How Messy Product Data Kills Retail Revenue (and How Brands Can Fix It)

For emerging consumer packaged goods (CPG) brands, few moments rival the thrill of landing a distribution meeting with a major retail buyer. Whether brokered through networking platforms like RangeMe or forged during meetings at a ECRM sessions, receiving that initial “yes” feels like the ultimate validation.

However, as Sudhanshu Gupta, Founder and CEO of Mirchi Labs, points out, that initial handshake is actually where the most complex phase of retail expansion begins. Getting a product approved is merely step one; getting that product set up, compliant, and visible across distinct retail portals is an entirely different operational hurdle.

For resource-constrained founder-led brands, navigating these distinct requirements can rapidly turn an exciting growth milestone into an operational bottleneck. Sales leads, administrators, and founders often find themselves bogged down in technical spreadsheets rather than driving core business growth.

“When the brands come, when they get ‘yes’ from the retailers, the very next thing is to get their product set up with those retailers,” says Gupta. “And every retailer comes with different requirements, different ways of accepting data. This is where Mirchi Labs comes in and helps brands set up their products and manage their product listings on various channels.”

I had the opportunity to speak with Gupta at a recent ECRM Session about the importance of keeping your product data on point, and how to do it. You can watch the full interview below!

Technology vs. Execution: Why Software Alone Isn’t Enough

The market is flooded with Product Information Management (PIM) tools, enterprise software platforms, and catalog management suites. Yet, despite these digital solutions, brands continuously struggle to onboard their products efficiently, says Gupta.

Drawing from over two decades of industry experience, Gupta emphasizes that the underlying challenge facing CPG companies is rarely a lack of software, but a deficit in operational execution.

“I spent over 20 years in product information management and content distribution to e-commerce retail channels,” says Gupta. “And one thing that I have seen is that the problem is not in technology. It’s in the execution part of it. And that’s where I formed Mirchi Labs: to help brands work on the execution side and help with the technology they have already chosen.”

According to Gupta, while modern software and PIM systems provide essential data storage and house raw digital assets, they still require extensive manual configuration. The missing execution layer bridges this gap by directly mapping unique retailer schemas, auditing missing specifications, and managing validation errors to guarantee compliance. Software provides the container for product specs, but it does not automatically resolve validation errors, fill missing attribute gaps, or translate a brand’s digital asset library into individual retail schemas. Without dedicated operational execution, even the most sophisticated software stack remains underutilized.

Decoding the Retail ‘Language Barrier’

Why is retail product setup so uniquely demanding? The core issue lies in the total lack of standardization across major retail networks. A single product SKU on Shopify or Direct-to-Consumer channels must be re-architected into dozens of distinct data structures depending on where it is being sold.

Every retail chain maintains its own taxonomy, mandatory data fields, and image specifications. What works for one marketplace or regional grocer will fail automated validation checks at a big-box retailer. “The retailer should know your product data as well as they know about your product,” says Gupta.

Here are some ways in which each retailer may differ in the product data they require:

Attribute Variations: One retailer might demand 20 unique product attributes, while another requires 50 or more detailed specifications for the exact same item.
Digital Asset Expectations: A channel partner may request 15 distinct high-resolution photography angles, whereas another only accepts 10 specific asset files.
Regulatory Disclosures: Selling regionally may require basic product information, while expanding into states like California triggers complex requirements like Proposition 65 consumer warnings.
Taxonomy Structures: Retail platforms categorize goods differently, requiring custom multi-nested subcategory mapping for each merchant system.

“Every retailer speaks a different language,” Gupta notes. “There could be one product for the brand, but the retailer looks to select 20 different products. How long does it take? It depends on what information is available. So if the brand comes with very limited information, then we help the brand kind of build a basic foundation catalog. From there on, we help match this catalog against retail requirements.”

Depending on the completeness of a brand’s baseline product documentation, bridging these data gaps can take anywhere from a few days to several weeks – time that directly impacts launch windows and buyer expectations.

