What is marketing analytics and why it is important?

Over the past decade, data has been increasingly important for all functions in companies. 

Companies have moved from blind guessing to making data-driven decisions. This is especially so for marketing teams as they need to rely on data to make informed decisions. 

The skill of being able to look at data, understand what they mean and derive business insights from them is permeating through more roles than we imagine.

Despite this, I still hear from many juniors that they chose marketing because they are “bad at math”  which leads them to believe that they are unable to do a role requiring numbers at all. I would like to address two misconceptions here.

• Firstly, being bad at math in your growing years does not mean you are unable to understand data.

• Secondly, good marketers need to be able to understand data to make business decisions.

Data interpretation is a fundamental skill for many jobs. In fact, Linkedin has elevated analytical reasoning skills as one of the top 3 hard skills in demand. 

I know the term “understanding data” sounds all too vague at the moment, but allow me to run through some marketing roles which require strong analytical reasoning skills. Note that this list is extremely summarised and non-exhaustive.

There are other roles that require a strong understanding of data that I was unable to include given the length of this article. I’ll also share some practical ways you can go about strengthening your analytical reasoning skills.  

Roles that require marketing analytics capabilities

1.     Media Planners/Buyers: 

In an agency, a fundamental role of any media campaign would be the media planner or media buyer.

As part of the role, they look at various datasets about their audience and the ad spaces to make media decisions. This includes the cost for ad spaces (such as paid social media ads or OOH advertising) or data about their audience (where they spend most of their time).

They also observe the results of past campaigns to better understand what was successful and what could be improved on. Media planners typically analyse the results using Excel, in order to utilise the data to come up with a good media strategy. 

2.     Digital Marketing:

This itself is a large umbrella and I’ll only cover some examples in the e-commerce industry. That being said, there are many job scopes that would fall under this category not mentioned here.

A digital marketer in an e-commerce firm could be looking after the brand’s paid media performance, social media and customer relationship management. For example, given a paid media budget, they would have to manage their paid ads and track KPIs. The KPIs would vary given the nature of the campaign. Otherwise, they may need to track the uplift of sales given varying voucher codes. Different companies would use different tools but common software would include Excel, Tableau, PowerBI, Google Analytics and SQL. 

These are just some examples of roles that would require strong analytical reasoning skills. Some of these roles would also require basic data handling – deriving data from a database and wrangling the data to what is necessary for business use. I’ll share some tools/courses that I found were really useful to sharpen my learning. There are various data science courses on the Internet but I’ll share some that I found particularly relevant: 

How to improve your marketing analytics capabilities 

1.     Tableau:

Tableau is one of the most popular data visualisation platforms. I find it to be a very good software to understand data and pick up insights from it. As a personal recommendation, get involved in the community and take part in various events including #MakeoverMonday! You can get started here https://www.tableau.com/learn/training/20201

2.     SQL:

As many companies use SQL to extract data, I would recommend learning it if it’s an area of interest. Personally, I found most online free courses use overly simple datasets. I learned SQL as part of my school curriculum and sharpened it during my internship. (If you’re an SMU student, take Data Management!). Otherwise, try paid courses on Udemy or other websites. 

3.     Data Handling with R: 

Using R is less popular to transform and visualise data as most companies in Singapore use Python. Nevertheless, I found this book to help me understand the basics of handling data and is easy to follow. The writers have kindly put this online and I would recommend reading it if you have the time. You can find the link here: https://r4ds.had.co.nz

As I have mentioned above, it feels as if I barely scratched the surface of this topic. Regardless, I hope you have a better understanding of the importance of analytical reasoning skills and how you can kickstart your journey.

If you have any questions or would like to discuss this topic further, feel free to reach out!

Till then, happy analysing!