FEATURE
A: Essentially, we now operate in an analytics environment where data can be analyzed both in a textual format, as well as the traditional nontextual format.
Within the traditional nontextual format, this is essentially about trying to determine who are our best customers, who are most likely to defect, and what are their likely next actions. This analytics information has always been readily available for those contact centers whose organizations are willing to make the necessary investment in both human capital as well as technology capital.
In the textual environment, the advent of AI has now accelerated the development of new tools which as we all know were pioneered with the release of ChatGPT.
These tools use the discipline of natural language processing( NLP) alongside the mathematics of AI to provide whatever script is appropriate based on the input of the user.
For the contact center rep( the user), this could simply be the conversational responses of the customers. ChatGPT would then provide the appropriate response as one option for the contact center rep.
Of course, the real limitation here is that the tool is only as good as what has been input or fed into the tool. Because of this, many of the bigger or more advanced organizations will build their own custom“ ChatGPT” tools, which are referred to as large language models( LLMs).
For contact centers assigned to a specific product line or if they are a business process outsourcer( BPO), dealing with a specific company and their customers, the use of customized LLMs would always be the preferred approach.
This is easy to understand because the customized LLM is being fed specific information based on emails, texts, and prior conversations with only that specific product line or company. It is easy to see that the superiority of this information input will yield better solutions.
WHAT ARE THE TOP OPPORTUNITIES AVAILABLE NOW AND IN THE WINGS FOR CONTACT CENTERS TO HELP MAXIMIZE THE NET BENEFITS, AND OBTAIN OPTIMAL RESULTS, FROM THEIR DATA?
A: If organizations can overcome the challenges as indicated in the preceding, the benefits are happier customers as their needs are being met. At the same time, we can develop these solutions in a much quicker timeframe.
But the key to all this is having better data and better tools that can use this data for a more optimized solution.
I don’ t want to look at these tools as mechanisms to simply reduce labor costs. Instead, these tools really allow organizations to work with their suppliers to build more and better solutions within a given cost structure.
Rather than being seen as a cost minimizer, the better perspective might be one of revenue enhancement, as we can now do more within our existing labor infrastructure.
As organizations move forward with this technology, the key will be training in not only how to use these tools but how the domain knowledge of the contact center rep can best be leveraged within these tools. This training would consist of using both generative AI( GenAI) tools as well as CRM tools.
Let’ s look at an example. Here I, Richard Boire, call up CIBC [ Ed. note, in Canada ] because I am unhappy with my current mortgage. John Smith at the contact center picks up the phone call and immediately begins the identification process.
Once I am confirmed as a customer, the CRM system then provides information that I am a high-value customer. It also identifies that I am at risk of leaving the bank entirely.
At the same time, it also identifies that the next best service for me would be a line of credit( LOC) for my mortgage.
Based on this information, the rep would input into the GenAI system that I am a high-value customer but also a highrisk defector where my most likely next product should be a LOC mortgage product. The GenAI system then outputs the following prescribed activities, based on its knowledge of CIBC Products and services as well as CRM knowledge:
• Offer LOC mortgage product at the lowest rate possible.
• Review when mortgage comes up for renewal and present options that reduce monthly payments as well as interest payment.
" AS ORGANIZATIONS MOVE FORWARD WITH THIS TECHNOLOGY, THE KEY WILL BE TRAINING IN NOT ONLY HOW TO USE THESE TOOLS BUT HOW THE DOMAIN KNOWLEDGE OF THE CONTACT CENTER REP CAN BEST BE LEVERAGED WITHIN THESE TOOLS."
-- RICHARD BOIRE
But this is not the end of the dialog. As the customer is responding back to these prescribed actions, perhaps with the comment about lower rates at another financial institution( FI), GenAI can then respond with other advantages of CIBC’ s current mortgage product over the lower rates offered by the other FI competitor.
The rep doesn’ t have to use the GenAI script. But they can use it as an aid in delivering a more appropriate response to the client.
OCTOBER 2025 7