Framing the Solution: Tracking the Real Impact of Tariffs with Alternative Data (Part II)

Before it reaches company results: Part I tracked upstream supply shifts. Part II tests for downstream effects on pricing, margin, and demand using alternative data.

Tariffs reshape economic behavior before the impact shows up in earnings. Companies respond by shifting supply chains, adjusting pricing, managing inventory, and changing strategy. Consumers react through substitution and trade-down behavior. These shifts are visible in real time using alternative data. This two-part series introduces a structured framework to track those effects.

In Part I, we outlined a seven-step causal framework to help investors monitor the economic impact of tariffs in real time. We focused on the early stages of the response—policy announcements, supply chain adaptations, and freight activity. Using datasets like AIS shipping¹ logs, bill-of-lading records², and trucking volumes, we showed how alternative data³ can surface behavioral changes weeks ahead of official statistics or earnings reports.

In Part II, we follow tariff impacts downstream: pricing and inventory adjustments, consumer spending shifts and substitution, and how these trends surface in earnings transcripts and guidance updates.

Tariffs make headlines, but their effects are operational. They show up in shipping logs, inventory lists, and earnings calls before they register in official data. With the right tools, those shifts can be tracked in real time.

Each section of this article focuses on how alternative data can sharpen timing, reveal margin pressure, and expose strategy changes before they are priced into the market.

• For Investors: This toolkit offers a practical roadmap: use alternative data to anticipate tariff-driven economic impacts before they appear in earnings, statistics, or consensus views.

• For Data Companies: This framework is an invitation to go beyond obvious, marketed capabilities of specific alternative data and build toward critical investable use cases.

What you’ll be able to detect using the Part II framework:

• Detect if pass-through pricing fails before margins compress in earnings

• Track inventory build-ups, stockouts, and discounting ahead of revenue misses

• Spot early reshoring activity using permit filings and investment announcements

• Nowcast inflation at the category level before CPI/PPI prints using online price signals

• Measure consumer trade-down behavior and substitution away from tariff-hit goods

• Follow new-to-used vehicle shifts as a signal of affordability pressure

• Monitor earnings call tone and risk language for rising investor concern over tariffs

The mosaic approach matters most here: triangulating from transactional patterns, supply chain footprints, sentiment shifts, and pricing anomalies gives you a fuller, more credible picture than relying on any one dataset in isolation.

Seven-Step Framework for Tracking Tariff Impacts

1. Monitor Tariffs and Exposure (Part I)

   Track policy announcements and effective dates using official government data and understand corporate exposure.

2. Supply Chain Response (Part I)

   Use AIS shipping data, bill-of-lading records, and port analytics to detect sourcing shifts and import behavior.

3. Logistics Tightness (Part I)

   Monitor freight volumes, rail activity, and manufacturing job postings for signs of stress or adaptation.

4. Wholesale/Distributor Signals (Part II)

   Analyze inventory levels, delivery timelines, and SKU⁴ availability from online sources and B2B platforms.

5. Retailer Adjustment (Part II)

   Track real-time price changes, discounting patterns, and SKU churn to identify margin pressure and cost pass-through.

6. Consumer Reaction (Part II)

   Use transaction data, price sensitivity models, and substitution patterns to assess spending behavior.

7. Company Results as a Signpost (Part II)

   Apply NLP⁵ to earnings calls and investor communications to surface tariff exposure, strategic shifts, and risk language.

Disclaimer: The article outlines data-driven solutions to key investment questions. The goal is to help readers frame the right questions and build their own evidence-based perspective using structured analysis and alternative data. This is not investment research. It does not include price targets or recommendations. It doesn’t attempt to answer the investment questions proposed. While I’m not presenting conclusions, I’ll say this: in past applications, these techniques have consistently surfaced signals ahead of consensus.

📣 Data Providers: If you're building the kind of signal decision-makers lean on before consensus forms, this might be worth a conversation. We occasionally feature data firms in interview-based writeups—focused on use cases where timing, relevance, and clarity actually show up in the data.

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Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

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Brainstorm Data Solutions to Address the Key Investment Question, Continued…

Step 4. Wholesale/Distributor Signals

Web-mined Inventory Levels at Retailers and Distributors

Median inventory days available across seasonal SKUs, which could run at lower than typical season levels if actual inventory depth is not up to standards. Or could be higher than season norms if stockpiled inventory

1. Investment Question and Use Case

   Are companies building up or drawing down inventories in an unusual way due to tariffs (e.g., hoarding parts ahead of tariffs or running lean because of uncertainty), and what does that imply for future earnings and supply chain stability?

   By web mining⁶ publicly visible stock levels and delivery lead times, investors can gain a near-real-time window into how retailers, manufacturers, and tech firms are operationalizing tariff risk. This technique has broad relevance: U.S. retailers exhibit early signs of clearance or stockouts. Manufacturing firms may initially respond to tariff announcements by pulling down existing component inventories, either to accelerate production ahead of expected cost increases or to delay new purchases while assessing the impact. These early inventory drawdowns often occur before any changes in sourcing or pricing flow through to financial statements. Tracking these signals across both U.S. and cross-border sites (Mexico, Canada) can indicate whether supply chains are being rerouted around tariffs.

2. Key metrics and analytics approach