Customer service is a must-have for any business today. With the global reach of many companies, there’s a real demand for convenient consumer engagement at any time. Chatbots ensure a 24/7 model for client care, empowering buyers to interact with the brand whenever they need assistance. But there’s another dimension to consider – language.
Introducing a multilingual chatbot offers immense value to any organization, enhancing customer support and reducing operational costs. Below, we explore the importance of such conversational tools, different implementation approaches, and how Master of Code has developed a solution using the Microsoft Azure stack of services.
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The Value of a Multilingual Chatbot
A multi-language assistant is a conversational AI system that can communicate with users in their mother tongue. This breaks down communication barriers and makes customer service accessible to a wider audience.
Let’s consider the client care aspect. The value of a multilingual chatbot lies in putting all consumers on an even playing field. Every person deserves equal attention and engagement with your brand. An omnichannel approach to their experience has long been a growth strategy, and with contact centers and bot platforms merging those channels into a single support interface, that advancement is well underway.
The next step is extending the reach of assistance across all touchpoints to the widest possible audience. This is where a multilingual chatbot shines. With these systems in place and the ability to analyze customer interactions, you can easily identify the languages your customers use. The insight allows you to strategically expand your bot automation to include additional vocabularies, enhancing the digital journey. And, if those common applications in other tongues can be handled by the virtual agent, it reduces the workload for your live support specialists.
Multilingual Chatbot vs. Multi-Language Specialists for Call Centers
Those agents make up the second consideration when weighing a multilingual virtual assistant. Today, when a bot is unable to answer a question or concern that a user is having, what happens? The conversation gets escalated to a support expert to work with the client. But there is nothing that says the live agent can engage with the buyer’s language either. Moreover, with the escalation already possibly frustrating the consumer, reaching someone who still cannot assist due to a communication barrier will risk increasing customer dissatisfaction and not look as positively on the brand.
Although it is possible to staff up individuals who can speak all of your target audiences’ languages, the impact on a business is hard, and can also be rather expensive. Consider the steps in bringing someone onboard to support a brand in a call center. You have to find and recruit the individual; coordinate a start time, usually with a larger group to optimize costs; perform the training, which on average can take from 6-12 weeks; not to mention procure equipment and enable setup and aid for the specialist.
Even if we look at the lower end of the timing, say 1 week for recruitment and another 6 weeks for coaching. That’s 7 weeks for effort and cost per agent. And in an industry that in recent years has seen attrition rates of 34%, the whole process is quite pricey. And many organizations deliver a premium to employees who can provide multilingual assistance. Such an approach increases expenses and can decrease customer satisfaction should those agents leave the organization.
The primary challenges leading to customer support attrition are overwork and frustration. But this is where a multilingual chatbot can come in and offer value. It’s able to handle the majority of the repetitive and simple-to-answer queries for a client before it hits the agent. As a result, the solution reduces their volume and workers’ stress and exhaustion. Within a sector with high turnover due to burnout and with 53% of support teams experiencing an increase in workload, minimizing churn and cutting onboarding costs can help businesses focus on the issues that are most important to customers.
Why Companies Need Multilingual Chatbots
In addition to reducing the volume of customer requests, lowering employee attrition, minimizing client churn, and cutting onboarding costs, a multi-language assistant offers a myriad of other advantages including:
- Personalized chatbot global experiences. A multilingual chatbot delivers support tailored to each buyer’s tongue and cultural nuances, building stronger relationships.
- Enhanced brand accessibility. The tool breaks down barriers and makes your business welcoming to customers worldwide, expanding your potential market.
- Improved data collection. With its help, your team can gather valuable insights into the preferences and needs of an international client base, fueling better decision-making.
- Boosted self-service options. Consumers with different cultural backgrounds can easily find solutions, increasing satisfaction and reducing dependence on live agents.
- Competitive advantage. A multilingual chatbot sets your company apart in the global market by providing an exceptionally smooth customer journey.
- Increased sales opportunities. You can also reach more prospects and remove linguistic obstacles from the purchasing process.
The potential is clear. But how do you actually implement this powerful technology? There are two major approaches depending on business needs and capacity. Let’s examine these methods one by one.
Implementing Multiple Bots for Multilingual Support
Historically, the most common method of creating chatbots in different languages is to create a single bot per tongue. Here is how it works: a system needs to first understand the lingo in which the user is attempting to interact and then route them to the appropriate language-specific virtual agent. The multilingual chatbot then takes over the conversation and starts helping the customer.
