- Posted by Daitan
- On August 11, 2018
- AI, Customer Experience, Digital Transformation, Machine Learning
We hear a great deal about customer-centricity and integrating the customers’ journey at all touch points in order to optimize the customer experience. Considering how customer demands continue to evolve, what does it take for brands to produce a high quality, continuous experience?
It’s Time to Reimagine
At Daitan Group, we believe that the migration of Artificial Intelligence (AI) to digital products and business intelligence was enabled by the emergence of big data and more available data. Although I mention AI generally, it really includes Machine Learning (ML) and its subset, Deep Learning, which leverage big data to produce the kind of benefits we are all reading about. Through this confluence of technology, we can re-imagine the possibilities around customer experience across the entire journey—so, every touch point intelligently engages the customer the way the customer wants to be engaged—and shares all of that intelligence with the right stakeholders.
Artificial Intelligence and Machine Learning can add substantial value by bridging the data, information and context gap, so humans and their virtual assistant colleagues can deliver a seamless customer experience. More specifically, it means holistically tracking and enabling engagements everywhere. Many refer to this concept as a true omnichannel strategy.
What Does It Really Take to Create a True Omnichannel Experience? Intelligence Everywhere.
This is very complex to accomplish, but, we believe AI/ML have the ability to help us get there. Think of AI/ML as becoming the system’s intelligent backbone—or brain as Telefonica refers to it in their Aura initiative—that is aware of everything about that user’s interactions, history, current status, sentiment, and preferences as well as the status of the services they are consuming and the experiences of their peer groups. That level of comprehension can enable those virtual assistants to choreograph answers when appropriate, escalate to a human when complicated and share the entire set of events and context accordingly. With that framework in place, you can even believe contact centers can evolve from a cost of doing business into a key focal point for a rich human-to-human experience—or do they ultimately disappear completely being replaced by the omni-present intelligent backbone.
Fundamental to realizing the benefit of true omnichannel engagement is the concept of enabling intelligence everywhere—website, apps, SMS, MMS, call centers as well as in the application or service that the customer uses itself. In software development and computer science, this concept is referred to as Ubiquitous Computing—where computing is made to appear anytime and everywhere, and can occur using any device, in any location, and in any format. To do that takes expertise in:
- Creating a user experience (UX) that works well across all those channels.
- Advanced API management, microservices and data integration.
- Developing and delivering solutions designed to enable the omnichannel.
Much of the work Daitan does for clients includes helping them understand how to get from today to their future, by bringing practical development practices (like the ones described above) together with new processes (like DevOps), new skills like data science and big data, and new technologies like AI/ML. This is our core competency and frankly the reason we become a strategic resource to our clients with relationships that span years.
AI/ML Should Augment Humans, Not Replace Them
While some believe technology will eliminate jobs and automate functions such as contact centers, we believe until there is an intelligent backbone like I described above, the core value of human interaction is an essential element to any brand’s success. A great point made by David Rowlands, Contact Center Solutions at 8X8, in his blog titled, Will Bots Replace Humans in the Contact Center, “Humans in the contact center aren’t on the way out. I believe people in this industry all around the world have a bright future–and to write them off entirely would be to ignore the strategic importance of human interaction to a company’s brand….. In an era where brands are defined by the service they provide, not just the products they sell, the actions of the contact center team are fundamental. In fact, they are the customer experience that make a brand what it is.”
So if humans will continue in the customer contact center, AI should be there to augment or extend them. An example of how AI can augment humans would be triaging inbound calls. AI can automatically address simple queries, alleviating agents of the burden; as well as provide smarter prioritization and redirection of customer calls—so, the agent’s value is applied to the most urgent and important situations. Of course this also means that the agents must have the right skill sets and training to really handle the customer problem, and not just read from a script. As this transition plays out, agents will learn how to work alongside their digital interactive colleagues as the workflow between them evolves. AI can also augment the human agent by monitoring their calls and identifying areas for additional training.
AI/ML Fit into a Business’ Digital Transformation Strategy & Planning
Although there is great promise, we have a way to go to refine not only AI, but more importantly, the infrastructures, data, training methodologies and operational processes needed to support it. At the heart of this is recognizing that these technologies need to fit into your business initiatives for Digital Transformation in order to reach the level of system-wide holistic comprehension I described above.
Unless you are a start-up doing greenfield development, you are a company with legacy systems and siloed data. These are hurdles that can be overcome only by understanding what it takes to digitally transform, as well as incorporate how AI/ML enhance the result. And clearly, focusing on improving customer experience throughout the journey is a keen objective AI/ML are well suited to achieve.
McKinsey Global Institute’s recent report, Artificial Intelligence: The Next Digital Frontier identifies customer experience as an area where AI can add value. It also describes the five elements of successful AI transformations, which I believe rings true for any digital transformation initiative.
- Use cases/sources of value
- Data ecosystems
- Techniques and tools
- Workflow integration
- Open culture and organization
According to McKinsey, “AI’s dependence on a digital foundation and the fact that it often must be trained on unique data mean that there are no shortcuts for firms. Companies cannot delay advancing their digital journeys, including AI………A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture. In particular, our survey shows that leadership from the top, management and technical capabilities, and seamless data access are key enablers.”
Separating Hype From Reality
In terms of AI hype, there are also hurdles to overcome based on current experiences with early implementations. Let’s face it, all of us have been frustrated by chatbot implementations that only respond based on a script instead of the real conversational meaning. And/or endless wait times to speak to a real human at a call center once you’re finally escalated only to have to re-identify yourself and explain your problem all over again, only to discover that the agent follows a script too and can’t solve your problem. This is a perfect example of an AI that is not aligned with a user experience design that really fits the customer’s needs; and a human agent relationship that fails. Solving this really requires understanding the customer’s journey across all channels in order to make sense of the role chabots/intelligent assistants can play and contact center agents fulfill. So yes, there is a lot of room for improvement, but the technology and tools are coming along.
I am reminded of a post by Al Cook, Director of Product Management and Engineering at Twilio who has multiple blogs on bots. In one titled, Using Bots to Route Customer Requests Based on Sentiment and Emotion, he discusses building a prototype to explore how the human/bot interaction could work. Cook said, “….What is a realistic view of how companies could be using bots today? I’m particularly interested in the possibilities for using bots within a call center. ….. To explore these ideas, I built a call center prototype to look at ways to merge human and bot interaction together……Users can message in through SMS or Facebook, and based on what they say they need help with (and how they say it), they will get routed to the best qualified agent. The agent will have all the context of the conversation so far, and details about the customer.”
A software developer Arjun Madan from Bandwidth.com shared a similar post—Building and SMS Weather and Image Bot—describing creation of a bot, but he takes the idea a step further demonstrating how bots can extend functionality over time using APIs, “Now that you have a simple back-end service listening for text messages sent to a particular number, you can do pretty much anything you want! Some things I think might be interesting to integrate with the service are translation APIs, payment APIs, news APIs, flight trackers, and IoT devices.”
Even though these examples are simple bots, it demonstrates pervasiveness of interest and future potential. As ML techniques for Natural Language Processing, Sentiment Analysis, Recommendation Engines and Conversational Interfaces mature, improvement will come.
Realistically, we are at a point where there is tremendous investment in research and development to achieve operational-ready results, and that is overlapping a pent-up demand for the benefit of that perfection now. Even though every business values the intelligence provided by data science research, the true value is realized after the core engineering work is done to integrate that intelligence into the business operation. In that regard we believe it’s best to build a solid team around the data scientist so research transfers to development which transfers to operations. (See my blog on this here)
Like any good engineering endeavor predicated on solid business need, implementation takes time, lots of data, and capturing the intelligence everywhere to create a customer experience that feels perfectly human—even if it’s technology working seamlessly behind the scene augmenting real humans.
From design processes and advanced analytics, to modern architectures that include ML and some cognitive techniques, these are the change agents shaping how a new generation of customer experience management can be delivered. And these change agents are how any company can digitally transform their current customer engagement model to achieve a reimagined seamless omnichannel experience.