What Is Data Science and Why Do Marketers Care?

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Ideas and Strategies for Real-Time Personalization
What Is Data Science and Why Do Marketers Care?

December 21, 2017 by

As technology has evolved to allow for real-time, one-to-one personalization, data scientists are getting more involved in marketing. As a result, the world of personalization is increasingly intersecting with the world of data science. But what do data scientists do? What is their role in the process? We set out to answer that question with last week’s webinar, “A Marketer’s Q&A with a Data Scientist about Personalization.” Our Director of Product Marketing, T.J. Prebil, chatted with Aaron Baker, Data Scientist at Brooks Bell, to give us all more insight into the life of a data scientist.

I cover some of the information he shared in this blog post, but be sure to watch the webinar replay for the full presentation and all the details.

What is a data scientist’s role?

There’s nothing I like more than a good analogy, and Aaron gave us a great one in the webinar. He told us to think back to our time in kindergarten when we got worksheets that looked like this:

data science for marketers

To complete the pattern in the worksheet, you have to identify what the pattern is and use it to figure out what comes next. Aaron says that data science is a lot like this worksheet: uncovering patterns in data.

So if you’re looking for a definition, a data scientist is a person who uncovers patterns in data to engineer a result.

At Evergage, that result is often a personalized experience for a prospect or customer. But data science is much broader than that. It can be about uncovering insights to define business strategy, identifying the next product or feature for a company, and more.

Essentially, data scientists find creative solutions to problems — with data at the core.

Always Start with a Question

“Every good solution starts with a good problem.” – Aaron Baker, Brooks Bell

To address a problem with a creative solution, you need to start with a good question. So data scientists always begin their work with a question, then use the data to provide an answer. Aaron gave us some good cross-industry examples of questions that can be answered through data, such as:

  • How do we reduce churn?
  • How can we improve customer engagement?
  • What’s the impact of our digital ads?
  • How do we optimize the supply chain?
  • What’s our risk exposure?
  • How do we spot fraud?
  • How do we identify at-risk patients?
  • Which patients are likely to have a certain disease?

And then, of course, since this is an Evergage webinar, he also provided some personalization-specific questions:

  • How do we display the optimal page layout?
  • How do we display the most relevant products?
  • How do we optimize the onsite search results?
  • When is the most optimal time to trigger notifications?

Data scientists always start with a clear question so they can ensure they come up with a clear answer to it.

Source the Data and Build the Model

To find a good solution to a good problem, you need to have good data. Data scientists spend a lot of their time sourcing data. They need to figure out what data they need, identify if that data even exists, and then determine if they can get access to that data.

Once they have identified their data sources, they need to bring all of that data together in a central location and build a model or algorithm to analyze it. Models require ongoing evaluation and monitoring to make sure that they work effectively and can actually answer the right questions. But once they have a working model, data scientists can provide helpful answers to tricky business questions, offering creative solutions based on real data.

Wrap up

As we move to a world of one-to-one personalization across channels, data scientists are playing an increasingly critical role in the customer experience. They are needed to help answer important questions, find value in disparate data, and apply insights to deliver relevant experiences at the individual level.

The right platform can help those data scientists in their efforts, from collecting data, delivering cross-channel experiences, previewing and testing those experiences, and analyzing results. To learn more about data scientists and the role they play in an organization, in marketing, and in personalization, watch the full webinar replay (there is much more that Aaron covered that I didn’t get to in this blog post!).

And to learn how Evergage can help you deliver one-to-one experiences with in-depth data and machine-learning algorithms (even without a data scientist!) request a demo today.

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