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This is the tech that will change customer service

Rather than obsessing about driving efficiencies, business leaders need to look at emerging technologies to find new revenue streams. IAN JACOBS of Forrester looks at five theologies set to change customer service.

As the age of digital business becomes pervasive, customer service professionals need to step up their planning. Technologies that seem futuristic now will become mainstream within five years. Moreover, completely new models of customer service, driven by new technologies, will require additional organisational models and ways of measuring their success – adding to the need for solid research and planning.

Forrester senior analyst serving application development and delivery professionals, Ian Jacobs, looks at five technologies set to transform customer care over the next five years.

Jacobs says that today’s digital reality has forever changed the way customers engage with companies. To illustrate, he points out how a customer of a large telecoms provider developed an automated bot which automatically tweeted the company as soon as his Internet connection dropped below agreed upon speeds.

Despite this new breed of customer, Forrester believes many companies are not prepared for a future where customers control the conversation.

“Emerging technologies proliferate through the consumer world well before they hit the enterprise, and yet only 16% of global business and technology decision-makers at firms that are prioritising improving customer experience are creating a dedicated user group for customer experience initiatives,” comments Jacobs in a new Forrester report: Plan Now for Customer Service in 2021.

According to Forrester, emerging technologies that can make a significant impact on the future of customer engagement and revenue generation include:

1. Two-way video allows customers and service staff to better engage

Despite the fact that the lower price of bandwidth and smartphone capabilities have brought video chat capabilities to an ever-greater portion of consumers, contact centres are not effectively making use of it. Those who are using video, also tend towards one-way video, which limits the benefits which they could be achieving through two-way video.

Two-way video allows the contact centre staff to see the troublesome router, fridge or radiator. Even the traditional service industries are making use of it, with a major global bank making use of two-way video along with co-browse facilities to help customers fill out complex applications.

2. Bridging the physical and digital worlds with augmented and virtual realities

The level of investment into technologies such as augmented reality (AR) and virtual reality (VR) is indicative that the technologies will have their day in the sun. As the cost of technology comes down, mainstream user adoption will increase.

“VR will allow customer service agents to project their presence into consumers’ worlds and be with them in their moments of need. There are already AR demos that show how consumers can take their mobile devices, hold them over an account statement, and have FAQs and account info show up right on their screens,” explains Jacobs in his report.

Although VR devices have a relatively low penetration rate at the moment, Jacobs says this will change.

“36% percent of US online adults are currently intrigued by the prospect of getting a wearable device; of that group, 25% would be interested in smart glasses. As adoption becomes more widespread, companies can create new experiences, such as an extension of the functionality of two-way video with step-by-step AR projections that walk consumers through technical repairs, whether for plumbing, printers, or pasta makers.”

3. Virtual assistants will continue the customer conversation

Improvements in speech recognition, natural language recognition and machine learning will lead to a new class of virtual assistants. Forrester says these developments will allow a conversational experience and, as the system watches agent-assisted interactions, it will learn what to expect and be in a position to supply answers on the fly.

Forrester does warn, however, that companies will need to carefully consider where and how they deploy virtual assistants, as well as how they escalate enquiries to agents without losing information already gained in the interaction.

4. Messaging apps will become the workhorse

Messaging apps have gone mainstream. Figures released from the messaging companies show that almost one in seven people on the planet make use of WhatsApp, Facebook Messenger is not far behind and there are 700 million WeChat users per month. The need for in-app support is abundantly clear.

Embedding other channels such as virtual assistants and ticketing agents in the app offers organisations additional opportunities.

That said, companies will need to factor in the fact that messaging is an always-on, multiple engagement channel which will require companies to forecast volumes and schedule agents appropriately. Hand over between agents across shifts and based on requirement will also require some forethought before being rolled out.

5. Connected devices mean more relationship-driven services

ABI Research shows that by 2020 there will be more than 30 billion connected devices. The Internet of Things will transform companies from being product-based to being services-based. Airline engine builders are already selling their turbines by the flying hour rather than as depreciating asset, making use of in-flight data to optimise maintenance and maximise revenue.

This example clearly shows how brands can shift to lucrative subscription models. It also allows for companies to make use of multiple channels to engage, including AR and two-way video. However, this demands a relationship-centric approach for service and support.

“Custom care decision-makers with a focus on driving ever-greater cost efficiencies have been highly risk-averse and slow moving. But the change of pace inherent in the age of the customer will no longer allow contact centers to simply take cost out of the business. Emerging technologies can drive the types of customer service experiences that better cement customer loyalty as well as advance new revenue-generating opportunities,” Jacobs says.

