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Why Do Customers Describe Your Product Differently Than You Do?

Distinguish company construction language from customer consequence language without implying that every difference is a defect. Five blue, green, yellow, orange, and red zigzag bands cross white between inside-out and outside-in annotations.

Customers describe products from the outside in—through use, comparison, consequence, and social meaning—while companies describe them from the inside out through architecture, production, and intended positioning; the useful response is to diagnose the gap by segment and context before changing the language. Summary

The product team calls it an intelligent workflow-orchestration platform. A customer calls it the thing that stops approvals from vanishing in email.

Both descriptions are accurate. Only one sounds like Tuesday afternoon.

This difference frustrates companies because it feels like a message has been lost. Sometimes it has. Customers may misunderstand the category, miss the main benefit, or describe an experience that contradicts the promise. But sometimes the difference is evidence that the product has entered real life. The company names what it built. The customer names what changed.

Customers describe a product differently because they observe it from the outside in—through use, alternatives, consequences, and social meaning—while companies observe it from the inside out through architecture, production, and intended positioning. The gap should be diagnosed by segment and context before anyone rewrites the homepage.

The company and the customer are looking from opposite ends

Inside a company, language follows the organization chart.

Engineering names systems, services, models, and dependencies. Product names features, workflows, and roadmap themes. Sales names packages, differentiators, and objections. Legal names controlled claims. Finance names revenue lines. Over time, these terms become efficient. A two-word internal label can carry six months of decisions.

The customer did not attend those meetings.

Customers organize the same product around different objects:

  • the task they were trying to finish;
  • the alternative they replaced;
  • the failure they wanted to avoid;
  • the person who recommended it;
  • the moment it became useful;
  • the effort required to make it work; and
  • the story they can repeat to someone else.

This is not merely a plain-language problem. It is a vantage-point problem. Internal and external language compress different histories.

Vantage-point map

The same product compresses two different histories

Internal language preserves how the product was made; customer language preserves what happened in use.
Reading note

Editorial synthesis from Griffin and Hauser, 1993; Tullis and Feder, 2023; and Sujan and Dekleva, 1987. Translation preserves both mechanism and decision context.

Abbie Griffin and John Hauser's foundational voice-of-customer research makes this distinction operational. Customer needs must be elicited and structured before teams connect them with design attributes. A customer may need to carry a device safely in the rain. Engineering may answer with a seal specification, enclosure geometry, material, and test protocol. Replacing one side with the other would damage the work.

The job is translation, not surrender.

Expertise changes what ordinary words appear to mean

People inside a product company know too much to simulate not knowing.

That sounds like an enviable problem until someone writes a pricing page.

Jonathan Tullis and Brennen Feder tested the curse of knowledge across four experiments. Participants learned trivia facts and then estimated what novices would know. Learning impaired both the calibration and resolution of those estimates. Telling people, in effect, to rely less on their own knowledge did not repair the problem. Once a concept becomes available, its absence in another mind is difficult to model.

Product experts experience a related distortion. They know which terms are technically adjacent, which distinctions matter, and which steps are implicit. A prospective customer may see one unfamiliar noun followed by another.

Even familiar sentences can split by expertise. Jeff Coon and colleagues studied how experts and novices interpret generalizations. Novices applied general statements more broadly than experts, while expert speakers did not reliably adjust their language when explicitly addressing inexperienced listeners.

Consider the claim "The platform automates approvals."

An expert may hear a bounded function: rules, events, permissions, exceptions, and an audit trail. A novice may hear that the platform will decide, route, chase, approve, document, and fix every approval process. The sentence did not change. Its implied scope did.

This is why replacing jargon with shorter words is insufficient. The team must also expose hidden scope, prerequisites, and category assumptions.

Customers place the product in the category they can retrieve

A company can select a category in a strategy workshop. A customer still has to recognize it in memory.

Mita Sujan and Christine Dekleva used product-categorization experiments to show that experts and novices respond differently to comparative and category-level framing. The effects appeared in perceived similarity, distinctiveness, and informativeness. The category frame changed the inferences people made.

That matters because customer descriptions are often compressed comparisons:

  • "It is like a spreadsheet, but everyone can update it."
  • "It is a bank account for the project."
  • "It is the Figma for data pipelines."
  • "It is basically a consultant in a box."

These phrases may make a product team wince. They can also reveal the comparison set that controls the purchase.

