The intersection of chatbots and humans

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In modern commerce, humans aren’t the only ones wooing buyers and providing after-sales service. Today, AI-powered chatbots have gained momentum. Human psychological attributes are used to program realistic chatbots that align with our empathy spectra. These futuristic chatbots can provide expert customer support and effectively market products. When human behavioral imprints are used to evolve artificial chatbots, machine learning modalities enable them to exhibit near-human interaction.


Designing algorithms to understand humans behaviour

According Drift’s 2020 State of Conversational Marketing Report, around 25% of consumers used chatbot functionality to communicate with brands in 2020 instead of other traditional tools such as emails, phone calls and social media interactions. With the emergence of terms such as chatbot human relations (HCR), the exploration to replicate human behaviors on computerized chatbots has gained momentum. These relationships operate on the psychological concept of self-disclosure, in which revealing personal information is the key and foundational factor in forging deeper relationships. All chatbots have algorithms designed from a previous set of information disclosed by the consumer, which are built to become more predictive and relevant with each passing interaction. As humans find it easier to express themselves in online chats (instead of face-to-face interactions), more information about them is collected and fed into machine learning algorithms to develop an experience aligned with the human spectrum of empathy.

Equipping chatbots to understand human feelings

Here, a terminology called anthropomorphism join the game. This concept attributes human emotions, intentions, and traits to non-human entities, including objects like chatbots. As humans anthropomorphize, chatbots gradually become useful in meeting their interactive needs. The simple tone and mood language of a text can help chatbots understand the emotional state of the interacting buyer. The pre-populated algorithmic database is designed to contain emotional vocabulary that makes the chatbot good at emotional intelligence. For example, words like joy, excitement, and pleasure can convey that the human counterpart is happy.

In contrast, words such as disappointed, upset, and bad mean the user is confused. Some chatbots go further by using data from cameras and sensors on users’ mobile devices to hone in on their emotional states. Infrared images and dots are used to read facial features and interpret emotions. Additionally, data from sensors such as accelerometers, heart rate monitors, temperature and light sensors are also processed to determine the moodscape closest to the shopper.

The need for human intervention in AI conversations

While not everything can be automated, it is also not advisable to rely entirely on AI mechanics. Human interventions are needed to verify the outcomes that AI algorithms predict on their interacting counterparts. Although AI has progressed over time, it has not reached a position where full confidence can be placed in its predictions. Whereas some reports state that prediction by face recognition AI methodologies is up to 96% accurate, a chatbot has its limits. Not all use facial recognition features, as requesting access to the camera may be flagged as a privacy issue by many users. In such cases, for the most accurate predictive interaction to qualify as true, human operators must analyze the AI ​​output. If something is out of alignment, the learnings from it are taught manually and the system is augmented to evolve further.

Importance of empathy when designing conversations

Empathy is the fundamental human trait that unites us. Without an empathetic trait, we wouldn’t be able to build longer, stronger relationships. The same goes for chatbots. To be more relevant to consumers by meeting their needs, machine learning and AI algorithms must be programmed to pick up empathy features in language. For example, a chatbot that can remember your favorite foods, favorite places, and current state of mind can provide a more comforting user experience. Such algorithms are very effective in the e-commerce industry, where proper predictions and suggestions can catalyze sales of relevant products. For example, in a food delivery app, if the chatbot learns that you like your pizza with garlic bread for lunch, it will automatically swipe those suggestions the next time you’re ready to buy.

Conclusion: a futuristic perspective

Conversational AI is the need for modern sales, and more emphasis has been placed on chatbots powered by machine learning tools. The more predictive a chatbot is in understanding the consumer’s emotional mood, the better and more relevant it becomes for consumers. While the disruption of chatbots in modern sales isn’t just driving down monetary spend, it’s become a new marketing tool.

Sharon D. Cole