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The advantage of this model is that each assistant can be developed in parallel because they are disconnected from one another. Parallelism allows for a faster time to market, ultimately enabling businesses to help more clients. This is a significant benefit. But there are some challenges to consider when approaching the solution in this manner:
- The use cases may be different for each bot. This can lead to an apples-to-oranges end result, with diverse bots providing specific information. In other words, the customer in one lingo does not get the same support as the rest of the users, which can also induce additional frustration from a live agent escalation in terms of how they need to handle individual consumers.
- The conversation design for chatbot and digital journey maps are disconnected from one another. This may make sense from a geographical standpoint when various services are available in certain areas, but not from a multilingual support solution for a single geolocation.
- Service costs can escalate quickly. In the above example, there are 3 or 4 bots to manage and assist clients – one for each language and then possibly the orchestration chatbot which allows the user to select the lingo. When changes are made, multiple standalone assistants need to be updated, trained, tested, and supported. This can require more manual testing, resulting in significant effort to effect a small change.
- You are limited to a single language at a time. There is no supportability for switching settings and maintaining context and positioning in the flow. Essentially, when the client switches their preferences, the conversation starts over again. This makes sense if not every flow is sustained across all tongues, but definitely not an optimal experience for the end user.
While the technique mentioned is valid for offering multilingual support, it does have limitations. Recognizing these challenges can help us appreciate the complexities of automating such tasks. Fortunately, alternative approaches exist.
Implementing a Single Multilingual Chatbot
An alternative method for integrating one bot that supports multiple languages is to leverage the ever-expanding cloud-based cognitive services to provide this expansion. In this instance, there is a single Natural Language Understanding (NLU) service implemented in a default setting for the multilingual chatbot. The use cases for this tongue are laid out, the persona and journey map exercise is developed, and the core of the assistant is built.
Check out our Case Study where a chatbot provides a 3x higher conversion rate than a website alone
Now, within the multilingual chatbot itself, a language detector component is implemented. This element will work with other services to translate the request. Afterward, the mechanism applies it to the NLU to understand the intent of the question, which exists in the default lingo. Once matched, the appropriate response will be identified and formulated, and then returned to the customer in their selected language.
That is a rather simplified explanation of the service, but let’s apply this approach to the challenges listed above:
- Different use cases for each bot. By approaching the solution this way, we mitigate this problem by having only a single multilingual chatbot. All applications are equivalent in every language that is available within the tool. Moreover, we can now ensure that the experience for a user in any of the provided tongues has parity with the other supported ones. This also means that the same activities before agent escalation are identical, so support specialists should have more confidence in what has happened before they engage with the customer.
- The journey maps are consistent because there is only one multilingual bot, ensuring a fully aligned experience in terms of services and offerings.
- Support costs exist, but now there is only a single multilingual chatbot to maintain. Additional expenses may be needed to leverage some of the other cognitive services as the underlying architecture has become more complex. Yet, a DevOps team now has fewer bots to monitor and sustain in production. Furthermore, instead of doing individual testing in languages and deploying the virtual agents independently, time can be optimized to deploy this solution in one fell swoop.
- Because the input is translated and understood as the communication happens, should the customer change settings mid-stream then the flow continues unabated. The multilingual chatbot will then adjust its own responses to mirror that of the client, as long as that tongue is enabled and made available within the bot. This level of adaptability provides an enhanced experience to the end user and creates some additional value to the tool, including providing interesting data points of language switching mid-conversation if that is of importance to the business.
Challenges of a Multilingual Chatbot Implementation for Enterprises
Even with these limitations mitigated somewhat, there is still some additional work in maintaining the AI assistant. For each language implemented, the responses need to be crafted and formulated to be correct for all the available options. Although it is reasonable to use NLU and automatic translation to understand the intent of the user, it is not necessarily the same as in how you create the response. Language is very nuanced, and when you are talking about value, you require the answers to be aligned with the persona of both the multilingual chatbot and the brand. The vocabulary used in the replies requires extra effort to ensure clarity and accuracy. You would be doing this in the previous example as well, but it would be done in the confines of the specific bot.
Because of this need to still manually craft the responses, the organization needs to decide on which languages they wish to perform multilingual support. Not everyone can be easily detected, so there are some technical limitations that may result in a hybrid of the above two solutions being implemented. But when the tongue is supported, then adding an additional option to the portfolio is more a matter of intent mapping, utterance development, response message crafting, and then training the multilingual chatbot to understand the updated language. The goal of this architecture is not to recreate the wheel with each subsequent tongue, but rather expand its assistance capacity as needed.
