Natural Language is the way we will connect-Digitally
Getting used to the New Normal-Natural Language Interaction
The Post COVID world has created a paradigm shift as new terms like WFH (Work from Home), Zooming, unmasking, and a host of conversations around staying connected while staying safe have crept in and replaced discussions around weather, and the traffic. Basic Human tendency to express and connect is opening a host of possibilities to harness and understand human intent and behavior to drive how we minimalize our extravagant Pre-COVID possibilities. The art of tapping into these conversations using AI, deep learning, language-to-language translation, and Chatting using BOTS have grown exponentially. The next-generation of predictive analytics and Pattern recognition is lying buried in the Data we have started to generate exponentially, as our meetings move from Board Rooms to Video Chats and Customer conversations are being mined for drawing inferences and creating the new norm in the hyperconnected Post-COVID world across Global boundaries.
As the era of the big System integrator, big Tech and Bid Data are all being repositioned, the picture past 2020 is getting fuzzier and the stakes of mistakes could cost jobs, entire departments are being made redundant and make way for more effective ways of running IT and managing the new wave of Customer interaction as it becomes a Digital reality.
Asked what will drive and has started to impact Digital transformation decisions in an organization in 2020: The winner-COVID 19.
Buckle up and reimagine the customer experience and the journey as the consumer reassess their interaction with enterprises as their curry is being delivered by a contactless delivery boy ordered on an app from a restaurant you've never visited! All at the command of your voice into the app manned by a BOT!
Data Explosion is an understatement
From a digital age to the post COVID era, it seems that the world is generating humungous amounts of data are collected every minute, each day. Over 2.5 quintillion bytes of data are created each day, and that number is exploding as we start to record more ZOOM sessions or maximize screen time organizationally and with customers. Each interaction is now, more than ever a digital record, and it is growing bigger. From tracking your online content consumption and search engine queries to recording your purchase history and social media activity, an insurmountable amount of data is being generated on a regular basis.
Pretty much any aspect of our lives and actions are being translated into data –that can be processed and analyzed to provide intelligent insights for businesses. It’s impacting every way in which businesses identify their target demographic and find out what their customers are really looking for.
With the help of data mining and exploring tools, businesses, governments and Social organizations are sifting through more information about consumers than ever before.
67% of customers are willing to share their information in exchange for discounts or freebies. Around 86% of consumers are happy to pay for better customer experience. Clearly, customer data analysis proves to be critical in providing better experiences for consumers as they are aided in product recommendations, communications, retention, focused and incentivized by tailored loyalty programs.
As we consume more Media, we are targeted with specific content and finger pushing impulses, like the Netflix pic that pops up for each profile may be different as my preferences and profile are understood. Watch out, you’re being watched, but is it all that bad?
- Data Science helps large organizations that handle billions of customer interactions each year anticipate and resolve customer needs before they arise has the potential to drive customer satisfaction up and costs down.
- Visualized data can help predictive solutions utilize customer profile data for insights into an individual’s needs and preferences from prior interactions across all channels to determine intent and select the best agent or resource to service their needs, by:
- Offering next best action to satisfy a consumer’s need through an IVR, Virtual Agent or a Live Personal Agent
- Reducing customer churn as one studies patterns and signals that a customer may be close to buying a product or canceling their service based on web activity, words, tone or sentiment
- Proactive Notification to customers of customized offers or impending renewals with the best fit offers and personalized deals
Every conversation can be stored, studied and aggregated to offer real‑time insights as the engine becomes smarter learning from past and ongoing live agent interactions, across the digital channels to help optimize behavior and improve the accuracy of agent/BOT’s responses.
Immersive-Passive Security
Pins, TIN’s, Passwords, OTP’s, based on what I know, what I have, and what I am and more…what has my identity come to? Beware, It’s on watch, and it's under siege! Social engineering and hacking are a whole new set of weapons to contend with.
My face, my ears, my tongue, my gait, my voice, and iris are all contenders depending on what interaction channel I choose…it’s a bloodbath of options and the stakes for your wallet are high. Why not multi-factor and multi-modal it’s a list of never-ending options, but dos that still keep my identity safe?
A multi-Billion $ industry is now becoming a reality based on man’s worst instincts. And it’s a real threat. Mitigating it is the next best option!
Active Vs passive. It’s a toss-up on what’s best to be used in a Voice-based industry where the search for the ultimate seamless experience is on the top of the heap of priorities and has to dodge the chances of being cloned, mimicked, or spoofed. But do I really want to repeat “my name is my password” in a crowded room, just seems counter-intuitive. Keep it easy, keep it passive and your voice will make the gates open. Creepy but effective!
Artificial Intelligence in Human Interaction (NLI)
Natural language Interaction is a combination of the above three factors when it comes to tailoring a Customer journey based on artificial intelligence and deep learning. NLI allows a language application to understand human language and interact, close to how an intelligent human would, multi-tasking, with no days off, and no mood swings at millions of transactions per second. Now that’s a whole new beast of a service agent.
AI is set to pervade the customer lifecycle as Enterprises look to drive Intelligent conversation, contextual analytics, and predictive responses, backed by cognitive engines and eventually Human Intelligence and learnings tweaking the Artificial Intelligence to provide human-like responses and interactive assistance.
Intelligent Conversations
Conversational BOTS lets consumers engage in a near human-like dialogue through text chats or Voice for immediate access to information effortless through otherwise long winding menus and button-pushing outcomes through IVR, texting, or Web channels.
The dialogue exchange allows for cross and multi-channel interactions that flip in and out of any channel or device to navigate seamlessly across multiple channels of comfort for the customer to enable an “easy” interaction unlike in the previous Contact center horrors-all through one most ubiquitous resource-Human Voice!
Caller intents are deciphered however they may frame their ask, and the system statistically arrives at the most-likely customer’s interest or need, gather the necessary information to assist the customer through self- or Human-agent assisted-service.
Over 90% of the transactions can theoretically disappear from the agent's responses as NLU deciphers concepts and meaning from user intents from their statements, disambiguate, and provide complex answers that require fairly deep searches from FAQ’s or Knowledge bases that store “typical answers” to a large swath of queries. If not sorted, it is handed off to an agent, and the responses are backed up into the knowledge bases for the continuous learning built into the AI.
Deep Analytics
Data Analytics delivers valuable insights into consumer preferences, behavior, sentiment, and intents that can be used to deliver differentiated experiences throughout the customer lifecycle. Taking a customer journey-focused approach to analytics is a key driver of success in today’s omnichannel world. Areas it can be applied include:
- Identity using Biometrics to fight fraud and allow customers to seamlessly authenticate for access
- Customer Profiling and Pattern synthesis by Natural Language Interaction (NLI), machine learning and deep learning
- Predicting rather than reacting for personalized, contextual and relevant personalized experiences
NLI allows the use of live customer interactions to create actionable insights across the customer journey to make quick, confident calls on the best ways to improve the customer experience and enhance Interaction center efficiency. Enhanced agent performance helps build a high‑performing Engagement center while reducing compliance risks.
Natural Language Interaction (eNelAI)
Natural language Interaction is a combination of the above three factors when it comes to tailoring a Customer journey based on artificial intelligence. NLI allows a language application to understand human language, either spoken or written encompassing coding, understanding, interpreting, and manipulating human language. NLI today helps translates languages, convert speech to text and text to voice, and create human-like conversational agents to help customers. The statistical machine learning and the use of neural networks and deep learning make it possible to build automated systems that can interact with people more naturally than ever before and handing off the automatable components in large Contact centers to these highly efficient BOT's so human agents can split a wide range of their processes for both customer-facing activities and internal operations with these intelligent BOT’s.
But humans are constantly flitting, searching across channels and Googling options, and are armed and informed before they contact the enterprise. The journey before a flight booking looks something like this:
Tips for the eNelAI (NLI) and Customer Journey enhancements
There is immense clutter in the space of Customer interaction and jargon is bursting through the clutter and one hears of a huge influx of new jargon that purports immense value while underdelivering because of poorly handled “Technology Implementations”. The challenge is looking at the technology as an enabler and the Human Interface and diving right into the User Experience helping to de-clutter one's project portfolio.
The wealth hidden in these transactions is often the insights, best practices, and behavior patterns that feed the Deep Learning that organizations seek to invest in.
Start from the infrastructure in place already
Someone has built the past and its working, so on the work that others have done already and look for genuinely skilled partners who have been there before. The big bang-consulting and then top-down model, tearing down what’s built is often a recipe for a burnt dish. What you don’t want is opening up so many cans of worms that your projects veer off course and you get lost in the weeds. Work with smaller proven but nimble organizations, that understand your issues and works with you operationally, commercially, and business intricacies and willing to do PoC’s and then expand. Pick your battle
Don’t go Big-Bang
The training process for an AI-led deep learning application is highly iterative so start small and lead with a PoC and go down one path, a division or revenue stream like the “Youth segment” that defines future markets and successes and see what sort of results you get. And then you go about a transition to a larger implementation through a process of iterative improvement that involves training and tweaking the models. Experiment and see failure as a path to success The partner you choose should be allowed to question the approach and to abandon unproductive approaches, to rethink things that you thought you knew for sure, and to go back to the drawing board to map out a new approach but in smaller low impact experiments.
List your wins and don’t take your eyes off that list
The development of an NLI application is a complex undertaking involving BOTs., languages, Voice accents, grammar, contexts, disambiguation, and statistical model that mimics the human brain's decision-making process. This isn’t going to happen overnight. The key is to recognize an area of “business intents” to about the top 20% of the high impact areas tune the system for a high Natural Language intent understanding success rate and expand toward the big goal — an Enterprise-wide NLI and integrated (Omnichannel) interaction platform that is designed for change in the businesses a customer preferences as the world opens up to a post-COVID reality.
Key Takeaways:
Small is beautiful-Start with a bite-sized project that is a quick win and build confidence and learning. Bring together existing infrastructure with the new project requirements by increasing investments incrementally as success breeds success.
Identify the right partner-who is aligned to the business and is willing to work iteratively and collaboratively to arrive at small wins first and willing to expand as stakeholder demands grow
Contract Partners, not vendors-Create a workable commercial model where all parties have skin and risks in the game and work toward a success based model-both parties need to win in ideally a gain-share structure where the more you win the high the stakes for the partner/s
Analytics and Data-driven- The Data that is generated is to be used to derive future decisions and fine-tune the Deep learning that comes from the patterns that the Data unfolds and allows for continuous Business process improvements
Service Model is partly outsourced-with bulk of the operational efficiencies defined and executed by the partner, but the Strategic inferences to be tabled to get management buy-in as the scope expands to other divisions and Lines of Business
Listen to the Voice of the Customer-where Customer Centricity is the key to change where the data throws up pointers to understand the customer journey and incorporate performance enhancements, so the entire engagement is uncomplicated
Author: Anil Kumar, Chief Strategist, Voiceweb
