Delivering service excellence and productivity through technology

For firms involved in the services sector – from hospitality through to banking – the trade-off between providing service excellence and improving service productivity has been widely acknowledged and remains a key challenge. However, advances in technology have the potential to dramatically improve the customer experience, service quality and productivity, all at the same time.

Think of big data, cloud computing, artificial intelligence, robots, drones, wearable computers, sensors, self-driving cars, virtual reality, speech recognition, biometrics and the internet of things that hold in them opportunities for a wide range of service innovations.

Let’s look at two examples of innovative, technology-based business models. One is a traditional people-processing service, and the other an information-processing service.

robot_hostess

Robot hostess 280
Robots will serve guests at the Henn-na Hotel near Nagasaki, Japan. It is a new hotel in a theme park near Nagasaki, Japan, that will be largely run by robots and almost entirely automated.

Scheduled to open around the middle of the year, the 72-room Henn-na Hotel aims to have 90 percent of hotel services provided by robots, including porter service, room cleaning, front desk and other services to reduce costs and ensure comfort.

The hotel will also feature human-like robot receptionists to welcome guests and check them in, while many other processes have been redesigned such as using facial recognition to give access to the hotel, rooms and other facilities.

As a result of reduced operating costs the hotel will reportedly charge room rates as low as ¥7,000 (S$80/US$59), well below other similar standard hotels in the area.

The second example is speech-to-text service TranscribeMe which uses highly automated processes, speech recognition algorithms, and crowd workers to deliver a service at better quality and lower cost than any “old world” transcription business. (Full disclosure: The author has been an angel investor in TranscribeMe since 2013.)

What fascinates me is the focus on automation and scalability. Once a customer has set up an account, it’s a simple process of recording a focus group, medical diagnosis or other event, and uploading it using the TranscribeMe App or website.

Then the sound file gets processed by three speech recognition algorithms that learn over time. Next, short snippets of processed text and the matching audio parts get distributed to its over 50,000 registered and quality monitored crowd workers who process the text in parallel – enabling transcription work to be turned around at high speed.

Full automation

transcribeme 280The core engine behind TranscribeMe is adaptable to a broad range of applicationsOnce done, the system then automatically pulls the text together into a single document and a quality controller then verifies and, if necessary, corrects the text against the original audio. (Any edits made automatically feed into a quality rating of the crowd worker who processed that particular portion and affect his future allocation of work and pay).

Once the quality controller signs off on the document, the system sends it to the customer’s email account, bills the work and collects payment. This is done without manual labour involved – all is seamless and fully automated.

Using the same core engine the system has been designed to cover a range of applications from legal transcription to academic work to closed captions for films and videos, including close attention to issues of customer confidentiality. Recently, the company also extended the same core engine and crowd platform to translation services.

The power of this business model means speech-to-text work is delivered with superior accuracy, confidentiality and speed, and at a lower cost than alternate services.

These are just two examples of how technology can move our economies towards cost-effective service excellence.

Hospitality and audio transcription are obviously quite different industries, but both examples show how the smart application of emerging technologies means that improved service excellence does not necessarily come at the cost of service productivity.

Both mini cases make fascinating class room discussion topics: Henn-na Hotel about using robots to interface with customers and the future of service workers, and TranscribeMe regarding the seamless integration of fully automated and scalable technology and an apparently unlimited pool of crowd workers, and the working conditions of crowd workers.