Methodology

What TalentTell measures

Does your talent communication help you stand out from the crowd, or is it making you blend in with everyone else?

That's the question TalentTell answers. It analyses what's arguably the most engaged with in your public talent communications and scores them across five dimensions - measuring whether what you say is specific and distinctive, or generic wallpaper that reads like every other company's careers page.

It matters more than it used to. Increasingly, the first thing a candidate learns about you doesn't come from you - it comes from an AI summary, a "what's it like to work at X?" answer. And an AI can only reflect back what your content actually says. Specific, distinctive content gives it something real to say about you. Generic content gets you the forgettable average. Standing out to a person and standing out to an AI have become the same problem.

So we read the two things you publish that candidates engage with most: everything you say about your culture, your values, your people - the lot - and how you talk when you're actually hiring. The number of pages doesn't affect your score. What matters is the quality, specificity, and distinctiveness of what you communicate.

These dimensions measure how effectively an organisation communicates its employer brand, not how good the employer brand itself is. The distinction matters. A company could have a brilliant culture but communicate it poorly. TalentTell measures the communication, not the culture behind it.

The five scoring dimensions

Each dimension is scored from 0 to 100. Scores reflect the proportion of content that meets the threshold - not whether any single phrase does. A few standout sentences in otherwise generic content do not elevate the overall score.

How your score stays consistent

Most AI tools are non-deterministic - the same content fed in twice can produce different scores. TalentTell isn't. This section explains how, where the honest limits sit, and what to expect when something looks unexpected.

Same content, same score

Run TalentTell on the same content tomorrow, you'll get the same score - byte-for-byte identical to the score you got today. We hash your content; if we've already scored it, we don't ask the AI again. Same content, same score, every time.

Take Luxoft, one of the IT consultancies on our benchmark. We've scored Luxoft five times across our benchmark runs. Four returned Caregiver. One returned Sage. Same scoring engine, same scoring prompt - so what changed? The content did. On the Sage run, our scrape captured a substantially different set of visible job adverts versus the other runs. Both reads are accurate to what was visible at scrape time. That's normal hiring behaviour: active companies cycle their visible roles, producing different but valid snapshots. When we re-scored the identical scrape from any single run, the result was byte-for-byte the same as the first time.

Same content, same score. Different content, different score. And the engine tells you when content changed.

When your content changes, we know

If your careers content changes between scores, the engine detects it. That's a feature, not a problem - drift signals are how the engine stays honest about what it's measuring.

On one of the companies in our benchmark with content-rich careers pages, drift detection surfaced unexpected variance across both content streams - far more than rotation alone would explain. Investigation traced it to a timeout in our content-splitting layer that was reallocating content under specific conditions. We diagnosed the issue, fixed it, and re-ran. Subsequent runs confirmed stability. The point: drift detection didn't just flag noise. It pointed at a specific engineering bug we then fixed. That's the difference between unreliable AI and well-instrumented AI.

We don't over-read small differences

Caching makes your score repeatable, but repeatable isn't the same as precise. An AI scoring content is making a judgement, not taking a measurement - so we're deliberately modest about how finely we read the numbers. Two scores that sit close together are saying essentially the same thing; we don't treat a small difference as meaningful.

A wider gap is a different story - it can cross a line that changes the quadrant or how the result reads. Where two scores are close, we don't claim a precision we can't stand behind.

Borderline cases, read honestly

Some companies sit genuinely between two patterns - their content carries traits of more than one archetype, or their scores land near a quadrant edge. The engine always produces a single archetype call. But when a score sits close to a boundary, that single call is better read as a lean than as a verdict.

We see this most often when a brand sits genuinely between two patterns - a company that's part-Hero (achievement-led, ambitious) and part-Innocent (warm, inclusive), for example. Reading either as "the" definitive archetype would overstate what the content supports.

The honest reading in those cases is qualitative: "tending toward Hero, with characteristics of Innocent" rather than "definitively Hero." When a score lands near a boundary, that's your cue to read it as a lean rather than a hard call.

Variance across your surfaces is the diagnostic

The best brands know that consistency is everything: consistency breeds familiarity, and familiarity breeds trust. When your careers site and your job adverts tell the same story, that story gets stronger every time a candidate meets it.

And it matters more than ever now, because of how AI synthesises you. When someone asks their LLM of choice what you're like to work for, it pulls from every surface you show up on - a coherent story comes back sharp and recognisable; a fractured one comes back muddled. So coherence across your two touchpoints isn't a nice-to-have. It's what makes the story land at the moment someone's deciding whether you're for them. The brand consistency score (below) measures it, and the Talent Palette is built to close it.

