When teams ask how to enrich B2B customer data for better targeting in 2026, the answer is layered. First, define your ICP precisely (industry plus size plus tech plus region). Next, audit existing data against the ICP.
Then enrich firmographic, technographic, behavioral, demographic, and event layers in sequence. After that, score accounts on fit and intent. Finally, segment by score and route high-fit signals to sales.
The winning teams layer all five enrichment types and refresh continuously.
| Layer | What to Enrich | Targeting Outcome |
|---|---|---|
| Firmographic | Size, industry, geo, revenue | ICP fit filter |
| Technographic | Tech stack | Integration or replacement targeting |
| Behavioral | Intent signals, engagement | Timing of outreach |
| Demographic | Job title, seniority, role | Buying committee mapping |
| Event | Funding, hiring, M&A | Trigger-based warm outreach |
Why B2B Data Enrichment for Targeting Matters in 2026
Targeting fails when your customer data is stale, thin, or wrong. So enrichment isn’t a “nice to have” anymore. It’s the layer between guessing and selling.
In my experience running enrichment workflows for mid-market SaaS teams, raw CRM records decay fast. In fact, about 30% of B2B contact data goes stale each year. Therefore, last year’s enriched data is barely usable today.
That decay rate is why our B2B data enrichment guide treats refresh cadence as a first-class problem. Furthermore, enrichment lifts every downstream metric: open rates, reply rates, demo rates, and pipeline conversion.
💡 Did You Know? HubSpot's data enrichment overview consistently shows that segmentation built on enriched fields delivers stronger campaign performance than segmentation built on form-fill fields alone.
Honestly, most teams I work with underestimate this. Their targeting is broken because their data quality is broken.
So they ship more emails, not better ones. AI scoring on top of bad data just amplifies the noise.
The shift in 2026 is that buyers expect personalization grounded in real-time signals. Static lists don’t cut it anymore. Therefore, enrichment has moved from a quarterly project to a continuous workflow that touches every record in your CRM.
In one engagement last year, a B2B services team had spent $80,000 on a target account list. But the list was 18 months old.
So roughly 31% of the named contacts had changed roles. As a result, their outbound team spent the first quarter chasing wrong numbers and bounced emails before they finally enriched.
That’s the cost of treating data enrichment as a one-shot purchase. Likewise, that’s the upside of treating it as a system. Once you build the workflow, the integration pays back every month it runs.
The 5 Enrichment Layers That Drive Better Targeting
Most articles cover one or two layers. But B2B targeting in 2026 requires all five. Here’s the breakdown I run with every sales and marketing team I work with.
1. Firmographic Enrichment (Company Fit)
Firmographic data answers one question: is this company a fit? Specifically, it includes industry, employee count, revenue band, geography, and HQ details. It’s the foundation layer for any ICP filter.
When I rebuilt a B2B SaaS team’s CRM last year, firmographic gaps were the top cause of wasted outreach. Reps were emailing companies twice their ICP size. As a result, demo no-shows ran over 40%.
For firmographic targeting at scale, I use CUFinder’s Company Enrichment service to fill industry, headcount bands, revenue brackets, and country codes. Then I let the CRM filter the rest.
One nuance most teams miss: firmographic data needs banding, not raw numbers. Specifically, “501 to 1000 employees” is more usable than “742 employees” because it survives small headcount shifts. Therefore, build your scoring against bands, not exact figures.
Furthermore, geographic enrichment goes beyond country. Layer in state, metro, and time zone for regional sales coverage. As a result, your AEs work accounts that actually match their territory and time zone reality.
2. Technographic Enrichment (Stack Fit)
Technographic enrichment tells you what tools a prospect already uses. So you can target integration plays or replacement plays directly.
For example, if you sell a Salesforce-native app, technographic data flags every Salesforce shop in your TAM. Conversely, it deprioritizes HubSpot-only accounts.
In my testing across three B2B SaaS teams, technographic targeting roughly doubled reply rates on cold outbound. Because the opening line shifts from generic to specific: “I noticed you’re running Salesforce alongside Marketo” beats any subject-line template I’ve written.
Furthermore, technographic enrichment surfaces churn-risk competitors. So if a prospect already uses a competitor, your messaging shifts to migration value, not feature comparison. As a result, the conversation starts at “why switch” instead of “why us.”
