Machine Learning – the New Horizon in Marketing Success
As better methods of gathering and managing data are developed, and data analysis tools become more sophisticated, interest in the potential of machine learning is flourishing. The principles of machine learning, once reserved for academics and tech convention-goers, are now being applied in a wide variety of real-world contexts. Rather than following a set of static algorithms, machine learning provides an approach to predictive analysis that is able to evolve and improve, independently, in order to provide robust and actionable insights.
While most tech-savvy readers have some familiarity with big data and predictive analysis, the concept of machine learning is still transitioning from a vague buzzword into an exciting tool that can be implemented across diverse industries and applications. All of the current data analysis tools and methods provide the raw material that drives business decisions; machine learning promises to be the glue that turns several data sets into something bigger and more useful.
For the modern marketing professional specifically, the field of machine learning should be of particular interest. Marketing techniques have already improved dramatically over the last decade through the use of web analytics and rapidly evolving data tools. The downside is that marketing departments have to spend an increasingly large percentage of their resources parsing through the data to make decisions. Even with cutting-edge reporting and data aggregation software, there is a time-consuming process involved in converting that fleshed-out data into predictions, and then into action. Tools like SAS help with these issues. The SAS graphical user interface helps programmers build optimized machine learning models to make easy work of analysis. There are great SAS courses online designed to train programmers on machine learning processes, so they can teach machines do all the work for them. SAS is a vital skill for analysts to have, without which, data manipulation would be quite difficult.
Direct Benefits of Machine Learning to Marketing
Planning, executing, and pivoting campaigns is becoming more data-driven than ever before; machine learning principles provide a way to use that data in a way that is dynamic, incremental, and intelligent. The vast swathes of customer data that are collected can be used in real-time as training inputs to modify campaign activity. Instead of waiting to manually analyze an A/B test, a cutting edge marketing solution will be able to adapt automatically and rapidly respond based on customer engagement.
The benefit that machine learning provides to advertisers and marketers is a way to incrementally use data. In the past, a predictive model would be constructed using historical data sets, and then that model would become a static set of rules and algorithms to drive decisions. Now, that predictive model doesn’t have to be set in stone. With each new piece of data on customer demographics or content engagement, the model will be able to improve on its own, weighing that data with all of the historical training data initially ingested.
This is promising not only for improving campaign performance, but for delegating analysis and decisions that can now be handled by sophisticated programming. With marketing efforts being managed and fine-tuned by artificial intelligence, the tasks that are best left to human intelligence – creative direction, long-term strategy, and so on – can in turn receive more time and attention. Moving data management, and the decisions that follow, over to an adaptable AI, is more than the next step in data-driven marketing; it may lead to a fundamental redefining of the day-to-day activity of marketing teams by allowing them to focus their energies.
Staying Ahead of the Curve
Machine learning is rapidly shifting from being mostly theoretical to exceptionally practical; for industries that stand to benefit from the rapid application of nebulous data sets, the impact of easily accessible machine learning tools could be paradigm-shifting. While there is a copious amount of literature available on the topic, individuals without a PhD in Computer Science face a large barrier to entry in digesting and implementing the material.
For the non-tech professional who is eager to implement machine learning principles to drive business success, online courses provide the necessary knowledge and skills in a way that is time-efficient and cost-effective. This type of expertise is poised to become a necessity among professionals seeking to evolve with the shifting digital landscape, automating complex decisions and creating more time to spend on the business aspects that require a human touch.