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Integrating Analytics and Testing to Redefine Customer Experience

Integrating Analytics and Testing to Redefine Customer Experience
April 24, 2017

Everyday a new startup is born.

This creates the need for robust, fast-moving, and usable products along with the introduction of emerging tech - helping to target the end user market with additional quality and simpler substratum for rapid-fire delivery.

To do this we need to analyze the ways of ‘How’

This HOW is an integral part of our daily approach - in so many ways in which we tend to notice, appreciate, and ultimately forget - despite the pressing need to ideate and make our customers happy. In today’s rapidly growing digitization landscape, where time-to-market is vital, we notice how each day new avatars of the same app are launched in different play stores. Further, ad-spots are linked to the user history whenever the user comes online the next day.

This is the ‘magic’ of technology.

Therefore, the entire space has the customer at its core; driving new experiences, gauging trends, and creating a sense of satisfaction. To action the above, we must apply quality assurance activities like test analytics to answer some of these questions:

  • What are the principal customer pain-points?
  • What is the end user’s demand?
  • What are the issues bothering the customer and its end users?
  • How can good tests be leveraged?
  • How can the product quality be improved to boost user experience?

Customer pain-points might be a roadblock and test optimization through test analytics must be actioned accordingly, addressing critical functionalities, and thereby catering to essential customer demands. To architect a solution, we need to collect data points and perform predictive analytics. Data which needs to be analyzed include:

  • Test frequency
  • Test artifacts
  • The build release cycle
  • Defect history
  • Production incidents
  • Faulty feature history
  • Test environment matrix
  • Customer feedback & suggestions

All these data points when analyzed intuitively with the help of Predictive Analytics offers robust risk coverage and test optimizations - equipped with closer time-to-market without compromising on quality.

There are 3 major techniques which can be used in Predictive Analytics:

  • Predictive model
  • Descriptive model
  • Decision model

All of these predictive analytics models use various algorithms like regression algorithms, time bound algorithms, and are realized using machine learning. It is very important to gather a deep understanding of the system, before applying these algorithms, given that the nature of data applied to one algorithm may not be applicable for the other.

The scenarios where predictive analytics help in overall test optimization are:

  • Consolidation of test cases for daily builds
  • Risk analysis of features
  • Test cycle optimization
  • Early bug detection
  • Feature failure analysis
  • Quality of issues

It has been observed that 3% of test cost savings has been achieved through the above method - a popular technique in test analytics for the coming decade.

Beyond mere costs enhancement, greater productivity, testing efficiency, and quality is achieved – driving more focus and customer trust in the long-term. New business insights are gathered, not only by the service provider, but for the client as well. It helps us to see hitherto unseen areas or capabilities and achieve key strategic objectives.

Anticipating with a degree of precision allows an organization to make better decisions for their customers

In closing, here’s a favorite quote – which possibly summarizes all that we’ve discussed:

“Learning from data is virtually universally useful. Master it and you’ll be welcomed nearly everywhere! —John Elder”