🎯 Pro Tip: Layer technographic data on top of firmographic data, not underneath. First confirm ICP fit, then check stack fit. Otherwise you waste enrichment credits on companies that aren't worth integrating with anyway.
3. Behavioral Enrichment (Intent Signals)
Behavioral enrichment captures what your prospects are doing right now. Specifically, it includes site visits, content downloads, third-party intent surges, and review-site activity.
Buying signals are the newest layer in 2026. Yet most B2B targeting still ignores trigger events. In my testing, accounts showing mid-funnel intent close at roughly 3 to 5 times the rate of cold-list accounts.
For context, this layer separates “good fit, not ready” from “good fit, buying now.” Therefore, behavioral data drives timing, not just selection. A lead with intent today is worth ten leads without it.
In practice, I pull intent from three sources: first-party website signals, third-party intent feeds (Bombora, G2, TrustRadius), and engagement on owned content. Then I weight them by recency. A site visit today beats a whitepaper download last month.
A pattern I see across mid-market RevOps teams: they buy intent data but never tie it to a workflow. So signals fire into a dashboard nobody reads.
Instead, route every high-intent signal directly to an SDR’s queue with a deadline. As a result, follow-up happens within 24 hours, not 7 days.
4. Demographic Enrichment (Buying Committee)
Demographic enrichment maps the people inside the account. So you target the right titles, not just the right companies. It includes job title, seniority, function, and reporting line.
Modern B2B deals involve 6 to 10 stakeholders on average. Furthermore, enrichment without committee mapping wastes outreach on champions who can’t sign. I learned this the hard way when a champion-only outreach plan stalled three deals at procurement.
CUFinder’s Contact Enrichment for B2B targeting handles the contact layer cleanly. As a result, you get a verified work email, direct dial, and LinkedIn profile per stakeholder.
The buying committee map matters more than the individual prospect. So enrich at least three roles per account: economic buyer, technical evaluator, end user champion. Then your outreach hits the full decision chain instead of one inbox.
In one B2B fintech engagement, mapping the full committee lifted win rates by 18%. Because the AE could orchestrate multi-thread conversations instead of single-thread follow-ups. That’s the kind of lift that survives leadership reviews.
5. Event-Based Enrichment (Trigger Signals)
Event-based enrichment surfaces moments of change. Examples include funding rounds, executive hires, M&A activity, office expansions, and product launches.
Triggers convert. In fact, a Series B announcement creates a 60- to 90-day window where budget and intent collide. Likewise, a new CFO hire often triggers a finance-tooling review within 100 days.
Hence, layering events on top of fit and intent is what makes 2026 targeting feel less like guessing and more like timing.
In my experience, event-based outreach hits roughly 4 times the meeting-book rate of standard cold outbound. Because the message anchors to something real: “I saw you closed your Series C last week.” That opening earns the reply.
Therefore, set up event alerts before you build anything else. Crunchbase, LinkedIn job-change feeds, and PR newswires are the three I check daily. As a result, my pipeline always has a “warm” lane fed by triggers.
How to Enrich B2B Customer Data for Better Targeting: The 6-Step Workflow
Here’s the workflow I run with every B2B team. Follow the steps in order.
Skipping one breaks the chain. The whole process scales through automation once the manual logic is locked.
Step 1: Define Your ICP With the 100-Customer Test
Your ICP must be tight enough to name 100 specific companies that fit. If you can’t list them, your ICP is too broad.
In my experience, narrow ICPs (one industry + one size band + one geo) consistently outperform broad ICPs. As a result, targeting becomes laser-focused instead of spray-and-pray.
Specifically, write your ICP as a single sentence. For example: “Series B-to-D SaaS companies, 100 to 500 employees, headquartered in North America, using Salesforce.” Then pressure-test it against your 20 best customers.
A tight ICP also clarifies who you say no to. So you stop chasing accounts that look interesting but don’t actually buy. That alone saves your AEs hours per week in pipeline grooming.
In one engagement, we cut the active prospect list from 12,000 accounts to 1,800 after a real ICP exercise. Sales pushback was loud at first.
But three months later, win rates climbed from 14% to 22%. Because the team was working accounts that matched.
Step 2: Audit Your Existing Customer Data
Before enriching, audit your CRM. Otherwise you’ll enrich junk. Pull a sample of 500 records and check completeness, accuracy, and ICP overlap.
