatbots to cover internationalization (i18N) requirements. A chatbot should provide i18N support, similar to those provided by business applications such as-
If the chatbots support voice recognition feature then they must support this feature in multiple languages.
The biggest challenge will be to provide NLP in foreign languages. Translating the input into English and then using an English-trained NLP engine will not work out. This could be due to poor quality or training. You can interpret an expression in different ways in various languages.
The best way would be to train the NLP engine in a foreign language using native expressions. However, this will create a problem of scale because every time we look for a new supported language, we will need a new trained NLP engine.
We can say that it is fairly challenging to meet the requirements of an ‘enterprise chatbot’. Those chatbot providers that can provide these features will have a significant advantage when enterprises are evaluating chatbots for their needs.
However, the task is not impossible. We have reached this far in technology and we will definitely improve going forward. Further advancements will help us overcome the challenges that we see today. Until then, we will have more than enough data. It will help us work better with chatbots. Our conversations with them will improve.
Top 9 chatbots for enterprise customer service
One of the best chatbots for coffee-lovers. Starbucks has released a bot that orders coffee for those who need that extra boost throughout the day. Available on Facebook messenger, this chatbot orders coffees for those who need that extra boost throughout the day. Try this chatbot template now!
Sephora, one of the leading makeup brands, offers an engaging chatbot. users with quizzes to better understand the customer needs. It offers users advice on makeup, reviews on products, what is suitable for their skin type, and more. Visual Artist is one of the services Sephora offers, also known as the Sephora Shade Assistant bot. Based on images the customer provides, the Sephora Shade Assistant boy can shade-match and recommend anything from foundation colors to scanning a face of a celebrity to offer a list of matching lipsticks.
Another service Sephora offers in-store are makeovers, which are done on a reservation basis. Reserving has never been easier with the Sephora Reservation assistant. The chatbot has the ability to get 11% higher conversion rates through chatbot bookings for makeover appointments at the Sephora stores. ShopBot (eBay) Ebay, the legacy mega-auction site, has an AI-based chatbot called ShopBot. ShopBot is used to help customers find the best deal on the website. The more the customer interacts with ShopBot, the more personalized their search results they get. ShopBot additionally has a feature that allows a user to search for products via image search.
Whole foods, one of the most popular health food chains in North America has a chatbot on Facebook that helps customers deal with groceries, deliveries, meals, and catering. With the bot on Facebook, users spend up to 50 minutes engaging with the bot to decide what to eat! Whole foods arranges recipes, products, and can even offer inspiration for users to cook many types of dishes from all over the world (based on their preferences, of course).
The Facebook messenger chatbot also helps users with their recipe options. Say you have a gluten allergy, Wholefoods will offer your recipes that are gluten-free. And if you like the recipe, you can directly place an order for the ingredients you need using the bot. It’s a great way for customers to discover new foods, and to build the customer-client relationship.
Air France’s enterprise customer service chatbot, Lucie is offered on Facebook. She allows users to plan trips while talking to (or perhaps, coordinating with) friends. Lucie also has the unique ability to sense the mood of the customer, based on an emoji sent. Customers have the ability to plan their dream holiday with Lucie by describing their ideas, and preferences. Then, Lucie suggests travel destinations, articles, etc., so customers can plan their dream trip. Lucie informs you about the destination, popular sites to see, and other events happening which is much more satisfying than just booking a flight!
Now, we have a bot that is trained to find shoes for a customer based on their personality? Available on the Facebook platform, ‘Stylebot’ is Nike’s bolder Chatbot for women. Stylebot’s aim is to build a personal experience for each customer. It allows users to send in photos to create their own, custom Air Max 90 colorway, which gives the customer an enriched, personal experience while buying Air Max shoes.
The bot displays information about shoes and further directs you to Nike’s website. The bot provides style options based on emojis which gives the bot an overall youthful feel. It manages to make the act of browsing for shoes far more exciting than it already is. So whether you’re the working girl, the sporty girl, or something in between, Stylebot has something for you.
YouDrive, an Australian rental company developed a bot to make the process of notifying car or rental problems convenient for users. It was also created to accelerate request-processing. The bot offers solutions on the spot for the user. Queries from “how do I start my car?” to “I’ve gotten into an accident, what do I do?” are offered instantly. It sends user-requests to the correct department for quick solutions to problems which is why it's on our top t.
TechCrunch is a leading technology magazine that uses customer support chatbots to send users more personalized content. Interacting with the chatbot and selecting the type of stories you prefer, adds to TechCrunch’s database to offer recommendations to you. With so much content available online, Techcrunch narrows it down to your taste and preferences, which then leads to positive brand association.
