Conversational Analytics – is your chatbot fully reliable?
- January 23, 2020
- Posted by: Subham Paul
- Category: Blogs
Gone are the days when users of software applications would always rely on text commands or graphical user interfaces (GUIs) to get their jobs done. Instead, we seem to have gone back to older times when errand boys would listen to our instructions and accomplish the tasks on our behalf. That’s right, this is the age of conversational user interfaces and enterprises have rapidly adopted chatbots that continue to benefit the businesses and users alike.
Analyzing conversational data
Whenever a user chats or speaks (i.e. converses) with an application interface, some data gets preserved in the back-end systems. This conversational data is collected in real-time, it becomes a great source of analysis of customer behavior. It helps businesses to derive general conclusions about their services as well as chalk out personalization plans for their individual customers. Moreover, metrics related to users, messages and bot performance aid in continuously improving the services of the organization. We shall look at some of these metrics below:
- User metrics – total users, active users, engaged users, and new users.
- Message metrics – conversation starters, sent message count, received message count, missed message count, total conversations, and new conversations.
- Bot performance metrics – retention rate, goal completion rate, goal completion time, fallback rate, user satisfaction, and virality.
(for a detailed explanation of these metrics, read this article)
Chatbots all around us
Advancements in AI, Machine Learning and Natural Language Processing have made it possible for organizations to deploy chatbots as customer interfaces as well as consumer goods. 20 years back, nobody could have fathomed someone using an Alexa for daily uses, nor could someone have imagined non-descript websites presenting a chatbot offering assistance right after opening. The convenience offered by chatbots makes them a champion in customer service and seal their existence for a long time to come.
Conversing with a chatbot for the first time is as good as talking to a stranger. Would you share private information? While this is subjective, there do lie vulnerabilities associated with chatbots (both text-based and speech-based). Integrations between the chatbot’s server and other applications are created so that the data is shared between applications for further analysis. This can have loopholes that can be exploited by hackers. It is crucial for an organization to ensure that its chatbot is reliable and complies with all security requirements, lest it should jeopardize customers’ trust and tarnish the brand value of the company.
Here are some ways by which the security features of a chatbot can be ensured:
- Data specific to the user and the conversation should be deleted. Temporary data should be encrypted for usage during the conversation only.
- Secured access should be guaranteed using token-based authentication.
- Authentication tokens should have a specific expiration time (such as 60 minutes) and all incoming requests must comply with secured protocols so that malicious activity can be detected.
- The chatbot’s architecture must be protected from cross-site scripting.
- Sticking to the basics always helps. The chatbot’s system must be built on an N-tier architecture so that every layer meets its reliability standards and the overall system stays robust and secure.
It is always good to have someone to talk to, rather than having a machine to use. Chatbots have made our lives simpler and helped businesses flourish. However, bad actors are always in the look for suitable entry points. With added security, we can certainly enjoy a more hasslefree experience with conversational platforms and live the future that we envisaged decades back.