How Messy Data Directly Kills Retail Revenue

In e-commerce and retail channels, product data is not merely administrative back-office work – it is the direct catalyst for discoverability, consumer trust, and sales velocity. Incomplete or inaccurate product data creates severe downstream operational failures that directly destroy brand profitability.

“Messy data means incomplete descriptions,” says Gupta. “There are not enough images. Your feature bullets are not there. Your dimensions are wrong. Your weights are wrong. And that’s some examples where if you don’t have accurate information, then it leads to problems downstream. Then you don’t have the right prioritization on all those things, and hence, it kind of does not translate into revenue that the brand is expecting.”

The Downstream Financial Cascade of Bad Data

Supply Chain Fines & Chargebacks: Inaccurate box dimensions or product weights can lead to warehouse intake errors, shipping re-calculations, and vendor penalty fees.
Onboarding Delays & Delayed Revenue: If product attribute sheets fail compliance checks, shelf placement is postponed, leaving inventory sitting idle in storage.
Consumer Returns & Lower Conversion: Missing digital assets, improper feature bullets, or inaccurate product descriptions lead to poor online customer conversions and elevated product return rates.
Eroded Buyer Trust: Retail buyers operate on tight planogram schedules. Brands that fail to provide complete, compliant data risk losing their assigned shelf real estate altogether.

Meeting Brands at Their Level: From Early Startups to Mass Enterprise

Whether an emerging brand is launching its very first SKU or managing an expansive catalog across multiple channels, product data requirements evolve significantly. Mirchi Labs addresses this spectrum by adapting its operational execution based on where the brand stands in its growth lifecycle.

Phase 1: Early-Stage & Emerging Brands (1 to 20 SKUs)

For early startups, product data is often scattered across informal spreadsheets, emails, or supplier specification sheets.

Foundational Setup: Establishing official Global Trade Item Numbers (GTINs), Universal Product Codes (UPCs), and master SKU definitions.
Core Documentation: Building accurate physical specifications, including gross/net weights, individual item dimensions, master carton sizes, and core imagery.
Content Creation: Formulating compliant feature bullets and SEO-optimized retail descriptions.

Phase 2: Scale-Up & Enterprise Brands (Up to 20,000 SKUs)

Established brands often possess existing digital infrastructure, such as Shopify storefronts, ERP systems, or standalone PIM platforms.

Data Ingestion & Mapping: Extracting catalog data directly from existing platforms like Shopify and re-mapping fields to match retailer expectations.
Ongoing Dynamic Compliance: Updating listings as retail requirements evolve – such as adding California Proposition 65 consumer warnings when expanding distribution into Western regions.
Validation Error Resolution: Continuously handling portal errors, schema changes, and back-and-forth listing rejections on behalf of the brand.

AI Innovation with a Human Safety Net

The integration of artificial intelligence has transformed content creation, allowing CPG companies to generate product copy and attribute sheets at unprecedented speeds. However, in the environment of retail compliance, automated AI outputs alone carry substantial risk. Hallucinated dimensions, inaccurate ingredient lists, or non-compliant claims can result in immediate listing suspensions.

To eliminate these risks, Mirchi Labs leverages a hybrid operational framework that combines automated AI speed with rigorous human validation. Raw product data is first ingested and processed through AI models for initial content generation and quality audits. Before any listing is submitted to a retailer, however, a human domain expert reviews and validates the output to ensure 100% accuracy. This “human-in-the-loop” strategy guarantees that automated efficiency never compromises regulatory accuracy or retailer compliance rules.

Gupta emphasizes the critical balance between technological speed and human precision. “We do leverage AI in terms of content creation and also content quality,” he says. “But in spite of all the AI that we use, we always have a human-in-the-loop layer, which validates everything which is done by AI. So AI gives you speed, but we make sure that there’s accuracy in everything that is created by AI, and hence we have the human layer.”