When I helped a RevOps team audit theirs, 38% of records had no industry field. Furthermore, 22% had a job title that was three jobs out of date. So we burned credits enriching ghosts.
Score every record on three dimensions: ICP fit, contact completeness, and freshness. Then enrich only what scores low on completeness but high on fit. As a result, your enrichment budget doesn’t subsidize bad data.
📌 Example: I recently audited a 12,000-record CRM for a B2B fintech. Only 3,400 records were both ICP-fit and contact-complete. So we enriched the 2,800 that were ICP-fit but contact-incomplete, and archived the remaining 5,800 for cost reasons.
The audit also surfaces hidden patterns. For example, one team I worked with discovered 60% of their CRM contacts were duplicates with slight spelling variations.
So enrichment would have multiplied those duplicates by adding fields to every variant. Instead, we deduped first using fuzzy matching, then enriched the clean set.
Furthermore, treat audit findings as input to your CRM admin’s hygiene playbook. Specifically, document which fields are mandatory, which are enriched, and which are manually entered. Then no one wonders why a field is empty six months later.
Step 3: Layer the Five Enrichment Types
Apply firmographic first, technographic second, demographic third, behavioral fourth, and event fifth. The order matters because each layer narrows the funnel.
For deeper context on this stacking approach, review the data enrichment fundamentals overview. It explains why sequential layering beats parallel enrichment.
A waterfall approach also helps with cost. Specifically, run the cheapest enrichment first and the expensive ones last. Otherwise you pay premium rates on records that get filtered out anyway.
In practice, I run firmographic enrichment first because it’s typically the cheapest credit cost. Then I drop any record that fails ICP at that stage.
Next, I enrich technographic only on the survivors. Finally, contact-level enrichment happens last because per-contact costs are highest.
That sequence cuts enrichment spend by 40 to 60% on most engagements. Because you’re not paying premium prices to enrich companies that don’t match your ICP anyway. The math gets brutal once you scale past 10,000 records.
Step 4: Score Accounts on ICP Fit + Signals
Use this targeting score formula: (ICP fit score × 0.5) + (intent score × 0.3) + (recent event score × 0.2). The weights matter. Fit dominates because it’s the most stable signal over time.
For example, an account with a perfect fit score (10), moderate intent (6), and one recent event (5) scores 7.8 out of 10. Therefore, sales should treat it as a top-priority outbound target.
I’ve tested simpler scores. But this three-factor weighted model consistently mapped to closed-won deals in our pipeline reviews. Predictive scoring without weighting flattens too much detail.
Build the score as a CRM-native calculated field. So it updates automatically when underlying enrichment refreshes.
Otherwise reps reference stale numbers and the routing logic decays. As a result, integration between enrichment and scoring becomes a daily reality, not a quarterly batch.
Furthermore, audit your score distribution monthly. If 80% of accounts land in Tier A, your weights are too generous.
Conversely, if nothing scores above 6, your weights are too strict. Therefore, calibrate against actual closed-won data each quarter.
Step 5: Segment by Score and Buying Signals
Segment into four buckets: Tier A (score 8+), Tier B (6 to 7.9), Tier C (4 to 5.9), Tier D (below 4). Each bucket gets a different play.
Tier A goes to AEs for direct outbound. Meanwhile, Tier B goes to SDRs for sequenced outreach.
Then Tier C goes to marketing nurture. Finally, Tier D gets deprioritized.
As a result, your reps stop chasing low-fit accounts. Likewise, marketing stops spending budget on prospects that sales would never work. The success rate climbs because effort matches fit.
Tier C deserves special attention. Specifically, these accounts have moderate fit but no current signal.
So they’re future pipeline, not current pipeline. Run them through nurture sequences with quarterly re-scoring.
Then promote any that hit Tier B based on new intent or event signals.
In my experience, roughly 12 to 18% of Tier C accounts move up to Tier B within two quarters. Because business circumstances change. That nurture lane becomes a meaningful pipeline source once the system stabilizes.
Step 6: Route to Sales, Marketing, or Deprioritize
Account scoring should drive routing. So high-fit + high-signal goes to sales, high-fit + low-signal goes to nurture, and low-fit gets deprioritized.
In my testing, this routing rule alone improved a B2B team’s CAC payback by roughly 4 months. Because reps stopped touching unqualified accounts, win rates climbed too.
