Stop Generic Campaigns: Use Claude to Pinpoint High-Intent Leads
Marketing teams are under pressure to generate more qualified leads, yet many campaigns still rely on broad segments and generic messaging that burn budget without converting. This guide shows how to use Claude to diagnose your targeting issues, design granular segments, and deploy personalized campaigns that reliably boost lead generation performance.
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The Challenge: Generic Campaign Targeting
Most marketing teams know their targeting could be sharper, but day-to-day pressures push them toward broad segments and generic messaging. Campaigns are built around high-level personas and simple rules like geography, industry, or company size. The result: ads and emails reach large audiences where only a small fraction ever had a realistic chance of converting.
Traditional approaches like manual analysis of CRM exports, gut-feel persona work, and basic platform lookalikes no longer keep up with the complexity of modern buying journeys. Channels fragment, buyers research anonymously, and signals are hidden in unstructured data: win–loss notes, call transcripts, free-text form fields, and campaign reports. Without the ability to process this volume and variety of data, marketers default to coarse segmentation and one-size-fits-all messaging.
The business impact is significant. Media budgets are wasted on low-intent audiences, cost per lead climbs, and sales teams are flooded with poorly qualified leads that erode trust in marketing-sourced opportunities. It becomes hard to scale profitable acquisition, because every incremental euro spent seems to produce less. Competitors who can identify high-intent pockets and tailor offers to micro-segments will systematically outbid you on the best opportunities.
This challenge is real, but it is absolutely solvable. With the right use of AI — especially a tool like Claude that can digest your CRM, win–loss notes, and campaign data — you can move from generic campaign targeting to precise, evidence-based segmentation and personalized offers. At Reruption, we’ve seen how AI-first approaches can transform how teams qualify, route, and speak to leads. In the rest of this page, you’ll find practical guidance on how to do this in your own marketing setup.
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Our Assessment
A strategic assessment of the challenge and high-level tips how to tackle it.
From Reruption’s perspective, the real opportunity is not just using Claude for ad copy, but turning it into a targeting and segmentation engine for lead generation. Because we build AI solutions directly inside organisations, we’ve seen that the biggest gains come when Claude is connected to real CRM exports, win–loss notes, and campaign performance data, and then used to redesign how you define audiences and offers.
Treat Targeting as a Learning System, Not a One-Off Setup
Most marketing organisations still treat targeting like a one-time campaign configuration step. With Claude-powered lead generation, you need to approach targeting as a continuous learning system. Claude can surface patterns in who converts and why, but only if you regularly feed it refreshed CRM data, channel metrics, and qualitative feedback from sales.
Strategically, this means defining a clear feedback loop: which data is exported when, who reviews Claude’s insights, and how those insights translate into new audience rules and creative variations. Make someone explicitly responsible for the “learning layer” of your campaigns so that the system gets better every month instead of resetting with every new campaign brief.
Start with Clear Definitions of “High-Intent” and “Good Fit”
Before you unleash Claude on your data, align the organisation on what a high-intent lead and a good-fit account actually look like. Marketing, sales, and leadership often have different mental models, which leads to conflicting instructions and confusing training data for any AI system.
Invest upfront time to articulate the attributes that matter: firmographics, behaviour signals, pain points mentioned in calls, typical buying committees, and deal-breakers. Then instruct Claude using these agreed definitions. Strategically, this alignment reduces internal friction and ensures that Claude’s segmentation and scoring logic reflects real commercial priorities, not just marketing vanity metrics.
Use Claude to Bridge Quantitative Data and Qualitative Insight
Ad platforms are strong at click-level optimisation, but weak at understanding the human reasons behind conversion. Claude excels at reading unstructured text — win–loss reports, call summaries, free-text survey responses — and turning them into structured patterns that can inform campaign targeting and messaging.
From a strategic standpoint, position Claude as the bridge between what your analytics tools tell you (numbers) and what your customers and salespeople know (narratives). Make it a standard practice: every quarter, Claude reviews the latest qualitative data and proposes updated segments, value propositions, and objections to address. This elevates targeting from “people who looked like this in the past” to “people who talk and decide like our best customers.”
