Customer deduplication: building a single customer view

Many organizations struggle with the lack of a correct 360° customer view. The same customer often appears multiple times across databases due to spelling variations, inconsistent formatting, or the creation of new profiles without checking for existing ones. Over time, this leads to poor data quality and operational issues.

Duplicate customer records pollute the data:

• Transaction history is spread across multiple profiles

• Marketing campaigns target the same customer more than once

• Customer insights and analytics become biased

• Manual data cleaning takes significant time and effort

This not only frustrates internal teams but also results in a poor experience for customers.

Customer deduplication addresses this by defining what makes a customer unique and enforcing it consistently.
The result is a clean customer table where duplicates are merged under a single customer ID and customer details are aligned across all related records.

Image

A structured data cleaning and deduplication pipeline

Customer deduplication is implemented through a clear, repeatable pipeline, tailored to the client’s data and business rules:

1. Data normalization
   Input attributes are cleaned and standardized. This includes actions such as standardizing street names, formatting names consistently, and validating values like age ranges or postal codes.

2. Hard matching
   Strict matching rules are applied to identify exact duplicates, for example based on identical email addresses, customer numbers, or fully matching personal details.

3. Soft matching
   Similarity-based comparisons are used to detect likely duplicates that are not exact matches. Configurable thresholds help identify records that are probably the same customer despite small differences or spelling errors.

4. Data alignment
   Once records are linked, customer attributes are merged or prioritized according to predefined rules, ensuring consistent and reliable master data.

Efficient and scalable execution

The deduplication process at one of our fashion retail customers runs as an automated workflow, optimized for project-specific requirements and scheduled to run daily. It continuously detects new master customer combinations by processing:

• Updates to existing customer records

• Newly ingested customer data

By leveraging the scalable compute and storage capabilities of Microsoft Azure and Databricks, the solution handles large volumes of customer data efficiently while remaining flexible as data grows.

The outcome is a trusted, up-to-date customer foundation that supports accurate reporting, targeted marketing, and a better overall customer experience.

Product recommendations: turning sales data into relevant suggestions

Product recommendation solutions help guide customers and sales teams toward the most relevant products based on real purchasing behavior. Instead of relying on intuition, recommendations are driven by data: what customers bought in the past, which products are frequently purchased together, and how buying patterns evolve over time.

This approach delivers clear business value:

• Improved customer satisfaction through relevant suggestions

• Higher cross-sell and up-sell rates

• Increased average basket size

• Reduced sales cycle time for sales teams

Recommendations can be used across channels: at checkout, in digital platforms, or by sales representatives during customer interactions.

How recommendations are generated

Recommendations are based on historical sales data and customer behavior. Different analytical methods can be applied depending on the use case and data maturity:

1. Association Rule Learning (Market Basket Analysis)

This method identifies products that are frequently bought together by analyzing past transactions. For example:

• Tiles and glue appear together in 25% of all invoices (support).

• Every tile purchase includes glue (confidence = 100%).

• Half of glue buyers also purchase tiles.

These insights form the basis for cross-sell recommendations such as suggesting glue when tiles are added to the basket.

Image

2. Recommendation systems

This approach looks at average customer behavior:

• What products are usually bought together?

• What did similar customers purchase?

Customers are then shown products that people “like them” often buy, making recommendations more relevant and easier to accept.

3. Predictive modeling

Predictive models estimate the likelihood that a customer will buy a specific product, given their previous purchases. This allows for more targeted recommendations and prioritization of products with the highest chance of conversion.

Practical use cases

• Suggest complementary products at checkout

• Help sales representatives propose frequently paired items

• Enable data-driven cross-selling based on actual buying behavior

Output and delivery

The result is a dynamic recommendation table that lists relevant product combinations and their associated recommendations. This output updates automatically when new orders are placed or when the data is refreshed, ensuring recommendations remain current and accurate.

Image

By grounding product recommendations in real transaction data, organizations can systematically increase revenue while making it easier for customers to find what they actually need.