What is Predictive Analytics?
Predictive analytics is a term referring to extracting information from data to identify patterns and predict future outcomes or trends based on those patterns. Predictive Analytics is used to study the existing data and trends to better understand customers and product behaviours.
The objective of predictive analytics is to build predictive models to forecast, what might happen in the future. The accuracy and reliability of these Predictive Models greatly depend upon the level of data analysis and assumptions made on the underlying pattern of data.
Process:
Collect Data –> Clean Data –> Identify Patterns –> Build Predictive Models –> Forecast / Predict Future events or outcomes
OR in other words,
Data Collection (Structured / Unstructured) –> Data Mining –> Statistics –> Modelling –> Deployment
Examples of Predictive Examples:
1. Customer/Employee Churn Prevention
Predictive Analytics help to prevent churn in customer base, by identifying signs of dissatisfaction among your customers, and identify those customers or customer segments that are at the most risk for leaving.
2. Customer Lifetime value
Identify the customer, that are going to spend the most money, in the most consistent way and over the longest period.
Ex: Bank, Insurance, Telecom Retail
3. Customer Segmentation
Identify target markets based on real data and indicators, and further identify the segments of those markets that are most receptive to what your company offers.
Ex. Automotive, Banking, Pharma, Insurance, Retail, Telecom etc.
4. Predictive Maintenance
Companies can predict both timelines for probable maintenance events and upcoming capital expenditure requirements, allowing them to streamline their maintenance costs and avoid critical downtime
Ex: Automotive, Manufacturing, Logistics & Transportation, Oil & Gas
5. Sentiment Analysis
Provide proactive recommendations to enhance organisations reputation by combining web search and crawling tools with customer feedback and posts, we can create analytics that give a picture of the organisations reputation within the key markets and demographics.
EX: Life sciences, Pharma, Education, Insurance, Retail, Telecom
6. Up- and Cross-Selling
Predictive analytics can provide suggestions on which products might be combined to appeal to which market segments, to increase both you value to your customers and the revenue derived from customers.
EX: Banking, Insurance, Retail, Telecommunications
7. Quality Assurance
Good predictive analytics can provide insights into potential quality issues and trends before they become truly critical issues.
Ex: Automotive, Life Sciences/Pharmaceutical, Manufacturing, Logistics & Transportation, Oil & Gas, Utilities
8. Product Propensity
Predictive analytics to help not only customers are more likely to buy our products and services, but what channels are most likely to reach those customers, allowing you to maximise those channels that have the best chance of producing significant revenue.
EX: Banking, Insurance, Retails