One of the first florists to offer a chatbot service. It allows users to order flowers and have them delivered. It offers a variety of options. Firstly, the user decides which flowers they want. Then, this directs users to a page where they can customize their orders with notes. It’s a convenient way to make a transaction and saves time for users and clients. And it’s definitely one of the most innovative chatbots!
84 percent of consumers consider customer service to be a key factor when deciding whether to purchase, and only three percent say it’s unimportant.
— Oberlo report on Customer service statistics.
What are the requirements for deploying a chatbot in enterprises?
Most enterprise users use sophisticated applications having a rich user interface. Asking them to switch to only text messaging or voice may not find many takers. Using visual elements like menus, buttons, scroll bars mixed with text and voice will be more acceptable. Having a flexible and rich Chatbot UX will be a key criterion in increasing adoption.
This will perhaps be one of the major stumbling blocks for the adoption of enterprise chatbot platforms. For instance, the resistance when mobility made its entry into the enterprise. Most enterprise chatbot platforms operate in the cloud and will have to access information and interact with entities inside of an enterprise and outside too. Similarly, most enterprises have strict security compliance rules and chatbots will be expected to be in the clear.
As we know, most chatbot platforms operate in a public cloud. They also have to access information stored inside an enterprise and sometimes from external public services. Chatbot integration will be a challenge and any platform that can provide seamless integration capabilities will have a significant advantage. However, not all enterprises will provide or allow API integration to its internal systems. In such cases, here's how you can go about it-
As an add-on module, you may have to write custom integration software for legacy systems and plug them into the Gateway.
Large and global enterprises will process enormous chatbot interactions. In fact, chatbots will be expected to handle thousands of simultaneous users and interactions per second. Additionally, availability will also be a key criterion and the chatbots and their platforms will have to provide 99.99% uptime or more because they will become business critical entities for an enterprise.
Enterprise Chatbots will show the characteristics of a big data platform. In today’s business, a delay of a millisecond can cost a huge amount of money. Especially, if the delay is by chatbots in the fin-tech area. Chatbots have to access internal systems in enterprises. So, any slowness caused by them will first reflect on their performance. It is acceptable in the early stages of its functionality but might become an issue at the later stage.
The biggest challenge in enterprise
One of the biggest challenges of human-computer interface is that humans think and talk in natural language, which is unstructured. However most softwares organize data in a structured format. We then, expect employees to interpret these structured data sets and map it to the information they're looking for. Instead, have an enterprise chatbot platform conduct these tasks. It'll drive efficiency and lead to frictionless human-computer interactions
SQL is a high-level language which was designed to provide quick access to data in RDBMS without learning a full fledged programming language. With enhancements and improvements throughout the years, it now performs these tasks very well. Wouldn’t it be nice if we develop the capability to ‘translate’ natural language to SQL? What we are looking for is the ability to map natural language elements in a question/query to columns and rows in a table with filters.
Let’s take the employee details example again. The HR personnel may ask: "Give me the names and contact details of employees staying in ‘London’?' or can I get information of employees located in ‘London’? or can "I am looking for details of our ‘London’ employees". A human would ask the same question in many variations and chatbots have to understand all these variants and map them to the same question.
One idea is to start by identifying the ‘domain entities’ in the query
E.g., in this case ‘Name’, ‘Contact Details’, ‘Employees’, ‘London’. These are named entities that are relevant to the specific domain (in some cases they are generalized too). In the NLP world you use a NER engine (named entity recognition engine) to predict the named entities in the query. This can obviously happen after you train the NER engine with a good enough dataset of labeled named entities which are part of query variations.
A popular algorithm to train an NER engine is CRF. Many open source NER engines based on CRF are available but they're too generic. They may not work for specific domains. The better option is to take such open source code and train it with your domain entities till they can predict them. Since Deep Learning is the flavour of the day, researchers have also come up with Deep Learning networks for NER and these outperform the traditional CRF based NERs. E.g., Spacy NER engine.
Adding simple rules to map identified entities
Once you have identified the entities, you could Add simple rules to map the identified entities to columns/rows in a table and then use a SQL generator algorithm to translate this to an equivalent SQL query. For simple querying use cases which don't do no need complex analytical output, this mechanism will work well. For solving complex use cases which can translate a query to a complex SQL query with multi-table joins and filtering, the above mechanism may not be an elegant solution.
Conclusion
Chatbots are a great ally in enterprise customer services. We hope you like our list of top ten Chatbots for enterprise customer services. To use a chatbot for customer service, try one from Engati today!
Register with Engati today to explore the new age of customer service chatbots.
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