Predictive Analytics

Introduction to Predictive Analytics

Let’s dive into the concept of predictive analytics and what’s the big difference between analytics and Predictive analytics. To explain in simple words, Predictive analytics is like driving a car during heavy traffic, where you will anticipate vehicles at the back by viewing your front view mirror and being wary about the traffic, you will drive smoothly and reach the destination safely.

In similar fashion how predictive analytics create business value? Predictive analytics is nothing but a practice of extracting meaningful insights from data sets using data mining process, and keeps informed about the business activities that might affect future business outcomes. The term predictive means finding a probability of future events- for example a customer uses predictive analytics to deliver more relevant data, to improve overall profit. Here analysis is done in depth why customer satisfaction is not being attained.

Features:

·         Identify and observe patterns to unknowns in the Past, Present or future.

·         Use data mining techniques to discover hidden insights.

Analytics- It’s a foundation stone where we try to understand the existing data set using trends and patterns via comparison method. It’s one of the first step towards Predictive analytics. Below is a table that shows primary differences between Analytics and Predictive analytics.

Analytics	Predictive analytics
Purpose is to understand the past and observe trends	Purpose is to gain insights that leads to effective decision making that is oriented towards futuristic goals.
Data used is raw, structured and compiled	Information is structured and unstructured.
Benefits productivity improvements for producing reports and metrics	Benefits process improvements leading to enhanced decision making skills.

Above Diagram shows  How Predictive analytics is viewed today by the IT people?

Defining Business Intelligence and its relationship with Predictive analysis.

With helps of BI you will learn how to use data to learn about your customers and what’s the current state of your business. BI looks up to identify areas that is under performing. Areas like: Products, customer reach, partners, time and business dimensions.  As per Gartner “This knowledge baseline is shaped through descriptive analysis examining past data to extract useful customer/buyer/prospect information”. Whereas in Predictive analytics you will predict like how customer’s behavior likely to be in future? So Business Intelligence is gaining knowledge, strategy, Infrastructure and here analytics provides feedback to business people signaling success or failure of their model by predicting futuristic events.

BI relationship with Predictive analytics

Applications using Predictive analytics

How Predictive analytics is being used by many sectors and how it helps in transforming business growth.

Benefits:

 To improve customer relationship management and thereby revenue. Ex: It can be signing up for a newsletter, clicking on a promotion code etc. There are some vendors that helps retailers to track their customer engagement like ‘Lattice’, ‘SAS’ etc. Top companies like Netflix and Amazon use this to create a loyal relationship with their customer that results in enhanced customer satisfaction and revenue growth.

 

How Amazon and Netflix uses predictive analytics

Launching New promotional deals that attracts customers: All retail stores from mid-sized to large size organizations depend on promotional deals, discounts to succeed in the market. According to a study by Oracle, 98% of fast-growing merchants feel that segmentation & targeting are important for their online merchandising strategy, yet more than half are not satisfied with the tools they have for promotions. For ex: Macy’s has experienced the benefits of predictive analytics by deploying a solution from SAP that helps in retaining their registered customers. Reports suggest that It has 8-12% increase in online sales by combining browsing behavior within product categories.

Reference:      http://conversionxl.com/predictive-analytics-changing-world-retail/?hvid=352IDw)

1.       Optimizing pricing index to Maximize Profit growth: Predictive analytics play an important role by supporting real time pricing that accepts input from sources like:

·         Customer activity

·         Order History and Preferences

·         Historical product pricing

·         Available Inventory

Refer the below video that shows how Uber and AirBnB have been setting prices with analytics. https://www.youtube.com/watch?v=-KFe5pGMFbo

1    Reduction in fraud by detecting it

Fraud and theft in terms of data occurs in all industries where billions of dollars are lost every year. IBM’s SPSS Suite is one of the best Predictive analytics solution that helps retailers to analyze browsing patterns, payment types and purchasing patterns to detect fraud. For ex:

Corruption, IP theft, Phishing etc. Nowadays leading retailers like Walmart have started using algorithms that are helps in catching fraud before it happens using Predictive analytics.

Deploying methods for predictive analysis

1.       Try to leverage with best data scientist who is technically and functionally qualified to integrate with e-commerce platforms. There are several online predictive tools and plugins available where you can utilize it and reap the benefits Ex: Custora – a tool that generates great customer lifetime value.

2.   Use an Open source predictive analytics product that helps in creating more custom solutions using platforms R, Prediction IO etc. For this you should hire well qualified skill set programmers are required.

3.       One of the easy and expensive method is to buy a full featured suite like SAS that comes as a full package with offerings like SAP, Prediction. The features of this offerings is many pre-built in products are available for fraud, pricing management etc.

Predictive software players

Spotfire, R, SPSS (An IBM company), Rapid Miner, SAS.

Limitations

• Each data pint must be planned and collected properly for proper execution
• Expensive to design

Conclusion

In order to benefit from predictive analytics, people in the company should liaise proper communication between one another. Here comes the difficulty. BI professionals speaks about the output in SQL language whereas executives will try to understand that in terms of reports, metrics etc. So together both IT people and BI professionals should try to understand the language of strategy, business models while solving business issues. Organizations depend on predictive analytics for strategic planning, achieving profits and targets, financial outcomes, trying to b e a competitor in the market. So with the help of predictive analytics, organization can rely on timely feedback that explains about their strategic initiatives, and assist them in answering futuristic questions.