How to use data to improve customer service

Retail stores, restaurants, convenience shops, and similar businesses are facing increasing competition, prompting many to explore techniques to stand out in a crowded landscape. Providing better customer service can be the solution. According to a report from HubSpot, 89% of consumers claim they’re more likely to choose the same brand again after a positive customer experience. 

Your company may be aware that customer service is lacking, but not know how to strengthen this vital area. The first step toward a better customer experience is to understand what your customers need and want, which makes data that you can gather from your business a goldmine of information.

Let’s review how your business can use data to improve customer service. 

Understanding the Power of Data in Customer Service 

Customer service involves more than helping a customer with a return or solving an issue — it’s about creating a positive experience for your customers every time, even when they come to you with something going wrong. A positive customer service experience contributes to the overall customer experience a person has at each touchpoint along their journey interacting with your brand, from initial awareness of your products and services to support after the sale. 

Traditionally, businesses have taken a reactive approach to customer service, meaning representatives would respond to customers' questions and concerns to resolve problems. An increase in the amount of big data and technologies that allow businesses to more effectively gather this data has enabled proactive customer service where support is available at each stage of the customer journey. 

Incorporating data into decision-making has positive implications for business growth in general. Pragmatic Institute states advanced data and analytics maturity have translated into a boost in revenue for 54% of businesses and given 44% a competitive edge. 

Similarly, when companies invest in data analytics for customer service, the result is often greater customer satisfaction and brand loyalty. It helps them develop more precise and personalized marketing and support to meet their customers’ needs. As some research states, 58% of businesses experience a substantial increase in customer retention by leveraging customer service data and analytics. 

Types of Data to Enhance Customer Service

Many companies have relied on customer relationship management (CRM) systems to gather information about customers, such as buying patterns and related interactions. These systems prove valuable for uncovering customer data, but they often lack insight into customers’ feelings about service. The following are several types of data your organization can utilize to improve customer service: 

Demographics 

Knowing the average ages, income ranges and other demographic aspects of your customer base can help you create more personalized messaging. 

Purchase History 

What products and services have specific customers bought most? Businesses show they care about their customers when they know what offerings they’re most interested in purchasing. 

Customer Behavior 

This data helps you better understand how customers interact with your brand. For example, if more customers prefer interacting on social media sites, offering customer support via these channels can boost their experience. 

Customer Feedback 

Customer feedback is valuable for understanding customer sentiment about ease of navigation on your e-commerce  site, the convenience of support services, and more. Many customers share insights on social media sites like Facebook, Instagram, and LinkedIn. 

Collecting and Analyzing Customer Data 

Gathering the right data is necessary to improve customer service, but it’s important to remember that most businesses must comply with industry regulations regarding data privacy, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Therefore, businesses need methods for gleaning valuable data from customers while respecting privacy and remaining compliant. 

The following are several ways you can collect customer data ethically and effectively: 

Customer Surveys 

Asking customers for their insights directly helps you stay transparent while gaining the data you need. Feedback from surveys can help you enhance support services, marketing strategies, and sales initiatives. 

Social Media 

In addition to providing businesses with many opportunities to engage with customers, many social media sites offer built-in metrics for data analytics. You can combine these insights with other data for a deeper understanding of customer sentiment. 

Transactional Data 

This is data collected at the point of sale, such as products and services purchased and the dates and times of purchase. Compliance will prevent you from collecting personal data about customers, but information from transactions alone can help enhance customer service. 

Newsletters and Blogs 

Typically used in marketing campaigns, blogs, and newsletters can be hotbeds of customer data. In this content, you educate customers or provide them with discounts and other incentives in exchange for feedback or other information. 

Customer Complaint Program 

If your main focus is customer feedback, a customer complaint program can be beneficial. This program is more specific to your business than a standard survey, making for a personalized experience for customers. You receive feedback in real-time  at the store level — as opposed to the top of the chain — empowering more informed decision-making and showing customers you value their perspectives. 

