Reflection – Learning Launch   Challenge   I work for a Telecom organization and head the IT Infrastructure division. We have contact centers  where the customers can call the contact center and speak to the contact center agents for getting their  issues resolved. The main challenge in the contact center is the high churn rate of the agents. Due to the  high churn of agents, the skill levels of the contact center goes down as new agents take time to learn to  resolve the customer issues. Many a times the customer is not satisfied with the resolution and they  tend to call the contact center again. This results into higher service costs and customer dissatisfaction.  A small team was formed to find out a solution to scale the expertise of the new agents so that repeat  calls from customers are reduced.      Selection   The first task of the team was to identify the type of problem customers’ call and the resolution given by  the agents. For this they went to the contact centers and through observations/interviews they  observed the actual process. After a brainstorming session, the team put together a solution to address  the problem of scaling the expertise. The solution will use natural language processing and use machine  learning to predict the best possible solution to a customer problem. The team took two weeks to  develop this tool. For testing this solution we planned to use the Learning Launch Tool    Application   For our first learning launch, we selected one contact center. We then selected 10 skilled contact center  agents and 10 new agents. We then selected the Top 5 problems for which customers call the contact  center. We interviewed the skilled contact center agents for the best possible solutions for the top 5  issues. The tool which was developed uses natural language processing and machine learning.  Depending on what the customer reports as a problem, the tool will give a list of steps as the best  possible resolution to the customer problem.  We trained 10 skilled contact center agents to use this  tool. Initially the accuracy of the resolution of the problems was at 70%. The machine learning tool was  trained again based on the inputs of the skilled agents at the end of each day for a successive 3 days and  on the 4th day the accuracy reached 90%.  Now we selected 10 new agents to determine the efficiency of the tool. 5 agents were trained and were  told to use this tool during customer interaction and 5 were not given this tool. We measured the repeat  calls for all these 10 agents.  We also did a survey with the agents who used this tool on the utility of this  tool to resolve a customer issue. This pilot went on for 2 weeks.    Insight   By applying the learning launch tool, our team identified that the repeat calls reduced by 15%, for the  agents who used this tool.  This will reduce the operating costs. The team also found out that the 95%  agents also felt that this tool was very useful to give a proper resolution to the customer problems.    Approach   The Learning launch tool has been very effective in our design thinking process to resolve the scaling of  expertise levels of a new contact center agent. This learning launch tool provided us a good technique to  assess the effectiveness of the tool within the budget and within a 4 week time period. Hence we will be  in favor of selecting this learning launch tool in our further pilots and incorporate in our design thinking  process


Reflection – Learning Launch Challenge I work for a Telecom organization and head the IT Infrastructure division . We have contact centers where the customers can call the contact center and speak to the contact center agents for getting their issues resolved . The main challenge in the contact center is the high churn rate of the agents . Due to the high churn of agents , the skill levels of the contact center goes down as new agents take time to learn to resolve the customer issues . Many a times the customer is not satisfied with the resolution and they tend to call the contact center again . This results into higher service costs and customer dissatisfaction . A small team was formed to find out a solution to scale the expertise of the new agents so that repeat calls from customers are reduced . Selection The first task of the team was to identify the type of problem customers ’ call and the resolution given by the agents . For this they went to the contact centers and through observations / interviews they observed the actual process . After a brainstorming session , the team put together a solution to address the problem of scaling the expertise . The solution will use natural language processing and use machine learning to predict the best possible solution to a customer problem . The team took two weeks to develop this tool . For testing this solution we planned to use the Learning Launch Tool Application For our first learning launch , we selected one contact center . We then selected 10 skilled contact center agents and 10 new agents . We then selected the Top 5 problems for which customers call the contact center . We interviewed the skilled contact center agents for the best possible solutions for the top 5 issues . The tool which was developed uses natural language processing and machine learning . Depending on what the customer reports as a problem , the tool will give a list of steps as the best possible resolution to the customer problem . We trained 10 skilled contact center agents to use this tool . Initially the accuracy of the resolution of the problems was at 70 % . The machine learning tool was trained again based on the inputs of the skilled agents at the end of each day for a successive 3 days and on the 4th day the accuracy reached 90 % . Now we selected 10 new agents to determine the efficiency of the tool . 5 agents were trained and were told to use this tool during customer interaction and 5 were not given this tool . We measured the repeat calls for all these 10 agents . We also did a survey with the agents who used this tool on the utility of this tool to resolve a customer issue . This pilot went on for 2 weeks . Insight By applying the learning launch tool , our team identified that the repeat calls reduced by 15 % , for the agents who used this tool . This will reduce the operating costs . The team also found out that the 95 % agents also felt that this tool was very useful to give a proper resolution to the customer problems . Approach The Learning launch tool has been very effective in our design thinking process to resolve the scaling of expertise levels of a new contact center agent . This learning launch tool provided us a good technique to assess the effectiveness of the tool within the budget and within a 4 week time period . Hence we will be in favor of selecting this learning launch tool in our further pilots and incorporate in our design thinking process