Tableau is so great because it talks to all these different softwares. And for us, this was a big milestone—tying them together and getting text mining into a dashboard.
The analytics team analyzes 17 different attributes for each individual person in the customer base, looking at tenure of the customer, what products they use, call drivers, how frequently they call, average handling time, call sentiment upon contact, age, and many more. Customers are then categorized and put into different cohorts for treatment.
These individual cohorts are then routed to a special support queue where skilled call center representatives handle requests to avoid repeated problems. Tableau dashboards arm the call center reps with contextual information such as historical calling patterns of the customers they are handling so they can resolve issues effectively and reduce the need for customers to call multiple times.
The team used the alerts feature in Tableau to notify them when the call volume goes down below the conditional visual threshold. And when it does, an algorithm is activated to reveal new sets of high-request customers and trigger a new customer list for the representatives.
“It's basically taking the human element out of all of this, which is amazing,” explained Greg.
Having situational awareness of the customers calling behavior and operational capacity for call center staff, Verizon improved their effectiveness when handling customer calls, reduced the number of repeated calls, and achieved a 43 percent reduction in call volume. Tableau dashboards also reduced customer service analysis time by 50 percent with quicker resolution of customer issues. The dashboards are used by high-level managers to develop strategy as well as stakeholders on the front lines who answer calls daily and need to identify calling behaviors, patterns, and historical trends.
Insights from our dashboards help us optimize call center operations to reduce the need for customers to call us multiple times. Monitoring these timely dashboards shows us that as the resolution rate and satisfaction index of customers go up, volumes for calls and dispatches—which are key cost drivers—go down.
Geospatial mapping in Tableau helps team monitor impact of service dispatches
In Verizon Fios, there have been certain households that require multiple dispatches to their homes to resolve issues. In order to help reduce the need for multiple dispatches and improve customer satisfaction the ACE team built a suite of Tableau dashboards that helps dispatch teams monitor a geographical impact of field technician dispatch activity not only at the state and zip code level, but also at the individual household level.
The dashboards analyzed dispatch activity for 6.9 million Fios customers and included KPIs such as number of tickets generated, ticket rate, dispatches initiated, overall dispatch rate—and also investigated the cost impact from these dispatches. Users could further slice and dice these KPIs by dimensions such as the type of customer, the different trouble types driving dispatches, and several other wireline infrastructure attributes.
Mapping features in Tableau, like the Mapbox integration, allowed the team to identify a location-based impact through heat maps and revealed where ticket and dispatch rates over-performed or under-performed and what variables led to the frequent dispatches.
Consequently, Verizon reduced technician dispatch analysis time by more than 50 percent and discovered how geospatial mapping can support other organizational needs.
Tableau R and Python integrations enable deeper digital product analysis
Verizon Fios launches digital products with the intention of supporting stronger customer engagement. One of those products is the Fios chatbot on Facebook Messenger, released in 2017. This and other products help customers engage with the brand and ask questions. Having analytics and reporting to monitor key performance indicators (KPIs) related to acquisition, engagement, customer receptiveness, and product effectiveness is critical.
ACE designed KPIs specific to the Fios chatbot and monitored customer adoption and usage. Applying parameters with custom dates in Tableau, they gauged product performance over time—assessing day-to-day, week-to-week, and month-to-month changes. To extract meaning from chat sessions, they also performed text analytics using the Tableau R integration. Text pre-processing was applied to the customers chat transcripts stored as raw string fields in the table to extract categorical keywords. “All of this is done in R where data is aggregated based on frequency of occurrence of the words. Then I bring it in Tableau and visualize word clouds, allocating color and size to the frequency of the occurrence. This helped a lot in understanding our customer’s mindset while chatting with the bot,” added Sid.
The flexibility of Tableau helped ACE, the digital, and customer service teams track responses for each category of questions that customers ask the bot. The extracted information was used to train the bot’s intelligence so it recognizes more questions and responds appropriately to solve issues. With this knowledge, they also learned the most popular times for customer engagement and increased staff to handle high-volume service events like power outages or pay-per-view sporting events on Fios TV.
It was Verizon’s first time integrating R with Tableau. But seeing positive outcomes, like company executives understanding overall customer response to a product launch or using insights to inform product marketing and planning, encouraged Verizon to integrate other sources with Tableau. To further scale analytics adoption, ACE also leverages Python models in Tableau with TabPy.