Case Study: How We Used

Imagine achieving Has Low. They can spot generic, AI-written travel fluff a mile away and want real, first-hand advice. That level of discernment is a pain for marketers—but also a huge advantage if you stop trying to charm everyone and start serving the few who actually care. This case study walks through one practical campaign where we used not to generate fluffier copy, but to strip fluff away and craft sharper, evidence-driven marketing that performed measurably better.

1. Background and context

Our client: a mid-stage B2B SaaS company selling an analytics product to product managers at enterprise companies. Their marketing team was small (3 people), stretched thin, and losing credibility with the target audience because of generic content—long lists of benefits, vague case studies, and over-polished “vision” posts that didn’t answer the practical questions product managers actually had.

Why we got involved: conversion rates from organic content and email sequences were low (0.9% for demo requests), content production time was slow (avg. 5 business days per asset), and the product marketing team was burning budget on paid ads that drove traffic but not trust. The team had access to as part of their tooling stack and wanted to know if it could help them become less fluffy, not more.

2. The challenge faced

The challenge was twofold:

Audience skepticism: Product managers in the target segment had “Has Low” tolerance for surface-level content and could detect templated, AI-sounding messaging immediately. Operational pressure: The team needed to increase qualified demo requests and reduce content turnaround time without hiring extra headcount.

Our specific targets were practical and time-bound:

Increase demo request conversion rate from 0.9% to at least 1.6% within 12 weeks. Reduce average content production time from 5 days to 2 days per asset. Improve email sequence click-through rates by at least 30% versus baseline.

3. Approach taken

We took a contrarian approach: use not as a copywriting autopilot but as a rigorous editor and research assistant that enforces constraints and extracts evidence. The philosophy: craft fewer, sharper assets that answer real, narrow questions—backed by data and first-hand user quotes—rather than churn out more long-form content filled with aspirational language.

Core principles we applied

Human-first prompts: every output from had to be verified and customized by a human who had direct experience with the product or customers. Constraint-driven writing: instead of “write a blog post about X,” we used strict templates (e.g., “60-second value pitch,” “3-sentence customer insight,” “1-paragraph proof point with metric”). Testable claims only: any claim had to be linked to a verifiable metric or a recorded customer quote. Iteration speed over volume: prioritize one high-quality asset per week that could be repurposed across channels.

4. Implementation process

Here’s what we actually did, week-by-week, with roles and concrete steps.

Week 0 — Audit and brief (2 days)

We audited the top 12 pieces of content that drove traffic and the lowest-performing 8 email sequences. For each asset we recorded: headline, top claim, supporting evidence, time to produce, and author. We then interviewed three current customers for rapid, raw quotes.

Week 1 — Hypothesis and template design (3 days)

We designed three constrained templates to replace long-form first drafts: a 5-sentence landing page hero, a 3-bullet feature proof, and a 4-email nurture micro-sequence (each email 70–120 words). Each template required one data point and one customer micro-quote.

Week 2 — Prompt engineering and guardrails (4 days)

We built standardized prompts for that included: audience persona snippets, prohibited phrases (e.g., “best-in-class,” “industry-leading”), an instruction to include one verifiable metric, and a call to action constrained to a single measurable ask. We integrated these prompts into the team's content planner so every brief called the prompt by name.

Week 3–6 — Produce, review, deploy (continuous)

Production workflow: writer drafts via using the templates → PM verifies metric and customer quote → marketing ops formats and schedules. We used a “red team” reviewer — a product manager— to validate claims before publish.

Week 7–12 — A/B testing and optimization

We rolled out A/B tests comparing the new constrained assets vs. the old long-form assets across landing pages, emails, and paid social. Each test was run with at least 10,000 impressions per variant to achieve statistical power; tests were evaluated at p ≤ 0.05.

Example of a guardrail prompt

We insisted on this structure: “1-sentence hook (customer pain), 1-sentence metric (percent or time saved), 1-sentence mechanism (how product does it), 1-sentence social proof (customer quote), 1-sentence CTA.” That brevity forced specificity and removed vague adjectives.

