Selection Intelligence
Know when your brand enters the shortlist, and when it disappears.
Selection Intelligence is the evidence-based analysis of whether, where, and under what customer contexts a brand is discovered, understood, considered, and recommended across search and AI-mediated buying journeys.
It moves beyond:
Were we mentioned?
And answers:
Did we become a legitimate option for a commercially valuable need?
Hendricks observes four systems: Google AI Overviews, ChatGPT, Perplexity, and Gemini.
Beyond Visibility
Visibility is only the first stage.
A brand can be:
- Mentioned but represented inaccurately
- Cited but not recommended
- Recommended only for a low-value use case
- Strong for one customer cohort and absent for another
- Visible on one platform and unstable everywhere else
- Favored in a neutral test but removed after the customer adds important constraints
Selection Intelligence measures those differences.
Context Panel
Four controlled conditions, each answering a different question.
- Neutral baselineControlled tests without meaningful customer history or supplied personalization.Question answered: What happens under standardized conditions?
- Cohort contextTests that explicitly include relevant characteristics such as company size, use case, geography, budget, priorities, and constraints.Question answered: Which customer profiles cause the brand to enter or leave consideration?
- Journey contextMulti-step research and comparison journeys that become more specific over time.Question answered: Does the brand survive as the customer moves from exploration to a shortlist?
- Time and platform panelRepeated tests across relevant search and AI experiences, dates, models, and locations.Question answered: How stable is the observed outcome?
Hendricks observes four systems: Google AI Overviews, ChatGPT, Perplexity, and Gemini. Hendricks does not measure, test, monitor, or report on Google AI Mode or Microsoft Copilot.
What Hendricks Measures
Eight observations, not one unexplained score.
Observed visibility
Did the brand appear?
Brand understanding
Was the company, product, service, location, or expertise represented accurately?
Relevance
Was the brand connected to the customer’s specific need?
Consideration
Was the brand treated as a legitimate candidate?
Recommendation
Was the brand explicitly favored, shortlisted, or presented as a preferred option?
Citation and source patterns
Which domains, pages, reviews, databases, publications, and owned properties appeared with the result?
Competitor performance
Which competitors won, under which contexts, and with what recurring evidence?
Selection Stability
How consistently did the outcome survive reasonable changes in wording, context, platform, location, and time?
What a Selection Intelligence baseline produces.
- Selection Map
- Observed Consideration Rate
- Observed Recommendation Rate
- Selection Stability analysis
- Competitor Selection Matrix
- Source and Evidence Graph
- Brand accuracy report
- Topic and service association map
- Commercial Selection Gap
- Prioritized hypotheses for intervention
- Baseline dataset for future experiments
Where a brand enters or leaves the shortlist, for an illustrative question.
Which workforce scheduling platform fits a regional logistics company running several depots on shift work?Operations director, replacing a spreadsheet-based process across several depots, shortlisting now.
Illustrative. An intervention changes conditions the brand controls. The outcome shown is not a guarantee and not a client result.
- Brand A visible, accurate, considered, shortlisted, outcome connected
- Your Brand visible, accurate, evidence missing, considered, not shortlisted; exits
- Brand B visible, misunderstood, not considered; exits (described as a payroll product, not scheduling)
- Brand C visible, accurate, evidence missing, considered, not shortlisted; exits
- Brand D not visible; exits
| Brand | Discovery | Understanding | Evidence | Consideration | Recommendation | Selection | Impact |
|---|---|---|---|---|---|---|---|
| Brand A | visible | accurate | sources observed | considered | shortlisted | outcome connected | Impact Ledger |
| Your Brand | visible | accurate | evidence missing | considered | not shortlisted; exits | (exited) | (exited) |
| Brand B | visible | misunderstood: described as a payroll product, not scheduling | sources observed | not considered; exits | (exited) | (exited) | (exited) |
| Brand C | visible | accurate | evidence missing | considered | not shortlisted; exits | (exited) | (exited) |
| Brand D | not visible; exits | (exited) | (exited) | (exited) | (exited) | (exited) | (exited) |
Sources cited at Evidence
- news coveragenot cited in this scenario
Illustrative interface. Not a client result.
Metric Definitions
Every Hendricks measure is defined before it is reported.
- Observed Consideration Rate
- The commercially weighted percentage of defined test contexts in which the brand is presented as a legitimate option.
- Observed Recommendation Rate
- The commercially weighted percentage of defined test contexts in which the brand is explicitly favored or recommended.
- Selection Stability
- The consistency of consideration or recommendation across reasonable variations in context, wording, platform, location, and time.
- Commercial Selection Gap
- The value-weighted difference between the client’s observed position and the relevant benchmark.