Prepare Your Team for AI-Augmented, Not AI-Replaced, Targeting
Using Claude for campaign targeting changes the role of marketers. Instead of manually slicing spreadsheets, they curate data sources, review AI-generated segment hypotheses, and design experiments to validate them. Strategically, you need to prepare the team for this shift so that Claude is seen as an assistant, not a threat.
Clarify responsibilities: who owns data quality, who reviews Claude’s targeting suggestions, who translates them into platform setups, and who monitors performance. Invest in basic AI literacy so your team understands Claude’s strengths (pattern detection, synthesis, language) and limitations (no direct access to platform inventories, potential bias from skewed data). This will reduce resistance and accelerate adoption.
Mitigate Risk with Guardrails and Human Review
Even with excellent data, any AI system can drift or overfit. Strategically, you should design guardrails for Claude-generated targeting. Define non-negotiable constraints (e.g., geographic restrictions, regulated industries to avoid, brand safety requirements) and bake them into prompts and review checklists.
Implement a two-step workflow: Claude proposes segments and messaging variants, then a human marketer validates them against brand, compliance, and strategic direction before deployment. For critical campaigns, A/B test Claude-informed targeting against your current best practice rather than flipping everything at once. This controlled approach lets you capture upside while keeping your risk profile acceptable.
Used thoughtfully, Claude can turn your targeting from generic to evidence-based by connecting the dots between your CRM, win–loss notes, and campaign outcomes. The key is to treat it as the analytical brain behind your lead generation engine, while marketers remain decision-makers and designers of strategy and experiments. At Reruption, we specialise in embedding these kinds of AI workflows directly into marketing operations; if you’re ready to move beyond broad segments and want help scoping, prototyping, and rolling out a Claude-powered targeting engine, our team is available to explore what that could look like in your context.
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Real-World Case Studies
From Banking to Banking: Learn how companies successfully use Claude.
Best Practices
Successful implementations follow proven patterns. Have a look at our tactical advice to get started.
Use Claude to Build a Conversion-Focused Segmentation Model from CRM Data
Start by exporting a representative dataset from your CRM: closed-won and closed-lost deals, lead sources, campaign tags, contact roles, deal values, sales notes, and relevant timestamps. Anonymise sensitive fields if needed. Your goal is to give Claude enough context to infer which combinations of attributes correlate with conversion and high deal quality.
Upload the export to Claude (or paste a summarised version if dataset size requires chunking) and provide clear instructions for how to analyse it. A practical prompt template:
You are an AI marketing analyst helping improve lead generation targeting.
I will provide a CRM export with the following columns (one row per opportunity):
- Outcome (Won/Lost)
- Industry
- Company size (employees/revenue band)
- Lead source & original campaign name
- Channel (Paid Search, Paid Social, Organic, Referral, etc.)
- Contact role(s) involved
- Deal value band
- Sales notes (free text, including objections and reasons for win/loss)
Tasks:
1. Identify patterns and segments with HIGH conversion rate and/or high deal value.
2. Identify segments with LOW conversion or poor deal quality that we should avoid or treat differently.
3. For each high-performing segment, describe:
- Common firmographic traits
- Typical buying roles involved
- Common pain points or triggers (from sales notes)
- Messaging angles that seem to resonate
4. Summarise these as 5–10 concrete audience definitions we can use for ad platforms.
5. Suggest 3–5 segments that are likely poor targets based on the data.
Output the result as a structured list we can easily translate into targeting rules.Use Claude’s output to draft new audience definitions and negative targeting rules in your ad platforms. Expect the first iteration to be rough; refine by feeding back updated data every 4–8 weeks.
Generate Personalized Offer and Message Variations for Each Segment
Once Claude has helped you define segments, use it to generate personalized value propositions and offers tailored to each micro-group. Provide Claude with segment descriptions, key pains, and your product/service capabilities, then ask it to create messaging that addresses those pains directly.
