How Lumi Helps Reveal Hidden Pricing Opportunities

Pricing and commercial teams often adjust prices without full visibility into how demand actually responds. This can lead to underpriced items with margin left on the table, over-priced items that quietly depress volume, and promotions that mask underlying demand patterns. Lumi AI allows teams to test pricing hypotheses through simple, conversational prompts that surface data-backed elasticity insights within seconds.

Watch the Full Walkthrough

In this walkthrough, Liam from Lumi AI analyzes a full year of sales and pricing history, identifies which items show the strongest price–demand relationships, and narrows the results to the products that matter most for revenue. The session shows how these findings can guide pricing decisions across merchandising, planning, and finance.

What Is Price Elasticity?

Price elasticity measures how demand changes when price changes. It helps teams understand whether an item is sensitive to price adjustments or relatively stable.

Different elasticity patterns can signal:

High Sensitivity: Demand drops when price increases, creating margin pressure when prices move up.

Low Sensitivity: Demand remains stable across price changes, giving teams more flexibility.

Positive Price Response: Demand rises as price increases, often indicating promotional effects, shifts in product positioning, or data anomalies.

By comparing elasticity against actual sales performance, organizations can pinpoint where pricing strategy should tighten, where revenue is leaking, and where promotions may be distorting true demand.

Investigating Price Elasticity with Lumi

Lumi makes hypothesis testing around pricing fast and clear. Here’s the full analysis:

1. Asking a Natural Language Question

Teams begin with a straightforward prompt:

“Which items show the strongest relationship between price and demand?”

Lumi interprets the question and identifies the relevant signals across sales transactions, price changes, and demand patterns.

2. Context Gathering and Query Generation

Lumi retrieves the necessary inputs, such as:

• Item ID and description
• Historical prices
• Daily or weekly demand
• Correlation strength between price and quantity sold
• Estimated elasticity based on the full 12-month record

Lumi automatically builds the analytical query, applies consistent logic, and prepares the comparison across all items.

3. Adding Detail and Refining the View

As the team refines the question, Lumi adjusts the analysis instantly. In this walkthrough, Liam asks Lumi to:

• Add estimated elasticity to each SKU
• Include average price and average daily volume
• Restrict results to the top 5 percent of items by demand last month
• Further restrict to the top 1 percent by demand across the full 12 months
• Remove items with average prices below one dollar
• Sort by the strongest price-demand correlations

Lumi rebuilds the full pipeline each time, ensuring that calculations use the complete 12-month history even as the output filters change.

4. Analyzing the Results

The refined output reveals which items materially respond to price adjustments. For example:

• Several high-volume SKUs show strong negative correlations and significant elasticity values, suggesting that even modest price increases may reduce demand.
• A smaller set shows positive correlations, prompting teams to check for promotional timing, assortment changes, or shifts in classification that may influence demand signals.
• High-velocity items with stable elasticity emerge as candidates for margin expansion since demand remains steady across price changes.

These results can be saved inside Lumi boards for recurring use.

Driving Action Across Pricing and Planning

Once teams validate the insight, the findings can be shared across commercial, planning, and finance functions. Practical steps include:

• Adjust list prices for items with stable demand and low elasticity
• Re-evaluate promotional depth for items showing high price sensitivity
• Flag SKUs whose positive correlations may indicate promotional distortion or data quality issues
• Improve forecasting accuracy by incorporating elasticity into forward demand models

Why It Matters

Price elasticity often hides in fragmented reports or requires manual modeling that few teams have time to maintain. By pairing natural-language analysis with live operational data, Lumi gives organizations a consistent way to understand how price affects demand across the items that truly shape revenue.

With one prompt, teams can see where price changes carry risk, where margin expansion is possible, and where operational decisions may be influenced by outdated assumptions.

Frequently Asked Questions

Q1: What data sources does Lumi use for elasticity analysis?

Lumi connects directly to sales, pricing, and item master data inside the organization’s environment, ensuring elasticity is calculated from trusted operational records.

Q2: Can elasticity analysis be automated?

Yes. Once the analysis is set up, it can be saved to a Lumi board and refreshed anytime. Teams can track elasticity as prices or demand patterns shift.

Q3: Can elasticity be segmented by brand, category, or channel?

Absolutely. Lumi can filter or group the analysis by subclass, brand, store, or any other dimension available in the data.

Q4: How does this differ from traditional reporting tools?

Traditional tools rely on manual query writing or preset dashboards. Lumi generates the logic automatically, retrieves the data, and presents the findings in seconds, making exploratory pricing analysis accessible across commercial teams.

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