How Big Data Can Help Your Customer Service

How Big Data Can Help Your Customer Service

Digital transformation is impacting multiple business models. Among its most exciting changes is the use of Big Data in customer service, for example, through data collection and analysis.

They can originate from different sources, such as social networks, Analytics reports, user history on websites, management systems databases, and service reports.

Technology can interpret numbers and texts from these sources and generate corporate intelligence information for your business. For this reason, Big Data has been widely used in different industries to bring customers an increasingly better and more personalized experience. See how this is possible below!

What Exactly Is Big Data?

Collecting and processing data is not as simple a task as it seems. After all, this information shouldn’t be obtained without a clear purpose — you need a strategy to ensure it provides meaningful insights. Thus, the data can generate accurate results for companies.

This task demands robust software capable of recognizing and interpreting raw data, working them into relevant statistics and probabilities, and translating them into actionable information. Everything must be done in an automated and agile way, and that is precisely what Big Data promises. To understand the magnitude of the challenge, look at the types of data generated in fulfillment operations.

Structured Data

They are the ones that present themselves in an organized way. They are usually present in databases on other systems. They are called structured because they are stored in a particular computational language. This facilitates the work of analysis software, such as corporate management (ERP) and customer relationship (CRM) systems.

Unstructured Data

This type is not organized. So, Big Data software must know how to interpret the arrangement of information, collect it, and use only those applicable to the company’s strategy. On Youtube, for example, he will need to understand where the video, description, comments, etc. With each new site you find, this identification must be made in an automated and intelligent way. Ultimately, it should unify all these data types into a coherent analysis that decision-makers can use.

How Can Big Data In Customer Service Be Applied?

There are several applications of Big Data in customer service. See some of the possibilities:

Sending Discount Coupons Based On Consumer Behavior

There will be several exciting data for Big Data applications on your company’s website. After all, each customer’s path can be mapped: which products he accessed, placed in the cart or withdrawn, which purchases were completed, etc.

In this sense, Big Data will analyze this information to choose products the user would be more likely to buy. To improve the analysis accuracy in the service reports, the software will also be able to identify the most recurring keywords. Thus, it will suggest a personalized message to the customer, and the coupon will be sent in a highly targeted and conversion-oriented campaign.

Ad Targeting

This same logic applies to ads. For the most part, social media and Google already offer a good analysis of optimal targeting — and that’s because they invest heavily in Big Data. However, you can build on these insights with the information you get from your personalized customer service. Its Big Data system integrates Analytics data with reports and thus generates even more accurate segmentation.

Contact Dynamization

One of customer service’s most significant difficulties is offering dynamic and proactive communication. Generally, agents only respond to demands, but it is possible to go further with Big Data. He will view user behavior, personal information, relationship history, and real-time reporting. Soon, you can offer products and ask about resolving past pains, among other actions.

Improved Digital Presence

Many companies face difficulties in increasing their digital presence. After all, there are multiple media and channels available. So, how to direct the investment? Big Data analytics lets you know which devices and channels your industry uses most. In this way, you require your capital to stocks with more significant potential for return.

Optimization Of Service At Home Office

Big Data also enhances end-to-end home office service. It all starts with the implementation of this new working model. To do this, it collects data such as:

  • connectivity — testing your connection speed on the service provider’s systems;
  • technology — assessment of your employees’ technological knowledge and hiring needs ;
  • environment — Evaluating suppliers to carry out an environment checklist via photos and geolocation.

Big Data integrates all information into the analysis of scenarios favorable to the home office. From this, it is also possible to customize employee training with critical skills and knowledge in excellent remote service.

Thus, digital enablement is personalized, and the entire learning process can be accompanied by Big Data when assessing the gaps and opportunities that arise. During the service, the feature provides reports on the customer so that the entire journey can be personalized. Therefore, even at home, the attendant will have relevant information, capable of generating an excellent experience.

This technology is essential when evaluating indicators in real-time. It collects data from all systems and automatically calculates metrics such as average service and waiting time, rejection, and resolution rates, among others. With this, the manager can monitor the quality of service at the home office and propose changes.

Big Data in customer service enables your company to implement digital transformation successfully. This brings deep integration into the demands brought on by the new economy, incredibly remote work — a necessity in times of COVID-19 and beyond. Thus, you can offer a fantastic customer experience and gain much more competitiveness.

Also Read: Big Data: How Data Changed The Production Process

Editorial Team

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