Example prompt:
You are a B2B copy strategist. We want to improve lead generation by replacing generic messaging with segment-specific offers.
Here are 3 high-performing segments Claude previously identified:
[Paste segment descriptions and pains]
Our product/service:
[Short description of your offering and core differentiators]
Tasks:
1. For each segment, create:
- A primary value proposition (max 15 words)
- 3 supporting benefit bullets
- 2 example offers (e.g., assessment, calculator, trial, content) tailored to their pain points.
2. Suggest 2 headline variations and 2 intro lines for LinkedIn ads PER segment.
3. Highlight any language or topics that should be avoided for each segment (based on their objections/pains).
Return the output in a clean, sectioned format.Implement these variations in your landing pages, emails, and ads, and track performance per segment. This allows you to systematically move away from one-size-fits-all messaging.
Score and Qualify Inbound Leads with Claude Before Routing to Sales
Claude can also act as a smart scoring layer between your marketing automation system and sales. Instead of relying only on simple rules (job title, company size, number of page views), you can enrich leads with AI-powered lead qualification that considers all available context, including free-text fields.
Set up an integration (via API or middleware) where new or updated leads are periodically batched and sent to Claude with their attributes and activity history. Use a prompt like:
You are an AI assistant for B2B lead qualification.
I will give you structured lead data and free-text inputs from forms and chats.
Your tasks:
1. Score each lead from 1–10 for "Fit" (how well they match our ICP).
2. Score each lead from 1–10 for "Intent" (how ready they are to talk to sales).
3. Classify each lead into one of 4 buckets: "Sales-Ready", "Nurture-High Priority", "Nurture-Standard", "Disqualify/No-Action".
4. Briefly explain your reasoning in 2–3 bullet points.
Our ICP and high-intent definition:
[Paste agreed ICP and high-intent criteria]
Lead data:
[Paste JSON or table of leads with fields like company size, industry, role, pages visited, content downloaded, form comments, etc.]Write Claude’s scores and buckets back into your CRM/MA tool as custom fields. Use them to drive workflows: immediate sales alerts for “Sales-Ready” leads, targeted nurturing sequences for high-priority nurture leads, and exclusion from spend-heavy campaigns for poor-fit contacts.
Let Claude Design and Prioritise A/B Tests for Targeting and Creatives
Instead of brainstorming tests manually, ask Claude to recommend and prioritise A/B tests for generic campaign targeting based on performance data. Export campaign-level data from your platforms: impressions, clicks, CPL, conversion rates, audience definitions, and creative descriptions.
Prompt example:
You are a senior performance marketing strategist.
I will provide performance data for recent campaigns, including:
- Audience definitions
- Channels and placements
- Creatives (short descriptions or examples)
- Key metrics (CTR, CPL, lead quality proxy if available)
Tasks:
1. Identify where generic targeting seems to be limiting performance (e.g., broad audiences with high spend but low quality).
2. Propose 5–10 specific A/B tests that focus on:
- Narrowing or refining audiences
- Adjusting messaging per segment
- Testing different offers for the same audience
3. For each test, include:
- Hypothesis
- Implementation steps (for typical ad platforms)
- Primary success metric
- Recommended minimum sample size or runtime.
4. Prioritise tests by expected impact and ease of implementation.
Here is the data:
[Paste or attach data]Feed these tests into your experimentation backlog. Over time, you’ll build a systematic programme for eliminating generic targeting and scaling what actually works.
Use Claude to Clean and Enrich Targeting Data Before Import
Dirty, inconsistent CRM data is one of the main blockers for precise targeting. Claude is well-suited to normalise, categorise, and enrich messy fields before they are used in segment definitions. This is especially valuable for free-text job titles, industries, and reason-for-loss fields.
Periodically export problematic fields and use Claude to map them to standardised categories you can work with in your marketing stack.
You are a data cleaning and categorisation assistant for marketing operations.
