The majority of in-house marketing teams today consist of several different roles. These roles typically include a designer and developer, a product owner, a content or communications director and a growth hacker, just to name a few examples.

Of course, depending on the nature of your business, your own marketing team might look a little different. However, we are willing to bet that one role you don’t have filled is a data scientist.

Data scientists are paid well — and for good reason. The demand for their skills is increasing across various companies and industries, especially as the need for accurate quantitative and qualitative data analyses is more important than ever.

In fact, according to Glassdoor, the data scientist role has been the number one job in the United States for the last several years.

Furthermore, according to the U.S. Bureau of Labor Statistics, the rise of data science needs will lead to the creation of 11.5 million jobs by the year 2026.

Although data science roles extend far beyond the world of digital marketing, marketers have seen the value in data science projects, allowing them to better understand their customers, to understand what marketing channels and methods are working and which aren’t, redefine their goals and propel businesses forward. As a result, more and more marketers are filling data scientist roles in their organizations.

With access to this level of data, marketers can not only make more informed business decisions and reduce data-related risks, they can also bring some pretty impressive statistics to their CMOs.

What is data science and how can marketers use it to make more informed decisions?

What is data science?

Before we dive into the top three data science projects, if you aren’t entirely sure what data science is, let’s provide a little background about what exactly it is, what it involves and why it’s so important today.

Data science involves the interpretation and analysis of data and pulling out key, valuable insights and information from that data. Data can be classified as qualitative data or quantitative data, implicit or explicit. Regardless of the type or level of data, the goal behind data science is to enable marketers to think critically while also looking at the big-picture view of business goals.

Why are data science projects so important?

Why are data scientists so important? In our digitally-driven era dominated by technology, apps and tools, there is a greater need to analyze and measure performance KPIs for both the organization as a whole as well as for clients (if you are an agency). Data scientists can help businesses do this accurately, looking far beyond vanity metrics.

There are many benefits of data science projects, making them well worth the investments.

Data science projects allow marketers to:

  • Define, measure and refine goals based on KPIs
  • Track and analyze marketing funnels and pipelines
  • Look beyond raw numbers to identify trends
  • Provide accurate data and insights to help make more informed business decisions
  • Reduce risks associated with false assumptions, data misinterpretations or poor analyses
  • Strategize future campaigns

In addition to capturing the right data for making more informed business decisions, marketers can also leverage data scientists’ skills and knowledge to help drive other important projects and even future campaigns.

3 Marketing data science projects that will impress your boss.

3 Marketing data science projects:

Here are three data science projects that any marketer can complete, ensuring their position as a valued player on their marketing team:

1. Customer journey funnel analysis

It’s important for every marketer to understand what their customer journey funnel looks like and how it’s performing overall. This type of analysis shows how visitors and potential customers navigate a website, the paths they follow when doing so and the actions they take (if any).

For example, this type of analysis is helpful for marketers to understand why a customer abandons a site or shopping cart.

A look at how Google Analytics can help you with your data science projects in marketing.

Google Analytics is a common and popular tool to help analyze the customer journey funnel. A well-informed marketer can step in and help analyze this information at a deeper level, reducing risks associated with false assumptions or data misinterpretations.

The great thing is, it’s a simple report away in your Google Analytics suite. By taking accurate and verified data and analyses to your CMO, you and your team can confidently make more informed business decisions.

Here are a few easy steps for you to build and then access this powerful customer journey report:

  1. First, ensure you have “Enhanced E-commerce” enabled in your Google Analytics account. Here are some simple instructions, compliments of Google, for you to do that:
  2. After enabling the enhanced e-commerce options, you’ll need to wait some time for conversions to come through in your system. Google only tracks moving forward from when you enable the enhanced tracking, so you’ll need to be patient.
  3. The report can be viewed under Reports > Conversions > Ecommerce > Shopping Behavior Analysis.
  4. Even though this report is very easy to use (point and click easy) you may want to use the segment filtering device that is available on this and most other reports. Here’s an article that explains how you can use the segment filter to better drill in on the data you need to impress your CMO/VP of Marketing.

2. Anomalies and trends diagnostic analysis

As we mentioned in the point above, the biggest risks associated with analyzing marketing data is making false assumptions or misinterpreting the data altogether. This is the primary reason why leveraging a data scientist is so valuable.

In this context, “anomalies” refer to data that doesn’t follow a defined or set pattern, or doesn’t reach a specified confidence percentage. Furthermore, in addition to identifying data anomalies, it is also important to recognize and define trends. These could be trends in buyer or user behavior and are often presented as statistical models.

All in all, detecting and defining anomalies and trends generally requires manual data processing and analysis — a job for a skilled data scientist, or a sophisticated marketer (i.e. you!). By performing this type of analysis, or utilizing pre-built tools, marketers can arm themselves with the right data and information needed to make more informed business decisions and present those models and data to their CMO.

Leveraging anomalies and trends diagnostics reports from your pre-built analytics tools is a great way to impress your leadership team without needing to learn sophisticated new data querying and manipulation skills. Whether you’re using Google Analytics for anomaly reporting, or the more robust Adobe Analytics Anomaly Detection, you’ve got these reports easily at hand.

Data science projects for marketers: conversion predictive analysis.

3. Conversion predictive analysis

As experienced marketers, one of the most important metrics you are likely tracking is the performance of your content and conversion rates associated with your content. With marketers hyper-focused on increasing conversion rates, it’s important to have data to back up your work and decisions.

For example, as a marketer, you likely want to know which marketing medium or channel is driving the most conversions — and giving you the most bang for your buck. You likely also want to know how a campaign is performing. Of course, this is just one of the many questions marketers have when managing and overseeing marketing initiatives and activities.

Understanding campaign and marketing channel performance and how they are driving conversions is one half of the battle; the other is outlining and strategizing future campaigns. The best way to approach this is with a conversion predictive analysis. This type of analysis uses statistical data to help predict conversion rates and click-through rates.

Many marketers today are leveraging AI for conversion predictive analyses. Using AI can not only help build a data hub or profile to help marketers better understand customer and user behavior but it can also simulate test interactions and engagements to help predict certain actions, behaviors and patterns. This level of analysis provides information to marketers, guiding them on which campaigns they should invest in according to likely results.

For any marketer wanting to run their own predictive analytics, without learning and relying on AI, there are also four great solutions for you to get started relatively quickly.

These three solutions, ranked from easiest to most difficult, are:

  1. Google Analytics Smart Lists, similar to their smart goals, allows you to easily build remarketing lists of your site visitors that are most likely to convert if brought back to your site. In this help document, Google introduces Smart Lists and provides links to help you easily set them up.
  2. Google Analytics Smart Goals, made to use predictive analysis (based on your past conversions) to better drive future site visits that will generate additional conversions, can be easily configured, as outlined in this article.
  3. Facebook’s open-sourced Prophet project allows budding data scientists (and marketers) with only rudimentary coding skills to get started with forecasting out marketing campaigns/projects/initiatives. While you’ll need some R or Python knowledge to get started with the Prophet library, there are dozens of great tutorials out there to help you get going, starting with this blog post from Facebook Research.

Big data is now actionable data

In the last decade, as technology and digital marketing have consumed the business world, and continues to drive it forward, the need, demand and value for big data has skyrocketed.

Therefore, by having the right level of data, analyses and technology available to back up your work, make more informed business decisions and reduce risks, you can impress your CMO.