Research: Acquire First-Party Loyalty Data for Incrementality Proof

Acquire First-Party Loyalty Data for Incrementality Proof

The acquisition of first-party loyalty data is a critical mechanism for Consumer Packaged Goods (CPG) and beverage brands to establish direct relationships with shoppers, bypass retailer data monopolies, and mathematically prove that new product innovations—such as non-alcoholic extensions—drive incremental sales rather than causing internal cannibalization. By utilizing receipt scanning, omnichannel data integration, and artificial intelligence, brands can identify actionable cross-purchasing-behavior and calculate a true Return on Advertising Spend (ROAS).

Bridging the Offline-to-Online Retail Gap

A historic challenge for food and beverage manufacturers is the “retail wall,” wherein selling products through major supermarkets or convenience stores completely divorces the brand from the end consumer’s data [3]. To counter the power of retail-media-networks, brands are aggressively deploying zero-party-data-harvesting techniques to build proprietary databases.

Receipt Scanning and Reward Platforms

To track offline sales, brands deploy third-party loyalty platforms that utilize optical image recognition for receipt scanning [3]. Platforms such as Fetch act as verified intermediaries, exchanging reward points for real-time purchasing data. This allows brands to bypass traditional retail barriers and track long-term consumer habits, generating metrics such as a 33% repeat purchase rate in the beverage sector and a 39% repeat rate in food [2]. Advanced loyalty mechanics, including gamified profiling and tiered rewards, help sustain engagement and offset the-loyalty-paradox, where ubiquitous point systems fail to secure genuine brand loyalty [3, 5].

Household Scanner Data

Household-level reporting acts as another vital source of data. Market research firms utilizing tools like iri or Nielsen provide basket-level-scanner-data that links specific UPC transactions (including prices and applied coupons) with localized household demographics, such as income level and size [7, 9]. By synthesizing home scanner data with store-level POS data, brands can precisely track product adoption and attribute preferences, such as the increasing demand for “mood support” within the functional beverage space [9, 10].

Proving Category Incrementality

The ultimate goal of accumulating loyalty data is to prove “incrementality”—the metric demonstrating that a new product generates genuinely new revenue rather than merely cannibalizing a brand’s existing portfolio.

The Additive Nature of Non-Alcoholic Beverages

Loyalty and cross-basket data have been instrumental in defending the non-alcoholic (NA) beverage category against accusations of purely shifting share. Data compiled by nielseniq reveals that 92% of non-alcoholic beverage consumers also purchase traditional alcohol [11]. This cross-purchasing-behavior indicates that zero-proof beverages function as additive products that capture entirely new social or at-home occasions, rather than acting as a simple 1:1 substitute [11].

Loyalty platforms specializing in CPG can measure this incrementality over time. Reward-powered retention campaigns specifically designed to stretch performance windows have demonstrated an average incremental sales lift of 18%, alongside a 4x incremental Return on Ad Spend (iROAS) following the initial purchase cycle [2]. By proving these additive purchase behaviors, brands can confidently negotiate shelf space and category expansion without fearing immediate cannibalization.

AI-Driven Data Utilization and ROI

Once first-party loyalty and omnichannel data are acquired, organizations deploy specialized AI systems to translate behavioral patterns into measurable revenue.

• Behavioral Personalization: By mapping the consumer journey, brands identify high-value shoppers and target them with personalized campaigns. This generates a look-alike audience acquisition strategy that relies on targeted referrals and brand advocacy to boost Customer Lifetime Value (LTV) while minimizing Customer Acquisition Costs (CAC) [1, 4].
• Trend Identification: Legacy conglomerates actively ingest and analyze vast quantities of digital and social data to track macro trends. diageo, for instance, leveraged AI-driven trend analysis of social sentiment to confidently launch gordons-0-0 and Guinness 0.0, capturing a segment of the $11 billion NA market driven heavily by millennials reducing their alcohol intake [14].
• Omnichannel Retail Optimization: Retailers with vast product portfolios utilize AI to streamline the customer experience. The major U.S. alcohol retailer bevmo, which features dedicated non-alcoholic sets led by brands like heineken-0-0, partnered with AI marketing firm Breinify to leverage customer location and behavioral data. This AI optimization yielded a $125 million revenue increase in its first year, significantly outperforming the cost of manual analytics headcount [12, 13, 15]. Similar data requirements are standardized by whole-foods, which mandates GS1-certified product capture and strict Data Council specifications to power their adult beverage digital ecosystems [6].

Gaps and Contradictions

• Data Monopolies vs. Third-Party Costs: While tools like receipt-scanning apps successfully bypass the retail wall [3], brands must continuously fund these third-party intermediaries to glean insights, often resulting in high operational costs simply to buy back data regarding their own customers.
• Privacy Paradox: There is a contradiction between consumer desire for customized rewards (with 76% of consumers wanting rewards merely for entering a store) and increasing resistance to digital tracking [4]. Balancing deep profiling with data privacy constraints remains an ongoing compliance hurdle.

Suggested Additional Research

• Investigate opt-in vs. opt-out frameworks: Research how global data privacy policies (e.g., GDPR, CCPA) impact the acquisition rates of first-party data for beverage applications.
• Analyze the margin impact of reward subsidies: Investigate whether the discounts funded to drive receipt-scanning initiatives outpace the actual financial returns mapped through iROAS.
• Examine retail-media-networks (RMN) vs. D2C loyalty: Find exact cost comparisons between a brand paying a major retailer for targeted RMN access versus funding its own proprietary loyalty data ecosystem.

References

1. Food & Beverage Loyalty Programs: 2026 Best Practices — yotpo.com
2. How Fetch helps CPG brands acquire and retain | Fetch For Business — business.fetch.com
3. Food & Beverages Industry Loyalty Solutions | Antavo — antavo.com
4. [PDF] How retailers can leverage loyalty program data for exceptional … — oracle.com
5. 10 Best Retail Customer Loyalty Programs | SAP Engagement Cloud — emarsys.com
6. Wholefoods - Adult Beverage - The Data Council — thedatacouncil.com
7. [PDF] Understanding IRI Household-Based and Store-Based Scanner Data — ers.usda.gov
8. Functional Beverages: Market Trends and Opportunities … — freedoniagroup.com
9. [PDF] Firm Heterogeneity in Consumption Baskets - Thibault Fally — fally.are.berkeley.edu
10. [PDF] A Thirst For Change: Beverage Innovations - SPINS — spins.com
11. Unlock Real-Time Liquor Market Insights with BevMo Data Scraper | Web Data Crawler posted on the topic | LinkedIn — linkedin.com
12. BevMo! Privacy Notice — bevmo.com
13. Less Is More? — Non-Alcoholic Beer Sales Flat In 2019, With Important Exceptions — Good Beer Hunting — goodbeerhunting.com
14. What Non-Alcoholic Beverages Teach Us About Emerging Trends — harmonya.com
15. [PDF] Case Study: Retail & E-Commerce - Breinify — home.breinify.ai