The Modern Marketing Analyst: 7 Must-Have Skills for Success

Today's analysts have to be tech-savvy, understand data science, and be able to use complex tools. But that's not all. The transformation in marketing over the last 20 years has ushered in a new period where decisions are as much about numbers as they are about creative ideas.

Companies want marketing analysts who can do more than just look at numbers. Instead, they need experts who can understand the data, explain what it means, and help the company do better against their competitors.

If you want to become a marketing analyst or find a new job at a leading company, this article will help you. We'll discuss the primary skills you'll need as a marketing analyst and what companies expect from you in 2023.

By 2029, the U.S. expects an 18% rise in jobs for market research analysts. Companies are looking for experts good at understanding large amounts of data, especially those who are skilled in predicting future trends, understanding customers, and analyzing content.

So, what roles do companies expect marketing analytics to have?

Analysts should be able to dig into large sets of data to spot trends and research market conditions to see chances for growing products or introducing new ones. Using what they learn, they assist in forming company marketing plans. They also keep an eye on how marketing efforts perform and recommend improvements if required. Finally, they share their discoveries in easy-to-grasp ways, making complicated data clear and useful.

Data analysts need a mix of technical skills and a sharp eye. They should know programming and be good with numbers and stats. Let's look at the key skills that data analysts should have:

Being good at advanced data analysis is key for marketing analytics.

You'll often work with the following data:

For now, there are 3 main advanced data analysis skills that stand out:

Data analysis is the process of extracting, transforming, and interpreting information to discover noteworthy numbers, suggest conclusions, and support decision-making.

Most popular tools include Excel for basic tasks, while more advanced functions might need R, Python, SPSS, and SAS.

Read our latest case study to see how advanced data analysis made a difference.

Data normalization is organizing data to reduce redundancy and improve data integrity. It involves restructuring the data into tables (like in databases) to ensure that their relationships are correctly set, and any anomalies are removed.

For example, consider a company selling products in two markets using different currencies: dollars and euros. A normalization tool would standardize all sales data by converting it to a single currency, based on the exchange rate for that day.

There are several tools that can assist with the data normalization process. Some of the popular ones include OWOX BI, dbt, scheduled queries, etc.

Data visualization is turning data into easy-to-understand pictures or graphs. This way, people can quickly get the main idea or spot trends without digging through lots of numbers.

Popular tools for data visualization include OWOX BI, Looker Studio (formerly Google Data Studio), Google Sheets, Tableau, Power BI or D3.js for more custom visualizations.

Every marketer dreams of knowing the exact path a customer takes before making a purchase.

• Which ad did they click on?
• Did a blog post influence them?
• What role did that email newsletter play?

Attribution modeling is the answer to these questions. It breaks down the journey, highlighting key touchpoints that nudge a customer closer to a sale. For instance, you could have found that while social media ads introduced your brand to new customers, it was the email campaigns that usually sealed the deal. With this insight, you could allocate your budget more effectively.

By studying historical data, a data analyst can anticipate what's coming next, allowing businesses to prepare and make proactive decisions. The accuracy of forecasts depends on the quality of the past data. This might include sales data, website traffic, customer behavior, and market trends.

Analysts use various predictive modeling methods (linear regression or time series analysis) to make informed predictions about future events based on past data.

For proper forecasting, data analysts use Excel, Python, R, Tableau, and IBM SPSS.

A data analyst should be able to juggle various tasks. According to a survey by PMI, 77% of high-performing projects use project management tools.

Data analysts can lead teams that work on several campaigns. With project management techniques, they stay on track and identify overlapping tasks, saving both time and resources. So, while crunching numbers is vital, a data analyst's ability to manage tasks, teams, and timelines is equally critical.

A data analyst uses online surveys, analyzes social media sentiments, studies online behavior, and leverages big data to predict where the market is heading, see what customers like, and decide what to do next. Key tools to help in this process include SurveyMonkey, Google Trends, and SEMrush.

MARTECH, short for Marketing Technologies, refers to all the digital tools and platforms used by marketers to streamline, automate, and analyze their marketing tasks and data.

It is basically the techy side of marketing that includes tools that help businesses engage with customers, track results, and boost their marketing activities. For instance, an email marketing platform like Mailchimp is a piece of MARTECH. It helps send emails, track who opened them, and see if they led to sales.

Another example is RFM Analysis (Recency, Frequency, Monetary) – a technique that segments customers based on their purchasing behavior. By integrating RFM Analysis with MARTECH, analysts can determine which customers are most valuable, allowing targeted marketing campaigns and better allocation of resources.

Image: Add soft skill symbols (like communication bubbles and teamwork icons)

Soft skills play an important role for a data analyst, as they complement their technical expertise. Here's why:

Analysts don't just use numbers; they often interact with stakeholders, teams, or even end customers to understand requirements, explain insights, or gather feedback. Good customer service skills ensure that they can easily communicate, address concerns, and build rapport.

Data rarely presents itself neatly. Analysts discover missing data, anomalies, or conflicting information. A knack for problem-solving helps them navigate these issues and find solutions.

While tools and software can process data, human judgment is vital for interpreting results. Critical thinking helps analysts read patterns, question assumptions, and draw logical conclusions.

OWOX BI is a marketing tool that helps analysts work more efficiently. Here's what it offers:

Data Collection: It pulls data from different sources together, so analysts don't have to do it manually. This saves time and allows for quicker insights.

Forecasting: OWOX BI has features that predict market changes, helping businesses stay ahead.

Reports: The tool can create specific reports, helping analysts understand customer actions better. This way, marketing plans can be more precise.

Integration: OWOX BI works well with other platforms, making sure all data is easily accessible in one spot.

In short, OWOX BI gives marketing analysts the tools they need to be more than just data experts; they become vital strategy developers for businesses.