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What’s left after the machines take over?

KIERAN FROST, research manager for software in sub-Saharan Africa for International Data Corporation, discusses the AI’s impact on the workforce.

One of the questions that we at the International Data Corporation are asked is what impact technologies like Artificial Intelligence (AI) will have on jobs. Where are there likely to be job opportunities in the future? Which jobs (or job functions) are most ripe for automation? What sectors are likely to be impacted first? The problem with these questions is that they misunderstand the size of the barriers in the way of system-wide automation: the question isn’t only about what’s technically feasible. It’s just as much a question of what’s legally, ethically, financially and politically possible.

That said, there are some guidelines that can be put in place. An obvious career path exists in being on the ‘other side of the code’, as it were – being the one who writes the code, who trains the machine, who cleans the data. But no serious commentator can leave the discussion there – too many people are simply not able to or have the desire to code. Put another way: where do the legal, financial, ethical, political and technical constraints on AI leave the most opportunity?

Firstly, AI (driven by machine learning techniques) is getting better at accomplishing a whole range of things – from recognising (and even creating) images, to processing and communicating natural language, completing forms and automating processes, fighting parking tickets, being better than the best Dota 2 players in the world and aiding in diagnosing diseases. Machines are exceptionally good at completing tasks in a repeatable manner, given enough data and/or enough training. Adding more tasks to the process, or attempting system-wide automation, requires more data and more training. This creates two constraints on the ability of machines to perform work:

  1. machine learning requires large amounts of (quality) data and;
  2. training machines requires a lot of time and effort (and therefore cost).

Let’s look at each of these in turn – and we’ll discuss how other considerations come into play along the way.

Speaking in the broadest possible terms, machines require large amounts of data to be trained to a level to meet or exceed human performance in a given task. This data enables the bot to learn how best to perform that task. Essentially, the data pool determines the output.

However, there are certain job categories which require knowledge of, and then subversion of, the data set – jobs where producing the same ‘best’ outcome would not be optimal. Particularly, these are jobs that are typically referred to as creative pursuits – design, brand, look and feel. To use a simple example: if pre-Apple, we trained a machine to design a computer, we would not have arrived at the iMac, and the look and feel of iOS would not become the predominant mobile interface. 

This is not to say that machines cannot create things. We’ve recently seen several ML-trained machines on the internet that produce pictures of people (that don’t exist) – that is undoubtedly creation (of a particularly unnerving variety). The same is true of the AI that can produce music. But those models are trained to produce more of what we recognise as good. Because art is no science, a machine would likely have no better chance of producing a masterpiece than a human. And true innovation, in many instances, requires subverting the data set, not conforming to it.

Secondly, and perhaps more importantly, training AI requires time and money. Some actions are simply too expensive to automate. These tasks are either incredibly specialised, and therefore do not have enough data to support the development of a model, or very broad, which would require so much data that it will render the training of the machine economically unviable. There are also other challenges which may arise. At the IDC, we refer to the Scope of AI-Based Automation. In this scope:

  • A task is the smallest possible unit of work performed on behalf of an activity.
  • An activity is a collection of related tasks to be completed to achieve the objective.
  • A process is a series of related activities that produce a specific output.
  • A system (or an ecosystem) is a set of connected processes.

As we move up the stack from task to system, we find different obstacles. Let’s use the medical industry as an example to show how these constraints interact. Medical image interpretation bots, powered by neural networks, exhibit exceptionally high levels of accuracy in interpreting medical images. This is used to inform decisions which are ultimately made by a human – an outcome that is dictated by regulation. Here, even if we removed the regulation, those machines cannot automate the entire process of treating the patient. Activity reminders (such as when a patient should return for a check-up, or reminders to follow a drug schedule) can in part be automated, with ML applications checking patient past adherence patterns, but with ultimate decision-making by a doctor. Diagnosis and treatment are a process that is ultimately still the purview of humans. Doctors are expected to synthesize information from a variety of sources – from image interpretation machines to the patient’s adherence to the drug schedule – in order to deliver a diagnosis. This relationship is not only a result of a technicality – there are ethical, legal and trust reasons that dictate this outcome.

There is also an economic reason that dictates this outcome. The investment required to train a bot to synthesize all the required data for proper diagnosis and treatment is considerable. On the other end of the spectrum, when a patient’s circumstance requires a largely new, highly specialised or experimental surgery, a bot will unlikely have the data required to be sufficiently trained to perform the operation and even then, it would certainly require human oversight.