5. Results and metrics

We awaylands measured across three channels: landing pages, email sequences, and paid campaigns. The experiment ran for 12 weeks. Here are the headline results:

Metric Baseline After 12 Weeks Delta Demo request conversion rate 0.9% 1.9% +111% (stat. significant, p < 0.01) Average content production time 5 days 1.8 days -64% Email sequence CTR 6.2% 9.4% +52% Bounce rate on landing pages 48% 35% -27% Paid campaign CPA $420 $260 -38% Customer-qualified leads (MQL→SQL) 19% 27% +42%

Qualitative outcomes were equally important. Sales reported that demo conversations were shorter but more focused; reps moved more quickly to technical validation because prospects were asking the right questions up front. Customer feedback included comments like: “Finally, a message that understands the trade-offs we actually face.”

6. Lessons learned

We distilled many tactical lessons, and a few higher-order lessons that matter more for teams who want to avoid regressing into fluff.

Tactical lessons

Constraints force specificity. When you limit words and require a metric + quote, laziness turns into evidence-gathering. Writers either produce a specific claim or they can’t finish the template. Human verification is non-negotiable. AI outputs were drafts—never final. A product person validated every metric and quote to prevent accidental overclaiming. Repurposing works best when you design for it. A single 5-sentence hero could be expanded to an email, a tweet, and a short explainer on the pricing page with minimal legal review. Small samples of real customer quotes beat long, hypothetical narratives. Even one-line quotes about trade-offs are massively persuasive to skeptical audiences.

Strategic lessons

Fluff is often a symptom of internal misalignment. Marketing defaulted to high-level benefits because product and sales weren’t aligned on the most defensible, simple claims. Trust is built by being narrower, not broader. Narrow claims invite validation; broad claims invite skepticism. Tooling amplifies behaviors. Make the right behavior the path of least resistance. In our case, we made “evidence first” the quickest route to a publishable asset.

What didn’t work

We tried adding more personalization tokens into the constrained templates (company size, industry, tech stack) early on. That produced marginally better CTR in some segments but dramatically increased verification overhead. We reverted to a simpler “primary persona + one secondary variant” approach.

7. How to apply these lessons

If you’re sitting in the same spot—skeptical buyers, limited bandwidth, access to —here’s a step-by-step playbook you can copy in weeks, not months.

Run an audit (2 days)

Identify top 10 performing and bottom 10 underperforming assets. Note the claims and evidence (or lack thereof).

Create 3 constrained templates (3 days)

Design templates focused on the core conversion event (demo, signup). Each must include: 1 metric + 1 customer micro-quote + 1 clear ask.

Set verification rules (1 day)

Require a product or sales person to sign off on metrics and quotes before publishing.

Prompt-engineer responsibly (2 days)

Build prompts for that enforce constraints: banned phrases, required fields, output length limits. Store these in a shared library.

Produce and A/B test (6–8 weeks)

Swap the constrained assets into live campaigns and hold baseline variants. Run with sufficient impressions to reach statistical significance.

Measure and iterate (ongoing)

Keep the loop short: weekly check-ins, monthly review of templates, quarterly refresh of customer quotes.

Thought experiments to sharpen judgment

Remove all adjectives: What does the message say when you strip “best,” “powerful,” and “seamless”? If the message still communicates a distinct outcome and mechanism, it’s probably defensible. Replace the persona with one real customer: Write the hero message as if you were addressing an actual customer you spoke to last week. How different is it from your current hero? The five-sentence test: Can you communicate the core value, mechanism, metric, evidence, and CTA in five sentences? If not, you’re trying to sell a concept, not solve a problem.

Closing: authenticity as strategy

This case study shows that avoiding fluff is not a negative constraint; it's a strategic advantage. By using as an assistant that enforces constraints and surfaces evidence—not as a generator of shiny, vacuous prose—we improved conversions, shortened production cycles, and rebuilt trust with a skeptical audience.

The hard part isn’t the tool—it’s the discipline to demand specificity, human verification, and the courage to say less. If you want one practical next step: pick a single high-traffic landing page and run the five-sentence test this week. You’ll be surprised how much that exercise exposes—and how quickly your audience rewards clarity over fluff.