Improve Your Customer Service with the Help of Data Analytics

A dive into the importance of data analytics for enhancing customer service with the help of data privacy compliance


From small neighborhood stores to Fortune 500 corporations, every business relies significantly on its consumers, making customer service one of the most lucrative playgrounds for businesses. To put that into perspective, 90% of Americans say customer service is the deciding factor in selecting to do business with a firm, and 92% of consumers worldwide say a positive experience is a cause for a second purchase. Therefore, it is critical for companies today to focus on individualized customer service and comprehend and anticipate the demands of today's consumers who want immediate and customized help 24 hours a day, seven days a week.


Data is one such element that has proven to assist businesses in curating personalized experiences for their consumers. A chatbot, for example, might not only understand the context of the user's interaction but also assess the customer's mood and respond to their needs more efficiently. Similarly, consider data-driven post-sales email messages with personalized interactive GIFs or graphics based on the information obtained from that one customer to make communication clearer, engaging, and one-to-one. All of this and much more are possible with the help of data and business analytics.


Understanding Data Analytics

In simple terms, the science of studying raw data to draw conclusions about it is known as data analytics. Many data analytics approaches and procedures have been turned into mechanical processes and algorithms that operate with raw data intended for human consumption. Though statistics and data analysis have long been employed in scientific studies, sophisticated file analysis software and big data have opened many new possibilities in several industries.


The financial industry was one of the first to utilize data analytics to forecast market trends and assess risk, such as credit ratings, which have a variety of uses. To improve efficiency and decrease risk, these ratings employ a variety of data elements to assess loan risk, detect and prevent fraud, and improve efficiency.


Similarly, data analytics is already widely utilized in the healthcare industry to forecast patient outcomes, better distribute funding, and improve diagnostic processes. Furthermore, the traditionally challenging drug development process is being enabled to better comprehend the pharmaceutical business and anticipate sales using machine learning mixed with data analytics. But, just as data analytics cannot be done without a file management solution, it cannot be done without data privacy compliance.


Compliance with Data Analytics

To create tailored experiences with consumer data, you must first grasp how to properly acquire and handle it per existing regulations such as the General Data Protection Regulation (GDPR).


GDPR by the European Union (EU) provides standards for protecting EU citizens' and businesses' personal data. It does not ban the use of personal data for marketing purposes, but it does govern how it is used. This implies that consent will be required for customized communications, and companies must get the agreement of the persons involved before sending tailored messages to improve the customer experience and relationship through data. This consent must be particular to the objectives of direct marketing or direct marketing profiling. Though GDPR compliance is specifically for EU citizens, some other notable data privacy compliance regulations that have a similar notion concerning usage of personal data for marketing purposes are:


These laws and regulations differ from one industry to the next, and they might be confusing at times. However, when followed carefully and used to improve the data analytics process, it may pay off in the long term.


Data Analytics Done Right

Data analysis may help your company achieve a competitive edge, but it must be done cyclically, or the following steps will be meaningless.


Process and Clean Data

According to a study, most data in an organization is redundant, obsolete, and trivial (ROT), accounting for about 90% of the total organizational data. Hence, it is recommended by data scientists that data be cleaned and processed regularly to get more significant insights and analytics from it.


Explore and Visualize Data

Examine the processed data for patterns, trends, and clusters by visually inspecting it to investigate correlations and form hypotheses based on your findings. Visualization tools are the most efficient method of finishing this procedure; line graphs, stacked bar charts, box plots, and heat maps are a few examples of essential yet effective data visual tools.


Data Mining

Organizations that use predictive analytics to use and mine their data have a significant competitive edge over their competitors because they may acquire vital insights and respond fast to develop their business in ways that were not feasible before. That is where data mining and pattern recognition can help with various tools, including hierarchical clustering, market basket analysis, visualization maps, principal component analysis, factor analysis, and multi-dimensional scaling, and much more. 


Build Models

Make sure you have a variety of models such as decision trees, neural networks, support vector machines, and discriminant analysis to provide you diverse views on your data. However, every algorithm has its own advantages and disadvantages, and it is vital to remember that all models have limitations.


Right Tools

Several complementary software, such as file analysis software and in-place file management solutions that provide mathematical and statistical methods, can aid in the data analysis process. However, when choosing the best solution for your company, scalability, dependability, performance, data source consumability are all key factors to consider.


With the global data creation projected to grow to more than 180 zettabytes in 2025 from 64.2 zettabytes in 2020, it is fair to say "data is the new oil." Therefore, it is only reasonable to use that data for predictive analytics to improve your customer service in the long run. Moreover, data analytics appears to have infinite applications, as more data is created every day, allowing us to apply data analytics to new areas of business, science, daily life, and even customer service.

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