The Data Advantage Every Company Needs (With Hard-Won Lessons from CPG)
Contributors: Sebastian Kubalski, Technical Architect at PSignite
In today's data-driven world, CPG companies are awash in information from sales, marketing, supply chain, and more. Yet simply having data isn't enough – it must be refined and harnessed to create value. As British mathematician Clive Humby famously noted, "Data is the new oil… like oil, data is valuable, but if unrefined it cannot really be used." In other words, raw data needs to be cleaned, connected, and made actionable to truly fuel business success (Wikipedia - Data is the new oil).
To put this in perspective for the CPG industry, trade spend often consumes 10-20% of revenue for CPG manufacturers. The 100th largest CPG company generates approximately $5 billion in revenue, so if they spend 15% on trade and promotion, that equals $750 million requiring effective management. According to a recent McKinsey study, companies that implement Revenue Growth Management typically achieve a 4-7% gain in annualized gross margins. For a $5 billion CPG company with typical 40-50% gross margins, this could translate to a $125 million gain. The potential return on investment from optimizing trade spend management is substantial, making it a critical capability for CPG companies to start investing in around $50M revenue and greater.
Unfortunately, many organizations struggle with this: Forrester Research estimates 60% to 73% of enterprise data goes unused for analytics (Inc. Magazine/Forrester Research, 2018). Even worse, "bad" data (inaccurate, inconsistent, or siloed) can carry a hefty price tag – estimated at 15% to 25% of revenue for most companies due to inefficiencies and mistakes (MIT Sloan Management Review - Redman, 2017). Clearly, making data useful is both a tremendous opportunity and a critical challenge for CPG trade promotion execution (TPx) and digital transformation initiatives.
The Importance of Data in CPG TPx and Digital Transformation
Data is the backbone of modern trade promotion management and optimization in the CPG industry. TPx (Trade Promotion Execution) and RGM (Revenue Growth Management) processes are extremely data-hungry, relying on vast inputs from multiple sources – from ERP systems and financial platforms like High Radius, to syndicated data providers and increasingly, direct retailer portals like Walmart and Amazon that provide near real-time sales data.
One CPG expert observed that poor data quality can completely hinder a TPx implementation – causing low user adoption or, worse, bad decision-making – whereas investing in high-quality, consistent data yields great returns across the organization (CPGvision Blog - Whitehouse). In fact, a recent industry survey found 40% of CPG manufacturers say inadequate data cleansing and harmonization are holding them back from effective trade promotion analytics (POI State of the Industry Report). These numbers highlight that without a robust data strategy, even the best promotion plans or AI analytics will falter.
Why does data matter so much for CPGs? Consider these critical advantages:
Precision-Based Planning
Trade spend often consumes 10-20% of revenue for CPG manufacturers, so decisions about promotions must be data-driven (CPGvision Blog). Companies that unify data from sales, marketing, finance, and supply chain get a 360-degree view of promotional performance, enabling agile adjustments and better ROI on promotions.
For example, an egg manufacturer must align promotion planning with production constraints – it takes 18 months to raise chickens, so they cannot simply ramp up production when demand spikes. During a recent avian flu outbreak, having integrated data allowed them to quickly pull promotions when supply constraints emerged, preventing customer disappointment and maintaining brand trust. Without integrated data from demand planning systems and production, such precise coordination would be impossible.
Single Source of Truth
Modern CPG organizations pull data from numerous sources – ERP systems for financial information, Nielsen/IRI for market share, Crisp for distributor data, and increasingly, direct feeds from major retailer portals. When these sources are unified into a single, trusted view, teams across the organization can make aligned decisions.
"The real breakthrough happens when everyone – from sales to supply chain to finance – is looking at the same picture," notes Trey Roldan, CEO of Entrada. "We've seen clients reduce promotion planning cycles by 40% simply by eliminating the time spent reconciling different versions of the truth across departments. When your data is unified and trusted, decisions that used to take weeks of back-and-forth can happen in days."
This single source of truth is particularly critical when harmonizing different data formats – syndicated data may use Sunday-start weeks while your internal systems use Sunday-end weeks, or sell-out data arrives in units while pricing is tracked in cases. Without proper harmonization and conversion factors, these mismatches create confusion and errors.
Market Responsiveness
In today's rapidly changing environment – with new tariffs, supply chain disruptions, and shifting consumer behaviors – CPG companies must adapt quickly. Real-time, trustworthy data enables immediate course corrections. Companies with unified TPx platforms enjoy real-time integration and accurate data analysis, allowing more agile decision-making (CPGvision - Integrated TPx Software).
