How to Use Data Analytics to Improve Customer Support?

Data analytics plays a significant role in studying the performance of your business. You can learn how your users interact with your website, how the conversions occur, and how customer relations are maintained.

Customer data analytics will help you better understand your customers and how they interact with your businesses. It will improve the customer support you provide and have a positive impact on your customers.

Your level of understanding your customer needs and how you provide support can make or break customer relations. It will have a direct impact on your business as a whole.

Why is Customer Support Important?

The success of a business does not end with achieving sales goals. The ultimate success of a business is when they earn the loyalty of the customers. If a customer thinks not to come back to your business after the first time, it will have a negative impact on your business.

You should earn your customer’s trust and build a long term relationship with them. To earn their trust, you have to constantly communicate with them and solve every problem they face related to your business.

Backing them up with all the possible solutions and constantly supporting them will earn you your customer’s loyalty. Your loyal customers are your direct promoters. As they like your product/service and trust your business, they will recommend it to others.

More than any kind of advertisements and promotions, if buyers receive good reviews and suggestions from fellow buyers, they trust that brand/business more. So it is most important to have a customer support team within the business to keep a direct connection with customers.

What is Customer Data Analytics?

Customer data analytics is the process of collecting, analysing and assessing customer data to gain insights on customer behaviour. This will give you a better understanding of your customer’s needs, preferences, movies and actions.

Understanding the customers will help you give them excellent support, with which you can earn customers’ loyalty. Customer data analytics mainly focuses on product usage data with regard to customer data.

With customer data analytics, you can understand what the customers do behind the scenes before and after purchasing and using your products. By understanding customers and what they do or what they expect from your business at various points along their journey, you can provide supportive initiatives that can supercharge their efforts at each touchpoint.

By identifying the problems with your user’s experience, you can quickly determine what needs to change in order to fix the problems.

Types of Data Analytics

There are various types of analytics that businesses use to study and analyse customer data. We have listed out two main types of customer analytics -

Descriptive analytics

Descriptive analysis is the process of using statistical techniques to summarise a set of data. It is the method used to majorly to generate accessible insights. Regarding customer data analytics, it will help you gather uninterpreted information based on customer interactions. This includes data from market demand, prospects demands, conversions and web traffic.

Predictive analytics

Predictive analytics uses mathematical tools to generate predictions about an unknown fact with the data available from past data. Regarding customer data analytics, predictive analytics analyses past business trends and defines predictions on how the business will perform.

Predictive analysis is vital to analyse and predict the customer behaviours, demands, customer touchpoints, and it helps in upgrading customer experience too.

Prescriptive analytics

Prescriptive analytics is a process that analyses data and provides instant recommendations on how to optimise business practices to suit multiple predicted outcomes. Regarding customer support, prescriptive analytics provides optimised recommendations on how to act on data-driven trends and their possible outcomes.

In short, the three-tier modern, computerised data analytics are descriptive analytics - what we know, Predictive analytics - what could happen and prescriptive analytics - what should happen.

How does Customer Analytics impact Customer Service?

#1 - Provide a personalised experience

Personalisation is the base to gain customer loyalty. Your customer will be expecting a personalised customer experience from you, which makes them feel valued. When they get the personalised experience, they will tend to stay with your business for a long time. Customer data analytics will help you understand the customer needs, and you can personalise their experience with it.

#2 - Understand customer needs and expectations

Using the predictive analysis, understand your customer needs and expectations. Predict the customer needs and transform the way you interact and support your customers. It also helps you identify the potential prospects they are more likely to become your customers.

#3 - Analyse customer touchpoints

Know your customer touchpoints in your website, app or social media. Collecting the touchpoints and analysing them will help you understand what attracts your customer and gets the least attention. You can improve your website, and it will increase traffic.

#4 - Solve customer problems

Customer data analytics can help identify and resolve customer issues and complaints, thus leading to higher levels of customer satisfaction.

Summing up

Customer data analytics has a direct impact on customer support. You can understand your customers and provide them with hands-on support at the earliest. By analysing and gaining insights, you can maintain a good relationship with your customers. It will also help you stay loyal to your business and engage with you for the long term.

ReplayBird - Driving Revenue and Growth through Actionable Product Insights

ReplayBird is a digital experience analytics platform that offers a comprehensive real-time insights which goes beyond the limitations of traditional web analytics with features such as product analytics, session replay, error analysis, funnel, and path analysis.

With Replaybird, you can capture a complete picture of user behavior, understand their pain points, and improve the overall end-user experience. Session replay feature allows you to watch user sessions in real-time, so you can understand their actions, identify issues and quickly take corrective actions. Error analysis feature helps you identify and resolve javascript errors as they occur, minimizing the negative impact on user experience.

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With product analytics feature, you can get deeper insights into how users are interacting with your product and identify opportunities to improve. Drive understanding, action, and trust, leading to improved customer experiences and driving business revenue growth.

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Hi, I'm Afra, a content writer for ReplayBird. I write on all things SaaS and web analytics.

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