How Combination of Statistical Models and Machine Learning Algorithms Can Create Predictive and Prescriptive Analytics

They say the future can only be created, not predicted. However, modern business technology is sophisticated enough to help decision makers predict market behavior with unprecedented accuracy. Business intelligence is also capable of prescribing actions based on historical trends and real-time data for decisions to bear fruit.

Spend optimization is of prime importance in any business. Large industries had felt the need for predictive analytics decades back, but today we also have prescriptive analytics. Business intelligence software has progressed to such an extent that marketers are now excited about being able to buy media with the best possible levels of sanguinity. And it’s all automated!

Statistical models and machine-learning algorithms

Statistical models are data sets based on historic patterns of market parameters. The patterns can be used by machines to learn the behavior patterns—be it of people, prices, geographical trends, etc. To develop the technology, application developers enable computers to develop intelligence and influence business decisions using algorithms based on mathematical models. The bigger the data in statistical models, the greater the accuracy in predictions made by the machines. That is how predictive and prescriptive analytics work based on statistical data. The quality of algorithms is the primary factor influencing the quality of the predictions and prescriptions.

Relevance in online advertising

It is a subject of growing importance in media buying. Historically, return on advertising investments was a difficult parameter to calculate. Most advertisers would buy media with less knowledge and more hope. But technology is driving a change. Automated inventory buying involves machines choosing the right purchases. Analytics has steadily developed into a reliable instrument for calculating marketing ROI, and also helps marketers know why and how they should depend on an ad or inventory. If media buyers and inventory sellers wish they put their money on the right inventory or ad, the machines can do it for them.

Activities like inventory forecasting and ad targeting are crucial aspects of programmatic technology. Platforms developed for online advertising feature predictive and prescriptive analytics for marketers to know if certain media, websites, email schedules, and strategies are going to work.

Predictive and prescriptive analytics: The difference

With a purpose of predicting future market behavior, machine-learning algorithms are built into media platforms. They reveal how prices, impressions, ROI, etc. might fluctuate. That does not involve recommending actions—something which is a capability of prescriptive analytics.

A typical example of predictive analytics—how will inventory prices fluctuate for a category of websites over a specified amount of time?

Prescriptive analytics uses behavior data and real-time data to prescribe actions that will lead to a range of outcomes. Based on past outcomes of particular actions, and real-time market behavior, software can tell the user if an action will produce the desired result. The function here is to predict if a certain action will produce a specific ROI or minimum number of impressions—and it is based on real-time data feeds from a large gamut of sources.

A typical example of prescriptive analytics—to get ‘x’ number of impressions within a specified time limit, when should I post a tweet on a particular product category; what should be the typical keywords?

Media professionals involved in inventory trading and ad posting need to know what results will their spend fetch. Buying and selling of ad spaces now happen automatically and within milliseconds because of programmatic ad platforms. What drives the actions are machine learning algorithms and the levels of accuracy in doing that are only possible to achieve by machines, not we humans!

Author Bio:

Preethi Vagadia is currently a Senior Business architect with the Service operations practice at a well-known IT Industry in Bangalore. She has worked in several process improvement projects involving multi-national teams for global customers. She has over 8 years of experience in mortgage technology and has successfully executed several projects in logistics management, logistics integration, media planning process, warranty software and programmatic solutions.

 

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