Over the last five years, the conversational intelligence industry has undergone a significant transformation.
And it’s all due to the advancements in artificial intelligence (AI).
Five years ago, the market wasn’t saturated like it is now. If you google “conversational intelligence software”, you’ll see pages and pages of companies now offering a solution.
The industry has become more sophisticated, and more reliable, and is being used on a daily basis among sales teams, customer service teams, and more.
We’re taking a trip down memory lane to show you how far this tech has come.
Buckle up.
The tech behind conversational intelligence
The word AI is being thrown around with every company and most people don’t realize the depth of the technology.
Natural language processing (NLP) is a subset of artificial intelligence that fuels this type of tech.
NLP algorithms have improved the ability to interpret language and understand the context of conversations. Hence why the conversational intelligence industry is now able to do so much more than five years ago.
Where detection of words and phrases used to be the main focus, it has evolved to understanding intent and sentiment analysis.
Think of the complexity behind that. Every word may sound different based on who is saying it. That means technology will get to a point where it can understand the difference between you and me when we say “ok, that would be great”.
Maybe I’m ready to move forward, but you were more hesitant in how you said it. Those subtle differences can give cues for the best next steps based on that situation.
Mind blowing stuff.
Future of conversational intelligence for CS teams
Gartner forecasts that by 2026 conversational artificial intelligence (AI) deployments within contact centers will reduce agent labor costs by $80 billion.
Let that sink in for a second.
Now the good news is that this tech still needs to mature, and it can be very expensive to deploy such an operation.
But we have an example of how our product, Abstrakt, is helping fewer agents produce a higher volume than those agents without this type of technology.
Here is an example:
| Calls | Before Abstrakt | With Abstrakt |
|---|---|---|
| Average handle time | 8 | 5 |
| Calls in an hour | 6.25 | 10 |
| Calls in a day | 60 | 96 |
| Calls in a month (22 working days) | 1,320 | 2,112 |
| Calls in a year (includes 2 weeks PTO) | 15,180 | 24,288 |
| Average customer care salary | $40,000 | |
| 3 Agents w/o Abstrakt | 2 Agents w/ Abstrakt | |
| Calls in a day | 180 | 192 |
| Calls in a month (22 working days) | 3,960 | 4,224 |
| Calls in a year (includes 2 weeks PTO) | 45,540 | 48,576 |
| Costs of Agents | $120,000 | $80,000 |
| Cost of Abstrakt | $2,160 | |
| Total Cost | $120,000 | $82,160 |
| Total Savings | $37,840 | |
| Difference in call volume | 3,036 |
We have customers doing this every day at scale.
While customer service teams won’t be completely replaced by conversational intelligence tools, they will absolutely be using them to do more with less.
Future of conversational intelligence for sales teams
The amount of companies not using this type of technology for their sales teams is dwindling rapidly. Whether it be call centers, B2B software, healthcare, or even financial services.
Conversational intelligence software is a no-brainer when it comes to helping your sales team hit their numbers in this market.
Here is how teams are using it:
- A living repository to clone your top performers with all of your battlecards, playbooks, and objection handling techniques.
- Live, real-time guidance on calls or demos when they take a turn your team wasn’t expecting.
- Automated note-taking, coaching, and feedback for every call to help managers and reps improve on the spot. No more waiting for your weekly (or even monthly) coaching sessions.
Because we love numbers, here is just another example of how our customers are exceeding their numbers by using a conversational intelligence tool like Abstrakt.
| Calls | Before Abstrakt | With Abstrakt |
|---|---|---|
| Calls per day | 110 | 110 |
| Dials to connect | 15.78 | 15.78 |
| Connects per day | 7 | 7 |
| Conversions per day | 1 | 2.1 |
| Connect to convert rate | 14.29% | 30% |
| Conversion to Qualified Opp | 45% | 53% |
| Qualified Opp to Close within 90 days | 27% | 29% |
| Monthly Results (~23 working days) | ||
| Connections | 161 | 161 |
| Conversion into Discovery Meeting | 23 | 48.3 |
| Conversion to Qualified Opp | 10.35 | 25.60 |
| Qualified Opp to close within 90 days | 2.79 | 7.4 |
The focus of conversational intelligence
While companies would love to go all in on conversational intelligence (while others are the opposite), the end goal remains on the customer or prospect.
The Harvard Business Review released an article touching on how to improve the customer experience with AI.
The conversational intelligence industry should focus on the three R’s as they would say – recognize, request, and respond.
Technology like this should be used to complement your company’s capabilities, not substitute for them – even though that could change within the next five years.