6 steps to leveraging marketing data analytics

How to leverage marketing data analytics to boost your career

In the wake of the global pandemic, CEOs are increasingly relying on marketing leaders to deliver growth. 

Recently, McKinsey & Company surveyed 860 executives globally about their thoughts on the future of growth and productivity. The survey results revealed that 78 per cent of CEOs expect marketing leaders to drive company growth. 

This means that the pressure is on for marketers to deliver. In a landscape where rapid change has become the norm, marketing leaders are turning to data analytics to help them succeed. It’s why professionals with data analytics skills are in such hot demand. 

So perhaps you have aspirations for a career in marketing? Or maybe you want to get ahead or simply stay relevant in your profession? Either way, there’s never been a better time to upskill in data analytics. 

Of course, not all upskilling options have equal value. It might be tempting to do a quick crash course, or even learn from free online resources. But it’s important to be aware that, when it comes to marketing, studying a postgraduate degree can offer:

• increased salary and full-time employment prospects
• the opportunity to extend your professional network 
• access to knowledge delivered by world-class academics

In the meantime, if you want to learn how to use data analytics in marketing, we’ll walk you through the six essential steps to follow. First though, let’s explore why marketing data analytics can be so powerful in the first place. 

The benefits of data mining in marketing

‘Data mining’ means analysing large amounts of data to find the trends and patterns within it. It lets you convert raw, unstructured facts into comprehensible, actionable business information.

Iconic brands like Coca-Cola, Qantas, Coles, Netflix, Amazon and ASOS have all transformed their marketing practices by leveraging the power of data. They’ve enjoyed the benefits of data mining in marketing, such as:

• increased sales
• improved marketing efficiency
• growth in new customers
• more business from current customers
• improved customer experience
• reduced marketing costs
• reduced inventory and transportation costs

Many marketers want to transform their marketing practices to take advantage of data mining. However, they don’t always have access to the resources they need to do this. 

According to a recent Capgemini Research Institute report, “only 12 per cent of marketers have the requisite data access, capabilities and talent to drive and extract high value from real-time marketing.”

Beyond this, the challenge is knowing which areas to focus on to create a data-driven marketing environment in your organisation. 

You can learn exactly how to use data to drive results in RMIT Online’s Master of Marketing program. For now though, here’s a quick preview to get you started.

How marketing analytics can increase sales and customer loyalty

Using marketing analytics can increase sales. For example, Coca-Cola’s world-famous Share a Coke campaign used data to target millennials. Bottle labels featured the most popular first names of the target generation, which increased sales for the first time in roughly four years. 

Data-driven marketing also increased brand loyalty and strengthened an already stellar customer service reputation at Qantas. How? By continually evolving their customer service standards and Frequent Flyer program to match their customers’ needs. 

For example, Qantas offers members the option of redeeming Frequent Flyer miles on a variety of products and services using the power of data analytics in marketing. Instead of just flights and upgrades, customers can choose from luxury hotel stays, gifts and insurance. 

How data analytics can decrease marketing costs

Meanwhile, supermarket giant Coles uses data analytics to reduce their advertising costs. Coles combines data from its Flybuys program with online shopping records to tell customers when certain products are on special. These tailored customer specials are more efficient compared to blanket promotions, reducing marketing costs considerably.

And streaming entertainment provider Netflix also uses customer data to commission shows based on subscribers’ demographic information and viewing preferences. These insights drive critical product decisions to both save the company money and reduce risk. 

How marketing analytics strategy can protect brands from bad decisions

Finally, effective marketing analytics strategies can also save organisations from making costly decisions. For example, fashion retailer ASOS’s free postage and returns policy lets customers try on clothes without risk. 

Using data analytics in marketing, ASOS can then reduce any further risks by feeding historical purchase data into algorithms, presenting customers with items matched specifically to their size and taste. The result is fewer returns, more sales and increased customer satisfaction. 

Smart marketing analytics can even save a business from bankruptcy by creating competitive advantage. One example is Amazon’s dynamic pricing algorithm, which combines customer purchase, competitor pricing and inventory data to make real-time product pricing changes.

Six steps to leveraging marketing data analytics

If you want to become a data analyst or leverage data analytics in your marketing career, you’ll need to follow six essential steps. 

