fancybox fancyBox GP_Custom_Forms GP_Custom_Forms_RateIt
Menu

AI Makes It Possible (Blog Series)

Access the On-Demand Webinar Playback of  “How AI-Enabled Super-Agents Improve CX” with Kate Legget, Vice President and Principal Analyst Service Application Development and Delivery, Forrester Research, with Steve Nattress, Director, R&D and Jacki Tessmer, Vice President – Product Marketing, Enghouse Interactive.

Blog #2 of 3: With context, AI seeks to improve more quickly than ever.

Constant Learning Improves AI Recommendations

Natural Language Processing (NLP) is a key focus area, where “Conversational Analytics” can become the differentiating factor, helping AI to deliver the best possible solution.

Gathering, Aggregating, Assessing: Analysing all feedback via AI

Customer Feedback takes many forms, and most are relatively static and easily processed: surveys, feedback forms, polling questions and so on, all correlated to the customer’s continuity of loyalty. However, this information only provides part of the full understanding you need. The real data is buried in every conversation that takes place. Whether part of a query, a phone call, buried in the CRM system or posted to social media or online groups/forums, the sum of all these interactions gives us the whole story. Only recording, assessing, analysing and then aggregating all data from each customer engagement can provide a robust baseline of information to work from.

  1. Gathering – For effective agent support you need to capture (record) interactions in real time along with a recording of the agent’s screen activity. This will allow you to understand the agent’s side of the engagement to help understand issues they may have faced and ensure more comprehensive support is provided while improving first call resolution (FCR) and driving support time reductions.
  2. Aggregating and Assessing – The AI must use an appropriate industry-specific lexicon to provide a solid baseline against the expressions, phraseology and word combinations that the agent uses. Without this it cannot identify nuances in intonation, phrasing, directness, repetition, and various stress indicators that provide a substantive layer of context to the information gathered – otherwise it risks distorting results.
  3. Analysing Linguistic Analysis employs specialised algorithms alongside the above-mentioned industry-specific terminologies and phraseologies. Additional standardised baseline data sets allow AI-enabled platforms to then ‘listen’ to conversations to specifically understand what is being said, how and even why.

With additional algorithms and speech pattern recognition and tone analysis, AI can also help identify the customer’s overall sentiment regarding the current situation at hand. By leveraging the situational intelligence gathered and blending it with past customer choices (made in the same or similar situations), AI can then recommend solutions – to agents or the customer directly – that have the highest probability of resolving the problem.

With a dedicated focus on ensuring that this feedback loop is continually optimised, AI will increasingly be able to propose combinations of actions, in the best possible sequence for every situation, from an ever-increasing range of options, resolving situations with reduced customer aggravation and time spent.

Optional: Interestingly this capability – an element of voice-biometrics – can also be used as an additional level of customer account security, helping to identify if a customer is being forced to place some type of sensitive transaction or if they truly are who they say they are.

By extension, Al is also an excellent tool to determine if a customer is under stress of some sort or not, providing an additional indication of whether they are ready to, or contemplating, leaving (churn).

New Data: Refining the Models

Ultimately, the objective is ensuring that what is being said is completely understood, whether it’s being said directly, sarcastically, satirically, or using other nuances to convey the real message.

Keep in mind that, in order to properly contextualise the customer’s experience, each engagement should be looked at as a single experience, firstly against the aggregate of all customer interactions and then secondly as part of a collective group experience, in order to be able to improve everyone’s journey. Agents can’t do that themselves and even administrators can only do it manually with immense and prohibitive overhead.

Insight comes from Reinforced Learning and Control – Using fully aggregated data

In summary, viewing the customer journey holistically ensures that the organisation can better understand what meets the customer’s expectations – and most importantly, what doesn’t – enabling the organisation to better align its entire enterprise to deliver value to both customers and the business. 

Line Divider


Watch for our next Blog: “Key Learnings from our Webinar with Kate Legget of Forrester and Steve Nattress of Eptica”

Line Divider

Help make sure your organisation delivers the customer experience that exceeds their expectations. Doing so, will transform your contact centre from a cost-centre into a revenue generator.

Access the On-Demand Webinar Playback of  “How AI-Enabled Super-Agents Improve CX” with Kate Legget, Vice President and Principal Analyst Service Application Development and Delivery, Forrester Research, with Steve Nattress, Director, R&D and Jacki Tessmer, Vice President – Product Marketing, Enghouse Interactive.

See how your organisation can benefit from Super-Agents

Click here to view the playback: