Applying predictive analytics and data science to marketing can provide powerful insights for companies. Often, discussions about data science and predictive analytics quickly become bogged down by jargon and complex topics like artificial intelligence and machine learning. In today’s world, if you aren’t using some sort of AI/ML, you’re at risk of being considered “old school” and are probably experiencing some FOMO. 

The good news is that you don’t need to become a data scientist in your spare time to benefit from data analytics. Learning the basics will give you a leg up immediately and lay the foundation for future learning.

 

Reminder: What is Data Science?

Most marketers understand what is meant when we talk about data… things like names, contact information, demographics (age, income, education level, and so on), behaviors, revenue, etc. Each of these data points helps teams develop effective campaigns, better segment their database, and it drives decisions throughout a company. 

For example, if you know that your sales team has greater success when speaking with VPs of Product, modern marketers will work to figure out where those product leaders live and what matters most to them in order to invest in the right channels to engage with them.

But that’s a pretty simple example, and we all know that in order to break through the noise in the fully digital environment we’re all operating in, we need to think about data through a more complex lens. Plus, marketers are sitting on a MOUNTAIN of data. That’s where data science comes in. Data science describes the procedures and methods used to categorize, interpret, and make sense of all this data.

Data scientists use tools like predictive analysis and machine learning to detect patterns that can help marketers better forecast revenue and understand metrics like customer engagement. More on that next… 

 

Predictive Analytics, Machine Learning, and Artificial Intelligence 

Predictive analytics is one of those data science topics that can leave non-analysts feeling a bit dazed. At its core, though, the concept is straightforward — predictive analytics examines behavior patterns to predict future behavior. 

Predictive analytics helps marketers forecast the impact of future campaigns, anticipate customer response to messaging changes, and efficiently score leads based on their conversion potential. 

Here are a few examples of how we can apply predictive analysis in meaningful ways:

  • Identifying customers likely to abandon their cart
  • Targeting marketing campaigns to companies that are most likely to buy 
  • Making customer experience decisions based on predicted engagement with specific aspects of a service or product

Layer in artificial intelligence (AI) and you’ve got data analysis on steroids. Human analysts could never hope to sift through a company’s infinite stores of data to uncover the information they need to arrive at robust predictions. AI can make quick work of gathering, sorting, and preparing data for the tools data scientists use to conduct analysis.

AI can do more than serve as a gopher, however. Advances over the past few years have led to the advent of “machine-learning AI” that can actually make predictions, test them, and learn from the results, autonomously. 

Most machine-learning models require human input at some stage, but the latest programs are context-aware — they can be released into a new environment and develop a base understanding without human input. 

Why Should Marketers Care?

Put simply, these data science “tools” give marketers transparency and clarity, while also providing actionable insights and recommendations for how to leverage said data. 

At the end of the day, marketers are tasked with increasing revenue. Leveraging data in this way allows marketers to set aside uncertainty and confidently forecast their contribution to their company. Gone are the days of using reactive attribution models that force marketers to wait until after the deal is closed to understand what is and isn’t working in their marketing mix! In addition to this forward-looking crystal ball, this predictive magic can alert marketers when adjustments need to be made. 

At a time when businesses are overflowing with data — and when marketers are increasingly pressured to maximize ROI — data science is a vital tool within the marketing toolbox. In short, it is a game changer.

 

A Day in the Life of a Data Scientist

Let’s take a look at a data scientist’s typical workday. What do these professional number wranglers do all day? While each analyst has a unique set of duties and tasks they need to complete their daily work, there are a few elements common to most. 

Report Creation

One of the main goals for any data scientist is to take complex information and distill it into understandable and relevant reports for the teams that need them. This can mean creating separate sets of analyses based on the same data or diving back into the data to further examine specific metrics. 

Data Cleaning

The familiar saying “garbage in, garbage out” is significant when it comes to data analytics. Analysts need to manage the tools they use to ensure the right information is collected. Even a slight adjustment can make a difference in reporting, leading to decision-making based on misapplied analysis. No pressure!

Cross-Department Communication (Meetings… Ugh)

Interactions with other team members and departments is a large component to successfully leveraging data science expertise. Great data analysts are able to connect the dots between the data and the business value. They can take competing interests (between sales and marketing teams, for example) into consideration when preparing the analysis and communicating it back to stakeholders. 

Additionally, they can also expect to spend part of each day relaying and explaining data analyses to these teams. 

Deep Work Sessions

Oftentimes, especially in fast paced environments, data analysts are juggling multiple projects. However, each project requires quite a bit of effort. It is important that these scientists have time to spend with their data, which is where deep work sessions come in. You might find them blocking off large chunks of time on their calendar to really focus on a particular task or project.

There are, of course, other tasks data scientists perform each day, but these are some of the most frequent and essential. Interested in hearing another perspective? I’d recommend this article to get a closer look at the “typical day” of a real data scientist!

 

MadKudu: Your Data Science Sherpa

Our passion is empowering marketers to harness the power of predictive modeling and data analytics to deliver the most impactful results. Visit the MadKudu website to learn more about how predictive segmentation and data science can improve your marketing. 

Laura Kendall

Laura Kendall is the VP of Marketing at MadKudu, a martech company that helps marketing teams drive revenue through predictive marketing and analytics. With more than 13 years of experience in B2B marketing, demand generation, and leadership, Laura is passionate about the convergence of marketing and data when it comes to driving successful demand generation campaigns. She is actively involved in women in tech communities: She serves as a Salesforce Women in Tech User Group Leader and Committee Member for Women in Revenue.