If your company meets customers face-to-face or on the field, this isn’t for you.
But if, like many others now, 80% or so of your interactions with leads and clients go through some form of email, phone or videocall, you might want to have a look at Natural Language Technologies.
When you think about it, those phone calls, those 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. This is unstructured data we are talking about. Basically, a large flow of text and voice, but nothing your system can easily make sense of. Most companies don’t even have 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 analyze 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 3 different ways:
What we call Conversational AI is the creation of human/computer conversations using voice or text. You basically 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 deeper look, RASA wrote an amazing article on the matter.
A quick tech guide – Conversational AI has 3 main components. You want to look at those three to build a solution that fits what you need.
Now let’s keep going.
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 to store all client interactions under the same format. Much easier access, tracking, and the possibility to analyze 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. Helping your reps decide on the best course of action accordingly.
Some things can be automated as well. 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 customization. You can adapt it to any use case.
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 a lot of unspoken hints, that tells a lot about a buyer’s intentions. So, you can understand what part of your process is key to win and keep 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 actually say.
Your company is probably already handling a lot of data. You might even call yourself a data-driven company. And that’s probably true. But just take some time to consider if, in all that data, you have the most valuable of all, the voice of your customers. If not, Natural Language Technologies are not that difficult to implement.