The unprecedented outbreak of the COVID19 pandemic has caused many businesses globally to scramble to connect with their customers digitally. Many deeply introspected their individual approaches toward novel and adaptable forms of customer experience management. If there were a silver lining in this incident, it would be that this crisis has led the future of technological innovation to be preempted sooner than was ever expected. For many institutions and brands alike, analyzing digital interaction and assessing the ratio of digital dependency for daily sustenance such as banking services or e-commerce became the essential first step.
At Locobuzz, many of our clients faced volumes of customer concerns that were treated as critical, and logistical pain-points for the brand managers. Hence, our first step was to use this large influx of data to our advantage and strengthen our AI technology to build seamlessly accurate sentiment models.
If we have to apply the foundation of Machine Learning in daily, people-centric businesses, we have to consider constant change, whether evolving markets or customer preferences, CXM becomes highly dependent on the ability of AI technology deeply understanding different customer’s intentions. In ML terms, it becomes important to train AI to understand the difference between two responses that carry the same words, but can mean totally different things. For instance, someone enquires with a bank for a loan
Introducing the Smart Reply functionality is Locobuzz’s one of million ways to reduce an ORM agent’s biggest woe: TAT reduction. However, it also helps in segregating responses into different categories. It starts with the AI being fed responses that are bifurcated by query. Smart Reply not only helps by providing quick responses, but uses sentiment analytics and ML to efficiently increase the accuracy of the responses. But as the first example demonstrates, it’s not as easy as that.