This isn’t for you if your company meets customers face-to-face or on the field. But, if 80 per cent or so of your interactions with leads and clients go through some form of email, phone or video call (like many others now), you might want to have a look at natural language technologies.
When you think about those phone calls and emails, they represent pretty much every moment your customers directly talk to you. That’s when they tell you what they need and want, what they struggle with, what they like about your product, and what they don’t.
That is a lot of critical information for your business!
But here’s the thing. We are talking about unstructured data—a large flow of text and voice, but nothing your system can easily make sense of. Most companies don’t even have a clear record of their interactions. Let alone efficient analytics tools.
So, the only thing you can do is rely on your customer-facing teams. They are the only ones able to analyse what’s being said, make sense of all that data and report the findings to their managers. The heroes of unstructured data!
That is where natural language technology comes in. It can transform this mass of data into something structured and intelligible, lifting the burden off your teams. And it does it in three different ways:
- Conversational AI
- Agent Assist
- Behavioral Data Management
Also Read: How voice AI is revolutionising the fintech scene
Automating conversations, the power of conversational AI
We call Conversational AI the creation of human/computer conversations using voice or text. You add an AI layer between customers’ touchpoints and your team.
Used properly, it can automate most of the first-hand interactions your customers have with your company and seamlessly hand off the most complicated tasks to one of your reps.
Yes, we are talking chatbots, virtual assistants and the likes.
But technologies behind those solutions vary greatly. Depending on your needs, it can go from a simple interactive FAQ to a fully human-like conversation in different languages.
And it comes down to the level of end-user experience you want to achieve with automation. For a deeper look, Rasa wrote a fantastic article on the matter.
You want to look at those three to build a solution that fits your needs.
- ASR: Automatic Speech Recognition, or the ability to transform voice into text. Here, you want to consider the accuracy of the transcript, the different languages, accents, and the noise environment.
- NLU: Natural Language Understanding, or the brain. Those semantic algorithms analyse the meaning, intention, and sentiment behind a string of words. It also triggers appropriate responses.
- TTS: Text to Speech, or the voice of the assistant. It’s about the assistant providing accurate responses and being understood by the users.
Supporting teams with Agent Assist
Agent Assist is not that much about automating customer-facing tasks but rather transforming the mass of unstructured data into something that can help the teams perform better. Natural Language technologies can help your reps with real-time support.
It starts with a simple transcription of the calls that allows storing all client interactions under the same format: easier access, tracking, and the possibility of analysing each communication.
Natural Language Technology can go further than that. It can screen the transcript in real-time to uncover patterns. And highlight the sentiment of your client. You are helping your reps decide on the best course of action accordingly.
Some things can be automated, like pushing communication data into the CRM or crafting an offer adapted to this specific customer.
It’s basically for your reps to do their jobs better. And the beauty of this technology is its customisation. You can adapt it to any use case.
Also Read: These Artificial Intelligence startups are proving to be industry game-changers
Making data-driven decisions with behavioural data management
Natural Language Technology also supports managers and team leaders. It detects at scale what your customers want and how they feel. It identifies patterns in their voices, recurring sentences, blanks, and unspoken hints that tell a lot about a buyer’s intentions.
So, you can understand what part of your process is key to winning and keeping your clients. And what needs to be improved.
All that unstructured data is now accessible and understandable. You don’t have to rely on the opinions of your team anymore.
That means backing up business decisions with actual data—coaching reps with real tracking of their actions. And automatically know what your customers say.
Your company is probably already handling a lot of data. But in all that data, the voice of your customers is most valuable. If not, Natural Language Technologies are not that difficult to implement.
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