Predictive analytics, also known as advanced analytics, is often related to business intelligence. But are the two really related? And if so, what benefits do companies realize by combining their business intelligence initiatives with predictive analytics? Predictive analytics slightly differs from other forms of big data analytics and is the only form that gives futuristic forecasts.Does that mean we can predict what exactly happens in the future?
Let’s have a look!
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So, what is predictive analytics?
At its heart, predictive analytics helps predict future outcomes based on present data. Let’s dig into a little more detail.
Predictive analytics refers to using historical data, machine learning, and artificial intelligence to predict the future. This historical data is fed into a mathematical model that focuses on key trends and patterns in the data. The model is then applied to current (new)data to predict what will happen next.
Information that comes out from predictive analytics can help companies, business applications—suggest actions that can affect positive operational changes. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue. This helps organisations to become proactive, forward looking, anticipating outcomes and behaviors based upon the data and not on mere assumptions. But, predictive analytics goes even further and suggests what actions may benefit and also provide decision options based on the predictive implications.
Predictive Analytics in the Real World
Predictive analytics is increasingly used by various industries to improve everyday business operations and achieve a competitive advantage.
In practice, predictive analytics can take a number of different forms. So, where can we apply predictive analytics?
One of the main reason as to why predictive analytics has come to the forefront of the business world. Through big data analysis, there are systems in place that can combine multiple analytics methods to detect fraudulent patterns that indicate criminal behavior.
Today, one of the major concerns in the world is cyber security. With everything going online, to protect itself banks, hospitals, social media companies and even police stations have resorted to using predictive analytics to minimize network breaches that could expose valuable information. Through the use of analytical methods, companies can detect vulnerabilities within their systems as well as abnormalities that indicate fraud.
Big online financial providers like PayPal have long used predictive analytics to determine what kind of precautions they have to take to protect their clients against fraudulent users.
PayPal uses data such as your historical payments, to the kind of device often used as well as your PayPal user profile and country of origin, all these go into building machine learning algorithms that detect potential signs of fraud with every transaction.
Customer churn out ratio
Consider a yoga studio that has implemented a predictive analytics model. The system may identify that one of the customers will most likely not renew their membership and suggest an incentive that is likely to get her to renew based on historical data. The next time they come to the studio, the system will prompt an alert to the membership relations staff to offer her an incentive or talk with her about continuing her membership. Here, predictive analytics can be used in real time to remedy customer churn before it takes place.
Optimization of marketing campaigns
Long gone are the days of ‘spraying and praying’ all the while wasting valuable resources trying to capture an unsuitable market niche based on pure “assumptions”. Today, through specialized predictive analytics, companies can formulate effective strategies to identify, attract and capture markets for their products and services.
E-commerce websites like Amazon have been making use of predictive analytics to capture customer usage patterns and past search data of website visitors to recommend products. Amazon offers choices based on your likes and entices you to buy those products. From insurance companies to real estate, and almost every retail company, predictive analytics is now very much part of every operation.
Improve customer service
Businesses can better predict demand using predictive analytics. For example, consider a hotel chain that wants to predict how many customers will stay in a certain location this weekend so they can ensure they have enough staff and resources to handle demand.
Insight into the sales funnel
Getting into the mind of the average lead can be a tricky task, but predictive analytics can create a behavioral model of their journey through the sales funnel, and what individual actions—returning to the website repeatedly, or browsing related products—have to say about their intent to purchase.
Today, many airlines and big travel companies use predictive analysis to manage resources and forecast inventory. Virgin Atlantic and ‘Amadeus’ use predictive analytics to set ticket prices based on the predicted volume of traveling customers.
Hotels are using these systems to determine future occupancy rates to adjust accommodation prices. Similarly, most retailers use similar systems to determine what discounts can be given, when should those promotions be conducted and to figure out the expected ROI of the promotions, etc.
Every company has its unique challenges—but these challenges are little more than a new combination of a set of finite factors. So while you might not immediately think of a crisis approaching, using predictive analytics you can at least help your business come out of the whole mess before time. These insights can help navigate difficult financial times, putting you head-and-shoulders above the tide and giving you the tools to come out stronger than ever.
The amalgamation of Data science and Analytics has transcended into almost every sector. Applications of predictive analytics are not only limited in innovatively improving business processes but also make the system more data-dependent than based on the pure assumption of the top management. Irrespective of whichever industry you belong to, you will be analysing a lot of data sets, look around current processes, and be involved in making better decisions for the company and industry at large.
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