top of page

How AI/ML-Driven Marketing Strategies Helped Understand Customer Channel Behavior?

Discover how a prominent financial institution transformed its marketing approach and improved customer engagement by integrating AI and machine learning to analyze and act on consumer behavior and preferences.


Channel Preference

INTRODUCTION AND CLIENT BACKGROUND


In today's digital age, understanding customer preferences and behaviors across different communication channels is crucial for businesses.


Customers have become more diverse in their channel preferences, ranging from traditional methods like phone calls and emails to newer platforms like social media and messaging apps.


Recently, we collaborated with a leading financial services company serving acrosss the USA and known for its innovation and customer-centric approach in providing personalized solutions for payments, lending, and savings.


We partnered to identify the most profitable communication channel for their marketing campaigns. By leveraging advanced Machine Learning modeling techniques, they aimed to optimize their outreach strategies and enhance customer engagement.



BUSINESS GOALS


Specific end business goals were determined with the business to get the best communication channel for their brand's campaigns:


  1. Maximize response rates and profitability through targeted strategies tailored to customer preferences.

  2. Optimize their outreach efforts and enhance customer engagement by understanding which communication channels yielded the highest engagement and response rates.

To achieve these goals, we focused on:
  1. Analyzing and comparing the performance of different communication channels, such as Direct Mail, Email (Test) and No Contact campaigns (Control).

  2. Providing insights into which channels were most effective in reaching their target audience and driving desired actions, such as sign-ups, purchases, or participation in promotional activities.

DATA COLLECTION AND ANALYSIS


data collection and analysis

We utilized historical records of customer transactions and past campaign performance data. These records gave us valuable information about how customers had engaged with different communication channels in the past.


By analyzing this extensive dataset of ~10M records, we were able to:

  • Identify patterns and correlations between customer behavior and channel preferences.

  • Discover trends in channel usage among specific customer segments.

  • Determine the impact of different channels on customer response rates and overall profitability.

Thorugh analysis, we ensured that our strategies were grounded in real-world customer interactions and actionable intelligence.



BUILDING THE MACHINE LEARNING MODEL


BUILDING THE MACHINE LEARNING MODEL

1. Data Preprocessing


  • We started by cleaning and organizing the raw data from the client's past transactions and campaigns data.


  • This involved dealing with any missing information, making sure all data was in a consistent format, and checking for any errors or issues that could affect our analysis.


2. Feature Engineering


  • Using our industry knowledge and learnings from exploring the data, we created new features that represented different ways customers interacted with the business and how profitable those interactions were through various communication channels.


3. Model Building


  • Once we had prepared the dataset, we chose ML algorithms to build our model. Specifically, we opted for boosting algorithms - XGBoost, CatBoost and LightGBM because they excel at handling complex relationships in data and generating accurate predictions.

  • The model focused on existing cardholders who were the main audience for the campaigns. By concentrating on this specific group, we ensured that our model was customized to the audience most important to the client's business goals.

  • By utilizing ML algorithms, we successfully captured the intricate nature of customer channel preferences and built a strong model that provided accurate predictions and practical recommendations for marketing efforts.

KEY FINDINGS AND INSIGHTS


Solution findings


1. Channel Preference Segmentation:


  • We identified distinct customer segments based on their likelihood to respond to various communication channels.


  • By understanding these segments, we can tailor their outreach strategies to each group's preferences, increasing the chances of engagement.


  • For example, some customers may be more receptive to email campaigns, while others may prefer SMS notifications or Direct mails. By segmenting their audience in this way, our client can deliver targeted messages through the most effective channels for each group.


2. Uplift Analysis:


  • To measure the relative effectiveness of different channels, we conducted a comparative assessment of response probabilities between customer segments.

  • This uplift analysis allowed us to identify which channels had a higher impact on driving customer engagement and conversion. By focusing resources on channels with the highest uplift, we can optimize the campaign strategies and allocate budget more effectively.



DRIVING BUSINESS VALUE


business value and ROI

Here are the ways our solution brought value to the business, supported by specific metrics and outcomes:


Maximized ROI


We achieved a higher response rates (20%) and overall campaign profitability for the client by targeting customers through their preferred communication channels. By investing more in channels that specific customer segments prefer, the client saw a significant increase in their marketing campaigns' return on investment (ROI).

Optimized Campaign Strategies

Our model's insights guided decisions on where to allocate resources and how to craft messages, resulting in more cost-effective and impactful campaigns.


FUTURE OF CUSTOMER CENTRIC MARKETING


As we move further into the digital age, customer-centric marketing is constantly changing. Understanding and adapting to customer preferences requires using data-driven decision-making and advanced AI and Analytics.

1. Adopting Data-Driven Decision-Making: With so much data available, businesses need to use it effectively. By looking for patterns and connections in customer data, companies can find valuable information to guide their marketing strategies.

2. Embracing Advanced Analytics: By using machine learning algorithms and predictive models, companies can discover hidden trends, predict customer needs, and customize their marketing efforts accordingly.

3. Being Flexible in a Changing Environment: Customer preferences change over time, so businesses need to adapt their strategies. By actively monitoring and analyzing customer behavior on different communication channels, companies can spot new trends and adjust their approach as needed.

CONCLUSION


As a final message, We encourage readers to take proactive actions in understanding their own customers' behaviors and experiment with personalized strategies based on advanced AI and Analytics.


For any queries, please contact us at: info@maticsanalytics.com

Comments


bottom of page