google fiber case study
google fiber case study
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how to reduce the number of repeat calls to google fiber support team and increase customer satisfaction?
how to reduce the number of repeat calls to google fiber support team and increase customer satisfaction?
The following Case Study was done to address the Google Fiber Team's concerns regarding repeat calls from customers about their service. The aim was to find a way to analyze the available information and reduce the number of repeat calls which would in turn have a positive impact on customer satisfaction.
The following Case Study was done to address the Google Fiber Team's concerns regarding repeat calls from customers about their service. The aim was to find a way to analyze the available information and reduce the number of repeat calls which would in turn have a positive impact on customer satisfaction.
To accomplish this, the team needed to understand how often the customer calls customer support after their first inquiry, what market cities are generating the most repeat calls and what is the most common problem type that pertains to these calls.
To accomplish this, the team needed to understand how often the customer calls customer support after their first inquiry, what market cities are generating the most repeat calls and what is the most common problem type that pertains to these calls.
As per stakeholder requirements, three charts were required to be presented on the dashboard plus any others that the BI professional (in this case me) saw fit:
As per stakeholder requirements, three charts were required to be presented on the dashboard plus any others that the BI professional (in this case me) saw fit:
A chart or table measuring repeat calls by their first contact date.
A chart or table exploring repeat calls by market and problem type.
Charts showcasing repeat calls by week, month, and quarter
The image above represents the completed dashboard that includes charts on number of calls by Market and Type, Market and Type of first call and second call, percentage of Repeat Calls by day of week, Repeat call volume compared to first call and Repeat calls by month.
To accomplish this task, the first step was to import the dataset to BigQuery and use SQL to create a combined target table to be used for visualization. Additionally Google Dataflow was used to understand the data pipeline concepts pertaining to this case.
The dataset for this project was obtained as part of the Google Business Intelligence Professional Certificate course and was anonymized so that the three different cities were listed as market_1, market_2 and market_3. The problem type was also anonymized as type_n. The conclusions drawn from the dashboard would be beneficial to the Google Fiber team in identifying which problems are most persistent, which markets are the least satisfied and also which months or days of the week experience highest call volumes. Having this information would not just help them improve customer satisfaction as was their goal but also to manage employees better or root out any bugs in the product.
The dataset for this project was obtained as part of the Google Business Intelligence Professional Certificate course and was anonymized so that the three different cities were listed as market_1, market_2 and market_3. The problem type was also anonymized as type_n. The conclusions drawn from the dashboard would be beneficial to the Google Fiber team in identifying which problems are most persistent, which markets are the least satisfied and also which months or days of the week experience highest call volumes. Having this information would not just help them improve customer satisfaction as was their goal but also to manage employees better or root out any bugs in the product.
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