Utilizing predictive analytics in marketing can help companies improve their marketing ROI. While the examples in the video are from the financial services industry, the insights apply to B2C marketing in any industry.
By defining business goals, choosing the right data sources, cleaning and processing data, analyzing it, implementing and tracking results companies can turn big data (and even smaller datasets) into valuable insights.
However, there are several challenges in capturing marketing ROI from big data analytics. Companies will need to address data volume and quality, analytics skills and resources, integration with existing systems, data privacy and security, and measuring ROI.
A cross-sell use case demonstrates how big data analytics can help companies predict outcomes. In the webinar we highlight how to identify upselling and cross-selling opportunities, increase long term revenue, and boost marketing ROI.
Companies can capture marketing ROI from predictive analytics by following these steps:
Define business goals and objectives: Start by defining the specific business goals you want to achieve through big data analytics. This will help you concentrate your efforts and determine the right metrics to track.
Choose the right data sources: Select data sources that are relevant to your business goals and will provide the insights you need.
Clean and process the data: Clean the data to ensure that it is accurate and usable. Of course, this is easier said than done! Preparing the data and staging it properly for making predictive analysis is the most essential step.
Analyze the data: Use big data analytics tools and techniques to extract insights and identify patterns and trends.
Implement and track results: Based on the insights, you have to make marketing decisions and take action. No action, no ROI.
Data volume, variety, and velocity: Banks must deal with vast amounts of data from multiple sources, which can be challenging to manage, integrate, and process.
Data quality: Ensuring the quality of the data being analyzed is critical for accurate insights and decision making.
Analytics skills and resources: Companies need skilled data scientists, analysts, and developers to work with big data analytics tools and extract valuable insights.
Data privacy and security: Companies must ensure that the data they collect and analyze is secure and complies with privacy regulations.
Measuring ROI: Capturing marketing ROI from big data analytics requires tracking the right metrics and demonstrating the impact of the insights generated.
These challenges can be overcome by investing in the right tools and resources, as well as developing a clear marketing strategy. Ultimately, this is an iterative process and there is steady improvement over time.
What algorithms can you use to predict cross-sell?
As we demonstrate in the webinar video, you can use a variety of algorithms to predict if a bank customer will accept a marketing offer. For this kind of analysis, you don’t need real time data feeds and initial models often create an immediate benefit.
Some of the algorithms that might be used in this context include:
Logistic Regression: This algorithm is commonly used for binary classification problems and can help predict the likelihood of a customer accepting an offer, or not. The determination is based on customer behaviors and characteristics.
Random Forest: This is an ensemble learning algorithm that can help improve the accuracy of predictions by combining the outputs of multiple decision trees.
Gradient Boosting: This is another ensemble learning algorithm that can improve the accuracy of predictions by combining the outputs of multiple weak models.
Decision trees to create predictive models
We often start with the family of decision tree algorithms. One advantage of decision trees in this context is that they can handle non-linear relationships, which is important when dealing with complex data. Additionally, decision trees can also be easily interpreted, which makes it easier to understand the factors that are driving customer behavior. Another advantage of decision tree algorithms is that they can handle missing values in the data and can handle both continuous and categorical data. This makes decision trees a versatile and flexible tool for data analysis and can help ensure that the results are accurate and meaningful.
By utilizing big data analytics, companies can improve their marketing return on investment and drive long term growth for their business.
The predictive analytics marketing process involves setting business goals, selecting appropriate data sources, cleaning and processing data, analyzing the data, tracking results, and continuously adjusting and improving.
There are several obstacles to capturing marketing ROI from big data analytics. You’ll be dealing with vast amounts of data and need to ensure data quality. This means having a team with deep analytics, and the time available to tackle the project.
Can an AI consulting firm help you make better marketing decisions?
Time and cost savings
Working with an AI consulting firm can save companies time and money by allowing them to focus on their core business.
Deep expertise:
Based on best practices gained across industries, consultants extract valuable insights.
Objective perspective:
An AI consultant can help a company see their data and processes in a new light.
How can I get help with a predictive analytics marketing project?
Working with an AI consultant can help you clarify your business objectives and confirm that machine learning and AI can solve your business challenges.
Use unstructured text data to extract facts, categorize documents, or add text-based features to predictive models. Check out NLP and Text Mining Use Cases
