Language is the mark of humanity and cognizance, and conversation or dialogue is the most fundamental and a distinctive field of language. As we use more natural interfaces with technology, like language, our relationship is shifting to one where we increasingly humanize them.
The simplest kinds of dialogue systems are chatbots, systems that can carry on extended conversations with the goal of mimicking the unstructured conversations or ‘chats’ characteristic of informal human2human (H2H) interaction. Conversational AI expands the scope of today’s chatbots from stiff preset replies to one that can take astute & pliant actions. Conversational AI learns to allow humans and computers to talk and work together in a more natural way.
The maturity of the conversational technology is apace with the pandemic as businesses irrespective of industries were forced to go virtual in a jiffy resulting in a compulsion to provide digital solutions that are ubiquitous and meet the massive increase in customer support load — an optimal environment for chatbots and AI to breed in. When the idea of using human language in communication with machines arose in the early 50s’, never would have they imagined that overnight, millions of people will start using chatbots to get things done without any human intervention as a result of a pandemic called Covid-19. Though the pandemia has given it an extra pace in recent times, chatbots were already on the rise well before that, with a continuous growth curve, thanks to the companies that were eager to maintain a healthy equilibrium between these talkative machines which were capable of handling a huge influx of customer service requests with 24-hr customer support & their potential return on investment.
Now, this infatuation of businesses for the conversational AI in marketing space for the evolution of customer dialogues into a more customer-centric philosophy is here to stay! Chatbots are currently one of the biggest use cases for AI & the pandemic has made them even more enthralling given their vast use in business automation and the promise of great ROI.
In brief, what is Conversational AI?
One of the most followed spaces across the spectrum of AI in marketing is the Conversational AI systems. With more & more companies hankering to deploy their chatbots, voice assistants, and NLP-powered bots, in the coming time, we are going to see a lot more twirls in the market.
The use of Conversational AI Technologies is exploding across a wide variety of use cases. As reported by PRNewswire, The Global Conversational AI Market is Expected to Grow from USD 4.8 billion in 2020 to USD 13.9 Billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.9% during the forecast period. This clearly stipulate that several new players will enter into the market with new technology platforms, we will realise some industry specific best use-cases, also with time the growing data points will make the decision-making around conversational AI a little more complex, so the companies need to listen, iterate, improve & continuously understand what users are doing and what users are trying to do; that they can’t do inside their solutions right now.
Conversational artificial intelligence (AI) refers to technologies, like chatbots or voice assistants, which users can talk to. They use large volumes of data, machine learning, and natural language processing to help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages. (Definition Credit: IBM).
Here are some basics you should have a grip on before you decide the right route for your conversational marketing platform idea:
Scripted/Quick Reply Bots
AI or NLP-powered Chatbots
Social Messaging Chatbots
Context Enabled Chatbots
Some facets of an effectual Conversational AI System
Companies wish to build conversational AI that has the potential to amplify the experiences, it’s easier said than done & means much more than just spinning up any elementary chatbot. Instead, you need to give assiduous consideration to each of the key components of a conversational AI application. If done right, you are saved!! Because the costs of doing conversational AI inaccurate can be greater than never using it in the first place. When you start looking under the hood of bots or messaging apps with conversational capabilities, you will find the following coming together seamlessly, let’s look into few of the basics:
- Know what users want: A great conversational AI kicks off with natural language processing and the magic of cognitive resources (primarily pattern recognition and analytics) and applies it to intent recognition. Dig deeper into buyer’s personas as the building ground to help your AI system identify the right users. The analytics on your AI system’s interactions will flow into improving its efficacy with time. In short keep on experimenting and iterating 🙂
- Great design for a great conversational experience: Loved brands can transform an experience and evoke emotions, it can make you feel good, feel bad or simply react. So, just remember that these intelligent bots can act like a brand ambassador for your brand and thus needs to engage appropriately. You have to choose the right messaging platform and channels & make the dialogue flow flawless. It’s vital that conversational AI uses the same repository of information, regardless of what channel is used, this is to provide the same response with each channel. Devising an experience that effectively uses these elements will take some experimentation and iterations.
- The right content & tone: For conversational augmentation, companies need to identify the right points in a conversation for the system to offer suggestions to the human agents or the consumers, and compose the interaction in way that is seamless and intuitive without being intruding. Also, make sure to address the right opportunity or challenge. Point out the most critical features, people, and systems needed to deliver a valued service and finally evaluate is it feasible and viable for your organization. As both conversational agents and conversational enhancements will enable communication with the users, right content strategy becomes vital. If companies have the conversational data already, then they can curate the best of it and utilize that as the basis for the responses that the conversational AI application provides. If not, then use the human writers or natural language generation techniques to fill the gaps. Many organizations are now using content intelligence, a combination of OCR, machine learning and other AI technologies to create structured information from unstructured content. This creates a room for RPA bots to become smarter with cognitive skills & deliver human-like understanding of content, and to connect those skills with chatbots.
4. Fluid UI/UX: In conversational AI, you are not preparing just a series of independent speech acts, but rather a collective act performed by the speaker and the hearer that have a structure. So, while you are building an advanced technology system, bear in mind that ultimately, you are developing a tool for the conversational advertising. Hence, the user interface has to run in sync with your brand identity & a great user experience.
