Model Building, Pattern Finding, and Accurate Audience Predictions

Machine Learning Meets Modern Day Marketing

Targeting, deciding which offer to send to which customer, often relies on the marketing team’s ‘gut-feeling’ of what the right audience is for a campaign. Can machine learning, combined with data driven marketing, allow organizations to exceed traditional best-practice marketing approaches?

What is Machine Learning?

Machine learning is a method of data analysis defined as a sub-set of artificial intelligence (AI). Using computer algorithms that autonomously learn from data, a machine is capable of identifying patterns, insights, and providing predictions (particularly with new and unseen similar data), all while improving and changing its existing algorithms, completely unaided.

Finding hidden patterns and insights in data without being explicitly programmed where to look is typically the case in ‘unsupervised’ machine learning. In ‘supervised’ machine learning on the other hand, one knows the data contains a certain response variable, and would like to make predictions based on the unseen data. Machine learning models can produce repeatable decisions and predictions based on data. The more relevant data a model is exposed to, the more a model is able to independently adapt and grow in reliability.

Machine learning has been around for over half a decade and is experiencing great new momentum. Today, machine learning is applied in a variety of ways such as autonomously driving cars, offering movie recommendations, detecting fraud, and much more.

Data-Driven Marketing feat. Machine Learning

Data has become a standard in modern day marketing with data pouring into organizations from almost every angle. Gigabytes, terabytes, and even petabytes of data are collected on a daily basis and stored in massive databases. However, unless one has the capability to do correct analysis on the data and retrieve relevant insights and information, simply having these enormous amounts of data is useless.

Combining machine learning with data-driven marketing allows organizations to utilize their data in a more efficient and thorough manner. Machine learning takes the human error out of data analysis. People tend to find insights and patterns in data they were already looking for, machine learning systems base their findings on a wide range of datasets and mathematical algorithms, excluding all human bias from the process, fueling better business decisions.

However, machine learning algorithms and models are highly complex systems which require significant testing to validate results. Once this is complete, preceding data, together with new data and trends, can fuel the prediction of future outcomes.

Proven machine learning systems capable of discovering patterns, insights, and trends, as well as making accurate predictions on future data, are turning out to be extremely useful in data-driven marketing settings. With micro-targeted campaigns audiences accurately predicted, these discoveries allow marketers to precisely pinpoint their available resources, significantly increasing campaign performance, efficiency, and overall ROI.

Model Examples

Examples of models making predictions on future data include:

• Build a model to predict how likely a customer is to purchase a particular product based on their past behavior.

• Build a model to predict which potential customers have the highest propensity to become a customer based on past campaign performance.

• Build a model to extract homogeneous groups of potential customers with similar behaviors and preferences based on your current customers.

• Build a model to predict when and which audience segment to target, with what specific marketing campaign, based on previous campaign success.

Apply machine learning to produce models capable of analyzing enormous amounts of data, while delivering fast and accurate unbiased results on a large scale, generating high-value predictions that fuel better, faster, and smarter decisions.

Stop relying on ‘gut-feelings’, let the data speak for itself..