When organisations go to market for a talent intelligence platform, the evaluation process tends to follow a familiar pattern. A requirements document is produced. A shortlist of established vendors is assembled. Demonstrations are arranged. A scoring matrix is built around standard dimensions: data coverage, geographic scope, integration capabilities, reporting functionality, pricing. A recommendation is eventually made.

This process is thorough. It’s also systematically incomplete, because it evaluates platforms against criteria that most vendors in the market have in common, and misses the questions that would most reliably tell you whether a platform will actually improve your hiring outcomes.

The result is that organisations end up with platforms selected confidently on the wrong basis. Which is how you end up in an industry where many talent teams are paying for expensive intelligence infrastructure that, privately, they acknowledge has not changed how they actually hire.

Here are the five questions a rigorous talent intelligence evaluation should include, and that most procurement processes do not.

Question 1: Is this supply-side data or demand-side data – or do you have both?

This is the most important question you can ask, and it’s almost never asked in platform evaluations.

Supply-side talent intelligence tells you what employers are asking for: which skills are in demand, how hiring volumes are trending, how compensation benchmarks are moving. This is the core capability of platforms like Lightcast and Talent Neuron. It’s genuinely useful. But it’s also structurally incomplete, because it tells you nothing about what candidates actually want, how they make employment decisions, or what drives a candidate to choose one employer over another.

Demand-side talent intelligence captures what candidates are searching for, filtering by, and prioritising when making real job decisions. This is a fundamentally different type of data, built on different methodology, and it answers different questions. You need both.

Ask every vendor: where does your data come from? What proportion of your dataset reflects employer behaviour, and what proportion reflects candidate behaviour? How do you capture candidate-side data, and how do you distinguish between what candidates say they want and what their behaviour shows they are actually looking for?

Vendors that cannot give confident, specific answers to these questions are most likely working primarily with supply-side data, and presenting it as complete market intelligence.

Question 2: How current is this data, and how often is it updated?

Talent intelligence built on annual surveys, periodic benchmarking studies, and historical job posting analysis is inherently backward-looking. In a market where candidate priorities are shifting faster than ever, data that is six months old is not a reliable foundation for competitive decisions.

Ask each vendor: how often is the underlying dataset updated? What is the structural delay between data being collected, and it being available in the platform? For job posting data, what is the average lag between a posting going live and appearing in the dataset? For survey data, what is the publication cycle and how large is the sample?

The ideal answer is a continuously updating dataset where new signals are incorporated in real time or close to it. The acceptable answer is a high-frequency update cycle with transparent methodology. The concerning answer is an annual or biannual survey with periodic updates, which describes most of the established market.

Question 3: What can the data tell you about how candidates actually make decisions?

Most talent intelligence platforms are good at describing the talent market at a macro level. They are less good at explaining why individual candidates make the choices they make. Yet this is the insight that is most directly useful for EVP design and employer brand strategy.

Ask each vendor: what can your data tell me about the specific factors that lead a candidate to choose one employer over another? Can I understand trade-off behaviour, meaning what candidates are willing to compromise on and what they treat as non-negotiable? Can I segment this by persona, understanding that what a mid-career software engineer prioritises is different from what an early-career finance candidate or a returner to work prioritises?

The platforms that can answer this with real specificity, ideally with live examples from their data, are the ones that have genuine demand-side capability. The platforms that redirect to labour supply and demand curves when you ask about decision-making behaviour are the ones whose data does not extend to this dimension.

Question 4: How does the data connect to measurable hiring outcomes?

This is the question that separates a data product from an intelligence infrastructure. A data product gives you insight. An intelligence infrastructure gives you the ability to act on that insight and see what changes in your actual hiring outcomes.

Ask each vendor: which of your clients have seen measurable improvements in hiring outcomes, and what was the connection between the intelligence they accessed and the result that changed? Can they point to improvements in screening pass rates, pipeline diversity, time to fill, or offer acceptance rates that were directly linked to insights from the platform?

Be sceptical of generic case studies that describe improvements without specifying what changed and by how much. The best talent intelligence vendors will have specific, quantified evidence. They will be able to explain the mechanism: what insight was acted on, how the EVP or activation strategy changed, and what the measured effect on hiring metrics was.

Question 5: Does the platform help the right candidates find you?

This question reveals whether a platform is purely an intelligence tool or whether it also addresses the discovery problem: the fundamental gap in how talent markets work, where candidates cannot find employers based on what those employers actually offer.

Most traditional talent intelligence platforms are pure insight products. They describe the market to you. They do not make you more visible or discoverable within it.

Ask each vendor: does your platform also enable employer brand discovery? Can candidates find you based on what you genuinely offer, your culture, working model, benefits and values, rather than simply because they already know your name? What is the scale of the candidate population engaging with the platform, and what does their demographic profile look like? Is there any independent verification of the claims employers make about their culture and working model?

This question distinguishes platforms that combine intelligence with activation from those that provide intelligence alone. For most organisations, having both, understanding what candidates want and being discoverable by them on that basis, is significantly more valuable than having either in isolation.

What a good evaluation actually looks like

A rigorous evaluation of talent intelligence platforms should include all five of these questions alongside the standard dimensions of data coverage, geographic scope, pricing and integration. Demonstrations should explicitly include walkthroughs of how the platform handles demand-side data, candidate decision-making insight, and outcome measurement, not just the headline analytics dashboard.

The established platforms in this market like Lightcast, Talent Neuron, LinkedIn Talent Insights, are sophisticated tools that do what they do well. What they do is primarily supply-side labour market analytics. They are not, and do not claim to be, demand-side behavioural intelligence platforms. The gap between those two categories is where the most significant competitive advantage in talent strategy currently sits.

The organisations that understand this distinction are asking the right questions in their evaluations. The organisations that do not are selecting confidently into a market that looks comprehensive but has a meaningful capability gap at its centre.