Imagine the power of real-time, actionable, feedback on how every call is going. Imagine no more. Introducing Engage AI, the tone-based agent guidance and conversation analytics software, that supercharges performance for agents and call centers.
We believe the big potential in AI is to empower people, not replace them. Engage AI allows you to understand sentiment in a completely new way, through tone of voice. It increases agent engagement, brings teams closer and works in complete harmony with your preferred contact center platform.
Research on over 1.5 million real-life calls has shown that using Engage AI improves the experience for both customers and agents1
You have better things to be doing than trying to integrate IT. So, we made the installation of Engage AI as simple and seamless as possible, across all the leading platforms.
Now you can get on with the rest of your day, safe in the knowledge that in one simple step, you’ve stepped up your customer satisfaction scores. Easy.
See for yourself by booking a demo.
Find out more below
Why is Engage AI better at measuring customer experiences?
We’ve found Engage AI to be the best solution for measuring customer experiences for 3 main reasons:
How does Engage AI compare to established ways of measuring customer satisfaction?
Engage AI scores every single call, unlike survey tools that only have a 5-20% response rates and are often biased. Engage AI allows 100% measurement. Typical call monitoring/review by QM only selects a few random calls per agent. Engage AI allows all parties to see the whole picture instead of relying on the “luck of the draw”. And generally, if you evaluate customer sentiment or engagement based on words you have to literally evaluate every sentence. Most sentences will come across as neutral, but their tone will reveal sentiment and engagement quite easily.
Is Engage AI for contact centers only?
Engage AI is designed for people who are speaking to customers, so it is also for e.g., financial advisors that would not typically refer to themselves as “contact center agents”, but where calls have high value and where there is typically some mechanism for ensuring customer consent to analyze the call.
Why is Engage AI a helpful tool for the agent?
Engage AI boosts motivation by giving praise when its warranted – for instance if an agent is delivering their best tone of the month. This can break the sometimes monotonous and tough days. This kind of praise is particularly valuable in hybrid work where supervisors lack the visibility to such moments.
Moreover, Engage AI finds in the data that agent’s tone levels vary throughout the day. For instance, many have a drop right after lunch. Engage AI gives them a small reminder if this happens and helps to pick themselves up and get back on track – and thereby lift the customer experience.
Finally, Engage AI gamifies the call experience by showing real-time avatars of the customer and agent where the sentiment and engagement of the avatars change based on the conversation flow. This delivers an experience you could call “video call without video” that – like a video call – can feel much more personal and engaging – and lift the performance of the agent as a result.
What is the difference between speech-to-text technology and the tone-based technology within Engage AI?
Speech-to-text is a very useful technology in the contact center. By transcribing all the conversations, it can provide a lot of value in terms of contact center automation (by automating the most frequent flows via chatbots or self-service), knowledge base improvements, voice of customer analytics (what are customers calling about), etc.
When it comes to real-time detection of sentiment and engagement, speech-to-text cannot detect the difference between a highly energetic and friendly sounding agent and someone using the same words. And it is very challenging to categorize sentiment and engagement levels in real-time using the words spoken. And finally, it is much harder to scale speech-to-text across different languages, whereas our technology works with any language.
How does Engage AI handle sensitive data and comply with GDPR?
Engage AI does not identify, or attempt to identify, the caller. Engage AI also does not link records for the same caller. Engage AI does not measure and record the words of the customers. Engage AI only measures customer sentiment (from “frustrated” to “happy”) and agent tone (from “low energy and unfriendly” to “energetic and friendly” on 0-10 scales during the call. We do our best to guide customers to correct product usage but ensuring the right level of compliance across their systems landscape is ultimately the customer’s responsibility.
How do I deploy Engage AI?
Engage AI is a software-as-a-service solution managed by Jabra. Each user in your organization will need to have a client application installed, which can be easily set up through MSI.
Will the data from Engage AI integrate into my existing systems?
There is a lot of value in the interfaces we provide directly, but of course you can take the data out and review it in your preferred BI/analytics systems. For this purpose, we have created a very simple data API to extract the data.
Will Engage AI work in any culture, language and/or dialect?
Yes! The AI model has been rigorously tested in most languages and accents and we have not been able to find any noteworthy biases at this point.
1Increase in CSAT and call length reduction based on the analysis of the top 10% of 700,000 Engage AI calls and measured when the agents perform at their best, for example, when their tone is in the top 10% of their calls. Improved data accuracy benchmarked against open source text-based sentiment analysis tools from Microsoft, Google, Amazon, BERT, and Assembly AI. Improved agent motivation and agent-supervisor connectivity based on Engage AI user surveys, 2022
2Based on Engage AI user surveys, 2022
3Based on Engage AI user surveys
4Average impact for agents when delivering their best tone
5Engage AI customer survey targeted agents and supervisors Based on data from Engage AI customers with A/B testing of agents. Samples of agents with outbound tasks (conversion rate) and inbound tasks (AHT)