Personalization and Customer Service 

A significant benefit of data-driven customer service is personalization. The insights you gather enable you to customize customer interactions, whether it’s using purchase history to suggest products or services that might interest them or looking at demographics to determine which messaging most resonates with the target audience. 

Consider these examples of well-known businesses that have utilized data to provide personalized customer service: 

Amazon 

This e-commerce  giant leverages data to recommend products to customers that they may not even have realized existed. Amazon collects data based on purchase and browsing history and compares it with similar customer profiles to deliver a highly personalized set of recommended items. This system enables customers to find the products they want quickly while also exposing them to offerings they might not have seen otherwise. This strategy increases customer satisfaction by responding to specific needs and boosts Amazon’s bottom line. 

Netflix 

Even as more and more companies release streaming platforms, Netflix remains on top. Why? Part of this dominance can be ascribed to its use of data. Netflix considers various aspects to create a more personalized experience for users, including the dates and times they watch shows, which devices they use, how long it takes them to complete a series, and even how often they pause during a program. These metrics are used to augment algorithms, creating a customer experience where it seems Netflix can read their subscriber’s minds. 

Starbucks 

As a leader in drive-thru service, Starbucks understands the importance of personalized customer experience and drive-thru speed of service. The company utilizes several methods to gather data from customers, such as Deep Brew, a recommendation system using artificial intelligence. It uses data to tailor menus based on customer preferences and even switches menu items depending on the weather, season, or time of day. 

Anticipating Customer Needs with Data 

As mentioned, the goal of improving customer service through data is to be proactive. This is where predictive data analysis comes in. Data-driven predictive analytics involves combining historical data with analytic tools like machine learning to make predictions about future customer needs and behaviors. The following are some ways that predictive analytics can help deliver a great customer experience: 

  • Simplifying checkout processes to provide customers with a seamless e-commerce  experience 
  • Creating more personalized marketing campaigns where customers receive the right message at the right time and on the platforms they most use to interact with a brand 
  • Forecasting customers’ needs to deliver relevant messaging to customers before they realize they have that need 
  • Enabling businesses to manage and allocate resources more effectively 
  • Empowering customer support teams to swiftly and accurately resolve issues 
  • Reducing churn by helping companies identify customers at high risk for churn to better meet their needs 

Data-Driven Service Recovery 

No business is perfect; it’s common to drop the ball with customer service from time to time. However, these incidents are opportunities to use data to improve the customer experience. These data-driven steps can help significantly improve service recovery and boost customer service: 

  • Gather complaint data from surveys, social media posts, emails, calls, and other sources. It’s helpful to have a system for storing and organizing this data. 
  • Analyze the data to detect trends or patterns in complaints. This step helps reveal possible service gaps. Different methods exist for evaluating complaints, such as SWOT analysis or root cause analysis. 
  • Develop and communicate steps to improvement with a plan that details the objectives, timelines, key performance indicators, and similar aspects. This plan should be shared with customers, employees, and other stakeholders. 
  • Establish methods and tools for evaluating progress, such as feedback surveys and customer satisfaction scores. This step allows for timely adjustments to improvement plans.

Invest in Customer Service Analytics from DTiQ 

If your company wants to improve customer service, start by looking at customer data. The insights you glean will allow you to better meet the needs and preferences of different customers, respond to their questions and concerns, and predict their future needs. What’s more, focusing on data-driven customer service can mean increased customer loyalty and revenue for your business. 

Restaurants, retail stores, and convenience stores that need guidance using data to enhance customer service should turn to DTIQ. Our business intelligence solutions deliver the insights you need to better respond to pain points and deliver an unforgettable customer experience. We also offer smart video solutions that can help augment customer service. Contact us to learn more about our solutions to customer service analytics or book a demo today. 

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How to use data to improve customer service

Retail stores, restaurants, convenience shops, and similar businesses are facing increasing competition, prompting many to explore techniques to stand out in a crowded landscape. Providing better customer service can be the solution. According to a report from HubSpot, 89% of consumers claim they’re more likely to choose the same brand again after a positive customer experience. 

Your company may be aware that customer service is lacking, but not know how to strengthen this vital area. The first step toward a better customer experience is to understand what your customers need and want, which makes data that you can gather from your business a goldmine of information.

Let’s review how your business can use data to improve customer service. 

Understanding the Power of Data in Customer Service 

Customer service involves more than helping a customer with a return or solving an issue — it’s about creating a positive experience for your customers every time, even when they come to you with something going wrong. A positive customer service experience contributes to the overall customer experience a person has at each touchpoint along their journey interacting with your brand, from initial awareness of your products and services to support after the sale. 

Traditionally, businesses have taken a reactive approach to customer service, meaning representatives would respond to customers' questions and concerns to resolve problems. An increase in the amount of big data and technologies that allow businesses to more effectively gather this data has enabled proactive customer service where support is available at each stage of the customer journey. 

Incorporating data into decision-making has positive implications for business growth in general. Pragmatic Institute states advanced data and analytics maturity have translated into a boost in revenue for 54% of businesses and given 44% a competitive edge. 

Similarly, when companies invest in data analytics for customer service, the result is often greater customer satisfaction and brand loyalty. It helps them develop more precise and personalized marketing and support to meet their customers’ needs. As some research states, 58% of businesses experience a substantial increase in customer retention by leveraging customer service data and analytics. 

Types of Data to Enhance Customer Service

Many companies have relied on customer relationship management (CRM) systems to gather information about customers, such as buying patterns and related interactions. These systems prove valuable for uncovering customer data, but they often lack insight into customers’ feelings about service. The following are several types of data your organization can utilize to improve customer service: 

Demographics 

Knowing the average ages, income ranges and other demographic aspects of your customer base can help you create more personalized messaging. 

Purchase History 

What products and services have specific customers bought most? Businesses show they care about their customers when they know what offerings they’re most interested in purchasing. 

Customer Behavior 

This data helps you better understand how customers interact with your brand. For example, if more customers prefer interacting on social media sites, offering customer support via these channels can boost their experience. 

Customer Feedback 

Customer feedback is valuable for understanding customer sentiment about ease of navigation on your e-commerce  site, the convenience of support services, and more. Many customers share insights on social media sites like Facebook, Instagram, and LinkedIn. 

Collecting and Analyzing Customer Data 

Gathering the right data is necessary to improve customer service, but it’s important to remember that most businesses must comply with industry regulations regarding data privacy, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Therefore, businesses need methods for gleaning valuable data from customers while respecting privacy and remaining compliant. 

The following are several ways you can collect customer data ethically and effectively: 

Customer Surveys 

Asking customers for their insights directly helps you stay transparent while gaining the data you need. Feedback from surveys can help you enhance support services, marketing strategies, and sales initiatives. 

Social Media 

In addition to providing businesses with many opportunities to engage with customers, many social media sites offer built-in metrics for data analytics. You can combine these insights with other data for a deeper understanding of customer sentiment. 

Transactional Data 

This is data collected at the point of sale, such as products and services purchased and the dates and times of purchase. Compliance will prevent you from collecting personal data about customers, but information from transactions alone can help enhance customer service. 

Newsletters and Blogs 

Typically used in marketing campaigns, blogs, and newsletters can be hotbeds of customer data. In this content, you educate customers or provide them with discounts and other incentives in exchange for feedback or other information. 

Customer Complaint Program 

If your main focus is customer feedback, a customer complaint program can be beneficial. This program is more specific to your business than a standard survey, making for a personalized experience for customers. You receive feedback in real-time  at the store level — as opposed to the top of the chain — empowering more informed decision-making and showing customers you value their perspectives. 

Personalization and Customer Service 

A significant benefit of data-driven customer service is personalization. The insights you gather enable you to customize customer interactions, whether it’s using purchase history to suggest products or services that might interest them or looking at demographics to determine which messaging most resonates with the target audience. 

Consider these examples of well-known businesses that have utilized data to provide personalized customer service: 

Amazon 

This e-commerce  giant leverages data to recommend products to customers that they may not even have realized existed. Amazon collects data based on purchase and browsing history and compares it with similar customer profiles to deliver a highly personalized set of recommended items. This system enables customers to find the products they want quickly while also exposing them to offerings they might not have seen otherwise. This strategy increases customer satisfaction by responding to specific needs and boosts Amazon’s bottom line. 

Netflix 

Even as more and more companies release streaming platforms, Netflix remains on top. Why? Part of this dominance can be ascribed to its use of data. Netflix considers various aspects to create a more personalized experience for users, including the dates and times they watch shows, which devices they use, how long it takes them to complete a series, and even how often they pause during a program. These metrics are used to augment algorithms, creating a customer experience where it seems Netflix can read their subscriber’s minds. 

Starbucks 

As a leader in drive-thru service, Starbucks understands the importance of personalized customer experience and drive-thru speed of service. The company utilizes several methods to gather data from customers, such as Deep Brew, a recommendation system using artificial intelligence. It uses data to tailor menus based on customer preferences and even switches menu items depending on the weather, season, or time of day. 

Anticipating Customer Needs with Data 

As mentioned, the goal of improving customer service through data is to be proactive. This is where predictive data analysis comes in. Data-driven predictive analytics involves combining historical data with analytic tools like machine learning to make predictions about future customer needs and behaviors. The following are some ways that predictive analytics can help deliver a great customer experience: 

  • Simplifying checkout processes to provide customers with a seamless e-commerce  experience 
  • Creating more personalized marketing campaigns where customers receive the right message at the right time and on the platforms they most use to interact with a brand 
  • Forecasting customers’ needs to deliver relevant messaging to customers before they realize they have that need 
  • Enabling businesses to manage and allocate resources more effectively 
  • Empowering customer support teams to swiftly and accurately resolve issues 
  • Reducing churn by helping companies identify customers at high risk for churn to better meet their needs 

Data-Driven Service Recovery 

No business is perfect; it’s common to drop the ball with customer service from time to time. However, these incidents are opportunities to use data to improve the customer experience. These data-driven steps can help significantly improve service recovery and boost customer service: 

  • Gather complaint data from surveys, social media posts, emails, calls, and other sources. It’s helpful to have a system for storing and organizing this data. 
  • Analyze the data to detect trends or patterns in complaints. This step helps reveal possible service gaps. Different methods exist for evaluating complaints, such as SWOT analysis or root cause analysis. 
  • Develop and communicate steps to improvement with a plan that details the objectives, timelines, key performance indicators, and similar aspects. This plan should be shared with customers, employees, and other stakeholders. 
  • Establish methods and tools for evaluating progress, such as feedback surveys and customer satisfaction scores. This step allows for timely adjustments to improvement plans.

Invest in Customer Service Analytics from DTiQ 

If your company wants to improve customer service, start by looking at customer data. The insights you glean will allow you to better meet the needs and preferences of different customers, respond to their questions and concerns, and predict their future needs. What’s more, focusing on data-driven customer service can mean increased customer loyalty and revenue for your business. 

Restaurants, retail stores, and convenience stores that need guidance using data to enhance customer service should turn to DTIQ. Our business intelligence solutions deliver the insights you need to better respond to pain points and deliver an unforgettable customer experience. We also offer smart video solutions that can help augment customer service. Contact us to learn more about our solutions to customer service analytics or book a demo today. 

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How to use data to improve customer service

Posted
December 18, 2023
by
Katie McCann
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Retail stores, restaurants, convenience shops, and similar businesses are facing increasing competition, prompting many to explore techniques to stand out in a crowded landscape. Providing better customer service can be the solution. According to a report from HubSpot, 89% of consumers claim they’re more likely to choose the same brand again after a positive customer experience. 

Your company may be aware that customer service is lacking, but not know how to strengthen this vital area. The first step toward a better customer experience is to understand what your customers need and want, which makes data that you can gather from your business a goldmine of information.

Let’s review how your business can use data to improve customer service. 

Understanding the Power of Data in Customer Service 

Customer service involves more than helping a customer with a return or solving an issue — it’s about creating a positive experience for your customers every time, even when they come to you with something going wrong. A positive customer service experience contributes to the overall customer experience a person has at each touchpoint along their journey interacting with your brand, from initial awareness of your products and services to support after the sale. 

Traditionally, businesses have taken a reactive approach to customer service, meaning representatives would respond to customers' questions and concerns to resolve problems. An increase in the amount of big data and technologies that allow businesses to more effectively gather this data has enabled proactive customer service where support is available at each stage of the customer journey. 

Incorporating data into decision-making has positive implications for business growth in general. Pragmatic Institute states advanced data and analytics maturity have translated into a boost in revenue for 54% of businesses and given 44% a competitive edge. 

Similarly, when companies invest in data analytics for customer service, the result is often greater customer satisfaction and brand loyalty. It helps them develop more precise and personalized marketing and support to meet their customers’ needs. As some research states, 58% of businesses experience a substantial increase in customer retention by leveraging customer service data and analytics. 

Types of Data to Enhance Customer Service

Many companies have relied on customer relationship management (CRM) systems to gather information about customers, such as buying patterns and related interactions. These systems prove valuable for uncovering customer data, but they often lack insight into customers’ feelings about service. The following are several types of data your organization can utilize to improve customer service: 

Demographics 

Knowing the average ages, income ranges and other demographic aspects of your customer base can help you create more personalized messaging. 

Purchase History 

What products and services have specific customers bought most? Businesses show they care about their customers when they know what offerings they’re most interested in purchasing. 

Customer Behavior 

This data helps you better understand how customers interact with your brand. For example, if more customers prefer interacting on social media sites, offering customer support via these channels can boost their experience. 

Customer Feedback 

Customer feedback is valuable for understanding customer sentiment about ease of navigation on your e-commerce  site, the convenience of support services, and more. Many customers share insights on social media sites like Facebook, Instagram, and LinkedIn. 

Collecting and Analyzing Customer Data 

Gathering the right data is necessary to improve customer service, but it’s important to remember that most businesses must comply with industry regulations regarding data privacy, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Therefore, businesses need methods for gleaning valuable data from customers while respecting privacy and remaining compliant. 

The following are several ways you can collect customer data ethically and effectively: 

Customer Surveys 

Asking customers for their insights directly helps you stay transparent while gaining the data you need. Feedback from surveys can help you enhance support services, marketing strategies, and sales initiatives. 

Social Media 

In addition to providing businesses with many opportunities to engage with customers, many social media sites offer built-in metrics for data analytics. You can combine these insights with other data for a deeper understanding of customer sentiment. 

Transactional Data 

This is data collected at the point of sale, such as products and services purchased and the dates and times of purchase. Compliance will prevent you from collecting personal data about customers, but information from transactions alone can help enhance customer service. 

Newsletters and Blogs 

Typically used in marketing campaigns, blogs, and newsletters can be hotbeds of customer data. In this content, you educate customers or provide them with discounts and other incentives in exchange for feedback or other information. 

Customer Complaint Program 

If your main focus is customer feedback, a customer complaint program can be beneficial. This program is more specific to your business than a standard survey, making for a personalized experience for customers. You receive feedback in real-time  at the store level — as opposed to the top of the chain — empowering more informed decision-making and showing customers you value their perspectives. 

Personalization and Customer Service 

A significant benefit of data-driven customer service is personalization. The insights you gather enable you to customize customer interactions, whether it’s using purchase history to suggest products or services that might interest them or looking at demographics to determine which messaging most resonates with the target audience. 

Consider these examples of well-known businesses that have utilized data to provide personalized customer service: 

Amazon 

This e-commerce  giant leverages data to recommend products to customers that they may not even have realized existed. Amazon collects data based on purchase and browsing history and compares it with similar customer profiles to deliver a highly personalized set of recommended items. This system enables customers to find the products they want quickly while also exposing them to offerings they might not have seen otherwise. This strategy increases customer satisfaction by responding to specific needs and boosts Amazon’s bottom line. 

Netflix 

Even as more and more companies release streaming platforms, Netflix remains on top. Why? Part of this dominance can be ascribed to its use of data. Netflix considers various aspects to create a more personalized experience for users, including the dates and times they watch shows, which devices they use, how long it takes them to complete a series, and even how often they pause during a program. These metrics are used to augment algorithms, creating a customer experience where it seems Netflix can read their subscriber’s minds. 

Starbucks 

As a leader in drive-thru service, Starbucks understands the importance of personalized customer experience and drive-thru speed of service. The company utilizes several methods to gather data from customers, such as Deep Brew, a recommendation system using artificial intelligence. It uses data to tailor menus based on customer preferences and even switches menu items depending on the weather, season, or time of day. 

Anticipating Customer Needs with Data 

As mentioned, the goal of improving customer service through data is to be proactive. This is where predictive data analysis comes in. Data-driven predictive analytics involves combining historical data with analytic tools like machine learning to make predictions about future customer needs and behaviors. The following are some ways that predictive analytics can help deliver a great customer experience: 

  • Simplifying checkout processes to provide customers with a seamless e-commerce  experience 
  • Creating more personalized marketing campaigns where customers receive the right message at the right time and on the platforms they most use to interact with a brand 
  • Forecasting customers’ needs to deliver relevant messaging to customers before they realize they have that need 
  • Enabling businesses to manage and allocate resources more effectively 
  • Empowering customer support teams to swiftly and accurately resolve issues 
  • Reducing churn by helping companies identify customers at high risk for churn to better meet their needs 

Data-Driven Service Recovery 

No business is perfect; it’s common to drop the ball with customer service from time to time. However, these incidents are opportunities to use data to improve the customer experience. These data-driven steps can help significantly improve service recovery and boost customer service: 

  • Gather complaint data from surveys, social media posts, emails, calls, and other sources. It’s helpful to have a system for storing and organizing this data. 
  • Analyze the data to detect trends or patterns in complaints. This step helps reveal possible service gaps. Different methods exist for evaluating complaints, such as SWOT analysis or root cause analysis. 
  • Develop and communicate steps to improvement with a plan that details the objectives, timelines, key performance indicators, and similar aspects. This plan should be shared with customers, employees, and other stakeholders. 
  • Establish methods and tools for evaluating progress, such as feedback surveys and customer satisfaction scores. This step allows for timely adjustments to improvement plans.

Invest in Customer Service Analytics from DTiQ 

If your company wants to improve customer service, start by looking at customer data. The insights you glean will allow you to better meet the needs and preferences of different customers, respond to their questions and concerns, and predict their future needs. What’s more, focusing on data-driven customer service can mean increased customer loyalty and revenue for your business. 

Restaurants, retail stores, and convenience stores that need guidance using data to enhance customer service should turn to DTIQ. Our business intelligence solutions deliver the insights you need to better respond to pain points and deliver an unforgettable customer experience. We also offer smart video solutions that can help augment customer service. Contact us to learn more about our solutions to customer service analytics or book a demo today. 

THE AUTHOR
Katie McCann
Manager, Content & Communications
Meet Katie, the creative force behind content and communications for DTiQ. When she's not at work, you'll find her soaking up the great outdoors with her four-legged sidekick or breaking it down in a spin class. But it's not all hustle – she knows how to kick back and enjoy some quality time with her friends, especially when there's charcuterie involved.

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