Data in demand

There was a time when data used to be gathered in the backend of a system without any importance. However, with digitalization, a huge pool of data is created every day broadening the insight of the business and analyzing customer behavior. Recently, organizations have started to utilize the accumulated data for their benefit, to expand the scope of business opportunity and growth. Organizations use the data generated from log files to enhance customer satisfaction. The goal here is to understand a customer and their need basis which the company can build a conducive environment. Although, the accumulated data can help to create an idea about the customer yet, predicting customer behavior remains a big concern for enterprises.

Staying ahead in the competition

Organizations have started to rely on the technology of smart data analysis for business development and successful implementation of strategies. However, there are still organizations that depend upon legacy systems to extract meaningful information from the data. While there are data used in innovative ways by few companies to forecast customer behavior in order to gain a cutting edge over their competitors, which in turn can add value to the business.

Need for data scientists

Now, handling and managing data is done by data scientists. It is the data scientists that try to identify potential or new data sources to place on the algorithmic application to extract value from it. The data scientists analyze and interpret data to gain more information on the customer. They also try to highlight company expertise by implementing new processes. With each data set, it enhances the scope of new learning to change the business.

Knowing your customer

The only traditional method of data analysis cannot derive desired results, thus, data analysis powered by machine learning can prove useful in understanding the need of a consumer. To elaborate further, the data accumulated can derive information on the customer purchasing history that can predict the potential purchases that the customer can make in the near future. Here, an advertising agency can display the specific customer-related ads to enhance customer satisfaction and initiate sell.

Understanding customer behavior through machine learning

The following are the steps through which customer behavior can be gauged and analyzed for business development.

• Data gathered from browsing history and log files need to be obtained and analyzed in a secure environment.
• Technologies such as Hadoop and Big data play an important part in dealing with such large data volume. The collected data is utilized for interactive investigation.
• It must be mentioned here that feature engineering is one such process that can transform raw data into a machine learning algorithm that can initiate an understanding of customer behavior. Spark for example is a tool that helps with data investigation.
• The classification model is used to categorize data based on a related set of information to arrive at a plausible conclusion.
• Based on the data, forecasting can be done in a more accurate manner.

Machines indeed can understand customer behavior

Hence, we can conclude that solely based on the data gathered from various sources, machines can derive a meaningful insight and data scientists can assist to analyze the information assembled in a concrete and valuable outcome.

- Kathakali Basu
Content Writer
Infoholic Research