The method of direct observation of target’s behavior pattern by considering natural contexts to gather information regarding potentially relevant, contiguous and environment related events. Descriptive analyses take into consideration correlated events which occur with some of the target audience and their responses. Before conducting any experimental functional analysis, descriptive analysis is used as a part of a comprehensive functional assessment of problem behavior.
According to Infoholic Research, the Descriptive Analytics market is anticipated to grow at a CAGR of 18.2% during the forecast period 2016–2022.
Listed are 6 popular descriptive analytics trends in the current business scenario:
Trend #1: Combination of information from multiple sources
Descriptive analytics gives a comprehensive view of past and future status covering different assets. It combines information from different sources and compares contrasting data which highlight key performance indicators such as per square foot cost and space utilization. Dow company has been able to produce reliable data for decisions to identify underutilized space using descriptive analytics. It has been able to achieve close to 20% increase in facility usage savings close to 4 million dollars annually.
Trend #2: Time and motion techniques
Descriptive analytics supports to calculate the frequency of customer’s buying behavior pattern and the time taken by the merchants to resolve them. It also calculates additional costs associated with different business transactions. While comparing third party reimbursed more than 15% prescriptions submitted are rejected by supermarket chain compared to more than 20% rejected by independent pharmacies. Additional costs on rejected prescriptions averaged close to $1.0 in supermarket chain compared to more than $2.0 at the independent pharmacy, the difference is caused by higher level of staff involvement in independent pharmacy.
Trend #3: Comparing past performance patterns
Descriptive analytics provide valuable insights while comparing past performance patterns. They play a key role in extracting data from the bottom of the big data value chain. In case of assessing credit risk. Descriptive analytics are very helpful for assessing credit risk of a firm by considering past financial performance to predict likely financial performance in the present year.
Trend #4: Search engine optimization using historical data
Descriptive analytics generates reports and provides information about customers, operations, finance and sales. It helps to derive correlations between the various variables. In case of Netflix, it uses descriptive analytics to derive correlations between multiple movies that subscribers rent which helps to improve the recommendation given by their search engine using historic sales and customer data.
Trend #5: Provides results based on standard aggregate functions
Descriptive analytics consider standard aggregate functions using different databases. They are extensively used for social analytics by creating groups based on counts covering different events. The results a business generates from the web server through google analytics tools provides outcomes which helps to understand past happenings and helps to validate if a promotional campaign was successful considering basic parameters like page views.
Trend #5: Segregates customers by their product preference
Descriptive models are useful in segregating customers by their product preferences and life stage. These tools can be further utilized to develop models to simulate large number sub segmented groups to make multiple predictions. In case of electricity usage descriptive analytics studies past data and provides insights to plan power needs which allow electric companies to set optimal prices.
Trend #6: Key performance indications
Descriptive analytics uses data to provide information on past and current trends or events. It categorizes the data into sub sets to provide key performance indicators and drills down data to give details like frequency of events, operations cost and cause of failures. Descriptive analytics are helpful to real estate, facilities and asset managers in providing context they need for future actions.