What is Descriptive analytics?
Descriptive analytics is a type of analysis that involves the use of statistics to describe or explain past events. Descriptive analytics can be used to answer questions like: What happened? How did we get there? and Why did it happen? Data from descriptive analytics can be used to detect trends in data, find correlations between variables, and identify cause-and-effect relationships.
Descriptive analytics differs from predictive analytics in that descriptive analytic data focuses on specific events or trends at a certain point in time. In contrast, predictive analytics predicts future events based on past data. While descriptive analytics won’t tell you what will happen in the future, it can help predict what may happen based on historical data. For example, if your store has historically been more profitable during December, and you have positive sales figures for November but not December, you may have reason to be concerned about January sales figures. You can use descriptive analytic methods to better understand historical trends to avoid overreacting or under-reacting to how these trends might influence future events.
Advantages of Descriptive analytics
Descriptive analytics collects large amounts of information to help businesses make better decisions. It can provide a more nuanced view of the customer by looking at their behaviors. For example, if you are a clothing retailer and want to know what colors consumers tend to like, descriptive analytics could answer that question for you. It also provides a historical perspective on your business or campaign, which means you won't have to keep running the same data over and over again. With this information in hand, you will be able to make better decisions about your marketing campaigns in the future.
One of the descriptive analytics's biggest advantages is its ability to help businesses understand their customers on a deeper level. Businesses can use descriptive analytics to learn how consumers behave, how they interact with their products, or how they respond to different messages. It also provides historical data, so businesses can look back at previous years' information for comparison. This can be helpful when you want to see what your business did right or wrong in order to improve marketing campaigns or make strategic changes in other areas.
Descriptive Analytics - Third Paragraph: Another advantage of descriptive analytics is that it allows you to analyze data that would have been impossible or very time-consuming before computers were invented.
Disadvantages of Descriptive analytics
Although descriptive analytics has its benefits, there are some disadvantages that should be considered. One disadvantage is that descriptive analytics cannot show cause-and-effect relationships. Another disadvantage to descriptive analytics is that it can only answer who, what, where, and when questions. This information may not be enough for decision-makers to make key business decisions.
Descriptive analytics can only answer who, what, where, and when questions. This may not be enough for decision-makers to make key business decisions. For example, if you want to know how many customers in Canada purchased your product in December 2016, descriptive analytics will give you that information. But if you want to know why they purchased your product rather than one of your competitors’ products or how much each customer spent on your product, descriptive analytics will not help you answer these questions because they are concerned with just describing what happened.
Applications of Descriptive analytics
Descriptive analytics can be applied to many different industries, from marketing to healthcare. The goal of descriptive analytics is to help people make decisions about the future based on what has happened in the past. For example, if you have a new product in the works, you can use this data to figure out how it will perform once released on the market by comparing it with other similar products that were successful in their own right. Not only can descriptive analytics help you forecast the future, but they also help you understand why things happen as they do.
For example, you can use descriptive analytics to determine which products are sold best in different regions. This information allows you to understand where future growth will likely occur, while also helping your business reduce its overall costs by allowing you to cut back on unnecessary expenses, such as shipping fees.
Another useful application of descriptive analytics is predicting who your best customers are likely to be and how profitable they’re likely to be for your business. By knowing who these potential customers are, you can direct more of your marketing toward them, which helps boost profits in a relatively easy way. Finally, businesses can also use descriptive analytics on their website traffic logs and social media presence to gain valuable insight into what aspects of their company need improvement.
Conclusion
Descriptive analytics can be used to find out what people like, don't like, or want to know. This type of analysis provides insight into consumer behavior on the Internet. It helps companies understand how consumers think and feel about their products.
Companies can use descriptive analytics to better market their product by seeing what the competition is doing well and not so well. They can also use this information in conjunction with other types of data analysis such as predictive or prescriptive analytics, which help determine future trends.
Descriptive analytics involves collecting data from a variety of sources. Companies usually start by gathering information on their customers, including sales records, questionnaires or surveys, and demographic profiles. They then put together that information with publicly available data such as sales figures or public opinion polls. These sources can be compared to find out which attributes appeal most to consumers in a certain region.
