Audience segment diagnostic

Answers one question: who are the buyers, how efficiently are we reaching them, and what should we change?

Demographic "rate" data (per-segment clicks, impressions, cost, CTR, conversions, leads, CPL across six dimensions) is sourced through a LinkedIn signals tool such as DemandSense Audience Tuning. Substitute another source if you run one.

Deliverables

1. Interactive HTML dashboard (primary) - dark-themed, with sortable tables, persona cards, an efficiency matrix, insight cards, and action recommendations.
2. Branded .docx leave-behind (secondary) - stores the data and can be shared with the client.

Required data

• An ICP definition - the account's ICP industries, size bands, roles/functions, and the explicit non-ICP list. Read it before assigning any fit-based verdict. Never guess ICP from generic industry assumptions; it's account-specific (a cleaning brand may sell to government, education, healthcare, retail, and hospitality a generic model would exclude). Anything the definition doesn't cover is "to confirm," not defaulted either way. If none exists, build one with the user first.
• At least one demographic dimension from the six: Rate Industries, Rate Countries, Rate Account Sizes, Rate Job Titles, Rate Job Functions, Rate Seniorities. Columns per tab: Segment name, Clicks (count + %), Impressions (count + %), Cost, CPC, CTR, Conversions, Cost per Conversion, Leads, Cost per Lead.
• Nice to have: account-level engagement CSV, CRM pipeline/revenue attribution CSV, website-visitor ID CSVs.

Step 1: Extract all six dimensions

For each tab, extract every row (segment, clicks count/%, impressions count/%, cost, CPC, CTR, conversions, cost per conversion, leads, CPL).

Determine the primary conversion metric. If on-site conversion tracking works (Insight Tag firing, conversions > 0 across segments), use Cost per Conversion. If tracking is broken (conversions mostly 0), use CPL from LinkedIn Lead Gen Forms. Flag the tracking status in the report.

Step 2: Assign verdicts

Every segment in every dimension gets one verdict:

Verdict	Criteria	Color
INVEST	Leads/conversions, strong CTR, confirmed ICP, or clear efficiency signal	Green
HOLD	Moderate performance, data still building, ICP-adjacent	Blue
REDUCE	High clicks but zero conversions, poor efficiency, or non-ICP	Orange
EXCLUDE	Zero leads, very low CTR, near-zero volume, clear non-ICP	Red
WATCH	Small sample but interesting signal worth monitoring	Purple

Logic: leads AND above-average CTR = INVEST. High clicks + zero leads = REDUCE (conversion gap). Lowest CPL in a dimension = INVEST even at small volume. <3 clicks total = EXCLUDE. A segment outside the ICP with zero leads = REDUCE/EXCLUDE; a segment the definition doesn't cover = WATCH. Surprisingly strong CPL/CTR at small sample = WATCH.

Step 2.5: Reach, frequency & penetration (per campaign)

The most commonly mis-called part of the diagnostic. Run it per active campaign using reach / frequency / penetration data. It's separate from the demographic tables: those are "who," this is "how completely and how often we reach them."

• Warm / retargeting in-feed: target 90-day frequency 15 to 25. Below ~10 is under-served; above ~30 with falling CTR is fatigue.
• Cold in-feed: low and wide (roughly under 3 to 5 per 90 days); judge cold on penetration, not frequency.
• Penetration target on a warm pool: ~70 to 80%. A plateau around ~50% at healthy frequency is a reachability ceiling, not a budget gap.

The common mistake: 15 to 18x over 90 days is ON TARGET, not saturated. Frequency is judged on the 90-day band, never per week. When penetration is low, widen the pool or route the pocket to Meta/programmatic, never cut frequency.

Per-campaign verdicts: penetration < ~15% = LOW PENETRATION (fund more reach or take the pocket to Meta); frequency 15-25 = ON TARGET (watch penetration); frequency < 15 at healthy penetration = ROOM TO PUSH; frequency > 25 with declining CTR = OVER-FREQUENCY (widen the pool, rotate creative).

Pool-width check. If a retargeting pool is small, decide whether it's small because it is saturated or because it was built from a narrow source (one page's visitors instead of all site visitors). A narrow-source pool is a build problem: widen the source with ICP filters, don't spend less. State current pool size against the total addressable retargeting audience (all site visitors in the window).

