Share this on:
What You'll Learn
The marketing and sales teams play a critical role in uncovering profitable opportunities within the customer base, both in the present and future. Finding high-value customer segments is crucial to maximizing return on investment while minimizing efforts spent on less valuable segments.
Building an automated pipeline that involves machine learning to identify these segments can help businesses improve their data-driven marketing strategies and effectively target valuable segments.
To help facilitate this, let’s build a conversational insurance quote advising bot:
A robust architecture is essential for achieving continuous improvement in a project
In this case:
- Real-time data from a Customer Relationship Management system (CRM) is ingested into Informatica - a popular data integration platform.
- The data then flows into serverless SQL within Databricks.
- An automation script written in Python within Databricks processes this data using machine learning techniques to segment customers, targeting them based on revenue.
The key outcome of this pipeline is the ability to segment customers based on profitability. The results are used for personalized marketing efforts, such as targeted emails using LLM techniques or a recommendation system. These results can be applied to several use cases that are further discussed in the blog.
A feedback loop continuously updates the data in Databricks, allowing the automation script to periodically rerun the segmentation analysis to reflect changes in real-time data.
By automating the segmentation process, companies can continuously monitor and adjust their strategies based on real-time data, ensuring they are always targeting the right customers.
Why Create Customer Segments
Customer segmentation is the backbone of effective marketing strategies. It involves dividing a broad customer base into distinct groups based on shared characteristics such as demographics, behavior, or profitability.
Finding the most profitable segments will help the company to allocate resources more efficiently and to develop targeted marketing campaigns.
Customer Segmentation Analysis
Our dataset comprises both demographic and transactional information, providing a comprehensive view of customer distribution and behavior.
While our dataset encompasses various types of information, we’ve highlighted some key examples below to give you an idea:
Transactional iInformation:
- CustomerID
- Revenue
- Quantity
- AvgPurchaseAmount
Demographic Information:
- Education
- Marital_Status
- Income
- Number of Children at Home (Kidhome)
- Age
- Gender
By performing segmentation based on demographic information and then targeting clusters based on revenue generated, we can derive valuable insights. This approach allows us to identify high-value customer segments, which can then be targeted with personalized marketing strategies. Ultimately, this method helps maximize revenue and enhance customer engagement.
K-Means Clustering
There are several clustering algorithms available for segmenting data, and one widely used method is K-means clustering.
K-means is an unsupervised machine-learning algorithm that partitions data points into a predetermined number of clusters.
The algorithm operates by iteratively assigning each data point to the nearest cluster centroid (the center of the cluster) and then updating the centroids based on the current cluster assignments.
This process continues until the clusters stabilize, meaning the centroids no longer move significantly.
K-means is favored for its simplicity and efficiency, making it suitable for large datasets. However, it requires specifying the number of clusters in advance, which is why methods like the Elbow Method are often used to determine the optimal number. Additionally, K-means assumes that clusters are spherical and of roughly equal size, which can limit its effectiveness in certain scenarios. Despite these limitations,
K-means remains a popular choice due to its straightforward implementation and the clear, interpretable results it provides.
Since our application is automated, selecting the number of clusters is also automated by incorporating a simple logic into the existing K-means algorithm code.
Using the Elbow Method to determine the optimal number of clusters for the analysis:
The plot generated after performing the Elbow Method helps in making this decision. Users typically examine the plot to identify where the graph bends, forming an elbow-like structure, and choose the number of clusters corresponding to that point.
For instance, in this plot, the graph bends and forms an elbow between clusters 2 and 3. It is suggested to use 2 or 3 as the number of clusters.
Mathematically, the bend/ the elbow is formed at a datapoint when the change in slope is high.
So, a code script can be added to the existing code that performs the elbow method to perform this mathematical calculation to automate this manual work.
inertia = []
# Perform K-Means clustering for k in range(1, 11): kmeans = KMeans(n_clusters=k, random_state=42) kmeans.fit(X)
inertia.append(kmeans.inertia_)
# Calculate slopes slopes = [abs(inertia[i] – inertia[i-1]) for i in range(2, len(inertia))]
# Choose the data point with the maximum slope optimal_index = slopes.index(max(slopes)) + 2
print(f”\\nBased on the elbow method, the optimal number of clusters is suggested to be {optimal_index}.”)
#Output Based on the elbow method, the optimal number of clusters is suggested to be 2.
After determining the number of clusters to form, the K-means algorithm is executed. This algorithm clusters data based on a target variable. In this case, we cluster customers based on their demographic characteristics, to target their revenue generation.
