Chatbot Data Analytic — The Next Big Thing for Business Optimization
Technologically, the Chatbot is developed to understand human behavior practices. As a part of self-learning process, they can make systemic records of data, metrics, preferences and trends that eventually help them in monitoring the user interactions, adapt the relevance and responses accordingly. Fortunately, this characteristic enables the Chabot to play a significant role in the area of Data Analytics as well and Chatbot development companies try to leverage best strategies, tools and technologies for the same.
Chatbots and Current Data Eruption Scenario
In this era, Chatbot dynamism is even more vital while the data eruption keeps moving towards higher and higher points. For example, according to an IDC Digital Universe study, the amount of digital data created per year would be 35 zettabytes by 2020. Further, that number was bumped to 40 ZB, and then to 44ZB considering the AI and IoT factor. In another study, IDC estimates that by 2020, business transactions (including both B2B and B2C) via the internet will reach up to 450 billion per day (and they will generate massive amount of data too).
Both technology and business world are exploring the utmost opportunities to analyze this data for the generation of meaningful and actionable insights leading to strategic business decisions. Being the part of those transactions or interactions, the extended functions of bots in the field of analytics make them even more phenomenal.
Chatbots and Data Value
The first business arena of the Chabot in context of data and analytic is, the user directly interact with data itself leveraging the interface of bots. Thus, the efficient bot architecture incorporated with data analytics capabilities delivers remarkable business value by providing operational experience, customer experience and analytics.
The second arena is, automated data collection. During the conversations, bots ask for more information that clarifies the user’s inclinations and preferences. Even several financial organizations such as banks use bots to automate their data look-ups. This attribute can be erxtended to information collection as well, for different purposes. The collected data can be processed and analyzed as per requirements.
Chatbot — Stages of Maturity in context of customer experience
Several companies use the Chatbot interactions to understand the customer’s preferences or choices. They use algorithms to ask several questions, and based on the answer they can sense the customer’s choice. Then it becomes easy for them to suggest the products. Whereas many companies use bots to increase user engagement leading increased customer acquisition as well as to automate processes as overall cost effective monetization. Depending on the purpose of action, there are three stages of Chatbot maturity in context of customer experience.
- Informational Interactions:
These bots understands natural language and respond the questions asked. They provide less interactive customer experience but good to go for FAQ responds or similar types of conversations.
2. Personalized Interactions:
Connected with relevant systems or applications at back end, these bots can generate user-specific responses. They ask simple questions as well to recognize the user intent. The interactions sound like more human here and there is possibility to deliver the amazing customer experience in such cases.
3. Transactional Interactions:
These intelligent bots are meant for helping users in completing the particular task, action or activity through a series of instructions or interactions. Connected with relevant back end system, they also can integrate the customer data. Point to point simplified instructions and keeping it quick and interesting for user is the key here to provide remarkable customer experience.
Chatbot Analytics and Data Interaction
In this connected world, B2B or B2C customer interaction landscape is changing in a big way and that too very quickly. Moreover, the Chatbot has already become a considerable part of remodeling. As the Bot carries out direct interactions with the end user, it has a bigger responsibility to fulfill in cultivation of new and growing data sets that also includes the business critical data.
With growing potency of Artificial Intelligence and Machine Learning, the scope of the Chatbots is not limited just as Conversation Agents or Virtual Assistants anymore. Businesses have started pondering over the kind of Bot Strategy in which they can align their bots with customer experience and data technology stack in the best possible manner.Below is a sample analytic report of a Cab Booking chatbot application helping the organization in understanding their user behaviors.
Chatbots and Predictive Analytics
In general, Predictive Analytics is all about observing and analyzing the ongoing as well as prior data based facts and to predict about the future. Even simple observations to Chatbot data can address the business queries such as what is happening in the context of customer behavior? Further, the analytic can help in finding out why it is happening and what may happen next?
Predictive Analytics relies upon various statistical techniques such as data mining, predictive modeling, and pattern matching and ultimately machine learning. Efficient usage of relevant techniques and algorithms for bots make it easy to obtain the kinds of insights that can optimize not only customer experience but nearly all aspects of the business.
Advanced Behavioral Analytics
The Chatbot enables the business to go much deeper and broader in their data analytics. Integrating advanced behavioral analytics technologies to Chatbot is a prevalent practice these days. May be as standalone software or as a built in feature, even Robotic Process Automation functions well, and provides tailor made solutions for customer experience analytics. As a result, companies can define the new heights of customer experience to use it as a competitive advantage factor.
Below is a basic report displaying ‘Key Query’ indicators of a user journey leading to notable user preference insights over the time.
Of course, the current bots will experience several intensifications in near future as a part of evolution process. For example, increased accuracy, tools and technologies beyond machine learning and having a right and high-yielding role for human individuals in the loop. As a result, bot technology will be able to place itself at more purposeful magnitude.
Conclusion:
Bot analytics enable us to understand the customer behavior, what drives them in making relevant decisions, what disappoints them, what makes it easy to retain them and more. Hence, there is no exaggeration is saying that in future bots and Chatbot development strategies both will have a role in enhancing or even restructuring business processes.