5. Figure out the correct use-case(s) for your Conversational AI: Identify which parts of your organization would benefit most from this kind of automation and decide on most relevant use-cases.
6. Get the right team: Organisational Buy-in across all levels can be crucial to your conversational AI’s success. This can vary from organization to organization but is a good starting point for how to set up a winning project team. To win on this turf, it’s important to develop a clear plan on how you are going to build your team?
7. Choose the right KPI’s: After defining the objective and scope of the conversational AI, the KPI’s will become quite lucid. Try setting target figures on few indicators closely linked to the original strategic stake of the project & the ones sufficient to evaluate the ROI. But be warned, KPIs should not be the only metrics taken into consideration when evaluating the overall impact of the solution. Thus, beyond the KPI’s directly linked also correlate these metrics with your pre-chatbot era.
8. Adapt to the evolving user needs, augment constant feedback loop for user Intelligence: Each conversation must take the system closer to the understanding of the user as well as improve the ability to design an efficacious conversation. Though a well thought AI bots manage user expectations expertly, but users naturally have higher expectations from conversational than non-conversational interfaces. This makes it even more important for these algorithms to constantly improve through feedback mechanism. Process, understand, and generate response…infinite cycle 🙂
9. Making sense out of Language (voice & text): while working with the voice interfaces, you use speech-to-text transcription to generate text from a user’s input and text-to-speech to turn your responses back into audio interface. For both the interfaces, language understanding techniques such as sentiment analysis, question classification, intent recognition, and entity and topic extraction are likely to make more sense in understanding what the user is trying to convey.
10. Refrain Robotic Responses: Conversational AIs’ are usually the first point-of-contact for the customers/users, so it’s imperative that their tone is coherent, consistent and compassionate & the communication flow is not robotic. “Permeate empathy and humaneness into the AIs’ framework when developing a conversational design.” This can greatly help to mimic the assistance of a service agent without the long-waited connection times for the customers.
11. Privacy and Security: In order to improve itself, conversational AI requires gigantic data sets. Every reciprocity that these virtual agents have with a customer helps it get better at answering questions and automating requests. These Conversations may often contain sensitive information/data that requires careful handling from both a technical and policy perspective. Thus, your network infrastructure is highly critical in protecting your system. A precise network topology can eradicate many security threats based on software flaws (at both the operating system level and application level) or network attacks such as eavesdropping. To create bots with built-in security features, the bot building platform must provide a secure environment for incoming data, storage, and outgoing data, basically stick to the fundamentals of security.
12. Carefully scrutinise the Social & Ethical upshots: Reasonable privacy expectations for the sensitive information & data, anonymization, behavior audit, explicable responses, gender equity & user awareness, any conversational agent must meet a large number of requirements in terms of security, transparency, traceability, usefulness & privacy. Not long back we saw the case of “Tay” gone rogue, when the Microsoft’s 2016 chatbot, was taken offline 16 hours after it went live. Tay began posting messages with racial slurs, conspiracy theories, and personal attacks on its users. Unfortunately, poor Tay was learning these biases and notorious actions from its training data, including from its users who carried hostile attacks on the system, purposely teaching it to repeat the foul language.
Give the user exactly what they’re looking for, nothing more, nothing less.
The biggest challenge for even the smartest bot is to fool a human into thinking it was a human. With the current technology even the best of the bots struggle to harnesses the micro-decisions consumers experience on a daily basis.
As people2people communication is fundamentally modifying, this shift is significantly altering their expectations from brands as well. The customers of today looks for facile “buying journeys” & “instant delights” thus making a trail for conversational AI to disrupt markets and consumption models.
As conversational AI becomes a business must-have, consumer obsessed & future ready brands will be looking beyond chatbots, voice skills, and smart speakers to an omnichannel, multi-device, and multi-modal future of highly contextualised and intelligent engagement for providing deeper insights about customers, hyper-personalizing interactions, customizing campaigns, predicting the future of consumer behavior, social predictions, building relevant brand identities, proactive customer support, learning from feedback loops and insights…
Not to forget, while attuning these technologies, it’s easy to become overly reliant on the data, so make sure there is both a data-driven and a human empathy stance lined up to your commercial decision making.
After years & years of disruptive ads, spammy emails, and cold calls, the game is no more about creating more content, sending more messages & keeping the fingers crossed, it’s all about choosing the right path & creating impactful differences in the life of your consumers.
About the Author: Kingshuk is the Co-founder & Director Strategy & Design Healthinnovationtoolbox/Co-founder Digital Machina. He has spent the last 15 years of his career immersed in digital delivery, designing and executing digital transformation strategies, digital operating models, products and solutions in companies of all types. Kingshuk’s mix of industry expertise with design and digital experience has enabled him to help clients across the globe to develop and implement brand and marketing strategies, establish customer touch-points and create blueprint for digital roadmaps. He loves working on consumer behaviour, market research, branding challenges, content design, digital strategy, new product development, digital products, business transformation and anything with an entrepreneurial smack.