Companies now have access to a wide range of data sets, including internal data on customer interactions, transactions, and profiles; widely available third-party data sets covering purchase behaviors and preferences, and digital behaviors, including social media activity; and new data sets on customer health, sentiment, and location (for example, in stores) generated by the Internet of Things.
Today there are numerous efficient ways to store and manage data. Companies target accurate forecasts with the gathered data that would benefit them for taking future decisions which will yield better insights.
Predictive analytics forecasts future trends using statistical algorithms mixed with internal and external data, allowing firms to optimize inventories, improve delivery times, enhance sales, and minimize operational expenses.
What is Predictive Analytics?
Predictive analytics is a type of analytics that makes predictions about future events using historical data, statistical modeling, data mining techniques, and machine learning. Businesses employ predictive analytics to detect data trends and identify risks.
Predictive analytics is all about being able to “predict” what might happen. These studies are aimed at gaining a better understanding of the future.
How does Predictive Analytics help in predicting the future?
Predictive analytics is a method of sifting through current and historical data to find trends and forecast events and circumstances that should occur at a given period based on specified parameters.
Organizations can use predictive analytics to uncover and utilize patterns in data in order to recognize threats and opportunities. Models can be created to uncover correlations between various behavior characteristics which enable taking informed decision-making.
Predictive Analytics Models
Predictive analytics is built on models, which are the templates that allow users to turn historical and present data into actionable insights, resulting in beneficial long-term outcomes.
Customer Lifetime Value Model: Identify clients who are most likely to buy additional items and services in the future.
Customer Segmentation Model: Customers are grouped together based on comparable qualities and buying habits.
Predictive Maintenance Model: Estimate the likelihood of critical equipment failures.
Quality Assurance Model: Identify and prevent flaws to reduce customer displeasure and additional expenditures when providing products or services.
Predictive Analytics Examples
Using Predictive Analytics in Retail Industry
The food retail business of the UK’s retailer, Waitrose sells a variety of high-quality groceries and wines. The demand for the products sold by the e-retailer varied depending on the season, event, or festival, but this was not taken into account previously because it was difficult for Waitrose to find out what customers wanted.
By integrating a big data analytics solution into an e-store and working with an e-commerce development business, retailers learned about demand at the store level and estimated what products they should have in stock and when they will be needed. The replenishment methods result in a 40% reduction in order changes.
Using Predictive Analytics in Inventory
A Fortune 500 telecommunications company was having difficulty maintaining optimal inventory levels and determining when and how much fresh stock needed to be ordered to meet new customer requirements and network upgrades.
GeakMinds strategy was to understand each part’s demand based on historical consumption patterns from various warehouses. To find the best models for each part/SKU, a time series classification and prediction methodology was used. This application’s main advantage was data-driven decision-making for Material Purchase quantities and timelines, ensuring optimal CAPEX spending.
Using Predictive Analytics in Healthcare
Clinical decision-making at MBSC (Michigan Bariatric Surgery Collaborative) is aided by clinical and patient-reported data paired with strong predictive algorithms. Participating doctors collect complete preoperative data for potential bariatric surgery patients using the MBSC registry and patient engagement tools.
The MBSC Predictive Outcomes Calculator, a publicly available tool that physicians can use to forecast a patient’s weight loss, comorbidity resolution, and complication rate after bariatric surgery, is fed with this information.
The tool forecasts the following using patient demographics, comorbidities, and other risk factors:
- For each of the various procedures, it predicts weight reduction in years 1, 2, and 3.
- Predicts the chance of weight-related comorbidities like diabetes or sleep apnea is resolved.
- Predicts the occurrence of negative outcomes, significant complications, and death.
Notably, the weight loss, comorbidity resolution, and probable consequences rates are patient-specific, based on risk-adjusted, real-world outcomes data from similar individuals. These technologies, along with other quality improvement measures, enabled MBSC and its members to reduce venous thromboembolism (VTE) rates by 43% and post-surgical mortality rates by 67%.
Conclusion
Predictive analytics will help a business get a lot more value out of its qualified data.
Be it any domain such as Retail, HealthCare, Telecommunication, CPG, and so on , valuable insights can be inferred from historical data. Thus, with predictive analytics, businesses can improve their respective departments and take them to the next level.