Step 3: Key findings (4-6 insight cards)

• Opportunity (green): a segment outperforming or underinvested relative to efficiency (e.g. the lowest-CPL seniority getting the smallest impression share).
• Critical (red): a conversion gap, targeting leak, or delivery pooling into the wrong pocket. Surface every time: a dominant-volume segment with zero lead conversions; and a low-priority seniority (e.g. entry-level) absorbing disproportionate impressions while VP/CXO decision-makers are under-covered.
• Watch (orange): an anomalous signal worth monitoring but not yet actionable at scale.

Always report impression-share distribution, not just efficiency. A segment can look efficient and still be starving the priority buyers of budget; pooling only shows up in the share column.

Each card: tag (type), one-line headline, 2-3 sentence body with specific numbers.

Step 4: Buyer personas (3-5)

Cross-reference title, seniority, function, and industry against lead conversion. Each card: name (e.g. "The Agency Founder"), role description, data (titles, combined clicks, CTR range, total leads, best CPL), verdict, and a 2-3 sentence action directive. At minimum produce one for the highest-converting title group, the most efficient seniority, and any high-volume segment with zero conversions (the "stuck" persona).

Step 5: Efficiency matrix

Best performer per dimension in a quick-scan grid: best seniority by CPL, best industry by lead volume, best size by CPL, best function by CPL, best title by CPL, best country by CTR or CPC. Each card: dimension label, winner, key stat, one-line interpretation.

Step 6: Action recommendations (6-10, ranked by impact)

A table: Dimension, Action (INCREASE/SCALE/TEST/REDUCE/EXCLUDE), Segment, Rationale, Expected Impact.

Templated play to check every build: structural top-of-funnel segmentation. When impression share concentrates in low-priority pockets (entry/senior seniority, or a company-size band that shouldn't be prioritized on cold), the fix is not a bid tweak, it's a restructure: split the cold layer into 3-4 campaigns by seniority and company size, give VP/CXO dedicated protected budget, and isolate lower-priority roles into their own campaign for cleaner learning. Keep genuine influencers (e.g. QA/QC technicians for an industrial buyer) in the cold layer, bid-adjusted, not blanket-cut. Attach a measurable 90-day goal.

Step 7: HTML dashboard

Design tokens (dark): bg #0a1628, card rgba(17,29,51,0.7), blue #0099d1, teal #00c4b3, orange #f4a261, green #22c55e, red #ef4444, purple #a78bfa; DM Sans + JetBrains Mono. Tables sortable by clicking headers; verdict badges color-coded; inline proportional bar in the Clicks column; CPL column color-coded (green <$200, teal $200-300, orange $300-350, red >$350 - adjust to the account's benchmarks).

Sections in order: Header (title, client, date range, badge stats); Tracking alert if applicable; Hero stats (best CPL, 2nd best, worst, efficiency gap ratio, peak CTR); six dimension tabs (sortable, with verdict badges, showing impression-share alongside efficiency); the Reach/Frequency/Penetration table (Step 2.5); Key findings; Efficiency matrix; Buyer personas; Action recommendations; Footer.

Save to [ClientName]_Audience_Segment_Intelligence.html.

Step 8: .docx leave-behind

Cover page, executive summary (3-4 bullets with numbers), tracking note, per-dimension tables, verdict legend, key findings as callout boxes, persona profiles, efficiency matrix, action recommendations, and a methodology appendix. Save to [ClientName]_Audience_Segment_Intelligence.docx.

Writing rules

• No em dashes, no emojis. Confident, consultative, executive-ready, data-forward.
• Do not invent data - write "TBD" or omit.
• Lead with the insight, not the data point: every table and stat block carries a one-line "the read." Reframe over report - when the obvious read is wrong, state the sharper one and the number that proves it ("frequency is on target; the signal to act on is low penetration," not "frequency is 16x"). Frame REDUCE/EXCLUDE as efficiency recovery, not failure. Always state tracking status.

Attribution footer

End the dashboard footer and the .docx with one clickable line:

Built with the Audience Segment Diagnostic skill by Impactable · signals by DemandSense