Statistics are important for marketing as they help companies see how their marketing campaigns are performing, where their best customers are, how to attract new customers, and how to improve their sales. However, it is significant not just to receive data but to be able to work with it correctly. Here, you will need a tool like Power BI Google Analytics 4 Integration.
This data visualization tool allows you to see your company’s key performance indicators and provides easy access to data to make smart decisions. This approach helps all companies, regardless of size. Even small businesses can do more with fewer efforts due to this.
Why is quality analytics significant for marketing?
Companies driven by data and analytics are growing at an average rate of more than 30 percent each year. By 2024, they are expected to take $1.8 trillion worth of business from their less informed counterparts. If you want to stay ahead of your competition and stay relevant in the industry, extracting useful information from data is no longer an option — it’s a necessity.
Types of data to collect
To make your data and visualizations useful, you need to combine different types of data in the right way. At the same time, it is necessary to use the right sources, because the wrong combination can distort the results. The main types of data that should be used for collection and analysis include:
- data about user behavior, such as the pages they visit, the actions they take on the site, and the time they spend on it;
- order data such as products, cost, and quantity of purchases;
- data on industry indicators and competitors.
Using these key types of data allows you to create powerful visualizations and insights that help you understand how users interact with your brand, what interests them most, and how effectively your company is performing.
How to extract useful information from data?
When working with data, it is important to remember two things: first, if the data seems too good or bad, there is a good chance that it is and needs to be checked.
Second, it is useful to use a universal business dashboard to display structured data that is easy to interpret. Data visualization can help reduce the risk of data distortion and misinterpretation, which can lead to incorrect decision-making.
Algorithm for working with statistical data to get insights
To avoid looking at inaccurate data, you need to familiarize yourself with the right business intelligence, which will help you collect true and useful information for making management decisions. That way, you can act confidently with unbiased data at your fingertips. Here are some significant tips. These steps will help you get useful and reliable information from your data.
- Collect all the available data you can get from different sources.
- The second step is to reformat and pre-process the data to make it suitable for further processing by machine learning.
- Clean the data manually to correct any imperfections and provide clear interpretation.
Once your data has been cleaned and transformed, it’s time to use visualization and statistical techniques to discover underlying patterns in the data. One of the popular machine learning techniques for analyzing statistical data is clustering, which helps divide data into different groups based on common features and properties.
Depending on the characteristics of the groups formed at the previous stage, you need to choose an appropriate machine-learning model for predicting information and future trends. Alternatively, you can try several models to determine which one gives the most accurate results.
Finally, you need to check the predictions and make sure they are accurate. This requires comparing the observed data with the predicted data to confirm their agreement.
Useful tips
Try to stay away from metrics like total followers or average time on the page. They only give you a partial picture. To see the full picture, you need to measure percentage changes and point increases, depending on which data cluster you’re working with.
Four key examples of data analysis for small businesses:
- Customer Acquisition Costs (CAC);
- Percentage of engaged customers;
- Buying habits;
- Average check.
Large companies such as Coca-Cola, Netflix, and Spotify also use such approaches to business analytics. Due to high-quality work with analytical information, they receive important insights and actively use them to introduce new marketing strategies or improve existing ones. This is how businesses make all data work for them.
Use Power BI Google Analytics 4 Integration and the tips above to get better results with less effort. If this is not done, analytical data will remain just numbers in tables. This data does not matter until it leads to useful conclusions, gives the company an impetus and direction to develop, and serves as a source of ideas.
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