Bridging data and decisions: Crisp’s semantic layer for the retail industry

Crisp’s semantic layer transforms disparate retail data into a common language, fueling a new era of CPG intelligence.

For CPGs, retail data holds the key to informed decision-making across the enterprise. However, unlocking the full potential of raw data requires transforming it into concise, actionable insights. This is where Crisp’s semantic layer comes in – a powerful tool that acts as a translator, taking disparate data sources and building a unified, business-friendly language around them.

This layer in our data platform, enriched with algorithmic models that define key metrics for CPGs, facilitates efficient analysis for agile decision-making; access to clean data in business tools like Power BI; and the power to leverage cutting-edge AI/LLM technologies to reveal trends and deeper insights.

Why do organizations need a semantic layer?

The semantic layer is a crucial bridge within the Crisp data stack, going above and beyond normalized data models to provide teams a consistent, business-focused language around retail data regardless of department or technical expertise. Without a semantic model, this level of clarity is often lacking, or lives within individual BI tools managed by separate teams. As a result, data definitions, like product categorization rules, or metrics like ‘Dollar Velocity’, might be inconsistent across the organization leading to errors, and wasted time spent reconciling data during analysis.

That’s why Crisp’s semantic layer embraces a ‘headless BI’ approach, allowing organizations to connect a wide range of BI tools and applications to a single, centralized data and metrics catalog. This eliminates the need to recreate metrics and definitions in each tool, saving valuable time and resources.

Normalization is the foundation

Crisp’s data ingestion process begins with cleaning and normalizing data across 40+ retailers and distributors, ensuring a common baseline for insights across disparate sources. This foundation is essential for the semantic model to add its value. It removes inconsistencies and duplicate data and creates a harmonized language: ‘Units Sold’ is always defined and measured the same way, regardless of whether the source data came from Target or Amazon. 

The semantic layer adds meaning and context to this normalized data. While an AI model might recognize terms like ‘Dollar Velocity’ or ‘Share of Category YoY Growth,’ it lacks the understanding of their precise definitions and business significance. The semantic layer explicitly defines these metrics, establishes their relationships, and encodes the underlying business logic, empowering CPG teams to draw insights that seamlessly inform inventory management, merchandising, growth strategies, and more.

Composability: build on a solid base

Crisp’s semantic model uniquely embraces composability, enabling flexibility and adaptability. Like building blocks, it starts with a solid base of pre-defined metrics and logic for core CPG data sources like POS and inventory. 

As your business expands, so can your data model – seamlessly incorporating additional retailers or distributors as your footprint grows. The model’s design further allows for seamless integration of external data sources like syndicated market reports, EDI, weather, and much more. The ability to query this constellation of diverse data using plain, high-level business language opens possibilities for CPGs to gain a holistic understanding of the diverse factors impacting their supply chain and sales performance.

For example, by analyzing the effects of weather patterns on sales, companies can help optimize future inventory levels, preventing out-of-stocks, or overstocking and waste. 

This composability feature positions CPGs to regularly enhance their data models, staying ahead of evolving business questions and the increasing availability of new information.

As your business expands, so can your data model – seamlessly incorporating additional retailers or distributors as your footprint grows.

Extensibility: customize and expand

While composability allows CPGs to build upon a strong foundation with new data sources, extensibility empowers them to tailor the semantic model to specific business needs. This is achieved by adding company-specific metrics to Crisp’s models and any additional data sets, ensuring focus on the KPIs that drive your company’s success. 

This fosters deep data exploration. Using the weather data example, consider the ability to define new top-line metrics like ‘Past Streak of Days with High Humidity’, which can now be computed with other values instantaneously. Companies can now ask questions like: “In the last year, how did regions with high-humidity periods affect sales of fruit-flavored beverages compared to regions with less humidity?” And that’s just one example.

These custom, complex analyses can inform targeted promotions, product development, and inventory adjustments based on localized weather patterns, and more. 

While composability allows CPGs to build upon a strong foundation with new data sources, extensibility empowers them to tailor the semantic model to specific business needs.

LLM and AI applications

Imagine not just asking questions like, “How did snowstorms affect on-time delivery rates in the Northeast in Q4?” figuratively, in querying data, but literally, leveraging your choice of LLM. Crisp’s semantic model makes this possible with its meticulous structure and clearly defined relationships providing the perfect foundation for interaction with cutting-edge AI and LLMs.

Non-technical team members can now easily access insights, saving time compared to traditional query methods. Additionally, the semantic layer ensures the accuracy of AI-powered results by protecting against the misinterpretations LLMs can sometimes make when interacting with relatively unstructured data. As AI plays an increasingly transformative role within the CPG industry, Crisp’s semantic model positions companies to stay ahead of the curve and future-proof their data-driven approach. 

Imagine not just asking questions like, “How did snowstorms affect on-time delivery rates in the Northeast in Q4?” figuratively, in querying data, but literally, leveraging your choice of LLM.

To learn more about Crisp’s robust semantic model and our flexible retail data solutions, book a demo today.