How scoring works

Scores are proportion-based. If 80% of a company's content uses standard employer brand clichés and 20% contains genuinely distinctive language, the distinctiveness score reflects the 80%, not the 20%. The distinctive elements are noted as exceptions, but they do not inflate the overall score.

Headlines and headings carry more weight than buried body text, reflecting how candidates actually read careers pages - top-down, scanning for signal.

Specificity and distinctiveness are scored in separate calls so the first answer can't anchor the second; the other dimensions ride with the first call.

There is no forced distribution. We do not aim for a target percentage of companies in each quadrant. If 60% of companies genuinely produce generic content, 60% will land in the bottom-left. That is a finding, not a flaw in the methodology.

The positioning map

Every analysed company is plotted on a two-axis map:

• X-axis (Generic to Specific): The Specificity score. Content backed by concrete details, named programmes, and real numbers scores higher; vague, aspirational language scores lower.
• Y-axis (Cliched to Distinctive): The Distinctiveness score. Content that could not appear on a competitor's careers page scores higher; interchangeable, sector-standard language scores lower.

This creates four quadrants:

• The Communication Cloner (low X, low Y) - Generic and cliched. Vague claims delivered in language that could belong to any employer.
• The Hidden Gem (high X, low Y) - Specific but cliched. Real evidence and concrete details, but packaged in standard employer brand language.
• The Smooth Talker (low X, high Y) - Distinctive but generic. Original-sounding language that stands out, but without the specific evidence to back it up.
• The Standout (high X, high Y) - Specific and distinctive. Concrete evidence delivered in genuinely ownable language. The target.

When a company's scores sit close to a quadrant boundary, the underlying content genuinely straddles two patterns. The map still plots it in one quadrant, but a position that close to a line is best read as a lean toward that quadrant rather than a hard placement. The "How your score stays consistent" section above explains why.

The 12 communication characters

Beyond the quadrant position, TalentTell identifies the dominant communication character - the archetype that best describes how an organisation talks about itself as an employer. These are based on established brand archetype theory, adapted specifically for employer brand communications.

The 12 characters are: Hero, Rebel, Explorer, Sage, Innocent, Jester, Ruler, Caregiver, Creator, Magician, Lover, and Regular Guy. Each has distinctive language patterns, motivations, and strengths. The character assignment is based on the overall pattern of language across all analysed content, not on any single phrase.

How benchmarking works

The comparison companies on the positioning map are real organisations whose public employer brand content has been analysed using the same methodology applied to your submissions.

How we collect content

For each company, we systematically crawl their careers site and corporate pages to discover every publicly available employer brand page. We do not cherry-pick pages or editorially select which content to analyse. Every employer brand page we can access is included.

Job adverts are discovered automatically from the careers page. We follow links to individual job listings on known applicant tracking systems (Greenhouse, Lever, Ashby, Workday, and others). We collect up to five adverts per company, deduplicating near-identical postings (a common pattern - the same intro paragraph copied across multiple roles) so we sample genuinely distinct roles, by a fixed rule that applies to every company identically.

All content is collected automatically with no human editorial selection, ensuring every company is assessed on the same basis. We assess everything a company has published. No content is excluded or truncated.

The two content streams

We read the two kinds of talent content nearly every employer publishes, and score each on the same five dimensions, weighted equally:

Within each stream, all relevant pages are pooled together and scored as a whole. A company with seven pages of careers site content and a company with one page of careers site content are each scored on the quality of what they communicated - not on how many pages it took.

Why weight them equally?

Your careers site and your job adverts contribute equally to the final score. That's a deliberate choice, not a default.

They're two halves of the same story. Your careers site is where you make your pitch; your job adverts are where you live up to it, at the moment someone is actually deciding whether to apply. Weighting them equally means both have to hold up.

It eliminates the shop-window problem. If one side counted for more, you could game your score by polishing it and neglecting the other. Equal weighting means a beautiful careers site can't buy you a good score if your job adverts don't carry the same character through.

Volume doesn't advantage anyone. Because we score proportions - what share of your content is specific, what share is distinctive - publishing more pages doesn't lift your score. If anything, more content gives generic language more room to dilute the strong stuff.

If we can't find job adverts to compare against, your score reflects your careers site alone, and the consistency gap is marked "not assessed" rather than guessed.