Auditing Your Brand: The Shelf-Ready Scorecard

To help founders assess their operational readiness prior to buyer meetings, Mirchi Labs developed a specialized diagnostic utility available on their website. The Shelf-Ready Score provides instant visibility into missing data points and compliance vulnerabilities.

How the Shelf-Ready Score Works:

Spreadsheet Upload: Brands upload their current master catalog or product spreadsheet into the application.
Retailer Target Selection: The tool benchmarks the uploaded product specs against specific target retailer schemas.
Automated Audit: The software checks for missing digital assets, incomplete descriptions, invalid UPC/GTIN structures, absent dimension metrics, or lacking feature bullets.
Diagnostic Scorecard: The brand receives a clear audit score highlighting exact gaps, allowing them to proactively resolve issues before presenting to buyers.

“It’s not about the incomplete data; it’s the missing data altogether,” explains Gupta. “They can upload what they have, and the self-ready check tells them if they’re missing a certain number of images required by a retailer, missing a description, feature bullets, dimensions, or weights. It gives them a scorecard based upon what they’re missing and what they need to improve on.”

A Practical Action Plan for Emerging Founders

For CPG founders managing tight budgets and wearing multiple operational hats, offloading data management allows leadership to focus on core value drivers. Gupta shares crucial guidance for brands navigating retail expansion:

1. Build Data Foundations Simultaneously with Product Development

Product data should never be treated as an afterthought. The moment product formulations, packaging designs, or physical prototypes are finalized, GTIN generation, weight recordings, and dimension mapping should begin immediately.

“Start thinking about the product data as you start thinking about your product – because you will need both,” advises Gupta. “The sooner you start, the better it is. Because it’s not about just that data completeness and the data what retailer asks for. It’s also the timelines that you have to meet with the retailer.”

2. Demonstrate Operational Capability During Buyer Pitches

When sitting across from retail buyers at ECRM sessions or pitch meetings, buyers evaluate operational reliability just as rigorously as product flavor, packaging, or margin structure. Arriving with a retailer-ready data architecture proves that the brand can meet tight onboarding deadlines without logistics delays.

3. Divide and Conquer: Separate Product Innovation from Operational Execution

Founders drive growth when focusing on market strategy, sales relationships, and product formulation. Attempting to master complex retail EDI formats, attribute mapping, and compliance schemas distracts from critical commercial goals.

“A founder who is launching a product has too much going on,” says Gupta. “They have to think about R&D, they have to think about the market, they have to market the product, they have to take care of sales. What we encourage those brands is they can leave this operational execution to us because we understand the data pretty well. We tell them: You focus on the product development, we will focus on the product data development.“

Turning Data into a Competitive Advantage

To test your brand’s retail data compliance or run a free audit, visit www.mirchilabs.com to access the Shelf-Ready Scorecard tool and build a compliant foundation for your retail growth journey.

Key Takeaways: How Messy Data Kills Revenue

The Retail Execution Gap: Securing a buyer “yes” at trade shows or platforms like ECRM and RangeMe is only the starting line; launching products requires translating raw brand data into retailer-specific formats.
Technology vs. Execution: Software alone cannot solve onboarding friction; brands need operational execution to build foundational catalogs and satisfy complex compliance standards.
The High Cost of Messy Data: Inaccurate dimensions, incorrect weights, missing digital assets, or incomplete descriptions derail launch timelines and directly destroy revenue potential.
Retail Specificity: Every retailer demands different attributes (e.g., Retailer A requires 20 attributes and 15 images, whereas Retailer B asks for 50 attributes and 10 images).
AI + Human-in-the-Loop: Leveraging AI accelerates content creation and quality checks, but human oversight is essential to eliminate hallucinations and guarantee compliance.
Core Recommendation: Emerging founders must treat product data development with the same urgency as physical product development long before sitting across from a retail buyer.