How to systematically measure pricing power

Every quarter, thousands of companies report their earnings. CFOs deliver carefully scripted presentations. Analysts ask predictable questions. But buried in those transcripts, tucked between the prepared remarks and investor relations polish, lies one of the most powerful predictors of long-term investment success: pricing power.

Warren Buffett's entire investment philosophy centers on companies with "pricing power" - businesses so essential to their customers that they can raise prices without losing market share. But here's the challenge: the signals are fragmented. A transcript may reveal a successful price increase, a filing might hint at eroding customer loyalty, and a news headline could show retailers pushing back. Manually connecting those dots is time-consuming and often incomplete.

A systematic approach helps turn these scattered narratives into structured insights. It makes it easier to see who really has pricing strength, and who is struggling against competition or consumer resistance and this is exactly what happened when we first deployed our Pricing Power Analysis framework.

Workflow summary with Bigdata.com

This workflow brings together search, labeling, and structured scoring to organize messy narrative data into a repeatable framework for analysis. Instead of sifting through anecdotes, investors can run a consistent process across an entire watchlist and track how pricing dynamics evolve over time.

Here’s the breakdown:

1. Define the scope. Select your watchlist (e.g. S&P 500), document sources (e.g. earnings calls, news), and time window for analysis.
2. Search for signals. Retrieve mentions of both positive (pricing power) and negative (pricing weakness) themes, such as “successful price increase” or “demand drop after price hike.”
3. Classify narratives. Label each mention into meaningful categories, from “brand loyalty confirmed” to “failed price increase.”
4. Compare across companies and sectors. Aggregate results to reveal patterns: which industries hold pricing power, and which face commoditization pressures.
5. Track over time. Monitor how pricing narratives shift from week to week or quarter to quarter, highlighting when momentum changes.
6. Score confidence. Weigh positive against negative mentions to rank companies by overall pricing strength.

What emerged

When applied to consumer and tech companies, the analysis surfaced clear stories:

• Spotify raised subscription prices while maintaining user loyalty, showing strong brand leverage.
• PepsiCo hit limits, as European retailers resisted increases and pulled products.
• McDonald’s and Starbucks faced consumer pushback, demonstrating that even strong brands meet resistance when price hikes go too far.

Companies with Pricing Power

This chart displays companies that have been most frequently mentioned in positive pricing power contexts, organized by sector and ranked by total mention volume.

Companies lacking Pricing Power

This complementary chart shows companies most frequently mentioned in negative pricing power contexts, organized by sector and ranked by total mention volume.

Pricing Power confidence analysis

This assessment ranks companies by pricing power, balancing positive and negative signals. The chart illustrates the split, with 50% as the baseline. Companies to the right show stronger pricing power, while those to the left face greater challenges

These examples illustrate how pricing power is not static - it shifts with brand strength, competitive alternatives, and customer sentiment.

Ideal for

• Portfolio managers monitoring sector dynamics
• Analysts benchmarking competitors’ ability to sustain margins
• Investment committees preparing thematic or risk assessments
• Strategy teams seeking consistent measures of competitive positioning

Practical applications

• Idea generation at scale: Identify companies with durable pricing advantages
• Competitive benchmarking: Compare peers’ ability to pass on costs
• Risk analysis: Flag firms vulnerable to price resistance or market share erosion
• Portfolio monitoring: Track exposure to sectors with stronger vs. weaker pricing control

Technical stack

• Bigdata API — for access to transcripts, filings, and news with hybrid search (backed by vector storage for fast retrieval and cross-encoder re-ranking)
• bigdata-research-tools — a Python toolkit for running scalable financial research workflows on top of the Bigdata API (e.g., thematic labeling, scoring, visualization).
• LLMs (e.g. GPT-4o-mini) — for classifying narrative chunks into pricing power

Interested in building your own?

Within hours, you can go from scattered transcripts and news headlines to structured insights on which companies hold true pricing power. Check out the Pricing Power Analysis cookbook to build your own workflow. You can access the full framework, example use cases, and code on GitHub.