Following the analysis, we obtain clustered data that can be leveraged for various use cases, benefiting both marketing and sales teams. This segmented information helps tailor marketing strategies, optimize sales approaches, and ultimately drive more effective decision-making.
Utilizing the Clusters
Having this pipeline that produces top-performing clusters and has a feedback loop constantly improving the analysis can be beneficial to the company in a lot of ways. As discussed before, it can be used to tailor email campaigns improving personalized marketing. It can also be used for market analysis, benchmarking, building a recommendation system, and improving product development.
Use Case 1: Interpreting Results to Uncover Marketing Strategies
By examining the clusters and their distribution, the marketing team can derive valuable insights. These insights enable a deeper understanding of customer segments, leading to more informed and effective marketing strategies.
For instance, the company might initially target female customers based on product focus. However, clustering results reveal that gender does not significantly impact revenue generation, as the highest revenue-generating cluster shows an even gender distribution. This insight may prompt a reassessment of the marketing strategy.
Additionally, the analysis uncovers that a higher percentage of the top-performing customers have a teenage child at home, it suggests that targeting products geared toward this demographic could be more effective.
These insights enable the marketing team to refine their strategies, aligning them with the actual characteristics and preferences of high-value customers.
Use Case 2: A recommendation system
Now that we’ve identified the top-performing clusters, we can expand our use case and explore other ways to leverage the data we have. We developed a recommendation engine that focuses on the top-performing customers within these high-value clusters and analyzes their shopping history. We can generate recommendations in several ways, including:
- Suggesting the highest revenue-generating products to these customers.
- Offering personalized recommendations based on individual shopping history.
- Introducing popular products to less active customers.
We employed Singular Value Decomposition (SVD) for building this recommendation engine. SVD is a matrix factorization technique commonly applied in collaborative filtering for recommendation systems. SVD is effective in collaborative filtering, where recommendations are based on the behavior of similar users. For example, if users with similar preferences liked certain products, SVD helps in predicting which products a user might like.
SVD is employed in this context to generate personalized recommendations for customers by uncovering patterns and preferences through collaborative filtering.
This approach results in tailored suggestions, making the recommendations more relevant to everyone.
The output of this recommendation system consists of 5 recommendations per customer ID. The dashboard you see represents this recommendation system, showcasing the tailored suggestions provided for each customer.
Use case 3: Gen AI-based personalized marketing efforts
With the personalized recommendation system, Gen AI can be used to build marketing strategies. Based on their recommendations, GenAI can generate personalized emails with coupons to encourage repeat purchases or deploy chatbots that recommend similar products, enhancing the personalization of marketing efforts.
The OpenAI API is utilized to generate personalized emails, targeting specific demographic customer segments with recommendations for products. The API uses prompts to craft tailored email content based on the characteristics and preferences of each customer.
Subject: Transform Your Home and Life with These Must-Have Products!
Dear [Recipient],
As a busy mom, you deserve a home that brings you joy and relaxation. I’ve handpicked four amazing products that will transform your space and enhance your daily life:
1. HYACINTH BULB T-LIGHT CANDLES – Experience spring’s charm year-round with these delightful T-light candles. Their soothing aroma brings nature indoors, creating a calming atmosphere for you and your family.
2. SUNSET CHECK HAMMOCK – Unwind in comfort and style with our Sunset Check Hammock. Perfect for your backyard oasis, it’s the ideal spot to relax after a long day or enjoy serene sunsets with your loved ones.
3. FLORAL BATHROOM SET – Transform your bathroom into a blossoming oasis with our stylish and comfortable Floral Bathroom Set. It’s the perfect way to freshen up your space and add a touch of elegance to your daily routine.
4. WHITE HANGING HEART T-LIGHT HOLDER – Create a cozy ambiance with our white-hanging heart T-light holder. It’s perfect for romantic evenings or self-care nights, adding a warm and inviting touch to any room.
Treat yourself to these incredible products.
Personalized email sample
Final Thoughts
At LumenData, we have explored various use cases of customer segmentation to enhance our understanding and engagement with different market segments. There are numerous other use cases for customer segmentation beyond the examples discussed, including market analysis, product
development, and benchmarking. By tailoring our strategies to specific customer segments, we ensure that our efforts are targeted and effective, ultimately driving better results and achieving our business objectives.
Integrating this automation script ensures that our segmentation process remains dynamic and responsive to real-time data changes. The automation not only improves the accuracy and timeliness of our analyses but also supports more effective and data-driven decision-making across the organization. In all these endeavors, the importance of accurately segmented customer data cannot be overstated, as it forms the foundation for making informed and strategic decisions.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud, and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Xylem, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
Authors
Data Science Intern
Director, Analytics