Hendricks does not claim to inspect hidden model reasoning.
We do not assign invented weights to backlinks, schema, reviews, or individual sources.
We observe:
Then we identify patterns, test hypotheses, and state the evidence level behind each conclusion.
- Inputs
- Outputs
- Citations
- Sources
- Interventions
- Changes
- Business Outcomes
Common Questions
Six questions to settle before commissioning a baseline.
How do you account for personalization?
Hendricks treats personalization as a variable to be tested, not as noise to be removed. Every Selection Intelligence baseline runs the same commercial need through four context panels: a neutral baseline, cohort contexts that supply real customer characteristics, multi-step journey contexts, and a repeated time and platform panel.
Running one need through all four panels produces a distribution rather than a single answer. Hendricks reports an observed consideration rate and an observed recommendation rate across the defined contexts, alongside Selection Stability, which records how consistently an outcome survives reasonable changes in wording, context, platform, location, and time.
Hendricks does not claim to reproduce any individual person’s account history or prior conversations. Personal memory cannot be reproduced universally, so a baseline measures the conditions Hendricks can define, vary, and repeat, and every conclusion carries the grade of evidence behind it.
Is this the same as AI rank tracking?
No. AI rank tracking reports where a brand appeared for a prompt, and a Selection Intelligence baseline reports observed consideration and recommendation rates by context and by cohort, with the competitor and evidence patterns behind them.
Hendricks publishes the full definition, including what Selection Intelligence does not mean, on the What Is Selection Intelligence page.
Which AI and search systems do you test?
Hendricks observes four systems: Google AI Overviews, ChatGPT, Perplexity, and Gemini. No other system contributes to a Hendricks observed consideration rate, observed recommendation rate, or Selection Stability figure, and no result from one system is extrapolated to another.
Google AI Mode and Microsoft Copilot are surfaces that exist in the same information environment, and they are named here for that reason alone. Hendricks does not measure, test, monitor, or report on Google AI Mode or Microsoft Copilot. A Selection Intelligence baseline says nothing about how a brand performs on a surface Hendricks does not observe.
Reported scope is limited to the parts of AI-mediated search Hendricks can observe under controlled conditions and store for re-inspection. Each run is recorded with its exact question, supplied context, platform, date, location, response, and cited sources, so a client can audit an observation rather than accept a number.
How many prompts or contexts are required?
There is no fixed number, and a prompt is not the unit of measurement. Hendricks counts commercial intent contexts, each one a defined combination of customer need, customer profile, use case, constraints, geography, decision stage, and commercial value. A typical Search Intelligence Diagnostic covers approximately 100–300 commercially prioritized intent contexts.
One intent context is observed more than once. Each context is run across the four panels, across Google AI Overviews, ChatGPT, and Perplexity, and across dates, so the number of individual observations in a baseline is considerably larger than the number of contexts.
Final scope depends on the market, the customer journey, geography, data access, and the number of business lines. Hendricks sets the count per engagement, and the Intent Context Library that fixes it is built before any baseline measurement runs.
Can one brand be strong for one cohort and weak for another?
Yes, and cohort divergence is one of the most common findings in a Selection Intelligence baseline. A brand can be recommended for one customer cohort, treated as a marginal option for a second, and absent for a third, all within the same commercial need.
Cohort contexts supply the characteristics a real buyer brings to a decision: company size, use case, geography, budget, priorities, and constraints. Adding those characteristics changes which brands are treated as legitimate candidates. A brand favored in a neutral test can leave the consideration set once the customer states a constraint the brand does not visibly satisfy.
A single blended score hides the divergence. One averaged number can look stable while the highest-value segment is being lost, can credit a position earned in a low-value use case, and can conceal a competitor that wins every context in a priority market. Hendricks therefore reports the observed consideration rate and the observed recommendation rate by context and by cohort rather than as one brand-level figure.
How frequently should the baseline be repeated?
Hendricks publishes no fixed interval. A Selection Intelligence baseline is a dated observation of a moving environment, so re-measurement should be triggered by a change in one of the inputs the baseline depends on rather than by a date on a calendar.
Four changes are worth re-measuring for. The brand publishes, removes, or materially changes evidence a priority decision depends on. The competitive set changes. The commercial priorities or customer cohorts under measurement change. An intervention from Search Presence Engineering ships and needs a before-and-after read.
Low Selection Stability is its own trigger. An outcome that already varied across wording, context, platform, location, and time was fragile before anything changed, and one repeat run will not settle it. Model and search behavior also changes over time, which is why Hendricks records the date of every observation and reports a baseline as the state of a defined set of contexts on the dates it was measured.