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What are the best ways to use data analytics for call center performance?

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Data analytics is the process of collecting, analyzing, and interpreting data to gain insights and support decision making. For call centers, data analytics can help improve performance, customer satisfaction, and profitability. In this article, we will explore some of the best ways to use data analytics for call center performance in the context of telecommunications engineering.

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Juliano Kihara

CSM | Customer Success | Implementation Analyst | Customer Experience | B2B | B2C

1 Define key metrics

The first step to use data analytics for call center performance is to define the key metrics that measure the quality and efficiency of the service. Some common metrics include average handle time, first call resolution, customer satisfaction, service level, occupancy rate, and agent turnover. These metrics should align with the call center's goals and objectives, and be tracked and reported regularly.

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Gaurav Jain

Salesforce Administrator, Salesforc service cloud , Salesforce sales cloud , Salesforce Developer, Cloud Management and Computing , Double Star Ranger ? ? on Trailhead
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Data Analytics for Call Center Performance Data analytics can be used to improve call center performance in several ways. By tracking and analyzing call data, call centers can identify areas for improvement, such as reducing call wait times, improving first-call resolution rates, and increasing customer satisfaction. Key Performance Indicators Some of the key performance indicators (KPIs) that call centers can track and analyze include: Average call wait time First-call resolution rate Customer satisfaction score Call abandonment rate Average handle time This branch of analytics focuses on customer-agent interactions throughout the customer journey. The goal is to improve the quality of these interactions and gain a greater.

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Juliano Kihara

CSM | Customer Success | Implementation Analyst | Customer Experience | B2B | B2C
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Pela minha experiência o desempenho do call center é nutrido pela defini??o das principais métricas, servindo n?o somente como parametro das atividades desenvolvidas e dos contatos realizados, mas para o próprio engajamento do time de colaboradores do setor, que ganha um direcionamento claro, norteando suas a??es de forma uniforme e contínua. E para essa defini??o das métricas o mais importante é reconhecer o escopo pretendido: volumetria de liga??es, liga??es atendidas efetivas, liga??es perdidas, tempo médio das liga??es, lista de callback para as liga??es perdidas, s?o métricas importantes, mas necessário filtrar aquelas que fazem sentido ao dinamismo do cada negócio, para evitar engessamento da opera??o.

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2 Collect and integrate data

The next step is to collect and integrate data from various sources, such as call recordings, customer feedback, CRM systems, IVR systems, and network performance. Data integration is essential to ensure data quality, consistency, and completeness, and to enable cross-analysis and correlation. Data integration can be done using tools such as data warehouses, data lakes, or cloud platforms.

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Gaurav Jain

Salesforce Administrator, Salesforc service cloud , Salesforce sales cloud , Salesforce Developer, Cloud Management and Computing , Double Star Ranger ? ? on Trailhead
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The process typically involves: The client requesting data from the master server The master server collects data from external and internal sources Aggregating the data into a unified data collection Legacy systems: Data stored in legacy systems or mainframes often has missing markers, such as date and time for activities Here are some data integration approaches: Data virtualization: A process of presenting data for viewing purposes, providing real-time integrated information from multiple distinct sources in a single data set Data aggregation: The process of gathering and compiling data to be either stored in its raw form or prepared for analytics ETL: A traditional pipeline that converts raw data to match the target system

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3 Analyze and visualize data

The third step is to analyze and visualize data using various methods, such as descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics summarizes what happened in the past, such as the number of calls, the average handle time, or the customer satisfaction score. Diagnostic analytics explains why something happened, such as the root causes of customer complaints, the factors affecting service level, or the reasons for agent attrition. Predictive analytics forecasts what will happen in the future, such as the expected call volume, the optimal staffing level, or the potential customer churn. Prescriptive analytics recommends what actions to take, such as the best routing strategy, the most effective training program, or the optimal incentive scheme. Data visualization helps present the data analysis results in an easy-to-understand and interactive way, using tools such as dashboards, charts, graphs, or maps.

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4 Optimize and automate processes

The fourth step is to optimize and automate processes based on the data analysis insights. Process optimization aims to improve the performance and efficiency of the call center operations, such as reducing the average handle time, increasing the first call resolution, enhancing the customer satisfaction, or lowering the agent turnover. Process automation aims to reduce the manual and repetitive tasks, such as data entry, data validation, data transfer, or report generation. Process optimization and automation can be done using tools such as artificial intelligence, machine learning, natural language processing, or robotic process automation.

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Gaurav Jain

Salesforce Administrator, Salesforc service cloud , Salesforce sales cloud , Salesforce Developer, Cloud Management and Computing , Double Star Ranger ? ? on Trailhead
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The need for business process automation has been driven by a combination of economic, technological and competitive factors over the past few years. It has become essential for businesses to adapt and thrive in today’s dynamic and data-driven world. Increasing task complexity, driven by technological advancements and the globalisation of markets, has created a demand for streamlined processes that can efficiently handle intricate workflows. Moreover, the deluge of data in the digital era necessitates automation to swiftly process and derive insights from vast datasets, enabling data-driven decision-making.

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Juliano Kihara

CSM | Customer Success | Implementation Analyst | Customer Experience | B2B | B2C
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A análise de dados oferece oportunidade ímpar para uma melhor implementa??o dos processos necessários para melhorias contínuas. Colher dados, com tantas ferramentas disponíveis no mercado, tornou-se uma tarefa mais fácil, mas a usabilidade dessas informa??es que trará diferen?a dentro dos negócios: é o que irá qualificar empresas de sucesso.

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5 Monitor and evaluate outcomes

The final step is to monitor and evaluate the outcomes of the data analytics initiatives, such as the impact on the key metrics, the return on investment, the customer feedback, or the agent satisfaction. Monitoring and evaluation help assess the effectiveness and efficiency of the data analytics solutions, identify the gaps and challenges, and provide feedback and recommendations for improvement. Monitoring and evaluation can be done using tools such as key performance indicators, scorecards, benchmarks, or surveys.

Data analytics is a powerful tool for call center performance improvement. By following these steps, you can leverage data analytics to enhance your voice network design and optimization, and deliver better service to your customers and agents.

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Juliano Kihara

CSM | Customer Success | Implementation Analyst | Customer Experience | B2B | B2C
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Prender-se a um processo já estruturado sem avaliar os resultados de novas métricas é descartar a possibilidade de um insight valioso para a manuten??o e o crescimento de um negócio. O Acompanhamento diário das métricas, monitorando os índices alcan?ados, permite uma interven??o rápida de corre??o em casos críticos, além de nutrir as a??es de sucesso que est?o sendo apontadas na monitoria diária. Para a constru??o de uma experiência completa do cliente, atingido o sucesso durante o processo de usabilidade dos servi?os só é possível com coleta de dados, monitoria diária e avalia??o periódica das informa??es.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best ways to use data analytics for call center performance?

1

2

3

4

5

6

1 Define key metrics

2 Collect and integrate data

3 Analyze and visualize data

4 Optimize and automate processes

5 Monitor and evaluate outcomes

6 Here’s what else to consider

Telecommunications Engineering

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What are the best ways to use data analytics for call center performance?

1

2

3

4

5

6

1 Define key metrics

2 Collect and integrate data

3 Analyze and visualize data

4 Optimize and automate processes

5 Monitor and evaluate outcomes

6 Here’s what else to consider

Telecommunications Engineering

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