In the digital world, marketing plays a critical role in the success of business growth. Almost every modern business uses marketing to acquire new customers and build a brand. According to Statista, an estimated 616 billion U.S. dollars were projected to be spent on digital advertising worldwide in 2022.
Several marketing channels, like OTT platforms, have opened the door for marketers to double down on their marketing efforts. However, with the increase in marketing opportunities, it has become challenging to analyze marketing performance. Consequently, businesses need to streamline the marketing analytics process to allow marketers to make better decisions.
Marketing Analytics Definition
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Marketing analytics in businesses is used to evaluate marketing performance. The main objective of marketing analytics is to improve the effectiveness of marketing strategies and generate better ROI. Marketing analytics allows marketers to comprehensively view all marketing channels like pay-per-click (PPC) advertising, email marketing, and social media marketing.
With marketing analytics, businesses can clarify the big picture and dig into more granular marketing trends. Marketing teams in organizations use established business metrics like the cost of customer acquisition, conversion on the landing page, and traffic through ads.
Steps to build Marketing Analytics
There are many techniques to perform marketing analytics. To ensure you do not get into analysis paralysis, it is crucial to have a defined marketing analytics process. Generally, the marketing analytics process can be divided into the following steps:
Step 1: Setting Objectives
The first step in marketing analytics is to set short and clear objectives. Businesses need to determine precisely why they are implementing marketing analysis and where it will be beneficial. For example, if an organization wants to get more signups for its SaaS product. It needs to analyze its historical marketing campaign results, understand how existing customers engage on their platforms, and more. This will allow marketers to optimize their marketing strategies and boost signups.
Step 2: Collecting Marketing Data
After understanding business objectives, organizations must collect data from different sources. It could include data from websites, marketing platforms, social media platforms, CRMs, etc. To gather data from various sources, organizations need to deploy data integration techniques to centralize the data. The data sources would rely on the objective set in the previous step. Ideally, an organization would need to bring data from Google Analytics, advertisement platforms, and website engagement data and store it in a centralized location like a data lake.
Step 3: Data Cleaning
After collecting data, the next step is to clean and prepare it for analysis. The data cleaning process is usually carried out to ensure data quality. Data is collected from different sources and are in various formats. Implementing a cleaning process helps in handling missing values, duplicate values, and outliers. In data cleaning processes, businesses also structure the data to ensure it is fit for analysis.
For the data cleaning processes, you can use Python programming language and write custom code based on business requirements. However, if you do not have the right technical expertise, you can embrace low/no-code ETL tools. With several ETL tools available in the markets, it becomes easier to streamline the process of clearing the data. After cleaning the data, the data is stored in a data warehouse for analysis.
Step 4: Data Analyzing
After cleaning the data, it is important to carry out data analysis. There are four types of marketing analytics: descriptive, diagnostics, predictive, and prescriptive analytics.
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Descriptive Analytics: In descriptive analytics, businesses identify what has already happened with their marketing efforts. This type of marketing analytics is a simple and initial analysis that acts as a basis for more profound and meaningful insights. Descriptive analytics uses historical data to identify current market trends. For example, descriptive analytics could be the total number of visitors to a website, most viewed pages, bounce rate, and more.
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Diagnostic Analytics: Diagnostic analytics helps businesses answer questions like why something went wrong in marketing. Diagnostics analytics is an advanced form of analytics consisting of various data drilling and mining techniques. Companies can identify current trends in marketing and the reason behind these trends through diagnostic analytics.
Predictive Analytics: In predictive analytics, companies use machine learning models to predict upcoming outcomes. Mainly, businesses use predictive analytics to identify future trends based on historical data.
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Prescriptive Analytics: Prescriptive analytics is the process of determining an optimal course of action. With prescriptive analytics, businesses can make optimized decisions for desired future outcomes. Prescriptive analytics is also used to identify the future course of action while considering possible scenarios.
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Businesses can use the above options to analyze their marketing data and work on its weaknesses to strengthen their marketing skills.
Step 5: Sharing Results
After carrying out all the above marketing analytics processes, businesses can achieve meaningful and actionable insights. The next step in marketing analytics is to share insights derived from different types of analytics with decision-makers. The decision-makers then collaborate with marketing teams to make effective business decisions for the organization.
Step 6: Analyze Failures
After sharing meaningful insights, businesses can take the necessary steps to optimize their marketing efforts and achieve their goals. However, it is not necessary that you will achieve all your goals on the first attempt. There can be failures in execution, or you might not be satisfied with the results. You need to revisit the steps, identify bottlenecks, and resolve issues to enhance the accuracy of your insights, decision-making, and results. There could be several possible issues in the entire process. For instance, companies might identify errors and missing or invalid data in data cleaning processes. If these errors are ignored, they might lead to inaccurate predictions in the future. Therefore, the quality of data has to be improved.
While the above six steps are the core of any superior marketing analytics process, the specificity of steps can differ based on business use cases. If carried out with proper due diligence, organizations can select the right KPIs and help companies progress rapidly. However, companies should establish data governance and security policies to protect customers’ sensitive information and gain stakeholders’ trust.
Here are some critical factors to consider when evaluating and selecting a marketing analytics platform:
- Business Objectives: An organization’s marketing analytics platform should support its existing and future business requirements. Initially, businesses must identify the core objectives of their business and create a list of their desired business outcomes. After that, businesses can break down their objectives into measurable analytics goals. They can finally select an analytics platform to provide access to data and reporting features to achieve their business objectives.
- User Interface and Visualization: Along with technical persons, non-technical users must find it easy to use marketing analytics tools. Deploying an intuitive analytics tool can simplify everyone in an organization to generate insights and make quick decisions. You could embrace self-service analytics tools with a user-friendly interface to support different user types.
- Advanced Analytics: It is crucial to have an analytics tool that enables you to perform advanced analytics. Your analytics platform must be able to facilitate complex pattern recognition and predict future trends, events, and outcomes. It should also allow you to build statistical models for your business.
- Pricing: Before selecting the marketing analytics tool, you should be aware of the costs associated with the analytics solution. Analytics solutions often have different cost structures. The pricing could be based on queries or a fixed price. Considering your business objectives, you should embrace analytics tools that fit your needs.
Key Takeaways
Based on what we’ve discussed so far, here are the key takeaways:
- To improve business growth, it’s important to invest in marketing analytics. Start by identifying your marketing goals and the metrics that matter most for measuring success. Use data from various sources (e.g., website analytics, social media, customer surveys) to track and measure these metrics regularly. Analyze the data to identify patterns and trends, and use those insights to refine your marketing strategies for better ROI.
- Make sure your marketing analytics process is well-organized and efficient by breaking it down into specific steps. Start by defining clear objectives, then collect and clean data from different sources. Use different types of analytics to analyze the data (descriptive, diagnostic, predictive, and prescriptive) and share your findings with key decision-makers in the organization. Finally, analyze your failures to optimize future marketing efforts.
- Proper implementation of marketing analytics can help your business identify the right key performance indicators (KPIs) for tracking progress and making informed decisions. Start by identifying which metrics matter most for your business, then set specific goals for improving those metrics. Regularly analyze your progress using marketing analytics tools and adjust your strategies as needed. With a data-driven approach, you can quickly identify what’s working and what’s not, and make informed decisions to grow your business.
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Manjiri loves data science and produces insightful content on AI, ML, and data science. She applies her flair for writing for simplifying the complexities of data integration and analysis for solving problems faced by data professionals businesses in the data industry.