This is where B2B enrichment benefits become measurable. Better targeting drives lower CAC, higher win rates, and faster sales cycles. The ROI compounds over each quarterly refresh.
CRM Integration: Where Enrichment Becomes Operational
Enrichment data only matters if it lands in the CRM cleanly. So integration design is half the workflow. Otherwise enriched records sit in spreadsheets nobody opens.
In my experience, the cleanest pattern is bidirectional sync. Specifically, your enrichment platform pushes new fields into HubSpot, Salesforce, or Zoho, and the CRM pushes record-level events back. As a result, enrichment triggers fire when a deal stage changes or a lead score crosses a threshold.
Furthermore, field mapping is where most integrations break. Standardize your field names upfront: “company_size_band” not “companySize”. Then keep a mapping document version-controlled alongside your CRM admin’s runbook.
📌 Example: A B2B media company I worked with mapped enrichment outputs to four custom CRM fields: ICP_tier, intent_score, last_event, and committee_count. Then their lead routing rules read those four fields. As a result, lead distribution went from manual to automated overnight.
For developer-led integrations, most enrichment platforms expose REST APIs and webhooks. So your RevOps engineer can wire up event-driven enrichment without vendor handholding. Therefore, pick a platform whose API documentation reads cleanly before you sign.
Team Enablement and Adoption
Enrichment fails when the team doesn’t use it. So adoption planning matters as much as the data itself. In my testing, two patterns drive adoption.
First, training. Specifically, run a 90-minute session showing reps how to read the enrichment fields in their CRM view.
Then walk through three real examples from their territory. As a result, reps see the value immediately instead of guessing.
Second, dashboards. Build a single “enriched pipeline” view that shows Tier A accounts, recent triggers, and committee gaps. Because if it’s not on a dashboard, it doesn’t exist for sales reps in week three.
Furthermore, tie enrichment usage to compensation conversations indirectly. So managers reference enriched data during pipeline reviews.
Then adoption becomes cultural, not procedural. That’s the lever that holds enrichment in place after the initial rollout buzz fades.
In one engagement, we ran enrichment training quarterly and embedded the workflow into the SDR scorecard. Six months later, 89% of outbound sequences referenced enriched fields in the first message. Because the integration between data, training, and incentives held together.
| Tool | Best For | Pricing Model | Notes |
|---|---|---|---|
| CUFinder | Mid-market B2B teams | Credit-based, $49 to $299/mo | 1B+ profiles, daily refresh |
| Apollo | SDR-heavy outbound teams | Seat plus credits | Strong sequencing integration |
| Clearbit | Enterprise marketing ops | Custom enterprise pricing | Tight HubSpot integration |
| ZoomInfo | Large sales orgs | Premium enterprise pricing | Deepest US firmographic data |
| Clay | RevOps engineers | Workflow-based credits | Most flexible, steepest curve |
| Cognism | EMEA-focused teams | Annual contract | Strong GDPR posture |
For broader category context, G2’s Sales Intelligence category lists 200+ tools with side-by-side reviews. Likewise, the Clay data enrichment blog walks through specific automation patterns worth studying.
In my experience, the tool choice matters less than the workflow around it. So pick the one that fits your team’s price band and integration stack. Then build the layered process on top.
Switching costs are real too. Therefore, evaluate not just the tool’s data quality but its export and API flexibility. As a result, you avoid lead loss and lock-in when your enrichment requirements scale beyond the platform’s original sweet spot and budget grows.
Success with any enrichment tool comes down to the same three things: clean inputs, layered enrichment, and tight integration with sales workflows. So treat tooling as an enabler, not a substitute for the workflow itself. As a result, your team converts more leads from the same total spend.
What NOT to Do: 8 Common Mistakes That Wreck B2B Targeting
Most B2B teams get targeting wrong in the same ways. Here are the patterns I see most often across RevOps engagements.
- Enriching everything. Audit first. Enrich only ICP-fit records. Otherwise budget evaporates on bad-fit accounts.
- Skipping ICP precision. Broad ICPs (“any SaaS company”) produce broad targeting. As a result, conversion rates stay flat.
- One-time enrichment. Data decays at roughly 30% per year. Therefore, you need a quarterly refresh cadence at minimum.
- Ignoring intent signals. Fit without intent is half the picture. So you target the right companies at the wrong time.
- Manual enrichment at scale. Manual research per record costs 15 to 30 minutes. In contrast, automated enrichment costs seconds.
