Thursday, 27 April 2017

The modern marketer’s guide to machine learning algorithms

Most marketing (and sales) teams have seemingly simple goals: identify your best customers, target prospects who look like them, facilitate a positive buying experience, and bring these prospects into your customer community.

The challenge with this is that companies are faced with an onslaught of data, making it impossible to economically throw humans at each of the aforementioned objectives. Much business learning and many routine tasks can be done faster and better with automation and AI. People simply will never be able to sift through all that data at the same rate as machines, and certainly not with the precision that well-designed algorithms can bring to the table.

These days, every business is in the data business, and in order to make better decisions, leaders need machine learning and analytics to find actionable patterns in the data. But first, it’s important to clearly define the problem you’re trying to solve. For example, if your company struggles with customer churn issues, you might want to employ algorithms to figure out how to reduce the churn. In that case, you’d need to understand when someone is likely to churn so you can make an offer to reduce the risk. Another question might be why someone churns, but that’s a completely separate problem for a different algorithm to solve.

Once you’ve narrowed down your scope to an initial marketing problem, machine learning algorithms will undoubtedly be well-suited to tackle it with the historical prospect and customer data from your CRM, marketing automation or data warehouses. As you get started, it’s helpful to be able to speak the same language as your data practitioners and/or predictive analytics vendor. Let’s take a peek behind the curtain to gain a basic understanding of the key algorithmic approaches used in marketing.

[Read the full article on MarTech Today.]

Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.

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Author: Sean Zinsmeister

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