How can SPINS improve its product attribute data to better serve niche or emerging product categories?

Introduction

To improve SPINS' product attribute data for niche or emerging product categories, we need to focus on enhancing data accuracy, expanding coverage, and increasing the granularity of attributes. This improvement will better serve our clients in the rapidly evolving consumer packaged goods (CPG) industry. I'll approach this challenge by analyzing our current data collection methods, identifying key stakeholders, and proposing innovative solutions to capture and categorize emerging product attributes effectively.

Step 1

Clarifying Questions

• Looking at SPINS' position in the market, I'm thinking about the scope of our current product categories. Could you provide more information on which specific niche or emerging categories we're targeting for improvement?

Why it matters: This helps us focus our efforts on the most relevant and high-potential areas. Expected answer: We're particularly interested in plant-based alternatives, functional beverages, and sustainable packaging. Impact on approach: Would tailor our data collection and categorization strategies to these specific areas.

• Considering the rapidly changing CPG landscape, I'm curious about our current data collection methods. How are we currently gathering and updating product attribute data?

Why it matters: Identifies potential gaps or inefficiencies in our current processes. Expected answer: We rely on a combination of manufacturer-provided data, in-store audits, and third-party data sources. Impact on approach: Would focus on enhancing or supplementing these methods based on their strengths and weaknesses.

• Thinking about our clients' needs, I'm wondering about the primary use cases for our attribute data. What are the most common ways our clients utilize this data in their decision-making processes?

Why it matters: Ensures our improvements align with client needs and use cases. Expected answer: Clients use our data for trend analysis, product development, and competitive intelligence. Impact on approach: Would prioritize attributes and improvements that directly support these use cases.

• Considering the technical aspects of our data infrastructure, I'm interested in understanding our current data model. How flexible is our current system in accommodating new or evolving attribute categories?

Why it matters: Determines the scope of potential improvements and any technical limitations. Expected answer: Our system has some flexibility but may require significant updates to handle more complex or granular attributes. Impact on approach: Would consider both short-term improvements and long-term infrastructure upgrades.