Decoupled scoring

Specificity and distinctiveness are scored independently in separate analyses to prevent anchoring bias. The model assessing distinctiveness has zero knowledge of the specificity score, and vice versa. This ensures that a company's distinctiveness is measured on its own merits, not relative to how specific the content happens to be.

Company selection

Companies are selected globally across industries and size bands to provide meaningful comparison within your sector and beyond. Within each industry, we aim for representation across company sizes - from 50-person startups to 10,000+ enterprises. The final position on the map reflects the equally weighted average across both content streams.

Minimum threshold

Companies must have careers site content - a careers page or equivalent - to be included in the benchmark.

Companies below this threshold are excluded rather than scored unfairly. Scoring a company based on job adverts and a leadership page without ever seeing their careers site would be like reviewing a restaurant based on the takeaway menu and the decor, but never tasting the food.

Exclusion is a fairness measure, not a limitation. Companies without a discoverable careers site presence have not published enough for a fair assessment of their employer brand communication.

Brand consistency

Beyond the overall score, TalentTell measures the consistency gap between your careers site and your job adverts. This is one of the most actionable insights in the benchmark. When a company has no job adverts we can compare against, the consistency gap is reported as "not assessed" rather than guessed at.

A company with a strong, distinctive careers site but generic job adverts has a brand leakage problem. Candidates experience the weakest content at the moment of highest intent - when they are actually considering applying. The careers site attracted them; the job advert failed to close the deal.

We compare your employer narrative scores against your job advertisement scores across all five dimensions. A small gap means your employer brand carries through consistently. A large gap means your recruitment marketing is not living up to what your careers site promises - or, occasionally, that your job adverts are stronger than your careers site. We'd caution against over-reading a small gap on any single dimension - on its own it says little; what matters is the pattern across dimensions and the size of the largest gaps.

This is where the real advice lives. Your careers site might be great, your values page might be great, but your job adverts might be what is letting the side down. Or vice versa. Knowing where the gap is tells you exactly where to focus.

For every consistency-gap finding on a benchmark company, the audit data behind it captures which pages contributed to each side of the comparison and the per-stream scoring underneath. If a finding looks wrong, we can pull the source pages and the per-stream breakdown - the gap analysis is checkable against specific content, not asserted.

What this is (and what it isn't)

TalentTell is a directional insight tool, not a scientific measurement instrument. The 0-100 scores are AI-assisted structured assessments - informed judgements applied consistently through a clear framework, not calibrated measurements with statistical confidence intervals.

Think of it like a Michelin inspector. Nobody asks a Michelin inspector for inter-rater reliability coefficients or margin-of-error bars on their star ratings. They have a clear framework, they apply it consistently to every restaurant, and they explain their reasoning. The value comes from structured expert perspective applied at scale, not from pretending the result is physics.

TalentTell works the same way. AI applies a structured content analysis framework consistently to every company - the same dimensions, the same proportion-based scoring, the same cliché detection. What is defensible is this: AI-assisted content analysis using a structured framework applied consistently to every company. What is not defensible - and what we will never claim - is "scientifically validated scores" or "statistically significant positioning".

Same content, same score, every time. We don't rely on the AI being naturally deterministic - we cache by content hash. Run your analysis tomorrow on the same content and you'll get the same score, byte-for-byte. The "How your score stays consistent" section above explains why this matters and where the honest limits sit. Both user submissions and benchmark companies are analysed using the same AI model and the same scoring prompt, ensuring a fair comparison.

Scope - English-language content only

The benchmark is limited to English-language content. This is a deliberate limitation. Scoring employer brand communication is sensitive to idiom, tone, and nuance - qualities that don't survive translation reliably. Scoring a German company's German-language careers page using an English-calibrated framework would produce misleading results.

In practice, most global companies publish English-language versions of their careers content by default - it's their international hiring channel. When we encounter a company whose primary careers content is not in English, we exclude it from the benchmark rather than score it unfairly.

Transparency - show your working

The strongest defence of any methodology is transparency. For every benchmarked company on the positioning map, you can see exactly what we analysed and how we scored it:

• Pages analysed - every URL we crawled and included, organised by content stream
• Per-stream scores - how each content stream scored across all five dimensions, with the per-page contributions underneath each score
• Brand consistency gap - the comparison between careers site scores and job advert scores, with per-dimension breakdown
• Aggregated result - how the stream scores combined into the final position on the map
• Audit row per scored stream - the exact prompt the engine sent the AI, the exact response received, and metadata identifying which version of our scoring approach produced it

If a score looks wrong, we can show you the exact content the engine scored and the framework that was applied. The methodology is public; the per-page breakdown backs every score. The verdict is the score against our published framework, not the AI's prose commentary on it. This is a tool for informed conversation, not an oracle delivering verdicts.

