Guest article by Jochen Wirtz
The trade-off between service excellence and 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 me provide two examples of technology-based innovative business models, one a traditional people-processing service, and the other an information-processing service.
First, the Henn-na Hotel in Nagasaki, Japan will be largely run by robots. It aims to have 90% of hotel services provided by robots, including porter service, room cleaning, front desk and other services to reduce costs and ensure comfort. Many other processes were also redesigned such as using facial recognition to give access to the hotel, rooms and other facilities, effectively replacing cumbersome room card systems. This hotel will be full of technology – see here for an introduction the hotel:
and watch the robot hostess introducing the hotel:
Figure 1: Robots serve guests at Henn-na Hotel, Japan
Second, TranscribeMe (www.transcribeme.com) uses highly automated processes, speech recognition algorithms, and crowd workers to deliver speech-to-text transformation at better quality and lower cost than any “old world” transcription business. What fascinated me was the focus on automation and scalability. Once a customer has set up an account (which is easy), she just has to audiorecord a focus group, medical diagnosis or court hearing with her mobile device using the TranscribeMe App which then uploads the file to TranscribeMe (sound files can also be uploaded conveniently via its 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 (that’s why transcription can be turned around so fast). Once done, the system then automatically pulls the text together into a single document and a quality controller listens to the audio while reading, and if required, corrects the text (any edits made automatically feed into a quality rating of the crowd worker who processed that particular text snipped and affect his future allocation of work and pay). Once the quality controller signs off the document, the system sends it to the customer’s email account, bills the work and collects payment via credit card. This is done without manual labor involved – all is seamless and fully automated.
To understand the detailed design that has gone into developing tailored value propositions for key applications while using the same core engine see http://transcribeme.com , click on the vertical solutions for “legal transcription” (top quality courtroom transcription), “medical”, “closed captions” (e.g., subtitles for movies, YouTube videos or MOOCs), “corporate” for transcription of focus groups, or “academic” for transcription of research interviews. Then click on “What makes us the best” to understand the power of this business model! Audio and text files are converted to text with superior accuracy, confidentiality and speed at low cost. For instance, huge amount of volume text can be turned around in 24 or 48 hours rather than taking days or weeks. Confidentiality is of upmost importance and TranscribeMe is fully Health Insurance Portability and Accountability Act (HIPAA) compliant – it breaks up all text into micro tasks and each transcriber sees only a short fragment. Finally, it is cost-effective with prices ranging from US$1/minute of spoken text (large volume, single speaker, 4-7 days turnaround time) to US$5/minute (for 8 or more speakers on the same audio recording, the transcribers have to attribute text to each speaker which is more time intensive and algorithms are less effective in doing that, 1 business day turnaround).
Recently, the company extended the same core engine and crowd platform to translation services.
These are just two examples of how technology can move our economies towards cost-effective service excellence. It seems the service revolution is finally taking off!
Figure 2: TranscribeMe has a crystal clear value proposition and delivers against it!
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 scalable technology and an apparently unlimited pool of crowd workers, and the working conditions of crowd workers (have a look at the discussion rooms how they share their challenges, gripes and likes of working with TranscribeMe).
Disclosure: Jochen has been an angel investor in TranscribeMe since 2013.