The economic point is a particularly important one. To automate the activity in a mine, for example, would require massive investment into what would conceivably be an army of robots. While this may be technically feasible, the costs of such automation likely outweigh the benefits, with replacement costs of robots running into the billions. As such, these jobs are unlikely to disappear in the medium term. 
Thus, based on technical feasibility alone our medium-term jobs market seems to hold opportunity in the following areas: the hyper-specialised (for whom not enough data exists to automate), the jack-of-all-trades (for whom the data set is too large to economically automate), the true creative (who exists to subvert the data set) and finally, those whose job it is to use the data. However, it is not only technical feasibility that we should consider. Too often, the rhetoric would have you believe that the only thing stopping large scale automation is the sophistication of the models we have at our disposal, when in fact financial, regulatory, ethical, legal and political barriers are of equal if not greater importance. Understanding the interplay of each of these for a role in a company is the only way to divine the future of that role.

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LG unveils NanoCell TV range

At the recent LG Electronics annual Innofest innovation celebration in Seoul, Korea, the company unveiled its new NanoCell range: 14 TVs featuring ThinQ AI technology. It also showcased a new range of OLED units.

The new TV models deliver upgraded AI picture and sound quality, underpinned by the company’s second-generation α (Alpha) 9 Gen 2 intelligent processor and deep learning algorithm. As a result, the TVs promise optimised picture and sound by analysing source content and recognising ambient conditions.

LG’s premium range for the MEA market is headlined by the flagship OLED TV line-up, which offers a variety of screen sizes: W9 (model 77/65W9), E9 (model 65E9), C9 (model 77/65/55C9) and B9 (model 65/55B9).

NanoCell is LG’s new premier LED brand, the name intended to highlight outstanding picture quality enabled by NanoCell technology. Ensuring a wider colour gamut and enhanced contrast, says LG, “NanoColor employs a Full Array Local Dimming (FALD) backlight unit. NanoAccuracy guarantees precise colours and contrast over a wide viewing angle while NanoBezel helps to create the ultimate immersive experiences via ultra-thin bezels and the sleek, minimalist design of the TV.”

The NanoCell series comprises fourteen AI-enabled models, available in sizes varying from 49 to 77 inches (model 65SM95, 7565/55SM90, 65/55/49SM86 and 65/55/49SM81).

The LG C9 OLED TV and the company’s 86-inch 4K NanoCell TV model (model 86SM90) were recently honoured with CES 2019 Innovation Awards. The 65-inch E9 and C9 OLED TVs also picked up accolades from Dealerscope, Reviewed.com, and Engadget.

The α9 Gen 2 intelligent processor used in LG’s W9, E9 and C9 series OLED TVs elevates picture and sound quality via a deep learning algorithm (which leverages an extensive database of visual information), recognising content source quality and optimising visual output.

The α9 Gen 2 intelligent processor is able to understand how the human eye perceives images in different lighting and finely adjusts the tone mapping curve in accordance with ambient conditions to achieve the optimal level of screen brightness. The processor uses the TV’s ambient light sensor to measure external light, automatically changing brightness to compensate as required. With its advanced AI, the α9 Gen 2 intelligent processor can refine High Dynamic Range (HDR) content through altering brightness levels. In brightly lit settings, it can transform dark, shadow-filled scenes into easily discernible images, without sacrificing depth or making colours seem unnatural or oversaturated. LG’s 2019 TVs also leverage Dolby’s latest innovation, which intelligently adjusts Dolby Vision content to ensure an outstanding HDR experience, even in brightly lit conditions.

LG’s audio algorithm can up-mix two-channel stereo to replicate 5.1 surround sound. The α9 Gen 2 intelligent processor fine-tunes output according to content type, making voices easier to hear in movies and TV shows, and delivering crisp, clear vocals in songs. LG TVs intelligently set levels based on their positioning within a room, while users can also adjust sound settings manually if they choose. LG’s flagship TVs offer the realistic sound of Dolby Atmos for an immersive entertainment experience.

LG’s 2019 premium TV range comes with a new conversational voice recognition feature that makes it easier to take control and ask a range of questions. The TVs can understand context, which allows for more complex requests, meaning users won’t have to make a series of repetitive commands to get the desired results. Conversational voice recognition will be available on LG TVs with ThinQ AI in over a hundred countries.

LG’s 2019 AI TVs support HDMI 2.1 specifications, allowing the new 4K OLED and NanoCell TV models to display 4K content at a remarkable 120 frames per second. Select 2019 models offer 4K high frame rate (4K HFR), automatic low latency mode (ALLM), variable refresh rate (VRR) and enhanced audio return channel (eARC).

To find out more about LG’s latest TVs and home entertainment systems, visit https://www.lg.com/ae.

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