Every benchmark score is backed by an immutable audit row, which we can produce on request. Challenges aren't "take our word for it" - they're a record we can pull.

How to read your score

"Why is my score low?"

Scores reflect proportions. If 80% of your content uses standard employer brand language - "passionate team", "collaborative environment", "we value diversity" - and 20% contains genuinely distinctive material, your score reflects the 80%. The distinctive elements are noted, but they do not define the overall score. Exceptions do not change the proportion.

"You missed some of our content"

We crawl careers sites systematically and include every employer brand page we can access. If you believe we missed publicly available content, we are happy to re-run the analysis with updated URLs. The methodology is designed to capture everything - but websites change, pages move, and some content sits behind authentication or JavaScript rendering that our crawlers cannot access.

"Our careers site is strong but our score is low"

Check the per-stream breakdown. A strong careers site with generic job adverts will pull the overall score down - and that is intentional. Your job adverts are the content candidates see at the moment of highest intent. If they do not reflect the same quality as your careers site, that is a gap worth knowing about. The brand consistency indicator shows exactly where the disconnect is.

"We only have a simple careers page"

A company with one excellent, distinctive careers page can score higher than a company with twenty pages of generic content. Volume does not advantage anyone in this methodology. What matters is the proportion of your content that is specific, distinctive, and authentically written - whether that content lives on one page or fifty.

"What if I score the same content again - will I get the same result?"

Same content, same score, byte-for-byte. We hash your content and cache the result. If the content hasn't changed, the score is identical. If the content has changed - even slightly - the engine detects that and re-scores. Your report shows the most recent measurement; the engine retains an audit row capturing the prompt, the response, and the metadata behind it. For benchmark scores, we can produce that audit row on request.

"What if my archetype is different the next time you score me?"

If your content has changed, that's an honest reflection of what's now visible to a candidate reading your careers content - job rotation, hero copy updates, new testimonials all produce different but valid snapshots. If your content is identical and the archetype changes, that would indicate an engine problem; we have a cache invariant that prevents it and verification runs that confirm it consistently. Show us; we'd want to investigate too.

"What does drift mean on my measurement?"

Drift is our term for "your content changed since the last time we scored you." The engine flags it internally and captures the detail in an audit row. It's a signal, not a problem - it tells us whether your most recent measurement is comparable to the previous one. For benchmark scores, the audit row is queryable on request.

"My score is right on a boundary - which quadrant am I really in?"

Both, genuinely. When your scores sit close to a quadrant boundary, the content sits between two patterns rather than landing definitively in one. The engine still produces a single quadrant call - but that close to a line, the honest reading is qualitative ("tending toward Hidden Gem with characteristics of Smooth Talker") rather than treating the single call as the final word. The "How your score stays consistent" section earlier explains why reading it as a lean is honest rather than evasive.

Limitations

• Communication, not culture. TalentTell analyses how effectively you communicate your employer brand. It does not measure whether your actual culture is good, bad, or somewhere in between. A company could have a brilliant culture but communicate it poorly - and vice versa.
• Directional, not definitive. Scores are structured assessments, not scientific measurements. They indicate where your communication sits relative to others, not an absolute truth. Use them to start conversations and identify opportunities, not to declare winners and losers.
• Scrape determinism vs scoring determinism. Scoring is byte-deterministic on identical content. The scrape itself isn't always identical - some careers pages serve rotating widgets, geo-personalised copy, or A/B-tested hero text, so two scrapes on different days can capture slightly different bytes. Our drift detection surfaces this as a signal rather than masking it. Making the scrape itself more deterministic is on our roadmap.
• Point in time. Analysis reflects the content as it existed when it was submitted or scraped. Companies update their content regularly, and a score today may not reflect changes made tomorrow.
• Public content only. Benchmark analysis is based on publicly accessible careers pages and job adverts. Internal EVP documents, onboarding materials, and other non-public content are not included in benchmark scores (though you can submit them for your own analysis).
• AI-assisted, not AI-decided. The analysis is performed by AI applying a consistent framework. Like any analytical tool - human or automated - it is one input to inform decisions, not the final word. The transparency features exist precisely so you can evaluate the reasoning yourself.

Questions about our methodology? Get in touch at talenttellcontact@revealthereal.co.uk