Rui Costa - Programming Google Cloud

Pigeon Travel is an online travel agency. Pigeon Travels website and mobile application are used to book airline tickets, hotel reservations, and car rentals. They also have a large call center to provide support for their customers. The agents in the call center support their customers via live chat and call-in support.

For measuring customer satisfaction and calculating their Net Promoter Score, they provide the user with a survey at the end of the call or chat. A Net Promoter Score is a measurement used to answer the question: Will the customer recommend our product or service to a friend? Customers who respond 9 to 10 are called promoters, responses from 7 to 8 are called passive and responses from 0 to 6 are called detractors. The small percentage of users that leave feedback, the feedback provides for an overall low customer satisfaction and Net Promoter Score. Issues customers have raised are:

• Call Availability: Long hold times before speaking or chatting with a customer representative

• High Handling Rates

Pigeon Travel also noticed a high abandonment rate which correlates to the call availability issue listed by their customers.

The business has asked you as the lead developer to put a plan together and a prototype on how Pigeon Travel can increase the Net Promoter Score, increase customer satisfaction, and provide the following metrics to the business:

• Customer Sentiment

• Call Topics (Why are users calling?)

• Understanding Trending Topics

• Understanding call quality including Silence Scores, Call Duration and Call Escalation Paths.

We can meet some objectives of the business by taking a potentially biased and inefficient manual approach which would require analysts to randomly select calls to collect the key performance indicators. However, the framework which we will cover in a few will allow us to use Google Cloud services to meet the goals and analyze all recorded calls to get insights in near real-time. These insights can include:

• Overall call sentiment.

• Sentence-by-sentence sentiment.

• Insight into which agent quality metrics to track (such as call silence, call duration, agent speaking time, user speaking time, and sentence heatmaps).

• Insights on how to reduce call center volume by analyzing keywords in transcripts.

Now that we know the business objectives, we need to define the framework. How we can define a framework without knowing the services available from Google Cloud? The rest of the chapters in the book do just that. For now, I have defined a framework for you that will guide you through the book, and shows the architecture we will work through.

Figure 1-1. Framework Diagram

The framework presented in this book uses Google Cloud AI services such as Speech-to-Text and Cloud Natural Language API. The framework also employs Pub/Sub, a messaging system, and Dataflow, a data processing framework, used for data streaming and transformation. The resulting framework consists of a cloud-native and server-less job running in Google Cloud. To implement the framework, you dont need any machine learning experience, and all the data infrastructure needs, such as storage, scaling, and security, are managed by Google Cloud. While we have chosen Google Cloud as the platform, you have many choices such as Amazon Web Services and Azure. They both have similar offerings to Google Cloud that will allow you to translate the Framework components to their offerings to fully a working framework. However, we wanted a wide partner ecosystem of Business Intelligence Tools, as well as have Google scale querying for the large amounts of data that will be created from our transcription of the audio files thus Google Cloud is the best fit for our requirements.

Throughout the book, cloud-native applications are loosely defined as an approach to building applications that take advantage of cloud computing resources. It is often associated with public clouds, but can also be used with on-prem clouds. Cloud native applications are defined by how they are deployed, rather than where they are deployed. A cloud-native application will leverage the perceived limitless resources available. For Google Cloud, a cloud-native application can scale up and down as needed, the application is built to use and release resources based on the rules defined.