Conversational artificial intelligence (AI) is becoming a standard fast. Industry giants are pioneering the trend, deploying virtual- and voice assistants and AI-powered chatbots.
Apple’s Siri, Microsoft’s Cortana, and Amazon’s Alexa are the best-known examples.
How will this technology impact customer experience going forward?
Conversational AI in a Nutshell
The term “conversational AI” is as straightforward as it gets. Behind the scene, it portends human-computer interaction through speech or text. The more sophisticated the app is, the more natural interactions will be.
The technology is evolving rapidly, with more and more businesses using it in their day-to-day operations, notably in customer service.
AI Chatbot Impact Is Yet to Be Evaluated
Customer service is taking big strides toward the future, with artificial intelligence (AI) changing processes and approaches.
Novel frontline trends necessitate the deployment of AI chatbots, which combine natural language processing (NLP), cloud, and machine learning (and, in some cases, biometrics as well).
However, the hype has yet to materialize into palpable results. Evaluation of AI chatbot service quality is still hindered by the lack of relevant evaluation instruments.
That’s why understanding conversational AI has become a necessity for businesses hoping to keep customers in the future.
No doubt new tools to aid in the attempt will keep emerging but, for the time being, take all stats with a grain of salt.
What is certain, however, is that these tools can address a number of critical issues, notably the trend pioneered by millennials, who look for dynamic web-behavior.
Another obvious benefit is that customer service is available around the clock. Presently, AI chatbots can’t address personalized issues, but they’re developing fast to address this critical point.
Can Conversational Intelligence Mitigate App Fatigue?
On the other hand, mobile users are increasingly experiencing the so-called app fatigue. As the term portends, it is the trend among computer and mobile users to lose interest in using new apps.
The chief reason for this is the vast number of apps that have managed to become counterproductive. It is becoming increasingly difficult to find new tools capable of meeting customer needs without being overbearing.
The most illustrative example is the rising number of instant messaging service users, as opposed to social network users’ information overload at its finest.
As a direct result of the trend, businesses are witnessing decreased retention rates. Using conversational intelligence for sales has become critical, urging corporations to focus on promising AI-powered tools and setting up viable data-driven strategies.
Outlining Meaningful Customer Relationships
With even human interactions becoming a case study for digitalization, it is more important than ever to stick to human sentiment. In corporate terminology, this translates into building meaningful relationships with customers.
To achieve this goal, businesses need to re-think their engagement strategies, which have been greatly altered due to digitalization. Digital interactions have taken over; “humanizing” them has become the greatest focus.
Customers Are Becoming More Demanding
The other side of the coin is that customers have become “spoiled” thanks to new tech. Research shows a significant change in customer behavior, driven by the instant gratification trend.
Customer service is particularly challenging, as people have less patience to wait for an agent to pick up. AI-powered tools have been deployed to alleviate the issue but, as mentioned above, they have yet to achieve optimal levels of self-service.
In the meantime, businesses are focusing on more intuitive customer care, which is rooted in the following parameters:
- Faster issue resolution
- Personalized self-service
- “Humanized” customer interactions
- Lower rates of customer issues
- Higher customer engagement scores
- Extended customer lifetime value
Types of AI-Powered Apps
It’s always challenging to attempt the systematization of new tech in development. However, due to significant gaps in AI usage and implementation in industry and services, a certain distinction needs to be made in order for businesses to be able to tell the forest for the trees.
To this end, we’ll use the classification by Shai Rozenes (Department of Industrial Engineering and Technology Management, Holon Institute of Technology, Holon, Israel) and Yuval Cohen (Department of Industrial Engineering, Afeka Tel-Aviv College of Engineering, Tel Aviv, Israel), who propose the following “hierarchy of AI techniques, tools, methods, and implementations into three distinct levels”:
- General AI techniques
- Domain specialization AI techniques
- Application-tailored AI solutions
General AI techniques can be deployed for all kinds of purposes and may include “pattern recognition, data mining, machine learning, deep learning, rule-based reasoning, fuzzy logic, expert systems, etc.”
Domain specialization AI techniques focus on a single domain specialization, e.g. natural language processing (NLP), speech and tone recognition, face and emotion recognition, gesture recognition, and case-based reasoning (CBR).
Application-tailored AI solutions address specific user needs.
Domain Specialization AI Techniques
Of these three, domain specialization AI techniques can benefit corporations greatly, as they allow for meaningful synergies, according to the same authors.
They propose that “in the context of service provision, the main identified AI clusters” are:
- Speech-related cluster (focus on speech analysis and generation)
- Text analysis cluster
- Emotional recognition cluster (focus on recognition of emotions)
- Collaborative cluster (focus on human-computer collaboration)
- Computer vision cluster (focus on analyzing pictures, photos, and videos)
- Awareness cluster (focus on awareness capabilities, “such as self-awareness and context awareness”)
Classifying the Service Quality of AI Chatbots
A proposed methodology to classify and measure the service quality of AI chatbots in the frontline needs to take into account seven service quality measurement dimensions, as follows:
- Understanding meaning (explicit and implicit) and the emotional implication of the text
- Close human-AI collaboration
- Human-like behavior
- Continuous improvement
- Culture adaption
- Responsiveness and simplicity
For businesses struggling to integrate AI chatbots to fit their specific goals, this means a lot of experimenting.
Because technology is changing rapidly based on continual experiments and beta testing, the corporate focus should be on best-use scenarios.
Focus on Humanization
Finally, the most critical issue businesses need to weather is low customer acceptance rates of conversational intelligence.
Forbes has found that 87% of customers prefer human interaction to interact with AI chatbots. The reason for the trend lies in the fact that chatbots are still machines; people prefer human agents because they understand them better.
This is particularly evident when customers reach out to solve complex issues, which is the main challenge AI developers are trying to solve.
Things are likely to change in the future as research progresses but, for the time being, corporations need to find a fine balance between conversational intelligence and human customer care.
A word of wisdom, though the development of new tech is accelerating, complex concepts take time to materialize in their best form.
In the context of conversational intelligence, suffice it to say that text and natural language interface research had been big in the 70s and 80s of the past millennium before graphical user interfaces made their grand debut.
Chatbots have been around for far longer than people think. The first one, MegaHAL “chatterbot” was developed by Jason Hutchens in 1996.
In plain words, it is essential that businesses focus on their targets and deploy conversational intelligence only where they can boost customer satisfaction. Forget the word “innovation” for a while. Customers are having a difficult time keeping up with novelties. Focus on simplicity, fast response, and feedback until finalized solutions pop up.