I will provide a list of free-text entries from our CRM. Your job is to:
1. Normalise job titles into standard seniority and function buckets (e.g., "VP", "Head", "Manager"; "Marketing", "IT", "Finance").
2. Map company industries to a standard list of 15–20 industry categories.
3. Categorise free-text "Reason for Loss" fields into a controlled list of reasons (e.g., "Budget", "Timing", "Competitor", "No Fit").
Return the result as a table with columns:
- Original value
- Normalised job seniority
- Normalised job function
- Industry category
- Loss reason category (if applicable).Import these cleaned and categorised fields back into your CRM and use them to define more accurate audiences and exclusion lists, reducing waste from irrelevant impressions.
Expected Outcomes and Metrics to Track
When implemented properly, these practices should lead to measurable improvements rather than just nicer reports. Realistic expectations within 3–6 months of systematic use of Claude for campaign targeting and lead generation include: 15–30% reduction in cost per qualified lead, 10–25% increase in opportunity rate from marketing-sourced leads, and a visible shift in spend from low-intent to high-intent segments. Track metrics such as segment-level CPL, MQL-to-SQL and SQL-to-opportunity conversion, pipeline value per segment, and time-to-contact for “Sales-Ready” leads. Use these numbers to continuously refine Claude’s prompts and the underlying data you feed into it.
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Frequently Asked Questions
Claude helps by analysing your existing CRM data, win–loss notes, and campaign performance to uncover which combinations of attributes actually predict conversion. Instead of broad segments based only on job title or industry, Claude can surface micro-segments defined by behaviour, pain points, deal size, and buying roles. It then helps you translate these insights into concrete audience definitions, negative targeting rules, and personalised messaging for each segment, so your ads and emails focus on high-intent, good-fit prospects rather than everyone who loosely matches a persona.
You do not need a large data science team to start. For most companies, the core requirements are: someone who understands your CRM and campaign data well enough to export relevant datasets, a marketer comfortable with prompt writing and interpreting Claude’s outputs, and basic technical support to automate data flows if you move beyond manual uploads.
Over time, it helps to involve marketing operations or IT to set up secure, repeatable integrations between your CRM/MA tools and Claude’s API. Reruption typically works with a small cross-functional pod (marketing lead, ops/IT, and a business owner) to get from first prototype to a stable AI-augmented targeting process.
Timelines depend on your campaign volume and data quality, but many organisations can see early signals within 4–6 weeks. In the first 2–3 weeks, Claude can help you build improved segments and messaging variations based on historical data. Once deployed, you need at least one full optimisation cycle—typically another 2–4 weeks—to gather enough volume for statistically meaningful comparisons against your current targeting.
More substantial improvements usually emerge over 3–6 months, as you iterate on segments, refine prompts, and expand AI-driven targeting to more channels. The key is to treat this as an ongoing optimisation programme, not a one-time switch.
The direct usage cost of Claude is typically modest compared to media spend, as you are primarily using it for analysis, segmentation, and content generation. The main investment is in the initial setup: cleaning data, defining your ICP and high-intent criteria, designing prompts, and wiring Claude into your workflows.
In terms of ROI, realistic outcomes include a 15–30% reduction in cost per qualified lead, better MQL-to-SQL conversion, and less time wasted by sales on poor-fit leads. Because these gains compound across channels and campaigns, even modest percentage improvements can easily justify the implementation effort within one or two quarters, especially for teams with significant paid media budgets.
Reruption works as a Co-Preneur, embedding with your team to design and ship working AI solutions rather than slideware. For this specific challenge, we typically start with our AI PoC offering (9.900€), where we validate that Claude can meaningfully improve your targeting using your real CRM and campaign data. You get a functioning prototype, performance metrics, and a concrete plan for production.
Beyond the PoC, we support you in engineering the integrations, setting up secure data flows, refining prompts, and enabling your marketing team to operate the new system. Our focus is to build an AI-first targeting capability directly inside your organisation, so that your team can continuously learn, adapt, and scale lead generation without depending on external agencies for every optimisation.
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