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What is predictive analytics and how can it be applied across different industries?

Shikha Pariha
July 29, 2024

In the first of a three-part series, lead software engineer Shikha Parihar explores the concept of predictive analytics and how the SAP analytics cloud is a powerful solution to facilitate it.

When I explained my line of work to my eight-year-old son, he was immediately drawn to the word “predict” and asked, “Mummy, are you like Hogwarts professor Sybil Trelawney who predicts futures?” Inspired by his inquisitive mind, I succinctly explained to him the great realm of machine learning and artificial intelligence (AI). Confident that AI is clever because it uses his browser history to suggest video games and encourages him to click ads for gaming laptops, the next query I received was: “Can AI predict when I can get a PS5?”  

The answer to that question will have to wait for another blog post and instead, here I’ll focus on the concept of predictive analytics and how almost all major industries have adopted it. 

What is Predictive Analytics? 

These days, organisations are loaded with data, which is stored in many data repositories spread throughout the company. Data scientists employ deep learning and machine learning algorithms to identify patterns in this data and forecast future events to extract insights. That’s predictive analytics. 

It’s safe to say that nearly every industry now uses predictive analytics in daily operations – be it Netflix to analyse viewer data and trends, identify gaps in content offerings, and make strategic investments in original productions; or as my son cleverly pointed out, Google to predict user behaviour. 

What’s the business use case for using predictive analytics?      

Predictive analytics can be deployed by businesses across various industries – such as manufacturing, marketing, finance, and healthcare – to target customers and improve operational outcomes. Here are two powerful examples: ,

1. Predicting buying behaviour 

One of the main applications of predictive analytics is in the retail industry, where brands utilise these tools to get comprehensive customer insights and use analytics to determine customer purchasing patterns based on past purchases. For example, a global wholesaler uses predictive analytics to precisely forecast future demand patterns, optimise product assortments, and schedule inventory replenishment activities by assessing past data, market trends, and external factors. To obtain insights and make wise decisions, the organisation uses data from a variety of sources, such as supply chain data, consumer behaviour data, and sales data.  

Predictive analysis is also extensively applied in the banking sector to predict customers’ past usage/spending patterns and effectively cross-sell the right product, at the right time. 

2. Supply chain 

The first step in supply chain predictive analytics is selecting a mathematical model that best captures the trend you wish to analyse. This requires testing several forecasting models by using known historical data to evaluate the model and iteratively improve it until it can be forecast accurately.  

The second stage is adding high-quality, current data and running the model to produce trends. It’s crucial to understand that while the model can’t foresee the future, it does use probability theory to determine most of the scenarios. 

Lastly, you can visualise results in supply chain analytics modelling. The global wholesaler is relevant again here, as the retail giant uses predictive analytics to identify areas for process improvement, optimise transportation routes, and enhance overall supply chain performance. 

How does SAP Analytics Cloud enable predictive analytics? 

SAP Analytics Cloud (SAC) offers an all-in-one solution based in the cloud for all your analytics, planning, and business intelligence requirements. SAC, which is powered by machine learning and artificial intelligence, offers predictive analytics capabilities through a variety of predictive models, including time-series, regression, and classification. More on that in blogs two and three of this series.  

Multinational healthcare company F. Hoffmann-La Roche AG (Roche) turned to SAC to streamline and accelerate its financial forecasting process with predictive planning with excellent results: 

  • 70% of forecast data entry points automated with SAC 
  • 2 hours to generate a US$4.2 billion financial forecast – reduced from several weeks 
  • 14,000 forecast data entry points automated 

In the age of data abundance, high competition, and shifting consumer behaviours, having that agility to move from insight to action is crucial. That’s why, whatever the business use case, predictive analytics is being adopted across industries with increasing pace, as organisations harness the power to discover deep insights, simplify access to critical information, and make informed decisions. 

Capgemini and SAP 

With four decades of experience with SAP solutions, serving 1,800 clients across the world, we are a leader in SAP certifications, an SAP Global Strategic Services Partner, and an SAP Global Platinum Reseller Partner. We can help you innovate, integrate and transform, so you can continue to grow, quickly adapt to any context, unlock and enhance business value, and stay ahead of your competition. 

Get in touch to start the conversation today. 

Stay tuned for the next two blogs in the series, in which we take a deep dive into how predictive analytics uses AI, and how to train your Smart Predict tool. 

Author

Shikha Parihar

Lead Software Engineer
Shikha Parihar joined Capgemini in March 2023 after a career break of 5 years. With a strong focus on visual data analytics and 11 years of experience, she is an SAP BI/BW skilled professional.

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