Virtual assistants are steadily gaining in popularity, with an estimated 40% of all mobile interactions due to be carried out by mobile assistants by the year 2020. The convenience and speed of these assistants have set a new standard for customer service that cannot be ignored.
Customers now expect things to move quickly and be a simple voice command away.
The value of a conversational UI is clear. Here’s how to launch one for your business.
Identify pain points
The first step in creating a conversational UI sounds simple enough: What’s it going to do? What’s it for?
However, as you dig in you’ll find that these questions aren’t easy to answer. There is some market research to be done. You’ll need to identify a real business problem that could benefit from being automated – look to areas like customer service, tech support, and sales for ideas.
Are the staff in these roles bogged down by inquiries? It could be time to streamline their work by filtering out queries that don’t require a human assistant, taking pressure off staff and improving customer satisfaction.
For example, 86% of B2B executives prefer using self-service tools for reordering, indicating that B2B orders with existing clients could be a relevant area of focus.
When you’ve established which problem or problems you’re going to tackle, the next step is to identify the specific productivity or cost-saving opportunities. If the voice UI can save money or make things faster, draft projections of exactly how much money, time, or other resources will be saved by implementing the UI. This can be weighted against the estimated cost of introducing your product or service to the market via an AI assistant – providing a clear, positive ROI target.
Plan and Prepare Data Sets
At the backend, your AI wants to learn and hone its conversational skills, becoming more of a social butterfly as it goes along. To do this, it needs to learn from data sets which can be acquired either from within your enterprise or by purchasing third-party data to work against. Bring your IT staff in on this along with any relevant stakeholders who should be involved so your AI can get started.
There are separate data set categories for question-answer functions, dialogue, customer support, etc. Now, considering how you have outlined the function and nature of your AI, it’s set to use machine learning to absorb as much (or as little) information as you can feed it.
This is a job for your IT staff, and like all aspects of AI creation, it can be daunting and often ill-advised to start completely from scratch.
Conversational AI platforms can offer a significant shortcut to a market-ready deployment. These AI platforms can offer crowdsourced data curation, domain optimized data sets and training learning algorithms that all greatly reduce costs as well as reduce the amount of specialized staff required to develop the conversational UI.
Even with a great enterprise conversational AI platform, you’ll need to assemble a team of smart and inquisitive people to oversee the process. Here’s one of my favorite guides on assembling the right team for your conversational AI from Georgian Partners.
Plan Your Assistant’s Personality
Take any of the most successful AI assistants. Alexa, Siri, Google Voice – they each have a distinct “personality.” These distinct personalities make it feel more like a “human in the room” and help distinguish each brand to consumers.
Alexa, for example, is programmed to answer genuine or serious questions in a highly professional manner, but can also sometimes detect jokes, sarcasm, and mischief. This advanced personality delights users and helps them to form a connection with their assistant.
In planning a conversational UI, it’s important to consider the following:
- How does your voice AI communicate?
- How does it represent your company?
Personality, tone of voice, level of formality, style of humor (if any) – it all needs to be considered. Is the AI going to be designed to carry out full conversations? Or is it only required to answer basic questions?
Your AI represents your brand and company, and it’s important that the marketing team outlines a “character” and decides whether the AI expresses any form of personality, and if so, in what way.
User Flow and Demographics
Considering the personality of your conversational AI behind the Voice UI is only one aspect of your project. You also need to think deeply about the users. Who will be using your conversational UI?
Customer needs and online behaviors are items most likely already under scrutiny by your marketing team, and this information will help inform the design of the user flow. Use some of these common marketing tools to reveal these opportunities for your conversational AI to assist.
- Develop user personas
- Map out common use cases
- Identify typical user workflows
- Determine common pain and choke points
- Document common and ideal user journeys
Planning the user flow depends on the specific use case. Maybe you’ve developed a customer service chatbot to streamline processes in online banking, or a voice UI designed to automate clunky processes in drive-through food ordering.
Create a flowchart detailing every possible step of the consumer’s process and think about how the UI needs to handle each information transaction.
Create Your Conversational UI on a Platform
Now that you’ve acquired the data that will fuel the AI and you’ve outlined some design and functionality goals, it’s time to actually build your UI.
There’s a lot to be considered when doing this, and it’s best to make things easy on yourself by opting to use an established conversational AI platform.
We’ve already touched on how the AI needs to learn from data sets. The same principle essentially applies to using a conversational AI platform. Instead of building something from scratch, why not start with a foundation of tried and tested features and elements that are ready to go and proven to work.
These platforms offer all the essentials from natural language processing, conversation management, experiential learning, an AI engine, and multiple personality templates and customizations to fit your brand and appeal to specific users based on their age bracket and other demographic identifiers.
Platforms like Clinc offer end-to-end services that greatly reduce costs and the time it takes to get to develop a working product, allowing you to go to market in just a few weeks.
The democratized data features allow you to take advantage of the vast amount of crowdsourced data curated by the platform which can save up to 95% on data costs. Clinc offers 98% accuracy in natural language processing and has use cases in banking, food service, the automotive industry, customer service, and more.
AI Assistants are here to stay. Customers want to use their AI assistants to do business with you, and if they can’t do that, some of them will take their business elsewhere. Trying to launch a conversational UI is a major undertaking, but the process can be simplified with less risk and costs by using a conversational AI platform like Clinc to develop your product and get to market before your competitors do.