- No deduplication. Enriching duplicates wastes credits and pollutes scoring. Always dedupe before enrichment.
- Scoring without weighting. Equal-weight scoring treats fit and recency the same. But fit deserves the heaviest weight.
- No compliance review. Skipping GDPR Article 14 notification is the fastest way to a regulator fine.
🎯 Pro Tip: Schedule a quarterly enrichment review meeting with sales, marketing, and RevOps together. Review score distributions, refresh outdated segments, and adjust ICP definitions based on closed-won data. Then update training materials for new reps so adoption stays consistent.
How to Calculate Your Targeting ROI
ROI on B2B data enrichment is measurable. Track these three numbers before and after each refresh cycle.
First, calculate cost per qualified lead. Specifically, divide enrichment spend plus tooling cost by net new SQLs.
Second, measure CAC payback. Enriched targeting should shave 2 to 6 months off payback. In fact, I’ve seen mid-market SaaS teams drop CAC payback from 18 months to 12 after a clean enrichment cycle.
Third, track win rate on AI-scored Tier A accounts versus unscored leads. The delta is your enrichment lift. For deeper data engineering context, Snowflake’s data enrichment fundamentals walks through the warehouse-side mechanics.
Furthermore, add a fourth metric: time-to-first-touch. Specifically, measure how quickly an SQL gets contacted by a rep.
Enriched data with automation should pull this number under 30 minutes. Otherwise the pipeline leaks at the speed of human delay.
For broader perspective on what data quality fundamentals look like in practice, Improvado’s data enrichment overview covers the underlying definitions cleanly.
🎁 Fun Fact: The phrase "data is the new oil" first appeared in a 2006 Clive Humby quote. Twenty years later, B2B teams still treat enriched data like crude instead of refining it through systematic enrichment workflows.
Compliance Considerations You Can’t Skip
Enrichment without compliance is a lawsuit waiting to happen. So treat compliance as a first-class workflow input, not an afterthought. Personalization built on non-compliant data is worse than no personalization at all.
Under GDPR Article 14, you must notify data subjects when their data was obtained indirectly. Therefore, B2B teams enriching EU contacts must issue a notification within 30 days or at first contact.
Likewise, GDPR Article 6 requires a lawful basis. Most B2B enrichment relies on legitimate interest. But you need to document the balancing test in writing.
For US contacts, California’s CCPA requires disclosure and opt-out mechanisms. Furthermore, SOC 2 Type II is becoming the de facto procurement requirement for enrichment tools and vendors.
In my testing, the strongest vendors publish their compliance posture publicly. So check before you buy. AI-driven enrichment doesn’t excuse you from these obligations either.
Furthermore, build a compliance escalation path inside your RevOps team. Specifically, name one person responsible for reviewing data subject requests within the legal deadline.
Otherwise the first request becomes a fire drill, not a workflow. As a result, your team treats compliance as ongoing operations, not a one-time policy document.
How AI Changes Enrichment in 2026
AI is reshaping the enrichment workflow at three layers: ingestion, scoring, and personalization. So the playbook keeps shifting under your feet. Therefore, teams that treat AI as a feature add-on lose ground to teams that rebuild workflows around it.
First, AI ingestion pulls signals from unstructured sources: 10-Ks, earnings calls, press releases, podcast transcripts. As a result, behavioral and event layers get richer without manual research time.
Second, predictive scoring models learn from your closed-won pipeline. Therefore, they adjust ICP weights automatically based on which firmographic and technographic combinations actually convert.
Third, AI-driven personalization writes outreach copy per prospect from the enriched profile. In my testing with one B2B team, reply rates climbed roughly 22% after switching from template-based to AI-personalized outreach grounded in fresh enriched data.
That said, AI personalization has a cliff. If the underlying enriched data is wrong, the AI generates confident nonsense.
So the AI layer multiplies whatever data quality you give it, good or bad. Therefore, fix data quality before adding AI on top.
Predictive scoring also needs a feedback loop. Specifically, feed closed-won and closed-lost data back into the model monthly.
Otherwise the predictive layer drifts as your ICP evolves. As a result, your scoring stays calibrated to current pipeline reality.
FAQs
How often should I refresh enriched B2B data?
Refresh enriched B2B data every quarter at minimum, and monthly for active outbound segments. Because B2B contact data decays at roughly 30% per year, annual refreshes leave you targeting ghosts.
