The martech landscape is cluttered and crazy. Each day there is some new marketing technology being introduced that claims to drive engagement. Keeping up with this ever-changing technology landscape can be overwhelming. One of the hottest areas in today’s martech landscape is the management of customer data.

Marketers are constantly trying to wrangle large amounts of customer data, either from IT or from their large data science teams, in order to uncover insights from vast, ever-growing amounts of customer data. Add to this the challenge of trying to drive more value out of existing technology investments and leveraging customer data locked in various platforms/data warehouses in a timely manner, and suddenly, the task seems impossible.

Marketers have realized that data curation is the biggest barrier to the $18.3 billion analytics market. This has led organizations to hire and train legions of citizen scientists — equipped with self-service data prep or analytics tools — to try and exploit the mountains of available data.

The majority of data scientists only spend 20% of their time working on data analysis. On top of this, three-quarters of companies that focus on big data initiatives report that their revenue increase from this effort has been less than 1%. Manual analytics and data visualization tools are becoming obsolete, and we need to look toward other solutions to save the state of big data customer analytics.

2018 is set to be the year that embedded analytics and automated machine learning will start to replace manual/ad hoc analytics.

So What Is AutoML?

There is a growing interest in creating tools that automate the usual tasks of understanding customer data and driving deeper insights.  This concept is often referred to as automated machine learning or AutoML. While there is no universal definition, the organizers of the AutoML workshop at the yearly ICML conference offer a reasonable description on their website.

AutoML plays a key role in helping a marketer form a deeper relationship with their customers. Customers today expect to receive highly contextual and individualized offers. You must continually offer the next best action with each customer on a one-on-one basis or risk losing them. But to really understand your customer, you need to converge relevant customer data across hundreds of silos and build a complete understanding of each customer. This data must then be cleaned, unified and run through relevant algorithms to find insights and recommendations. AutoML can automate this process and keep up with advanced customer analytics without the need to hire armies of data scientists. At Zylotech, we created an AutoML platform that solves data variety and quality challenges. There are a plethora of other companies that have engaged this technology with meaningful results. Google’s Cloud AutoML is being used by a variety of brands such as Disney and Urban Outfitters to aid in the ease of online shopping.