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Actioning Customer and Employee Experience Data with AI

December 20, 2023
7 min read
Actioning Customer and Employee Experience Data with AI
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Read Summary

  • There is a lot of untapped value in the data companies already have, like transcripts, recordings, emails, and chats. The challenge is accessing and analyzing it.
  • AI tools can help analyze large datasets to identify trends, patterns, and insights (e.g., employee sentiment analysis).
  • Modernizing legacy systems and making data accessible to AI is key to unlocking the value.
  • AI assistants can quickly synthesize data to have more informed conversations (e.g., managers' preparing for 1-on-1s).
  • Reducing time spent searching for data can help employees stay focused and productive. AI assistance enables more proactive work.

Read Transcript

We have these recordings. We have these transcripts. We have this content. What can we do to get more value out of it? Because you mentioned the value of the data and the factuality of it.

But are there other scenarios that you could see or are seeing that are really helping with both customer experience and employee experience today?

Look, one big one, and I just had a conversation and saw some pretty powerful things this morning with a company we know is all around just sentiment analysis and unlocking and getting a sense of the vibe, the sentiment, if you will.

From emails, chats, voice conversations, and meetings down to what topics are evoking what sentiment, how different departments are feeling about certain things that are going on, I think is massive and will be, because again, today, all of this is very anecdotal.

Oh, I heard this is not going well, or that person said there's an issue over there, but there's no real way to substantiate any of this stuff unless you have a ton of one-on-one conversations, which organizations do, and we will continue to. But pulling out some of the basis of there's actual data and facts happening around this stuff, I think, will be massive to investigate.

Hey, if there's smoke, is there fire, or is it not really a big deal? And there needs to be an issue to validate when this thing comes up. But if you're worried about employee engagement or some people are leaving the organization, there are some real ways that we can use this data to identify issues and help address them and correct them before they become big ones.

Yeah, I love that example. Let me use a really quick. This is a quick demo of Viva, Glint and Copilot. One of the problems with Glint historically is in a large organization of like, say, a couple thousand people, you might get hundreds or large organizations, tens of thousands of comments. Feedback items in an employee engagement survey.

That's really hard to rationalize. But being able to use AI to reconcile these large populations and figure out what are the trends, what are the patterns, and talk almost naturally through it means that you can be on a meeting now with like a department lead, somebody who can action these things, who wants this data right.

You can actually, as HR, have that conversation. When they ask you things, you can potentially get an answer really quickly and verify. You should probably look after the meeting's done, double check your transcript, and make sure that insights and observations you made were correct when you have more time to really rationalize over it.

But at the end of the day, this means that there's a lot more opportunity for us to respond faster and to be more informed. To your point, even when the data sets are in the tens of thousands, or in sometimes cases millions of records and things like that. So it's really interesting.

This is one example with Viva Glint, but there are lots of these kinds of scenarios where we can use it to reason over a much larger set of data. I think that, to your point, it's not that we don't have this data; most of us do in different systems. It's unlocking that. So making sure that these AI systems can access it, making sure it's modern, that's important.

A lot of times, we have this data in older systems that are a bit more legacy. We need to modernize them and make them accessible by AI. And then I think once we do that, there's so much opportunity, we kind of need to then prioritize it a little bit better.

I love that example you gave. I wanted to show a really quick visual of how that works, but you just said something that I think is really important and that, I don't know if we think about it when we're coming through with these types of AI conversations. Like, our problem today is not that we don't have data.

Everybody has more data than they know what to do with. It could be. But getting access to that stuff, that is what is so difficult. And that's why we don't use, probably most of today, because to do it, you need ten analysts, and you have to ask the right questions and still pull it out into a spreadsheet and work it out.

By that time, you kind of lost interest already. Or it's at least something that you can't do consistently.

Having access capability to kind of mine this data and pull out the stuff that's important the way you want it, by simply just asking a question for it, that is huge, and that lets us move fast, be productive, and make this stuff real. That's the beauty of this.

I think you have a good point here, but, no, I think it's really perfect. It's exactly what you see in this example here. Right. It's this idea of you do like a natural know.

I want to know what's going on in the store, openings in Phoenix, or whatever your query is. What happened with Fabricam last week? I have three direct reports. What happened with Judy in the last week? What results, what work was completed, all that sort of thing.

Then it pulls all those activity feeds from SharePoint, these files were updated, it synthesizes that. Now, when I have that meeting with Judy, let's say I can be as a manager or leader, it can be a very different meeting because I don't need to be like, again, low-value collaboration.

I don't need to say, hey, what happened with X? Or what have you been up to? I can just say, hey, I think you've done this. This is what I observed. I think there's more of this kind of work coming. How can I help prepare you for that? So again, it shifts everything to more proactive.

That agency is critical. And I just thought, hey, I have a visual of that. Let's use that for our reference. And like even to this now, how are we unlocking the value from this stuff, getting this quick access?

I read a study, I think it was the other week, it was a McKinsey study, and they said every employee spends about 2 hours a day just searching for data and gathering data. I mean, it's such a waste of time if you think about it. We have no choice today, right?

But that's where things get a little bit dull, and then it's easy to get distracted. Then I could check my social media feed because 2 hours of doing that kind of stuff, I know it's not consistent 2 hours in a row, but when I'm in the zone of working, and I'm looking for information to be able to query something, get it and keep moving on, like in that flow of work, I think is a big buzzword these days. Yeah, in the app I'm using and the experience I'm using, absolutely.

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