Harte Hanks, Inc (HHS) Fair Value & Analysis
Industrials · US · Market cap $17.8M
Fair value as of: Jun 26, 2026
Analysis
Harte Hanks, Inc (HHS) currently trades at $2.40, while our model-based Fair Value estimate is $1.85 — implying the stock looks roughly 22.9% overvalued today. We read business quality at 95/100 (high quality), in the Industrials sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: medium).
About the company
Harte Hanks, Inc. operates as a customer experience company in the United States and internationally. It operates through three segments: Revenue Solutions, Customer Care, and Fulfillment and Logistics. It offers data and analytics, including audience identification, profiling, segmentation and prioritization, and predictive modeling and data strategy; research and intelligence, helps in understanding of customers, category, competitors, and capabilities; strategy, which plans and executes omnichannel marketing, demand generation, and customer experience programs; creative and content, including creative concepts, messaging and content assets for print, broadcast, direct mail, website, app, display, social, mobile, search engine marketing, and voice; marketing technology, a website and app development, e-commerce development and enablement, database building and management, platform architecture creation, and marketing automation; digital and multi-channel marketing execution and ad…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.