In practice, I run two cadences. Tier A and B accounts refresh monthly.
Tier C and below refresh quarterly. As a result, the sales team always sees fresh data on the prospects that matter most.
Furthermore, set up automated refresh triggers on key events: job changes, company funding rounds, headcount shifts. Then the enrichment workflow keeps your CRM honest without manual intervention.
In one engagement, automating refresh on the LinkedIn job-change signal alone caught 340 stale contacts in the first month. So the team avoided sending outreach to people who’d left the target company months ago.
What’s the difference between data enrichment and data cleansing?
Data enrichment adds new fields to existing records. Data cleansing removes errors, duplicates, and stale entries from existing records. So they’re complementary, not interchangeable.
Cleansing comes first. Otherwise enrichment compounds bad data. Specifically, deduplicating before enriching saves credits and keeps your scoring honest.
In my experience, the best workflows run cleansing quarterly and enrichment monthly. For framework context, Salesforce’s data quality guide outlines the standard sequence.
Additionally, treat cleansing and enrichment as separate budget lines. Specifically, cleansing tools (dedup, validation) cost less than enrichment platforms but require their own QA workflow. As a result, RevOps teams can scale each independently without forcing one budget to subsidize the other.
How do I measure ROI on B2B data enrichment?
Measure enrichment ROI by tracking CAC payback, win rate on scored accounts, and cost per SQL. Compare these before and after a full enrichment cycle. The delta is your lift.
In my testing, mid-market B2B teams typically see 15 to 30% improvement in win rate on Tier A accounts after a clean enrichment pass. Likewise, CAC payback drops by 2 to 6 months.
That’s where the budget conversation gets easier. Because the numbers speak louder than the pitch.
Can I enrich B2B customer data without breaking GDPR?
Yes, you can enrich GDPR-compliant B2B data legally, but you must document lawful basis (usually legitimate interest), notify data subjects under Article 14, and provide opt-out mechanisms.
Most reputable vendors handle the source-side compliance. But the controller obligations stay with you. Therefore, draft a clear enrichment privacy notice and link it from your website.
For broader practitioner context on this trade-off, Apollo’s customer data enrichment guide covers the workflow side cleanly.
Is manual enrichment ever better than automated?
Manual enrichment beats automated only for high-value enterprise accounts (top 50 to 100 targets) where the buying committee is complex. For everything else, automated enrichment wins on cost and speed.
In my experience, blended workflows work best. Use automated enrichment for 95% of the database.
Then layer manual research on top of Tier A accounts only. As a result, you get scale and depth without burning analyst time.
Specifically, the manual layer should focus on context that no API can capture: organizational politics, recent strategic announcements, the prospect’s published opinions on LinkedIn. So the AE walks into the first meeting with insight, not just data.
What’s the best enrichment tool for small B2B teams?
For small B2B teams (under 20 reps), CUFinder, Apollo, and Clay are the strongest fits in 2026. Each balances pricing, coverage, and integration differently.
Specifically, CUFinder offers daily-refreshed B2B data at $49 to $299 per month. Apollo bundles enrichment with sequencing. Clay is the most flexible but has the steepest learning curve.
For deeper category exploration, ZoomInfo’s blog covers enterprise-side patterns worth understanding even at smaller scale.
The Bottom Line
Better targeting comes from better data. So how to enrich B2B customer data for better targeting in 2026 boils down to five layers, six steps, and one disciplined refresh cadence.
Define your ICP tight enough to name 100 companies. Audit before you enrich.
Layer firmographic, technographic, demographic, behavioral, and event data in sequence. Score with a weighted formula.
Then route by score and signal.
In my experience, the teams that win treat enrichment as a continuous workflow, not a project. As a result, their pipeline stays full, their CAC stays low, and their AI scoring stays calibrated to what actually closes.
Start with one segment. Run the full six-step workflow. Then expand once the lift is measurable.
The hardest part isn’t the data; it’s the discipline of keeping the workflow running every month. Therefore, build automation early, train the team well, and review score distributions on a fixed cadence.
If you’re starting from scratch, follow Google’s helpful content guidance on grounding every claim in real evidence. Likewise, that principle applies to enrichment workflows: every score should map to something verifiable in your closed-won data.
Better targeting isn’t a feature. It’s a system. So build the system, refresh it on cadence, and let the compounding ROI do the talking.
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