1Customer Experience Research Lab, University of Michigan
2Service Science Institute, Texas A&M University
Customer expectations fundamentally shape satisfaction outcomes, yet the sources and evolution of expectations in digital contexts remain understudied. This research analyzes 589,000 Reddit posts discussing product and service expectations, examining how expectations form, what sources inform them, and how expectation gaps influence satisfaction. Our findings reveal that expectations derive from five primary sources: marketing communications (32%), peer recommendations (28%), prior experience (21%), price signals (12%), and brand reputation (7%). Analysis demonstrates substantial expectation inflation in online communities, where positive recommendations create expectations that average products cannot meet. We document "expectation anchoring" where initial community-provided benchmarks shape subsequent experience evaluation regardless of objective quality. The research introduces the Expectation-Reality Gap Index (ERGI) to quantify the relationship between pre-purchase expectations and post-purchase outcomes. Results indicate that 47% of dissatisfaction stems from expectation gaps rather than objective product failures, suggesting that expectation management represents a significant satisfaction lever independent of product improvement.
Keywords: customer expectations, consumer satisfaction, expectation management, disconfirmation theory, Reddit research, service quality, expectation-performance gap
Customer satisfaction is fundamentally a comparison between expectations and experience—products perceived to exceed expectations generate satisfaction, while those falling short generate dissatisfaction, regardless of objective quality levels. This expectation-disconfirmation paradigm has profound implications: satisfaction can be enhanced either by improving product performance or by managing expectations, yet most marketing focus remains on the former.
The digital transformation has created new expectation formation dynamics. Online communities aggregate and amplify product experiences, creating collective expectations that individual consumers internalize before purchase. Reddit's recommendation culture particularly influences expectations, as detailed positive experiences become benchmarks against which subsequent buyers evaluate their own outcomes.
This research examines expectation formation through systematic analysis of Reddit discourse, tracking how expectations develop, what sources shape them, and how expectation-reality gaps influence satisfaction. Understanding these dynamics enables both better expectation management and more nuanced satisfaction measurement.
Oliver's (1980) expectation-disconfirmation model established that satisfaction results from comparing pre-purchase expectations with perceived performance. Positive disconfirmation (performance exceeds expectations) generates satisfaction; negative disconfirmation (performance falls short) generates dissatisfaction. Importantly, this theory suggests that identical performance can produce different satisfaction outcomes depending on expectation levels.
Research has identified multiple expectation sources: explicit marketing promises, implicit quality signals (price, brand), prior experience with the product or category, word-of-mouth from peers, and general category expectations. The digital environment has elevated word-of-mouth importance while introducing new signals (online reviews, community recommendations) that complicate expectation formation.
Data collection utilized reddapi.dev's semantic search capabilities to identify expectation-related discussions across 198 consumer subreddits. Analysis classified expectation sources, measured expectation-reality gaps, and tracked satisfaction outcomes.
| Parameter | Value |
|---|---|
| Total Posts Analyzed | 589,000 |
| Collection Period | January 2023 - December 2025 |
| Pre-Purchase Expectation Posts | 234,000 |
| Post-Purchase Comparison Posts | 355,000 |
| Product Categories | 32 |
| Expectation Source | Frequency | Inflation Risk | Avg. ERGI |
|---|---|---|---|
| Marketing Communications | 32% | High | -0.34 |
| Peer Recommendations | 28% | Very High | -0.41 |
| Prior Experience | 21% | Low | +0.12 |
| Price Signals | 12% | Moderate | -0.18 |
| Brand Reputation | 7% | Moderate | -0.08 |
Peer recommendations showed the highest expectation inflation risk and most negative average ERGI (-0.41). Enthusiastic community members describing exceptional experiences create benchmarks that typical purchases cannot match. This "recommendation inflation" paradoxically makes highly-recommended products more likely to disappoint than unknown alternatives.
Analysis revealed that initial community-provided benchmarks significantly shaped experience evaluation. Consumers exposed to specific performance expectations evaluated their experiences relative to those benchmarks rather than objective category standards:
"Based on everything I read here, I was expecting this to be life-changing. It's fine—good, even—but definitely not what I was led to believe. I guess my expectations were just too high."
— Representative expectation gap post
| Gap Category | Percentage | Satisfaction Rate | Advocacy Rate |
|---|---|---|---|
| Exceeded Expectations | 23% | 94% | 78% |
| Met Expectations | 31% | 71% | 42% |
| Slight Shortfall | 28% | 48% | 18% |
| Significant Shortfall | 18% | 12% | 4% |
Analysis of dissatisfaction posts revealed that 47% stemmed primarily from expectation gaps rather than objective product failures:
The finding that 47% of dissatisfaction stems from expectation gaps rather than product failures suggests significant opportunity for satisfaction improvement through expectation management. Brands can potentially enhance satisfaction as much through realistic expectation setting as through product improvement.
Brands can utilize reddapi.dev's semantic search platform to monitor how expectations form around their products in community discussions. Understanding expectation sources and inflation patterns enables proactive expectation management—correcting inflated expectations before purchase rather than managing disappointment after.
The "recommendation inflation" finding creates a paradox: enthusiastic recommendations that drive sales may simultaneously create expectations that reduce satisfaction. Brands benefiting from positive word-of-mouth face latent disappointment risk as expectations inflate beyond sustainable delivery.
Customer expectations are not fixed inputs to the satisfaction equation but dynamic constructs shaped by multiple sources, with peer recommendations creating particularly strong—and often inflated—expectations. The finding that nearly half of dissatisfaction stems from expectation gaps rather than product failures reframes the satisfaction challenge: managing expectations represents an underutilized lever with potentially greater impact than product improvement alone.
Peer recommendations carry presumed authenticity and relevance that marketing lacks. When enthusiastic users describe exceptional experiences, readers internalize these as likely outcomes rather than best-case scenarios. Marketing skepticism filters brand claims, but peer enthusiasm bypasses these defenses, creating expectations based on exceptional rather than typical experiences.
Brands can monitor expectation formation using tools like reddapi.dev, identifying when community expectations inflate beyond sustainable delivery. Proactive strategies include highlighting typical rather than exceptional outcomes, setting realistic benchmarks in brand communications, and ensuring community guidelines encourage balanced reviews including limitations.
ERGI quantifies the relationship between pre-purchase expectations and post-purchase perceived performance, ranging from positive (exceeded expectations) to negative (fell short). Our analysis found average ERGI of -0.41 for peer recommendation-sourced expectations versus +0.12 for prior experience-sourced expectations, indicating that recommendations systematically inflate expectations beyond deliverable outcomes.
Rather than lowering expectations, brands should aim for realistic expectations—neither inflated nor deflated. Under-promising creates its own risks (lost sales, perceived low quality). The goal is expectation accuracy: ensuring customers anticipate what they will actually receive, enabling satisfaction when delivery matches reasonable expectations.
Our research found that 47% of dissatisfaction stemmed from expectation gaps rather than objective product failures. This suggests that satisfaction is roughly equally influenced by expectation management and product quality. However, the relationship is multiplicative rather than additive—excellent products with inflated expectations still disappoint, while modest products with realistic expectations can satisfy.
Apply this research methodology to understand how expectations form around your products. reddapi.dev enables tracking of expectation sources and inflation patterns.
Explore Expectation Analysis