Boost Marketing ROI with Predictive Engagement Modelling in RapidMiner

Hello"All About Digital Marketing & Data Analytics" readers! Before I start, I want to share good news with you! My last edition was highlighted in the Women in Tech SEO Newsletter, which is truly a privilege to have my name listed alongside talented women under the "Brilliant Pieces by Brilliant Women & GNC Folks" section!!! If you haven't read my last edition, you can read it here.

After we discussed the data analytics universe for digital marketers, I want to provide specific use cases and demonstrate some techniques that we can easily apply to machine learning algorithms. Stay tuned, we're going to RapidMiner planet!

All About RapidMiner - Data Analytics and AI Platform

It is invaluable to turn the data into actionable insights that can inform marketing strategy and improve the effectiveness of campaigns. This is where predictive analytics comes in! Predictive analytics is the practice of forecasting the future based on current and historical datasets, machine learning techniques, and statistical algorithms.

This is where we landed "RapidMiner planet" – a powerful data science platform!

RapidMiner is a powerful tool for business intelligence thanks to its user-friendly interface, end-to-end data science platform, diverse machine learning algorithms, data preparation features, data visualisation, and reporting capabilities. The visual, drag-and-drop interface enables users to quickly prepare data, develop models, and deploy predictive analytics. Users can connect to various data sources such as databases and cloud storage to access the data they need. Robust visualisation and exploration tools help analyse and understand data before modelling.

RapidMiner provides a wide range of machine learning algorithms to suit different use cases, from regression to deep learning. The platform simplifies building models using techniques like automated machine learning, requiring just a few clicks. Users can thoroughly validate models through built-in techniques like cross-validation to avoid issues like overfitting before deployment. RapidMiner also enables teams to collaborate by sharing data connections, templates, and workflows.

With RapidMiner, organisations can build a robust data science foundation. The combination of ease of use and advanced capabilities allows users of all skill levels to rapidly develop impactful machine learning models. Integration and automation features help scale predictive analytics initiatives across the business. I believe RapidMiner empowers digital marketers to leverage analytics to drive better business outcomes with easy-to-use interface and auto-model features.

My Favourite Key Features of RapidMiner:

• Pre-built templates for customer churn, fraud detection and other common use cases
• One-click connections to diverse data sources like databases, data warehouses, cloud storage, social media
• In-database data preparation and ETL to optimise data for analytics
• Visualise data patterns, trends and distributions with various interactive charts
• Quickly find and fix missing values and outliers in data
• Explore and analyse data with statistical overviews and visualisations
• Create machine learning models visually without coding
• Automated machine learning generates models in just one click
• Choose from hundreds of machine learning algorithms, both supervised and unsupervised
• Optimise models for business objectives using constraints
• Validate models thoroughly with techniques like cross-validation

Predictive Engagement Modelling with RapidMiner

Classification algorithms like decision trees allow more advanced segmentation by predicting customer value or preferences based on demographics, behaviours and other factors. This gives you insights into your high-value customers so you can optimise marketing strategies and revenue. Decision tree models can classify new customers as high or low value to target them accordingly.

Overall, RapidMiner empowers businesses to segment customers precisely using diverse techniques. Segmenting customers with RapidMiner provides actionable insights to tailor marketing campaigns, target high-quality leads and maximise customer lifetime value. The advanced analytics capabilities make customer segmentation achievable for organisations to boost marketing ROI.

Let's think of use cases!

Classification lets you answer questions with a Yes/No or True/False response.

For example,

"Is this customer at high risk of churning?"

"Can we classify customers as having high or low lifetime value?"

"Will this customer accept an upsell offer for a premium service?"

"Can we predict which customers will click our email campaign?"

I used a dataset from Kaggle and calculated metrics like LTV and Customer Value as High or Low. You can configure your customer's value based on business objectives. After assigning values and prepping my model, I used RapidMiner's Auto Model feature. I'll walk you through my analysis and findings step-by-step.

Let's start:

1) I uploaded my file and connected it with res. Then I hit the blue "Run" button.

2) Here's my dataset. It includes customer ID, acquisition channels, cost, conversion rate, revenue, LTV and calculated value based on the company's aims. All of them are attribution factors for the calculated value (last column on the right) as High or Low.

3) After clicking run, we see details in the statistics section. It's a helpful summary showing min/max/average values and missing ones. As you can see, I left the value columns blank for our use case. We'll predict them using a machine-learning algorithm.

4) In the visualisation section, you can select your chart and change the dimensions you want to analyse deeper.

5) After prepping the data, RapidMiner offers an Auto Model feature for prediction, clustering, or finding outliers. In this case, I chose to predict and highlighted the value to predict. My dataset has missing values for a customer's value -engagement level- as high or low.

6) In this step, I set the class of highest interest as high instead of low.

7) RapidMiner automatically detected important values affecting our model. For example, customer ID does not affect a customer's value.

8) Finally, we can choose machine learning models. However, I selected all of them and left the final result to RapidMiner to determine the best model.

9) As a result, I chose the accuracy metric to determine the best model. Originally, RapidMiner used "classification error metric" but I changed it to accuracy or you can use AUC as RapidMiner recommends. You can also see orange symbols indicating the best-performing models that you can select.

10) In the ROC comparison - it's like a time series analysis - you can compare different models by plotting the ROC curves for each on the same graph. This provides a simple visualisation of predictive performance variations across classification thresholds, enabling easy discernment between models.

11) RapidMiner recommends the Naive Bayes algorithm in this case. Attributes on the left are fully controllable. You can check which attributes have high or low engagement rates. In our case, referral and social media channels acquire more engaged customers (as high in red).

12) The Simulator in RapidMiner can generate synthetic data to test and validate analytical processes. It lets users easily simulate complex datasets with full control over data distribution, attribute relationships, noise levels, and more. This facilitates iterative testing, uncovering edge cases, and building confidence in process reliability.

13) In the prediction table, you can see missing customer value predictions from rows 316 to 326. You can predict customer engagement levels using RapidMiner's prediction feature. RapidMiner predicted the missing ones in the third column of the table.

14) I just wanted to show the machine learning models menu and sections you can investigate and analyse.

15) My favourite algorithm! I think life gets easier when machines help guide the way :) Decision trees are especially used in churn prediction - I can do another analysis for churn cases. If LTV is over 0.032, a customer's value -engagement level- is mostly high, so you can automatically label this customer data and target this audience with different campaigns.

For the rest, if LTV is under 0.031, you can try different scenarios to convert them from low to high engagement. Also, cost is another factor impacting customer value -engagement level- to consider.

16) Look at this table of highly correlated data - LTV has -0.721. You can recalculate excluding highly correlated data for deeper insights.

You can make many predictions and use classification algorithms with RapidMiner. In this episode, I just wanted to show what are RapidMiner's capabilities and how we as digital marketers can benefit. That's all for now, but I’ll use clustering algorithms in RapidMiner for customer segmentation analysis in the next episode! So, don't forget to subscribe to the All About Digital Marketing & Data Analytics Newsletter!