In today’s digital era, descriptive analytics allows an organization to use historical data to understand and act upon new data. It is a base for advanced analytics, such as predictive and prescriptive analysis. In this article, we will define descriptive analytics, describe how it works, and how it relates to other forms of analysis, including the advantages and disadvantages of performing descriptive analytics across primary dimensions of several descriptive analytics examples for practical purposes.
What is Descriptive Analytics
When considered historical analytics, descriptive analytics means looking back to provide context to trends, events, and actions. It helps make sense of data by analyzing large quantities of data into small pieces that address the question, What has occurred? What is going on? Features and analysis. It has no flow forecasting or prescriptive factors that explain what approach should be adopted next. Prescriptive analytics is not available, but rather analytic graphics of what has happened so far, and it helps organizations improve their decisions regarding their processes.
Descriptive Analytics: Definition
Descriptive analytics can be defined as the interpretation of past or historical data in order to account for certain events or behaviors. This aspect of analysis usually includes finding average values, totals, and counts. Such analytics can be found in almost all industries and are referred to as performance, customer, and operational analytics.
Descriptive analytics, for instance, in a retail scenario, allows a store manager to comprehend the increase or decrease in product sales, the most suitable hours for retail, and the extent to which marketing strategies work.
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Examples of Descriptive Analytics
Descriptive analytics has been established in several sectors and is particularly important in the examination of past data to determine what has been achieved. Some of the industries where descriptive analytics has been put into use include the following:
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Retail Sector
In retail, descriptive analytics serves to establish customers as well as sales patterns. A case study could be of a clothing outlet using previous sales data to, for example, identify which clothes sell best at which times of the year or festivals; this knowledge helps the store reduce stockouts and boost marketing strategies.
Example: The company claims that in any given year, it analyses the sales data of the previous year to forecast shopping seasons, the most patronized products, and the normal trends in customer purchases. It aids in coming up with the right marketing activities and product stocks for the expected selling periods.
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Healthcare
In healthcare, descriptive analytics is used to evaluate patient progress, healthcare facilities, and management processes. By looking at past events, some decisions regarding patient seating, resource usage, and patient treatment can be made at hospitals.
Example: A hospital collects information about patients who were admitted through the emergency department for the last year in order to find the most frequent diagnosis, the most crowded month, and the month with the most constant flow. This is useful in staffing, resource management, and patient care enhancement.
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Marketing
In the domain of marketing, descriptive analytics is useful for measuring the performance of marketing activities, customer interactions, and the performance of a website. It assists companies in improving their marketing plans using information gained from the analysis.
Example: To measure the effectiveness of an ad campaign that was conducted online, the organization evaluates how many new visitors to the website came from ads, how many fans of the brand have commented, and how many converted. This information helps make changes to future campaigns in order to enhance their effectiveness.
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Finance
In addition to operational activities, financial institutions utilize descriptive analytics to measure past financial performance, transactions, and consumer usage trends. It enables them to understand their positions and take appropriate measures with regard to risk management, investment, and even operations.
Example: The client transactions records are reviewed by the banks to determine any anomalies or patterns that could be of interest in fraud detection. Moreover, the analysis incorporates time series data to assess trends in customer expenditure and provide suitable services to customers due to their actual spending behavior.
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Supply Chain Management
Descriptive analytics also improves the understanding of the past performance of companies by their supply chains, including logistics, inventory management, vendor management, and other related fields. Such knowledge or evidence helps them streamline processes, cut down on waste, and become more effective.
Example: Logistics company examines delivery data to find weaknesses in the supply chain system. The company can manage procedures to prevent delays in shipment by understanding the duration of various processes within the product delivery.
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HR
HR departments primarily rely on descriptive or lever analytics to enhance their staff productivity, performance management programs, and staff retention and recruitment approaches. Leveraging past HR data can enhance talent management and workforce planning in the organization.
Example: Taking notes, last year, the company adopted descriptive analytics to manage employee turnover, carry out job satisfaction surveys, and assess the success of recruitment efforts. This is beneficial in shaping the recruitment processes as well as maximizing the retention of employees.
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E-Commerce
As for e-commerce, planning user activities and assessing sales and products have been done through descriptive analytics on the e-commerce site. This information assists companies in understanding their clients and adjusting their online marketing activities.
Example: A section of the company has been monitoring patient buying behavior by looking at historical data on purchases, customer visits to sites, and rates of customers who buy things and do not complete hotel vouchers. This has enabled them to aggregate products that are most sought after by clients and suggest them to enhance sales.
What is an Example of Descriptive Analytics?
An example of descriptive analytics is a retail corporation preparing its historical sales data to know the purchasing behavior of customers and the design of various products sold. With the use of such sales data, a company may reveal what products performed well or poorly during given periods of the year, like the holidays or promotions, which customers bought more products, and what users’ actions have caused the change.
Example: A company providing clothing to clients applies descriptive analytics to assess festive season sales of the previous year. The January analysis indicates that winter coats were the fastest-moving items in the period of November and December, and customers aged 25-34 bought the items the most. This kind of information assists the retailer in devising future advertisement campaigns as well as stocking up on fast‐moving goods for the holiday.
Under this type of analysis, there is no specification of how future events will be, and there is no non-prescription provision either. It assists the organization in making decisions utilizing past information.
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How does Descriptive Analytics Work?
Descriptive Analytics focuses on evaluating the performance of a particular period in the past by examining the underlying historical data. Here is a more detailed workflow of DDA:
Data Collection
What: It is the historical data capturing of differences from any manner of interchangeable source that collects these disparate records, for instance, sales, customers, finances, or operational logs.
How: Data is obtained from intra-organizational sources (for instance, CRM and ERP systems) and areas outside the organization (for example, online social network markets).
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Data Preparation
What: It is the task of inspecting the prepared data to check for its quality [or deficiencies] and usability.
How: Data cleansing (purging redundant records, replacing missing data), data formatting (conversion into standard forms), data synthesis (merging data from multiple sources).
Data Analysis
What: Extract meaningful insights from the processed data based on their identified useful patterns or correlation of the time-series data.
How: Information is manipulated through different statistical, aggregation, and data presentation approaches. Usually, the following ways are employed:
- Descriptive Statistics: This is where various statistics like the mean, median, mode, and standard deviation are computed.
- Data Aggregation: This involves counting data in distinct categories, such as the number of sales made in a month or the average customer satisfaction surveys scored for each month.
- Data Visualization: This involves depicting information through the use of charts, graphs, and dashboards.
Data Interpretation
What: Focus on presenting factual details of what the analysis outcome is resulting from and what was performed in the past.
How: Explain the changes observed, for example, in sales in the context of seasonal effects, changes in trends of customer behaviors, etc. The information interprets what has been done previously and for what reasons.
Reporting
What: Underscore the critical insights and findings learned from the analysis activity to the stakeholders.*
How: Create reports, dashboards, and presentations that include storytelling features and visually present the data with metrics, trends, and patterns. The presentation of governance needs must also allow decision-making people to understand the information therein.
Actionable Insights
What: Imply synergies, where there is a plan, and the reasoning will be guided by the insight in question.
How: Make use of the findings to make changes that enhance operations, for example, better marketing strategies derived from an understanding of customer behavior or better appropriate inventory levels relative to sales.
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About SG Analytics
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