Consider how retailer portal data has transformed responsiveness: Instead of waiting 6 weeks for syndicated data, companies can now see yesterday's sales from Walmart or Whole Foods. This near real-time visibility means detecting a promotion underperforming on day 3 instead of week 6 – the difference between salvaging a campaign and writing off the investment.
Accountability and Continuous Improvement
When decisions are based on data, organizations can objectively review outcomes and learn. Post-event analysis becomes standard practice, comparing actual results to forecasts and identifying what drove variances. This creates a feedback loop essential for refining strategies and achieving long-term digital transformation goals.
Business users who review performance reports as part of their daily routine catch issues early. When anomalies are detected – like historical sales of 100 units/week suddenly showing 10,000 – proper communication channels ensure the root cause is addressed, not just the symptom.
Common Data Pitfalls – Why "Set It and Forget It" Won't Work
Ensuring useful data is not a "set-it-and-forget-it" task (Alation - Data Quality Monitoring). Data in the CPG environment is dynamic and messy, with sources scattered across multiple partners and formats (McKinsey Digital - CPG Growth). Here are the most common data pitfalls and failure points to watch out for:
Data Gaps and Missing Information
Critical gaps appear when product codes aren't mapped correctly between systems. Sell-out data operates on UPC codes while internal systems use SKUs – incorrect mapping means promotional lift calculations fail. Similarly, missing ACV (All Commodity Volume) data from retailer portals versus syndicated sources can skew distribution metrics.
Master Data Changes and Mismatches
Key reference data constantly evolves. When a product is rebranded or a retailer switches identifiers, reports break if systems aren't updated in sync. Unit of measure inconsistencies are particularly problematic – when sell-out data arrives in units but prices are tracked in cases, missing or incorrect conversion factors destroy margin calculations.
Poor Data from Third-Party Providers
CPG companies rely heavily on external data. Syndicated providers might deliver late or incorrect data, while retailer portals may have gaps. Week definition differences (Sunday-start vs. Sunday-end) require careful harmonization. Data inconsistencies from external sources significantly affect decision-making unless caught and corrected (Retail Velocity - Data Governance).
User Input Errors
Manual data entry remains a major risk point. Typos in invoice entry lead to wrong customer assignments. Duplicate promotions or invoices create double payment risks. Sales teams might enter the same promotion twice or incorrectly assign spending. These errors often stem from lack of understanding rather than operational issues – users may not realize the downstream impact of their entries.
Siloed Systems and Integration Breakdowns
Even with integration efforts, data pipelines fail silently. A broken API, failed FTP upload, or legacy system not updating leads to incomplete analytics. When finance adjustments to trade spend don't flow to the promotion management system, decisions get made on partial data.
Stale or Out-of-Date Data
Data decays in value rapidly in fast-moving consumer goods markets. If you're analyzing last quarter's retailer sales because current data isn't loaded, you're always reacting to the past. Without processes to continuously update data sources, your "single source of truth" slowly drifts from reality.
Lack of Ongoing Data Quality Ownership
Perhaps most critically, if no one "owns" data quality, it will degrade. Companies might invest heavily in initial data cleansing during implementation, but without continuous oversight, new errors creep in. Data quality ownership is not a one-time project – it requires continuous improvement (Fivetran - Data Observability, Alation - Data Quality Monitoring).
Notably, in a 2024 survey, 40% of CPG companies acknowledged that insufficient data cleansing and harmonization processes were holding back their trade promotion optimization efforts (POI State of the Industry Report). Over a third said they need to improve data ownership and management to drive better results.
If you made it this far, please consider signing up for my weekly newsletter. It's currently free and takes just 15 seconds — click here.
How to Keep Data Accurate, Accessible, and Useful
Given these pitfalls, how can CPG firms ensure their data remains a high-quality asset? The key is treating data as a living, strategic resource requiring automation, monitoring, and ownership on an ongoing basis. Here are proven strategies:
Automated Data Validation
Don't rely on manual checks. Implement automated validation capturing 80-90% of standard cases, flagging only exceptions for human review. Systems should alert when promotion spend exceeds budget by set thresholds, or when expected retailer POS files don't arrive.