Multilingual Chatbot in Action: 4 Inspiring Success Stories
Despite these challenges, the potential benefits of launching AI chatbots for business still outweigh the implementation hurdles. Let’s examine how real-world companies are successfully leveraging a chatbot in different languages to enhance their customer interactions.
Duolingo
The company has integrated multilingual chatbots into its app to enhance the learning experience. These AI-powered bots enable users to practice French, Spanish, and German through simulated dialogues. Duolingo has imbued each assistant with a unique personality, such as Chef Robert or Renée the Driver, to make the interactions more engaging.
The solution demonstrates flexibility in understanding responses. Multilingual chatbots also provide corrections when needed and even offer suggested replies for those moments when you’re lost for words. This approach aims to bridge the gap between traditional learning and the fluidity of real-world conversations.
WestJet
The organization has taken a pioneering step as the first Canadian airline to launch an AI multilingual chatbot, named Juliet. Available in both English and French on Facebook Messenger, Juliet simplifies travel management for guests. From finding trip inspiration to booking flights and accessing essential information, the bot provides a convenient support channel.
This conversational tool reflects WestJet’s commitment to digital innovation. Their aim is to enhance the customer experience through readily accessible platforms. Juliet’s use of reinforcement learning ensures continuous improvement over time, promising an increasingly sophisticated and helpful assistant for travelers.
Unilever
The company has revolutionized its HR services with the introduction of Una, an advanced multilingual chatbot designed to transform the employee experience. Una functions as a digital colleague accessible through Skype for Business. The bot offers HR assistance to employees across 106 countries.
Remarkably, Una can converse in 32 languages, breaking down communication barriers and offering a truly global support system. Unilever’s focus on strong digitization foundations, along with a commitment to change management, has been key to the successful implementation of this innovative bot.
Esso
Esso, in collaboration with Master of Code, launched a bilingual chatbot (English and French) game called “Pass the Puck” celebrating Canada’s love for hockey. This creative campaign aimed to engage fans and promote the brand leading up to the 2019 NHL season.
The results were impressive: an 83% sign-up conversion rate, a 94% player retention ratio, and a staggering 772,970 puck passes nationwide. The virtual puck traveled over 1.2 billion kilometers, reaching every Canadian province and territory within just four days. This approach demonstrates the power of bilingual chatbots to drive engagement and brand awareness in a fun and interactive way.
How Master of Code Global Developed this Multilingual Chatbot
There are many cloud infrastructure providers in the market today offering numerous cognitive services that can be creatively combined to provide a solution such as this. The Bilingual Messenger Chatbot mentioned above, we have put together with a focus on using the various benefits of the Microsoft Cognitive Services Suite.
This suite provides all of the necessary tools to make either of the discussed solutions happen. At the core of both methods in the stack is Microsoft Conversational Understanding, which is the further step beyond LUIS, the previous iteration of NLU provided by the company. Both of the products work with the Microsoft Bot Framework successfully and have APIs for use by other systems. Yet, the future of NLU at Microsoft resides with this next-generation tool which allows for many of the services outlined above, including the language orchestration activities.
The activities for Conversational Understanding are supported with Azure Language Detection, another one of the cognitive elements provided in the suite. This service, which is continually advanced by Microsoft, allows for the growth of language quality and support, creating new opportunities over time for additional functions to be bundled on top of this solution.
With all that, there are also services from other hosting vendors that can provide matching capabilities. For example, AWS has Amazon Comprehend, which can perform detection on content to reliably understand the language that the inbound request is in. So although we talk about how Master of Code has developed a Conversational AI solution, specifically a multilingual chatbot, using Microsoft Azure services, there are similar options available from the other major market players that can be leveraged based on the underlying architecture that an organization chooses to utilize.
How Do I Choose The Multilingual Chatbot Solution That is Best for My Business?
That depends on your customer base. There is no one right answer as to how to approach developing and implementing a multilingual chatbot. Each organization needs to consider its clients, the volume of queries they have in some of the additional languages, and the value of those ones to maintain and reinforce their brand.
If the company’s website is available in a certain tongue, then consumers will expect assistance in that language as well, and so those should be the minimum number of options supported. It also is based upon the technology stack that your business utilizes, leveraging what you already have present without necessarily needing to go through a new onboarding and procurement process.
If you want to research which options are best for you and your client engagement strategy, reach out to our team of specialists at Master of Code Global. With our extensive expertise in AI chatbot development services, we will help you map out a path to customer experience success using automation to support your existing contact center activities.
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