8 Must Do’s In Enterprise AI Chatbot Development
The promise of fast and cheap customer service by leveraging an omnichannel chatbot is seldom realized to its fullest extent. Lack of experience in development for enterprises can be attributed for most of the failures. The problem is that bad chatbots are easy to develop. Here are 8 tips to help build an enterprise AI chatbot.
Step 1: Determine What You Want Your Chatbot To Do
Determine what actions you want your chatbot to take. Please be as specific as possible.
Begin by asking yourself these questions:
What is the purpose of the chatbot? Customer service automation, customer experience improvement, or lead generation? Or might it be all of the above?
What are the most common client scenarios? Examine your questions and make a list of a few examples.Examine your questions and write down a few examples.
What would be the most useful chatbot feature for you?
Answering questions automatically?
Sending the inquiries to the customer service department?
Is it better to save abandoned carts or qualify leads?
Once you’ve answered these questions, deciding where to put the chatbot will be much easier.
Step 2: Choose a Location
What is your primary means of communication? Do most of your consumers reach out to you through social media or a live chat widget on your website?
In any case, make sure your chosen chatbot platform interfaces with the technologies you already use so you can serve your consumers wherever they are.
Your internet site. The bulk of chatbot development tools integrate with popular website platforms like WordPress, Magento, and Shopify.
WhatsApp, Facebook Messenger, Instagram, and Telegram are examples of social media channels.
You have other messaging systems and technologies in your stack (such as Slack).
Alternatively, see if the integration can be configured via a code snippet or an open API.
Many chatbot creation platforms include various connectors, allowing you to use chatbots across multiple sites.
Step 3: Select a Platform For Your Chatbot
It’s time to choose a provider now that you know the chatbot versions you want to produce and which channels you want to cover.
You can choose between the framework and the platform.
Frameworks for AI. Chatbot frameworks (such as Google Dialog Flow, IBM Watson, or Microsoft Bot) serve as libraries for enterprise AI chatbot developers to use in creating chatbots.
Platforms for chatbots They offer simple chatbot builders that allow you to design a chatbot using building components. They’re becoming more popular because constructing bots with their assistance is considerably easier and takes less time while producing comparable outcomes. Not to mention that certain platforms, like Tidio, provide programs that are completely free forever!
Gartner ranks IBM Watson Assistant highest in the magic quadrant. Organizations can use IBM Watson to identify potential issues and complications earlier and respond accordingly. This also saves them money in the long run.
Step 4: Give your bot a personality
When creating your bot you will want to give it its own personality. While creating this personality consider gender, age, location, language, income, their industry and job title, hobbies and interests, purchasing behavior, and the most significant challenges when developing your boy’s persona. Don’t be concerned if you don’t have all of the information in your client database, you can send surveys or conduct customer interviews to fill in the gaps.
Analyze the data you have gathered. Are there any patterns or things that your customers have in common? Examine your conversations with these clients and try to highlight things that connect them.
It is important to note that you are not required to have only one buyer persona. If necessary, you can create two or more profiles.
Step 5: Have Your Chatbot Tested.
The most effective technique to test a chatbot is to converse with it and pay attention to details such as:
User Experience (UX) Response Speed
Accuracy of Chatbot
What happens if the bot doesn’t understand the user?
Is conversing with a machine entertaining?
There are several methods for locating users for testing.
One method is to invite your coworkers to participate in the testing and collect training data from their chatbot interactions. However, keep in mind that your information may be skewed because they are familiar with specific language, your organization, services, and so on, and their interactions with bots may differ from those of your chatbot’s target audience.
Step 6: Educate Your Bots
Education of a chatbot is not just the initial setup to answer basic questions, but an ongoing review of the type of questions being posed to the chatbot and how they are being posed. Processes need to be established to monitor the chatbot usage and incorporate the learning from the monitoring into modifications to the bot. There is no way to predict all of the ways people will interact with the chatbot, but constant monitoring will help to educate the bot over time.
Step 7: Collect user feedback.
Your visitors and customers will be the best judges of the efficiency of your chatbot efforts. What’s the best you can do? Allow chatbots to send an automated customer satisfaction survey to users, inquiring about their happiness with the chatbot encounter. You can observe what works and where you need to improve based on the results.
Step 8: Improve Chatbot Analytics By Monitoring Them.
Last but not least, make a commitment to keeping track of your chatbot’s behavior. Use the analytics from the conversations to identify where the conversations take the user in the journey. Use this to improve your conversation maps and always look for ways to reduce the time of the interaction. This will allow you to identify chatbots that do not provide the best customer experience and do not benefit your visitors.
The Dayhuff Group has pioneered machine learning systems for numerous industries and have been a leader in advancing enterprise AI chatbot development. Based on decades of AI research, experiences working with businesses of all kinds, and insights gained from over 30,000 IBM Watson engagements