Marketing is evolving right before our eyes, and this transformation is being led by data.
Clustering, regression, and machine learning are examples of data science approaches that have transformed marketing from a creative to a scientific arena. Marketing teams can discover product and customer insights at scale in an unprecedented way by leveraging data science.
Role of Data Scientists in Marketing
Data scientists use artificial intelligence and mathematical modeling to uncover new insights. For example, a data scientist in marketing can answer questions like: Who are your potential customers? What alternatives do your product’s customers have? What are people’s opinions of your brand? What other items do your customers wish to purchase? A marketing team can cut waste and target customers in cost-effective and personalized ways by enlisting the help of a data scientist.
Data Science in Marketing Workflow
Understanding the data science workflow is crucial to allow your marketing team to truly benefit from the insights and interact effectively with your data scientist. After you’ve defined your task and obtained access to your data, the data scientist will then conduct an exploratory data analysis to determine the best model for obtaining the insights you seek. This could mean testing models on historical data sets and determining their accuracy, or it could involve a variety of alternative approaches to establish a baseline that assesses the effectiveness of whichever model is picked.
After the model is chosen, the data is formatted in a more user-friendly way. This could include determining how to handle missing values, duplicates, or other variables that make the model more difficult to apply. It is first run on a subset of the data to train the model. The approach you select will adapt to the data, allowing you to apply the model to any dataset with the same parameters. Finally, the model is fine-tuned. This is to make sure that the model is not overfitted to the data and that it performs as expected.
What Makes Data Science in Marketing So Important?
Hyper-personalization
All businesses strive for true personalization in their marketing techniques. This means understanding an individual’s preferences for content types, communication channels, and frequency of communication and then customizing each experience at scale. Data science marketing can develop outputs that deliver these targeted insights using pattern recognition, clustering, and predictive modeling. This leaves you with more personalized and meaningful brand-to-consumer interactions, leading to improved engagement, increased conversions, and more revenue.
Better Acquisition
Data science marketing can put both prospect and customer data through models to allow marketers to develop specific campaign frameworks for all stages.
As a result of acquisition modeling providing the greatest available prospect data through a range of attributes, less money is lost during the customer acquisition phase.
On the other hand, by recognizing the most common reasons for attrition and implementing defensive marketing initiatives to avoid it, predictive attrition allows your marketing team to counteract revenue loss.
Data Science Marketing for High-Impact Marketing
Data science as a marketing function is a key component of high-impact marketing teams in the future. Predictive data is here to stay, and its use in marketing will allow for next-level marketing, such as:
- Revenue maximization through acquisition, first-purchase, and up-sell campaigns
- Increased customer loyalty
- Higher levels of personalization
- Less money wasted on testing
- Decreased attrition
At the end of the day, data science, machine learning, and artificial intelligence have revolutionized digital marketing. Data science will become an indispensable marketing function that your business will have to leverage to stay competitive.