Challenge

I have been working during 13 years for my country’s operation of the biggest telecom operator in the  Caribbean, as a manager of the Sales and distribution team. The company was doing great in this country  where, according to the latest 2012 household survey, more than 6 million of its people live below the  poverty line with less than $ 2.41 a day, and more than 2.5 million below the extreme poverty line with 1.  23 $ a day. We have 4.5 million subscribers from whom most are very price sensitive.

From 1999 to 2009, First with one competitor, then 2 competitors at the arrival in 2003 of a new operator,  our challenges were: 1- massively acquire customers and build outstanding market share to become the  number one cellular network in terms of capacity and customer service at a time when voice call was the  main source of revenue in the telecom sector; 2- add enough new customers to replace the ones who  leave our network to adopt our competitors. Became number one operator in the market, at the end of  the first third of 2008, we acquired our main competitor to reduce the competition to two operators  instead of three. Meanwhile, the business switches to data which essence of differentiation is speed. Our  only competitor now understands this and capitalizes on.  Months later, we started losing customers.  Month over month, it costs us so much money to re-onboard customers who left and onboard new ones  while maintain current ones should be profitable. From 2004, what is the Issue? We face Continuous  churn.

Selection

After six years trying to solve churn issue by offering bundle minutes, SMS and data packages, we decided  to tackle the problem from its cause or source, to know the exact reason why customers leave our mobile  operator network using the learning launch tools for having empathic understanding of the problem,  making our own assumptions on issue we are trying to solve.

Application

To identify precisely why customers say bye bye to our network and solve this issue, our Product, Sales,  marketing and technical teams need to know the real decision motivation of the churned customers  whom they are trying to solve problem, by empathizing with, by understanding the landscape, ideating  solutions, prototyping, and testing to see if we are moving in the right direction. At the phase of the  learning launch, we involved a consulting firm to conduct survey to find out more about the area of  concern through observing, engaging and empathizing with people to understand their experiences and  motivations, as well as immersing ourselves in the physical environment so we can gain a deeper personal  understanding of the issues to solve. In such a human-centered process with ‘empathy”, the team in  charge of solving the problem come to set aside their own assumptions about the problem in order to  gain insight into users and their needs. A substantial amount of information is gathered at this stage to  use during the next stage and to develop the best possible understanding of the users, their needs.

Insight

Despite of its complexity, churn is a solvable problem that usually can’t be pinned on a single  function or solved with a silver bullet. Understanding churn starts internally with a cohort  analysis, in which we analyze churned customers and compare them to our non-churned ones  along every dimension. Considering that Design thinking is a non-linear process but iterative,  flexible, we focused on collaboration within stakeholders solving this problem and actively  observing how users think, feel and behave.

At every stage of solving this churn issue, we work with the “why” question and the end in mind,  mixing many tools like structured brainstorming or Ideation: exploiting crossing data from  Commercial, Product and Technical departments creating many ideas, like empathy with real  users for understanding their motivations and needs to satisfy through a cohort analysis in which  we analyzed churned customers and compare them to non-churned ones along many  considerations, such as 1-what is important to consumers when selecting a mobile provider in  this context of price sensitive consumers and data trending; 2- features and plans are nice to  have or essential in consumers' willingness to switch carriers. 3-   Consumers'  feelings  on

emerging technologies and trends in the mobile industry. 4- Sales agents and Customer service  representative who on boarded clients. 5- Sales process in use in stores and onboarding speed -  Customer Net Promoter Score (NPS) - Product utilization rate /number of seats - Feature requests  and, the most importantly, stated reason for churn

The First learning and confirmation was that the ones who leave are mostly customers with low  Average Revenue per User (ARPU) of less than 5 $ a month. The second finding was that high  ARPU customers have been grown both their voice and data consummation month over month.  And, the ultimate discovery was that customers churned for a real reason: the competitor offer  a better internet speed than us at a better value for money.

Approach

This approach aligns with emotional intelligence needed to help our problem solver team act on human  insights, recognizing and collecting emotional data, and using it at the right moments in context that can  become the active ingredient in delivering the solution to our churn issue.

All that done, comes the time to prototype through an experimental phase with the aim to identify the  best possible solution for each of the discoveries during the learning launch. The solutions will be  implemented within the prototypes, and, one by one, they will be investigated and either accepted,  improved and re-examined, or rejected on the basis of the users’ experiences. By the end of this stage,  the churn problem solving team will have a better idea of the constraints inherent to the users and the  problems that are present, and have a clearer view of how real users behave, think, and feel when  interacting with our network.

