What is data-driven marketing?
To summarize, Data-driven marketing is a set of tactics and techniques that make extensive use of data obtained through customer interactions. This data enables them to develop effective marketing plans by better understanding customer behaviors, preferences, and purchase motives.
This valuable data is critical for all types of businesses because it helps them improve their marketing strategy and customer experience, resulting in increased revenue. Marketing professionals can predict their customers’ goals and future actions using customer data, allowing them to develop personalized data-driven marketing strategies with the highest ROI.
How to develop a data-driven marketing strategy?
Even in the absence of external factors such as a global pandemic or innovative technology, customer purchasing habits are constantly changing. While some marketers become frustrated with these changes and return to mass marketing techniques. Businesses that stick with their data-driven marketing strategy often can see double-digit increases in sales.
To develop a data-driven marketing strategy for your company. Follow the below steps and invest in marketing technology that will increase your efficiency and ensure you reach the right buyers.
Collect and centralize information about current customers
Look for trends and patterns in the data
Divide your target audience into groups
Create distinct marketing campaigns for each target demographic
Data-Driven Marketing Relies on Real-Time Insights
Let us have a detailed look on each point to know how it works.
Collect and centralize information about current customers:
Because you have already succeeded with them, your current customers are one of the best sources of information about how to convert future customers. Gather as much information about them as you can, including demographics, location information, and the steps they took during their purchasing process. Web analytics software and customer surveys can assist you in gathering this data.
With the rise of cloud applications, you may have client information stored in multiple locations. To best understand and use the data you have, you must first centralize it.
Look for trends and patterns in the data:
Once the data has been collected and centralized, it can be analyzed for trends and patterns. For example, Is one of your products more popular in the southeast than in other areas? Before making a purchase, most of your customers will read articles and information to help them determine what product is suitable for them.
You can improve your marketing campaigns and encourage those patterns in potential customers as you identify buying patterns in your current customers.
Artificial intelligence (AI) can help you spot patterns and correlations that you might have missed otherwise. It can analyze more data in less time than human analysts. AI is used in many BI platforms to improve data insights.
However, even the best AI cannot replace human expertise. You need to ensure that you have experienced data scientists on staff to interpret the AI algorithms and validate the insights.
Divide your target audience into groups:
As you learn more about your target audience, you will be able to categorize them. The groups you form will be determined by the trends discovered in your data analysis. You could make groups based on gender, location, or previous site behavior.
Many web analytics tools include predictive analytics to assist you in determining what actions potential customers are most likely to take. These are based on previous interactions with your brand. Google Analytics 4, for example, can even create groups based on these predictive metrics to assist you in encouraging leads to take the actions you desire.