Modern TPx platforms embed robust validation rules in the data pipeline (CPGvision - Trade Promotion Data Quality). For instance, automated checks catch unit of measure mismatches between sell-out data (units) and pricing (cases), preventing calculation errors that could misstate ROI by orders of magnitude. Automated scripts identify duplicate invoices before they create double payment risks, and flag when product code mappings between UPC and SKU don't align.
Data Health Monitoring & Alerts
Establish a "health dashboard" for your data. Monitor metrics for completeness, accuracy, timeliness, and consistency continuously. CPGvision's in-system data health monitoring provides real-time visibility into data quality (CPGvision FAQ).
Set thresholds triggering immediate alerts – if daily sales data shows anomalies (100 units/week becoming 10,000), or scheduled data loads from Kehe or Unifi distributors fail, system notifications go to data owners immediately. Weekly reviews of these health reports catch emerging issues before they impact decisions. Treat data quality as a KPI alongside sales and service levels.
Master Data Management & Clear Ownership
Strong data quality ownership (governance) handles master data changes and cross-system consistency. Assign "data stewards" for critical domains – product master data, customer hierarchies, pricing structures. These owners manage updates, merge duplicates, and communicate changes across systems.
Regular audits are essential. Monthly reviews should systematically check for anomalies, duplicates, or outdated entries (Retail Velocity - Data Governance Best Practices). When data lakes centralize information from SAP and other sources, they remove integration complexity but require vigilant maintenance to prevent degradation.
Clear Issue Resolution Process with Feedback Loops
Despite best efforts, issues arise. What matters is having a disciplined process connecting front-line users to data owners. When salespeople spot incorrect promotion volumes, they need to know exactly who to contact – not just to adjust the values but to address underlying causes.
Define how issues are logged, tracked, and resolved. If baseline models are wrong, correct them rather than overriding individual lifts. Maintain an issue log highlighting recurring problems. For example, if week definition mismatches between Nielsen and internal systems repeatedly cause errors, permanently fix the harmonization logic rather than patching individual reports.
This feedback loop is critical – salespeople and field teams are your early warning system for data problems. Make it easy for them to report issues and ensure they see the resolution, building trust in the system.
Continuous Training and User Awareness
People remain central to data quality. Regular training on why certain data matters prevents errors at the source. When users understand that entering promotions in wrong units can misstate ROI by millions, they're more careful.
Build data accuracy into performance metrics. Designate department "data champions" liaising with central teams, reinforcing good practices and identifying pain points. Focus human effort on exceptions and domain expertise – experienced business users leveraging their knowledge to spot patterns automated systems might miss.
Turning Data into a Strategic Asset
Making data useful is not a one-time project but a continuous journey. When done right, the payoff is substantial. Companies successfully harnessing data for TPx and digital transformation can unlock 6-10% incremental revenue uplift and 3-5% EBITDA growth over several years (McKinsey - Digital Future of Consumer Goods, McKinsey - Integrated Revenue Management).
Real-World TPx Value Creation
Post-Event Analysis Excellence: With harmonized data from syndicated sources, retailer portals, and internal systems, companies can accurately measure true promotional lift. This means understanding not just volume increases, but profitability after considering all trade spending, hidden costs, and cannibalization effects.
Revenue Forecasting Precision: By combining historical patterns with real-time sell-through data from distributors (via Crisp or direct integration), companies forecast next year's revenue with unprecedented accuracy. This enables better financial planning and investor communications.
Top-Down Promotion Planning: Starting with budgeted spend targets, companies can optimize allocation across customers and tactics. With accurate baseline models and lift predictions, they maximize ROI within constraints – impossible without trustworthy data.
Cross-Functional Collaboration: When sales, finance, and supply chain teams share the same data picture, planning cycles accelerate. Manufacturing can prepare for promotional volume spikes, finance can accrue accurately, and sales can adjust tactics mid-cycle based on early results.
The bottom line: Data should be treated as a strategic asset – cared for, refined, and leveraged to its fullest potential. That means investing not only in tools but also in the less glamorous work of data maintenance, quality control, and ownership. It means limiting "garbage in" so you don't worry about "garbage out."
Digital transformation in CPG is ultimately about making better decisions at all levels, faster than ever before. Those decisions can only be as good as the data informing them – a fact becoming more critical as the industry adopts advanced analytics and AI. As one industry leader put it, "decisions are no better than the data on which they're based" (MIT Sloan - Data Quality) – so make sure your data is right, and the rest will follow.
If you enjoyed this article you may also like:
Jon Flaherty
If you have any comments or questions, please join the discussion below.
