Post-call text notes are the unstructured data notated by your call center agents after every interaction with a customer. These notes are typically saved in CRM or call center systems after every interaction, along with other metadata, such as call reason.
Post-call text notes are a treasure trove of deep insights about the wants, needs, and pains of your customers. Effectively utilizing them can work wonders for your business by increasing customer retention, boosting customer profitability, and reducing revenue risk.
Most importantly, this piece of data contains insights into real emotions of the customer, along with many other insights.
Let's consider an example scenario. Suppose a customer, Mr. Jon Doe calls to complain about billing. The structured CRM fields record the call reason as billing issue.
However, this misses the fact that he may have also called multiple times before for the same issue. He may have spoken about your competition, or inquired about pricing or contract expiry date.
In fact, one minute earlier, when the agent was actually speaking to the customer, the customer is also telling us why he is calling by punching buttons on your IVR, or speaking to the IVR main menu, which most companies are not utilizing.
When customer value leaders rely on structured information, such as billing data and call reasons picked up from a drop-down list, they are missing out on so many other clues about the actual emotional state of the customer in the context of the experience he or she is being subjected to by your company.
Why developing clear customer insights is hard?
Contact centers are the nerve centers of customer interactions. Every year, customers approach contact centers millions of times to voice their grievances, ask for information, or request support.
These notes are perhaps the largest stash of human-interpreted member experience information, waiting to be leveraged.
Lack of appropriate technology: Analyzing the notes needs advanced text analytics, and piecing it together with other contact center metadata, customer billing, and CRM info, needs advanced machine learning technology, due to the complexities and volume. Most companies lack such technological capabilities.
Lack of trained resources: Working on this data and connecting the dots between insights and business goals requires a high level of data scientific proficiency, domain expertise, and experience, in order to see the big picture. Acquiring such expertise is beyond the reach of many companies.
Lack of processes that allow inter-functional collaboration: Post-call text notes and other customer interaction meta-data typically resides within the operations or customer care department while sales, marketing, customer experience etc. have their own separate hierarchies. This prevents many companies from driving collaborative leveraging of this data source, which can serve customer value use cases across the enterprise.
Unify other customer data sources, such as billing, CRM, surveys, speech-to-text, chats, support tickets etc., and enrich them with insights from the text notes.
Leverage advanced machine learning to predict future call reasons, as well as churn risk levels.
Drive multiple proactive engagement strategies to retain your high-risk, high-value customers.
Drive improvements in retention and loyalty, NPS, sales center performance, marketing ROI, upsell and cross-sell, customer service experience, and cost-to-serve.
VOZIQ's predictive retention leverages Behavioral Intelligence to deliver breakthrough churn risk intelligence.