When chatbots arrived a few years ago, they were deployed to augment customer care and improve call center efficiencies. Increasingly, we are seeing chatbots in the pivotal role of driving the customer experience as they move beyond supporting customer care.
This shift is being precipitated by changes in consumer behavior and advancements in technology. Chatbots continue to evolve with improvements in artificial intelligence (AI), voice recognition and other related technologies. Meanwhile, consumers no longer purchase just products and services: they are buying the entire brand journey experience from discovery to post-sale.
A well-designed chatbot has the potential to change the way brands interact with their customers. It can connect customer touchpoints across multiple channels and enable customers to experience engagement at each point of their journey. While the value that chatbots can add to a brand has made their adoption a key objective for organizations, a successful adoption is not without challenges. This paper will outline these challenges and consider the best ways to address them.
Questions to be answered before implementing a chatbot interface
1) Why do I need chatbot interface?
Businesses planning to embark on this journey should first clarify their objectives and success criteria. Among the objectives to consider include improved customer experience and engagement, better customer care support, omnichannel fluidity, and cost optimization.
2) How do I ensure a brand connection?
Each customer holds an expectation of a brand based on past experiences and interactions. To maintain your customers’ connection to the brand, be sure to consider the entirety of the chatbot experience, including user interactions, messaging, and persona.
3) Should my bot behave like a human?
Customers don’t want to feel tricked. Set the right expectation at the start of conversation by letting customers know they are interacting with a bot, not a human.
4) What‘s my bot’s gender ?
There is no right or wrong approach to selecting a gender for your chatbot. You may decide to go with a male, female, or gender-neutral bot. In naming your bot, you can choose to forego a traditional name and instead select a name that references the bot’s specific task, e.g., “Master Chef.” You may also offer customers the option of choosing the gender they prefer to interact with.
It’s also important that the machine learning (ML ) models are built and trained by a diverse group of people to anticipate possible variations in customer interactions.
5) What is my bot’s persona?
With your bot’s objective in mind, develop a persona that the bot can embody so that it can connect with the customer. A bot can reflect multiple personas for the greatest flexibility to adapt to the conversation flow. For instance, a bot can take on different personas attached to different product categories. The bot may decide to switch persona during the conversation based on product category identified during the conversation.
6) What are my channels of interaction?
While building the conversation logic and flow, keep in mind the channels you want to support. A chatbot for Facebook may leverage a user interface (UI) element such as cards to display content. If the same bot is also intended to act as a voice assistant, such as Alexa or Google Home, that experience will require a different design. For every channel the chatbot is destined for, the conversation will require some tweaking.
Recommendations for building an engaging customer experience
Understanding user intent
The goal of a chatbot is to provide help to the user in the best possible way. Conversation design should focus on ensuring that users find what they are looking for. At the outset, be transparent with users about what a chatbot can do for them and develop contextualized help options for the user’s entire journey. If user intent is ambiguous, the bot should present the best options based on its understanding. The conversation design should account for all possible scenarios while supporting contextual awareness by drawing on past interactions and ingesting real-time signals.
Make interactions short and crisp
Keep your messages brief. If there is a need to give detailed information, provide URLs to help the user understand the processing details. Avoid cramming a large volume of text into a tiny chatbot window.
Cultivate contextualized and personalized interactions
A chatbot should be able to learn from past interactions with the user and leverage personalization, recommendations, and AI to build contextualized conversations. For example, if a customer searched for a product a few days ago, the system should provide recommendations based on that history. Chatbots should also be able to process a user’s context when an interaction is triggered on another channel, such as Twitter or Facebook, as a result of an advertising campaign.
Engage in proactive interactions
Move beyond passive experiences and add proactive interactions that alert customers to new offers and promotions. Proactive interactions powered by AI provides important opportunities to cross-sell or upsell products.
Provide a consistent experience across channels
In today’s multi-channel world, a user may connect with your business through multiple touchpoints. Users may start their interactions with your brand through Facebook Messenger but complete their purchase on Alexa or a mobile app. It is important to maintain context within a consistent brand experience wherever the customer chooses to interact with you.
Predict the next step
While designing the conversation, identify potential opportunities for the chatbot to predict the next questions based on the conversations that have occurred. If a customer asks a question about branch hours, the bot should provide additional information about services provided by the branch, such as ATM, insurance, funds, and investment services. These are questions someone planning to go to the branch may likely want answered, and anticipating them can make the customer feel cared for and connected to the conversation.
Bring gamification into the interaction
Gamification can break up a monotonous conversation. A bot could ask users, at appropriate times, if they would be interested in gaming activities such as quizzes. Be careful that such invitations do not become too intrusive as irritating the customer defeats the purpose of the chatbot.
Don’t leave the conversation open-ended
No matter how well you train your bot, you can expect a few failures. When your chatbot is not able to resolve a customer’s issue for any reason, it should continue to engage the user either through live chat, a customer care call, a ticket, or a callback so the user does not feel the conversation ended without a solution in progress. If customers do not feel the chatbot is addressing their problems, they will eventually stop coming back.
Don’t overlook the exit experience
Because so much of chatbot design is about ensuring the bot can resolve a user’s problem, it’s easy to miss opportunities to provide a great exit experience when the user leaves the conversation. This exit experience should go beyond a thank-you note; it should leave a lasting impression. You can include survey, feedback, or gaming activities as part of your exit experience, but never obligate the user to participate.
Discuss with your customer the personally identifiable information (PII) to be masked on the screen and in data storage. To comply with the general data protection regulation (GDPR), it is essential to keep users informed about how their data is displayed and stored and to obtain their consent on privacy matters before they use the chatbot.
Take a pause and realign for customer experience
Finally, keep the future in mind. With ongoing advancements in voice technology and the increasingly widespread adoption of smart devices such as Alexa and Google Home, businesses embarking on chatbot design must look beyond text-based interactions and embrace voice-enabled conversations. As part of its customer experience (CX) strategy, businesses should look to conversational interactive voice response (IVR) supported by technologies such as automatic speech recognition (ASR), text to speech, natural language, and AI to provide a better customer experience than traditional IVR and to reduce operational cost.
Enterprises undertaking the chatbot journey should consider these recommendations both before and during adoption. Designing conversations that are engaging, relevant, meaningful, contextualized, and personalized, and driven by AI, cognitive, and voice technologies can help enterprises not only better meet rising customer expectations but redefine the customer experience.