Correcting the customer too quickly can remove useful evidence. If buyers repeatedly call a governance product "an approval tool," they may be reducing a sophisticated system to its first visible job. Or the product may actually behave like an approval tool despite the company's larger story. Those diagnoses require different decisions.

The first needs better onboarding and category education. The second needs either a narrower position or a more capable product.

Meaning continues after the company finishes writing

Traditional branding treated identity as something an organization defined and transmitted. More recent research treats meaning as an ongoing stakeholder process.

Michael Merz, Yi He, and Stephen Vargo trace this shift toward stakeholder-focused brand logic. In that account, brand value emerges through collaborative activity and value in use. Vallaster and von Wallpach's online discourse study shows stakeholders using relationships, discourse strategies, and shared resources to negotiate meaning. A later five-case B2B study identifies four recurring performances: communicating, internalizing, contesting, and elucidating.

Customers do not merely receive a description. They test it against use, argue about it, simplify it, teach it, parody it, and attach it to other identities.

Albert Muniz and Thomas O'Guinn spent about eight months studying Bronco, Macintosh, and Saab communities. These communities displayed shared consciousness, rituals and traditions, and moral responsibility. Community members learned distinctions that were meaningful inside the group, including what counted as a legitimate example and how a proper owner behaved.

Susan Fournier's consumer-brand relationship research explains another source of vocabulary. People can experience brands through relationship roles and histories. They may describe one service as a dependable colleague, another as a demanding coach, and another as the friend who was exciting until every plan became complicated.

None of those descriptions appears in a feature taxonomy. They can still predict loyalty, disappointment, advocacy, or exit better than the taxonomy does.

A semantic gap can be measured—carefully

The language gap is not only a metaphor.

Rodriguez-Diaz and colleagues compared customer reviews with supplier product descriptions across 28 Amazon product domains. Their corpus contained about 6.9 billion words of reviews and 1.5 billion words of descriptions. They used compression and word embeddings to estimate whether the same words occupied different semantic neighborhoods in the two corpora.

The validation set included 280 high-drift word instances reviewed by a professional linguist. Across 26 usable domains, the compression measure correlated at 0.67 with the word-embedding validation. The authors also reported a modest inverse relationship between semantic gap and verified product ratings: r = -0.32, with p at or below 0.05.

Semantic-gap evidence

Customer and supplier language differed at corpus scale

A large preprint found measurable semantic drift and a modest inverse association with verified ratings, but not a causal effect.
Reading note

Rodriguez-Diaz et al., 2022. The corpora cover 28 domains; validation used 26 usable domains and 280 reviewed high-drift instances. Association is not causation.

This is intriguing evidence, not a copywriting law.

The study is a preprint. The satisfaction analysis operates across a small number of product domains. Product descriptions are only one form of supplier language. Reviews are written by selected customers after purchase. A correlation cannot establish that semantic distance caused lower ratings. Product complexity, description quality, expectations, and category culture could affect both language and satisfaction.

The defensible conclusion is narrower: customer and provider corpora can encode measurably different meanings, and the difference may contain useful information about the customer relationship.

That is enough to justify investigation. It is not enough to justify copying every review phrase into the interface.

Not every difference is a defect

Treat language divergence as one of four conditions.

Healthy appropriation

Customers invent a memorable shorthand that preserves the product's mechanism and makes its value easier to share. The new phrase may be better than the company's language because it emerged from repeated use.

Contextual variation

Different roles describe the same product by the part each one touches. A finance leader says "cost control." An operator says "fewer handoffs." A developer says "one API." None has to become the universal brand line.

Category or knowledge mismatch

Customers place the product in the wrong comparison set, misunderstand its scope, or cannot recover the category at all. This requires clearer framing, examples, boundaries, and onboarding.

Experience contradiction

Customers use language that directly conflicts with the promise: "manual," "fragile," "slow," "only works for experts," or "another dashboard." This is rarely solved by voice guidelines. It may expose a product or delivery failure.

Divergence diagnostic

Different wording can signal life, context, confusion, or failure

The response should change with the source pattern and the consequence of the mismatch.
Reading note

Editorial synthesis of the retained categorization and stakeholder-created brand-meaning research. The matrix diagnoses response, not linguistic quality.

The distinction protects teams from two equal mistakes. One is dismissing customer language because it is not technically precise. The other is obeying customer language because it feels authentic.

Authenticity is not the same as representativeness. A vivid phrase may come from one loud user, one industry, one support crisis, or one online community with its own norms.

Build four corpora before building a glossary

A useful language audit keeps its sources separate long enough to preserve context.

