Why Data Normalization Costs Consumer Brands Millions in Sales

Shelf Life: Expert Voices from the Consumer Goods Industry

In this episode, we dive deep into the complexities of data management within the consumer goods industry, focusing on how brands can achieve a comprehensive view of their business by connecting data across a multitude of retail, e-commerce, and supply chain partners.

Hosted by Abby Carruthers, Product Manager at Alloy.ai, the discussion features insights from Manfred Reiche, a subject matter expert in CPG data, and Matthew Nyhus, engineering team lead at Alloy.ai. Together, they break down challenges and solutions related to data normalization—a crucial process for standardizing data from various sources to ensure consistency and comparability.

From understanding product and location data normalization to tackling the intricacies of time and metric alignment, this episode explores how brands can transform their disparate data into actionable insights that drive sales growth and operational efficiency.

In this episode, you’ll learn about:

• Data normalization is critical for consumer brands to standardize data from various sources, such as retailers, e-commerce platforms, and supply chain partners, into a common language
• Integrating and managing data from multiple sources involves significant technical and operational challenges, specialized systems can automatically manage these hurdles
• Don’t shy away from the complexities of data normalization - seeking help and leveraging the expertise of others can save significant time and resources while ensuring accurate and actionable insights

Jump into the conversation:

(00:00) Introduction to Manfred and Matthew
(06:05) Using multiple retailers, integrate data sources for consumer insights
(09:55) Technology, people, and processes in master data management for product distribution
(14:47) Matching products from different sources for rich information visibility
(19:21) Consistency in managing changing product data
(21:59) Supply chain management with flexible, tailored database design
(25:24) How automation can reduce workload by 95% for all your teams
(29:45) Knowing servicing locations and translating insights for internal teams
(31:38) Distinguishing between brick-and-mortar and e-commerce sales
(34:17) Understanding net sales across channels, including returns and tax
(40:13) Backend stores metric values
(44:02) Retail data analysis pitfalls
(47:17) Being cautious with IT assumptions

章

1. Why Data Normalization Costs Consumer Brands Millions in Sales (00:00:00)

2. Data Normalization in Consumer Goods (00:00:02)

3. Master Data Management Challenges in Sales (00:10:15)

4. Handling Complex Product Matching Scenarios (00:13:43)

5. Standardizing Data Across Multiple Sources (00:26:47)

6. Data Normalization Across Time (00:37:11)

7. Modeling Edge Cases in Data (00:48:08)