Exploring the Current Use of Data Analytics in Digital Marketing
Defining Data Analytics
The Role of Data Analysis in Digital Marketing
Trends in Data Analysis: Current and Predicted
- Increased Use of Artificial Intelligence (AI) and Machine Learning (ML): AI and ML form the most cutting-edge advances that technology can offer the world. In the field of marketing, there has been a push to increase automation by incorporating self-correcting ML algorithms. Additionally, AI engines have been developed to manage data classification and storage. Some of these innovations may fail to make their expected impact, as is often the case with experimental technology. However, many of them will survive and find their way into the system as useful tools.
- Increased Cloud Adoption: Another cutting-edge advance available to digital marketing is cloud architecture for storing, accessing, and processing big data. Thanks to the massive volume of data coming into the system every minute, it would be increasingly difficult to process data with a traditional relational database model. As companies start adopting big data, the necessity of cloud computing will also increase.
- Focus on Customer Experience: Customer experience remains crucial to a business’s success. By leveraging AI chatbots to offer more “human” assistance — and then making adjustments based on customers’ questions — a company could offer consumers a higher level of service. Furthermore, incorporating ML into these chatbots could allow the system to learn from the questions and responses, enabling it to provide optimal answers to an increasing number of complex questions.
- Social Media Marketing: With platforms such as Facebook and Instagram offering easy access to demographic targeting, the use of social media advertising is likely to skyrocket into the near future. By using data analytics to collect information on consumers, a business can better inform its marketing department of the people it should be targeting for its digital ad campaigns. The campaigns, in return, generate more data through conversions, creating a positive feedback loop.
Skills Needed to Embrace These Trends
- Data Visualization: Making numbers usable is what data analysts do. Data visualization allows them to present the meaning found in the data rather than just a series of numbers.
- Critical Thinking: Like other scientific disciplines, the ability to interpret information is important to the field. Understanding what the data suggests to the company is the core responsibility of the data analyst.
- Communication: The data analyst should be able to present the data in a form that management and other departments can comprehend. Communication, in this case, includes both the development of reports and presentations as well as verbal communication between departments.
- Business Acumen: The only way for a data analyst to know whether something is essential to a business is to understand how the company functions. Business awareness will help the analyst determine what is critical and what is noise.
- Technical Skills: Proficiency with programming languages designed for big data (R, Python, etc.) is crucial to data analytics. For example, using algorithms on multiple data sets to define similarities produces insights that allow marketers to make informed decisions.