1. Internal language. Collect product names, architecture labels, roadmap terms, sales decks, support categories, analytics events, contracts, and training material. Mark which terms exist for operating precision and which appear in customer-facing decisions.

2. Inquiry language. Collect search queries, sales questions, interview transcripts, lost-deal notes, and first-session questions. This captures the product before the customer fully understands it.

3. Use language. Collect support conversations, usability sessions, implementation notes, reviews, cancellations, and success calls. This captures the product after it meets reality.

4. Social language. Collect community posts, comparisons, referrals, partner descriptions, analyst categories, and language used by people explaining the product to colleagues. This captures meaning as it travels.

Do not begin by pooling the text. Attach at least five fields to each phrase:

  • speaker role;
  • segment or industry;
  • expertise level;
  • journey stage; and
  • observed use context.

Griffin and Hauser found that interviews with roughly 20 to 30 customers could surface at least 90 percent of needs in a relatively homogeneous segment. The qualification is essential. Twenty interviews spread across five incompatible segments do not become a homogeneous sample by arithmetic.

Their research also shows why frequency is not importance. A need can be rare and decisive. Another can be frequent and trivial. Count language, but also connect it with decisions, behavior, failure, and consequence.

Turn the comparison into a language-delta register

A glossary records preferred words. A language-delta register records decisions.

For each consequential term, capture:

  1. Internal term: What does the organization call it?
  2. Customer term: Which groups call it what, and in which context?
  3. Meaning difference: Is the gap about category, outcome, mechanism, comparison, risk, effort, or social identity?
  4. Consequence: Does the gap impair retrieval, attract the wrong buyer, overstate scope, hide a requirement, or reveal a broken experience?
  5. Decision: Retain, translate, segment, teach, test, or retire the term.
  6. Evidence: What observation would show that the decision improved understanding without damaging accuracy?
Decision register

A useful language record preserves the difference

Record who said what, where the meaning changed, what consequence followed, and what decision the evidence supports.
Reading note

Editorial synthesis from voice-of-customer and stakeholder co-creation research. Illustrative entries demonstrate the structure; they are not measured findings.

This creates several legitimate outcomes.

Retain a technical term when accuracy, compliance, or professional recognition depends on it. Explain it at the decision boundary.

Translate a term when the internal label hides an outcome or use that customers must recognize quickly.

Segment language when different roles face different decisions and no single phrase can carry all of them.

Teach the category when the product creates a genuinely new model that customers cannot infer from an old comparison.

Test competing language when the consequence is uncertain. Measure recognition, comprehension, comparison, expectation, and task choice—not only preference.

Retire a term when it repeatedly creates the wrong expectation or protects an internal story that the product experience no longer supports.

Do not make the customer sound like the company—or the company sound like a transcript

Customer language is a source of truth about customer meaning. It is not the only truth a product needs.

Customers may describe an outcome without understanding the mechanism. Experts may describe the mechanism without making the outcome recognizable. Strategy connects the two.

A strong public description usually contains three layers:

  • a recognizable customer situation;
  • a defensible outcome or difference; and
  • enough mechanism and boundary to make the claim accurate.

The customer should be able to say, "Yes, that is what this helps me do." The product team should be able to say, "Yes, that is how it actually works and where it stops."

Perfect wording alignment is neither possible nor desirable. A product becomes useful in more contexts than one internal taxonomy can anticipate. A living brand will collect associations the company did not author. The goal is not to eliminate that life.

The goal is to know when a new phrase is a sign of understanding, when it is a segment-specific lens, and when it is the market politely informing the company that the product is not what the slide says it is.

References

Summary

Treat different customer wording as diagnostic evidence: identify which vantage point, category, segment, experience, or community produced it, then translate only the terms that obstruct recognition or misstate value.

  1. Collect separate language samples from internal teams, customer inquiries, observed use, support conversations, reviews, and communities.
  2. Keep role, segment, expertise, journey stage, and use context attached to every phrase.
  3. Mark whether each difference concerns category, outcome, mechanism, comparison, risk, experience, or social meaning.
  4. Test whether the customer term improves recognition while preserving the product's real mechanism and boundaries.
  5. Escalate gaps that reveal a broken promise, wrong comparison set, missing capability, or materially different experience.
  6. Record retained, translated, segmented, and rejected terms in a language-delta register.
  7. Repeat the comparison after product, audience, or market changes instead of freezing one permanent voice-of-customer glossary.

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