Data-driven decision making involves making decisions with the help of data and not gut feeling. Data is collected, organized, and analyzed to give meaningful insight so that the organization can meet its goals.
What do you want to measure?
It is crucial to know which performance metrics are most important for meeting your business objectives. It will help to lower costs, and you would not waste time going after things that won’t make a difference.
Measuring past campaigns is equally essential, as it provides a retrospective on what went wrong and where we can improve.
An organization collects information in multiple ways, and there is so much data that you might come across before pinpointing the appropriate one.
There are broadly two types of data-
1. Data from surveys, feedback forms, etc. are commonly used internal sources in a company.
2. Data from analytical agencies on market trends, competitors’ products are external sources that give a better view of the overall industry.
According to a survey by IBM, 80% of the time data scientist spent on cleaning and organizing data in the data warehouse, and the rest 20% on drawing inferences. Organized data is like an indexed telephone directory. Multiple organizations like Experian, Lexis-Nexis serve as a data lake for other companies; their information categorized adequately for analysis.
Deriving insights on the data is the most critical aspect. Many fundamental tools (e.g., Excel) to advanced (R and Python) can be used, but which one suits better is at the discretion of businesses and their needs.
Data-driven Marketing helps in strategizing multiple aspects of a business; some of them are:
• Campaign planning using demographics.
• Utilizing history for the preparation of new campaigns.
• Monitoring the current and future status and its impact on the campaign.
• Defining short term goals and tracking with data patterns.
• Investing in the right campaign strategy based on trend, e.g., social media vs. email.
Businesses are valuable when we put our heart and soul in them, but business goals are achieved, only if we use data to support the decisions.