There is nothing new about when technology disrupting human life with advanced concepts to cater to many businesses with one solution. Bot (chatbot) birth as a subset of AI is one of such innovations. chatbot use to support customer services in various industries produced tremendous results by reducing human intervention. This, in turn, saved cost to the tune of 30% as per various studies inducted to measure its positive impact. Be it financial services, banking, insurance, healthcare, retail or travel, the list is endless. Moreover, the chatbots are getting ready to read news and Wikipedia to participate in group discussion and talk shows.
There are a couple of issues need to be addressed to make chatbot more feasible.
- The developers need to develop chatbots capable of handling conversations more naturally like humans. In the current scenario, AI itself is not able to tackle natural language processing (NLP).
- The users are well aware of the fact that they are interacting with a machine and over time chatbot uniqueness would get over if it continued.
QSS strategy to add values to chatbot solutions:
QSS is a chatbot development company, as a technology and IT services provider has executed a number of chatbot development projects for its esteemed customers in this space. The QSS strategized to develop chatbot for addressing specific customer needs by giving due diligence with the following parameters.
The chatbots to sustain amongst current challenges suppose to be designed with simplicity and with limited conversations capability. The good solution is to develop utility chatbots for specific domains to perform specialized tasks, which in turn, suggest users expediting completing their interests in a speedy and meaningful manner.
In the long term, in order to achieve more personalized interactions, future bots can perform this with their enhanced ability to access, process data and respond appropriately by exploiting technologies like neural networks and machine learning.
Self-learning logical algorithm along with graphical database could assist machines better understand and interpret larger data, for what users are requesting, with minimized vagueness. This is also required to further update these semantic graphs dynamically to improvise chatbot response in terms of accuracy and rapidness. This would indeed improve the user experience over time in dealing with smarter chatbots.
- Identifying and recognizing the target audience of customer
- Developing worthy chatbot
- Suggest influencers to the customer for specific segments
- Steady approach for chat content
- Measure and evaluate conversations
- Keep improvising Bot intelligence