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Case Study · HVAC Manufacturing

Accelerating growth through a modern Product Information Management strategy

How Daikin unified enterprise product information across multiple acquisitions, reduced acquisition onboarding from approximately nine months to just twelve days, and built a scalable foundation for digital commerce with Convergence PIM.

DAIKIN
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Convergence Data
Product Information Management HVAC Data Governance Product Data AI Match & Merge Product Enrichment
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12 days
Acquisition onboarding
9 months
Previous onboarding time
Multi-brand
Portfolio standardized
HVAC
Industry data model
Customer
Daikin Comfort Technologies North America
Industry
HVAC Manufacturing
Solution
Convergence PIM
Focus
Acquisition Data Onboarding

Customer Overview

A growing HVAC leader built through acquisition

Daikin Comfort Technologies is one of the world's largest manufacturers of heating, ventilation, air conditioning and refrigeration equipment, serving residential, commercial and applied markets through a broad network of distributors and dealers.

Much of that scale has come through acquisition. Each new brand brought a valuable product portfolio, and with it a separate way of describing products: different taxonomies, different attribute names, different units of measure and overlapping catalogs.

To keep growing without slowing down, Daikin needed a product information foundation that could absorb an acquisition's data quickly, standardize it against an HVAC-specific model, and publish it confidently to digital and dealer channels.
  • HVAC and refrigeration equipment
  • Distributor and dealer channels
  • Multi-brand product portfolio
Business Challenges

Every acquisition restarted the data problem

Onboarding a newly acquired catalog was a manual, months-long effort before a single product could be published with confidence.

Multiple product taxonomies

Every acquired brand arrived with its own classification model.

Different attribute models

The same specification was captured differently across brands.

Duplicate product records

Overlapping catalogs created thousands of near-identical items.

Difficulty enforcing standards

Without shared rules, data standards drifted brand by brand.

Poor product data visibility

No single view of what products existed across the portfolio.

Slow acquisition onboarding

Bringing an acquisition's data into the catalog took months.

Legacy PIM limitations

Older tooling could not scale to HVAC-specific data models.

Poor ecommerce navigation

Shoppers could not browse or filter by meaningful attributes.

Limited data quality measurement

There was no reliable way to score completeness or accuracy.

Why It Matters

The downstream cost of fragmented product data

When acquisition data takes months to standardize, the business case for the acquisition waits with it.

Delayed acquisition value
Fragmented customer experience
Lower ecommerce conversions
Manual data rework
Limited cross-sell and up-sell
The Solution

Convergence PIM built around HVAC product data

Convergence Data combined an HVAC industry data model, AI-assisted match and merge, enrichment and relationship data into a repeatable onboarding process for every acquisition.

1

HVAC Industry Data Model

Convergence Data applied a purpose-built HVAC data model so every brand, unit and component is classified against the same industry taxonomy from day one.

  • Standardized HVAC taxonomy
  • Consistent attribute definitions
  • Reusable across acquired brands
2

AI-Powered Acquisition Match & Merge

AI-assisted match and merge compares incoming catalogs against the master record set, eliminating duplicates and standardizing attributes before anything enters the PIM. Acquisition onboarding fell from roughly nine months to just twelve days.

  • Automated product matching
  • Duplicate elimination
  • Attribute standardization and category mapping
  • Supplier data consolidation
  • Onboarding cut from ~9 months to ~12 days
3

Product Data Enrichment

The Convergence Data Factory attributes, normalizes and validates product information at scale. Richer, comparable specifications gave customers confidence and made products far easier to discover.

  • Data Factory attribution at scale
  • Data and unit-of-measure normalization
  • Validation rules and data quality checks
  • Missing information analysis
4

Product Relationships

Structured relationships connect units to replacement parts, compatible accessories, recommended upgrades, documentation and digital assets, turning clean data into commercial opportunity.

  • Replacement parts and compatible accessories
  • Recommended upgrades and complementary products
  • Documentation and digital assets
  • Stronger cross-sell, up-sell and average order value
  • Higher customer confidence and fewer returns
The Headline Result
9 months
Before
~12 days
After

Acquisition onboarding reduced from nine months to approximately 12 days.

Business Results

From challenge to business outcome

Challenge
Business Outcome
Multiple acquisition catalogs
Unified enterprise product catalog
Inconsistent taxonomies
Standard HVAC data model across the organization
Manual acquisition integration
Automated match-and-merge processes
Nine-month acquisition onboarding
Reduced to approximately 12 days
Poor attribute completeness
Structured enrichment and automated data quality rules
Limited ecommerce navigation
Rich category and attribute-driven browsing
Weak digital merchandising
Product relationships enabling cross-sell and up-sell
Difficult PIM maintenance
Flexible, extensible platform supporting future acquisitions
Why Convergence Data

Built for manufacturers, tuned for HVAC

HVAC Industry Data Model

A taxonomy and attribute library built specifically for heating, cooling and air handling products.

Automated Match & Merge

AI-assisted comparison collapses duplicate records from newly acquired catalogs.

Product Data Enrichment

Missing specifications are filled at scale so listings are complete and comparable.

Enterprise Data Governance

Ownership, workflow and validation rules keep quality high as volume grows.

Product Relationships

Accessories, parts and compatibility structured to drive commercial outcomes.

Future-Ready Platform

A governed foundation ready for new channels, new brands and AI use cases.

Conclusion

A repeatable path from acquisition to revenue

With a governed HVAC data model in place, Daikin turned product data from an acquisition bottleneck into a competitive advantage.

Nine months reduced to 12 days

Acquisition data onboarding moved from a multi-quarter project to under two weeks.

Standardized product data

Every brand now speaks the same product data language.

Better customer experience

Complete, consistent listings make products easier to find and compare.

Scalable acquisition strategy

New acquisitions can be absorbed without rebuilding the data model.

Improved digital commerce

Governed data feeds ecommerce, distributors and dealer channels.

"Acquisitions only create value when the product data behind them is standardized, governed and ready to sell "

— Convergence Data

Ready to Modernize Your Product Information?

Whether you are absorbing an acquisition or standardizing an existing catalog, a governed PIM foundation shortens the path from product data to revenue.