Ask the right questions

To correctly understand and diagnose a problem, you need to ask the right questions, taking into account your business goals and KPIs. Knowing which questions to ask is crucial for any data analytics career, so it’s important to have a great understanding of both data mining and marketing. Skipping this step risks making a poor decision that can result in significant fallout. 

This is particularly true if the decision is critical, such as deciding how to spend the company’s marketing budget. Making the wrong decision in this situation could result in serious financial repercussions. 

Get the right kind of data

The next step is collecting the right kind of data: information that’s directly relevant to the business goals and KPIs you identified earlier. Data analyst roles and responsibilities require in-depth research into current gaps in the market, both internally and externally. What’s missing? And how do we personalise the experience for these customers? 

For example, Coca-Cola collected the right kind of data when they developed their Share a Coke campaign. According to Lucie Austin, then Director of Marketing for Coca-Cola South Pacific, “research showed that while teens and young adults loved that Coca-Cola was big and iconic, many felt we were not talking to them at eye level…”

If Coca-Cola had pursued the wrong data, they wouldn’t have had the insights on common millennial names required to connect to their customers in such a meaningful way.

Clean the data

Clean data produces the best analytics results. Some typical data cleaning activities that marketers might do in this step include:

• checking the data for accuracy
• recording customer communication preferences
• removing any duplicate records 

Explore the data

After you've cleaned the data, it's time to explore and analyse it. This helps you: 

• discover the opportunities and limitations within your data
• uncover high-level patterns and trends that data analysis tools may not capture
• identify any problems or discrepancies before you do a detailed analysis

Data visualisation is a powerful tool to help you see patterns and trends in the data during this step. That’s why using it is an essential element of any marketing data analyst’s roles and responsibilities. 

Some key approaches to data visualisation include:

• graphical techniques
• bar graphs, line graphs and distribution graphs

You can also explore the data using descriptive summary statistics, and identify the mean, median, mode and standard deviation.

Model the data

Once you've analysed your data and answered the questions you set yourself in the first step, it's time to model the data.

Some modelling techniques used in marketing analytics include:

• clustering: this technique allows you to segment customers based on available information about their characteristics and behaviour.
• classification: these models use predictive analytics to identify new data based on categorising information from historical data. 
• recommendation systems: these systems seek to predict the ‘rating’ or ‘preference’ a user would give to an item, action or opportunity. A real-world example is ASOS's personalised product recommendations generated by data-enriched algorithms.

Data modelling can help you:

• automate routine marketing decisions: for example, a marketing recommendation system can identify actionable insights from your customer data without requiring any manual analysis
• personalise offerings to customers’ needs and wants: a great example is Coca-Cola’s Share a Coke campaign 
• nurture customer relationships: a good example here is the Qantas loyalty program redesign

Communicate the results

The final step in leveraging marketing data analytics is to present your insights to the project stakeholders. The way you present data is critically important to the impact of marketing analytics.

Whether you work in-house or at an agency, your role as a data-driven marketing professional is to bridge the gap between data and decision-makers. This means you need to ensure that any information you present is easy to understand and relevant to the stakeholders’ needs.

Why study a Master of Marketing with RMIT Online?

The decision to do postgraduate study is a big one. And with so many different institutions and study options available, it can be a little daunting to choose. So why choose RMIT Online for your postgraduate qualification?

RMIT Online’s Master of Marketing will allow you to transcend the buzzwords and gain fundamental marketing skills that deliver real business impact.

By studying a Master of Marketing with RMIT Online, you can expect to:

• learn how to take advantage of data analytics
• learn the principles behind how to become a data analyst
• develop a deeper understanding of foundational marketing principles to help you stay relevant in the future
• know how to apply proven marketing theories to business challenges and get results
• gain expertise in interpreting research and developing effective marketing strategies
• select courses that align with your career goals and interests
• discover the benefits of a data analytics career
• learn more about data analyst roles and responsibilities

Are you ready for the future?

As a marketer of the future, you’ll need the skills to harness the power of data to make informed business decisions. This can benefit both your organisation's return on investment and your career opportunities.

Interested? Learn how RMIT Online’s Master of Marketing program can deepen your knowledge of fundamental marketing principles. Then discover how to apply those principles today to drive results both now and into the future. 

Visit our website or call 1300 701 171.