Machine learning is generating a lot of chatter lately, making myriad marketers scramble to figure out to use it. Despite all the excitement surrounding machine learning, however, marketers still struggle with how to view it, what to expect from it, and how best to use this new technology.

Moreover, the digital marketing landscape has changed dramatically, largely due to rapid advancements in technology:

• CRM systems have added a digital history to customers and consumers.

• Web analytics and tracking have added granular data that can be analyzed in near real time to provide insights across Web and mobile channels.

• BI has added elements of quick and robust reporting to the marketing teams.

• Search engines and ad placement have added a new channel for reaching and understanding customers.

• Action systems (such as content optimization) have allowed for personalizing content.

• Detailed data collection and data mining have allowed for more precise segmentation.

The volumes and rates of data captured by those systems have increased substantially each year. Marketers are seeking to augment and blend that data with other sources, such as in-store visitor data.

The dizzying increase of data volume and growing number of data collection sources leave us marketers trying to figure out how to drive the right message at the right place, at the right time.

What to Do With All That Data

How can we use those data collection and action systems to identify, understand, and act on the most relevant data?

Understanding and identifying the most relevant data has been squarely placed in the marketing analyst area, with these teams utilizing new and exciting technologies to develop insights, design and develop campaigns, and improve customers' digital interactions.

Typically, campaigns and tests are planned and executed over a window of time. In most cases, however, campaign development and execution windows are not dynamic enough to take into account recent changes in customer behavior.

For example, testing and targeting content with a specific message may not take into account that the customer visited your site yesterday via a mobile device. Such a change might place that customer into a more profitable segment of a multichannel visitor, and your message might be different.

Machine learning has the capacity to quickly consume massive amounts of disparate data and locate patterns that allow an action system to read the output (typically a prediction) and act upon it.

Major Characteristics of Machine Learning

Consuming data, locating patterns, and learning from predictions to improve them over time are the major differentiation points for machine learning.

In the case of marketers in the near future, here are four key beneficial machine learning characteristics:

1. Massive data input from unlimited sources. Machine learning has the ability to consume virtually unlimited amounts of detailed data to constantly review and adjust your message based on very recent customer behaviors. Once a model is trained from a full set of data sources, it can identify the most relevant variables, limiting long and complicated integrations and allowing for focused data feeds.
2. Rapid processing, analysis, and predictions. The speed at which machine learning can consume data and identify relevant data makes the ability to act in real time a reality. For example, machine learning can constantly optimize the next best offer for the customer, so what the customer might see at noon may be different than what that same customer sees at 1 PM.
3. Action systems. Those systems can act upon the outputs of machine learning and make the marketing message much more dynamic. For example, newly obtained information may suggest surfacing a retention offer to a specific customer. Or perhaps no offer at all if the behavior suggests the customer might not require one to create a conversion event.
4. Learning from past behaviors. A major advantage of machine learning is that models can learn from past predictions and outcomes, and continually improve their predictions based on new and different data. A simple example is whether the weather at a particular moment has a correlated effect on conversion behavior.

Machine learning will not replace marketers but instead will allow them to think more broadly about the data sources they can successfully use and how they can gain automated insights against customer visits. This technology and associated algorithms allow marketers to expand the ability to continually run tests against actual customer behavior cost-efficiently.

Moreover, machine learning will be another powerful tool in the digital marketer's playbook that will provide never-seen-before insights and patterns within massive and blended data sets. At the same time, it will also provide the ability to act upon rapid understanding of the constantly changing data.