Marketing Analytics explained
Marketing analytics: this article explains marketing analytics in a practical way. It covers what marketing analytics is, what tools and software can be used, what action can be taken, who uses it, what the role of machine learning is and what the challenges of marketing analytics are. After reading this article, you will understand the basics of this marketing tool. Enjoy reading!
What is Marketing Analytics?
The definition of Marketing Analytics
Marketing analytics is used by commercial organizations to assess the results of marketing campaigns (return on investment (ROI)) and to make important choices about marketing activities.
Marketing efforts such as demographic studies, conjoint analysis, customer segmentation, and other activities provide vast amounts of information that marketers can use to optimize marketing strategy.
Marketing analytics uses both quantitative and qualitative data. Data analysts are concerned with:
- Marketing experiments
- Marketing automation (machine learning)
- Real-time sales information
- Predictive modeling
Different branches of marketing have their own challenges when it comes to data management and data analytics.
For example, an online marketer is interested in:
- The success of specific calls-to-action (CTA) online
- The amount of views on a blog post
- The amount of time spent on a page
- User experience on a website or in a mobile app
One tool that is popular among digital marketers is the free Google Analytics.
Data analysis and data analytics are used across the entire organization. Another department where data analysis is used is Human Resource Management, with HR Analytics.
Action based on Online Marketing Analytics
Marketing departments often have a wide range of options and methods for taking action based on insights gained through marketing analytics.
Advanced marketing analytics platforms measure consumer engagement and analyze how different customer segments respond to particular campaigns. That helps the marketer to measure the ROI of certain efforts.
Online marketers can use keyword analysis software to identify specific terms that are frequently searched for. These terms are incorporated on product pages and in advertisements to generate organic and paid traffic through web searches and mobile apps.
The data released when running an online marketing campaign can later be analyzed to gain insights into which types of content are best received with more followers and visitors. Marketers can then tailor the content of future campaigns to those best practices to increase traffic.
Marketers are also concerned with studying new market segments or launching campaigns that do not target traditional segments. In this way they try to uncover the potential of the market in other segments.
Machine Learning and Digital Marketing Analytics
Machine learning is defined as a class of artificial intelligence methods that train systems to apply solutions. A distinction is made between two types: machine learning with a ‘teacher’ and machine learning without a ‘teacher’.
In the case of machine learning with teacher, a person provides the machine with input. The system then analyzes the input and learns to classify the information based on known human solutions. In the case of teacherless learning, the machine receives unsorted information and learns to classify it without human guidance.
Marketers use machine learning, incorporated into analytics software, to find patterns in user data on a website. This helps predict online consumer behavior.
In psychology, a pattern is a particular set of reactions or a common set of actions.
For an example on machine learning and patterns, consider a pop-up window on a website with an offer. Often that window has several options to click it away. For example, by a cross at the top right, or a button with ‘no thanks’, or the user can click outside the pop-up window to close the window. Also, the window can close automatically after a while.
When hundreds of such parameters are collected, behavioral patterns can be uncovered in the vast amount of information. Based on those insights, a subsequent pop-window can be designed more effectively.
Digital Marketing Analytics Software
Marketing analytics tools are very popular software solutions for businesses to track marketing activities, analyze consumer behavior, and track campaign results. They provide the organization with crucial metrics, such as cost per lead (CPL) or the ROI of campaigns and investments.
Managing good software has proven time and again to be an essential tool for a company’s success in implementing online marketing strategies. These tools help marketers to optimize actions to reach the right target group at the right time and in the right place. It helps to create the most persuasive and successful campaigns possible.
These software solutions have become an important part of the business. Research shows that more than half of the companies that use it achieve higher profits than average.
Tailor campaigns to target audience
By analyzing user behavior using marketing software, marketers can better predict how likely customers are to respond to specific campaigns based on their behavioral profile.
For example, a marketer can use A/B testing by placing two ads with the same offer, but with different calls-to-action (CTA). With the right software, it’s possible to determine which ad elicits the most response, including information about the platforms from which the audience originates and other information about consumer behaviour.
With this data and knowledge it becomes possible to personalize future campaigns and tailor them to the target group. Without these tools, companies will continue to spend a lot of money on advertising on platforms that yield minimal benefits.
Who uses Marketing Analytics Tools?
Not only people who focus entirely on online marketing use these tools. The following groups of people will benefit from using marketing software:
- Marketing managers
- Website administrators
- SEO specialists
- Product Managers
- Business leaders
A company may require different tools depending on the purpose for which they are used. There is a wide variety of marketing analytics tools. Three examples of this are:
- Web analytics tools
- Social media analytics tools
- Email marketing tools
Challenges of Marketing Analytics
The biggest challenges within marketing analytics is understanding and correctly analyzing the enormous amount of data by marketers. So they need to determine how best to organize the data and transform it into a digestible format for stakeholders. The goal is to gain valuable insights.
Now that every click and every impression of every consumer is recorded online, an immense amount of data is created. Connected equipment also generates data. This is a challenge for data scientists. Big Data must be structured and analyzed before valuable insights can be derived from it.
Marketers struggle daily with the question of how best to organize and analyze the data. Research shows that data scientists are mainly discussing how data is treated, rather than actually analyzing the data.
The amount of information is not the only challenge. The data that comes in in raw form is often considered unreliable and inconsistent. Research shows that more than a fifth of the budget in data activities is wasted due to poor data quality. Organizations need a formal process that ensures that data remains of high quality.
Lack of data scientists
Once the challenges of quantity and quality have been overcome, the next problem arises: shortage of personnel. A shortage of personnel will be noticeable in many sectors from 2020, including in data science. Only 2 percent of employers believe they have the right people in-house to unleash the full potential of marketing analyses.
Determining the right model or format that provides the right insights can be difficult. Modeling different formats provides completely different insights.
Marketers collect data from a variety of sources. Therefore, they need to find a way to normalize this data and to ensure that the data can be compared.
Now It’s Your Turn
What do you think? Do you recognize the explanation about marketing analytics? Is marketing analytics used in your work environment? Do you know people who studied data science and became marketing analysts? What benefits do you think the use of data science has in practice?
Share your experience and knowledge in the comments box below.
- Sorger, S. (2013). Marketing analytics: strategic models and metrics. San Bernadino, CA: Admiral Press.
- Kitchens, B., Dobolyi, D., Li, J., & Abbasi, A. (2018). Advanced customer analytics: Strategic value through integration of relationship-oriented big data. Journal of Management Information Systems, 35(2), 540-574.
- Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.
- Iacobucci, D., Petrescu, M., Krishen, A., & Bendixen, M. (2019). The state of marketing analytics in research and practice. Journal of Marketing Analytics, 7(3), 152-181.
How to cite this article:
Janse, B. (2022). Marketing Analytics. Retrieved [insert date] from Toolshero: https://www.toolshero.com/marketing/marketing-analytics/
Original publication date: 09/26/2022 | Last update: 04/17/2023
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