Coursera: Design Thinking for Innovation:

Learning Launch Tool

Challenge: I'm part of a Performance Management team and I work towards  analysing Customer Satisfaction (CSAT) trends in the Call Centre space. The  challenge I’m faced with is that CSAT for a voice function (approx. 250 heads) is  much lower than the goal and needs improvement. We need to understand  what is bringing down the function CSAT performance and take corrective  measures to increase the overall CSAT for the function so they meet the goal.

Selection: I've decided to use the Learning Launch tool; the key here is to do  experiments that are cheap and fast. I pick a team of 3 individuals viz. a  process lead from another function, a specialist from the function and a  member from the Customer experience team. The team I’ve selected is diverse  and small as my cognitive blindness will be different from the others and vice  versa. Also, I felt that there is a need to have a sceptic within the team to  protect ourselves from confirmation bias.

Application: We begin by coming together and brainstorm on the reasons why  we are facing the specified challenge with each individual getting his/her  freedom of speech. Parallelly we have the MIS team gather customer data.  Post the brainstorm sessions and the data we gathered, we make a list of  assumptions that would help us experiment. We would deliberate if  assumptions are not likely to be true, we would change the idea or Trash can  and move on further. We select 2 teams (25 heads) as our population who  would be part of the experiment. We analysed that majority of the customer  dissatisfaction calls were when calls were cold transferred (directly transfer a  call to another specialist without any knowledge transfer between the  specialists) to another function. It was decided that the 25 specialist that were  selected would be trained to connect and introduce the customer to the  specialist of the other function with appropriate knowledge transfer between  them.

Insight: We learnt that there was an increase in customer satisfaction scores  for the analyst that were part of the experiment. The human touch made the  difference when the specialist connected and introduced the customer to his  colleague. The CSAT scores had seen an improvement of 10% to the baseline  for those specialists. This also positively impacted the overall morale of the  team.

Approach: Since the experiment was successful on the 25 specialists, the idea  is to roll it out to the entire function. We decide to have the training in batches  of 25 instead of the entire function so that there is no impact to service levels.  The roll out of the new process will be completed within a period of 2 weeks.  With the improvement of CSAT scores seen within the experiment, we would  estimate that the overall function would begin meeting its CSAT goal 3 months  from the time every specialist is onboarded with the new process. The  assumptions gathered in the brainstorming sessions which were not likely to  be true were tabled for possible latter use.


Coursera : Design Thinking for Innovation : Learning Launch Tool Challenge : I 'm part of a Performance Management team and I work towards analysing Customer Satisfaction ( CSAT ) trends in the Call Centre space . The challenge I ’m faced with is that CSAT for a voice function ( approx . 250 heads ) is much lower than the goal and needs improvement . We need to understand what is bringing down the function CSAT performance and take corrective measures to increase the overall CSAT for the function so they meet the goal . Selection : I 've decided to use the Learning Launch tool ; the key here is to do experiments that are cheap and fast . I pick a team of 3 individuals viz . a process lead from another function , a specialist from the function and a member from the Customer experience team . The team I ’ve selected is diverse and small as my cognitive blindness will be different from the others and vice versa . Also , I felt that there is a need to have a sceptic within the team to protect ourselves from confirmation bias . Application : We begin by coming together and brainstorm on the reasons why we are facing the specified challenge with each individual getting his / her freedom of speech . Parallelly we have the MIS team gather customer data . Post the brainstorm sessions and the data we gathered , we make a list of assumptions that would help us experiment . We would deliberate if assumptions are not likely to be true , we would change the idea or Trash can and move on further . We select 2 teams ( 25 heads ) as our population who would be part of the experiment . We analysed that majority of the customer dissatisfaction calls were when calls were cold transferred ( directly transfer a call to another specialist without any knowledge transfer between the specialists ) to another function . It was decided that the 25 specialist that were selected would be trained to connect and introduce the customer to the specialist of the other function with appropriate knowledge transfer between them . Insight : We learnt that there was an increase in customer satisfaction scores for the analyst that were part of the experiment . The human touch made the difference when the specialist connected and introduced the customer to his colleague . The CSAT scores had seen an improvement of 10 % to the baseline for those specialists . This also positively impacted the overall morale of the team . Approach : Since the experiment was successful on the 25 specialists , the idea is to roll it out to the entire function . We decide to have the training in batches of 25 instead of the entire function so that there is no impact to service levels . The roll out of the new process will be completed within a period of 2 weeks . With the improvement of CSAT scores seen within the experiment , we would estimate that the overall function would begin meeting its CSAT goal 3 months from the time every specialist is onboarded with the new process . The assumptions gathered in the brainstorming sessions which were not likely to be true were tabled for possible latter use .