First-time evaluator - what concrete prompts should I test for marketing ops?

Your list is missing the big one: channel-specific performance summaries. Try "Analyze this week's data: email open rate 22%, CTR 3.5%, LinkedIn impressions 45k, engagement 1.8%. Flag the top underperformer and suggest one concrete A/B test for it." That's the real Monday morning time sink.

Also, test segmentation prompts with messy data. "Segment these 500 leads using only company size and last activity date. Show me the segment definitions and a sample outreach subject line for each." If it can't handle imperfect inputs, it's just a theory tool.

You'll know in five prompts if it's a crutch or a prop.

Your channel summary prompt is a decent start, but it's still theory until you attach real cost data. "Flag the top underperformer" is meaningless without knowing what you paid for those LinkedIn impressions versus the email sends.

Try this instead: "Here's last week's spend and results: Email cost $200, opens 22%. LinkedIn cost $850, engagement 1.8%. Which channel had the worse ROI and what's one specific, budget-neutral shift to test?" That forces it to think about vendor bills, not just vanity metrics.

And segmentation using only company size and activity date? That's how you get useless segments. Any tool that doesn't push back on missing firmographic or intent data is just automating garbage.

I see you cut off your last prompt example mid-sentence. That's a fitting metaphor for most AI-generated workflows, honestly.

Your list is a decent start for testing logic, but you're missing the critical piece: procurement and vendor lock-in. The "nurture workflow build-out" prompt needs to force the tool to *choose a platform* based on your existing stack and contract realities. Otherwise you'll get a beautiful, useless diagram.

Try this: "Build a six-email nurture sequence for abandoned cart recovery. Assume we have a 12-month Marketo contract with send limits, and a separate Braze instance for mobile that's underutilized. Map the logic, specify which platform handles each step to avoid overage fees, and flag any additional costs."

If it can't handle that constraint, it's just playing in a sandbox. Your Monday mornings are spent wrestling with bills, not drawing boxes and arrows.

I'd run your "Campaign attribution modeling" prompt, but I'd modify it to force a concrete decision. The value isn't in comparing first-touch vs. last-touch; it's in deciding which one to use for budget allocation next quarter.

Try this more operational version: "Using a first-touch attribution model on this dataset [paste a simplified CSV], the webinar ROI is 22%. Using last-touch, it's 15%. Our sales cycle is 90 days and our content syndication partner pays on a first-touch basis. Which model should we use for Q4 planning and why? Provide a one-paragraph recommendation for the finance team."

This tests if the tool can handle the data transformation and also apply business constraints to generate an actual directive, not just an analysis. If it waffles or tries to present both options equally, it's failed the practical test.

Your channel prompt is a decent start but it's still playing with toys. Flagging the top underperformer is useless without context of historical performance and seasonality. A 3.5% CTR could be catastrophic or a record high.

Segmentation with only two fields? That's how you train the team to ignore data quality. If the tool doesn't refuse that prompt or at least flag the massive assumptions, it's actively dangerous. Garbage segments automate faster.

That's the correct angle, but it's incomplete. The attribution model selection needs to be tied to a quarterly planning action, not just a paragraph for finance.

Your prompt forces a choice, but the real operational test is whether it can translate that choice into a resource shift. A good follow-up would be: "Given your recommended model, reallocate our proposed $50k Q4 blog spend across these three channels [list them]. Show the new allocation and write the Jira ticket description for the marketing operations engineer to reconfigure the HubSpot dashboard."

If it stops at the recommendation without defining the downstream work, it's just a consultant, not a tool.

Your whole premise is flawed. If you're trying to replace a 30-minute checklist, you're already asking the tool to automate a process you probably shouldn't have in the first place.

Your lead scoring prompt is a perfect example. It'll give you a neat 0-100 point system, but it'll be based on the fields you gave it, which are probably garbage. Job title is a vanity field. Page views are inflated by bots. You're just getting a beautifully formatted rationale for a broken system.

And cutting off your nurture workflow prompt is the most honest part of your post. These tools always build the sequence. They never flag that you need legal review for abandoned cart messaging, or that your ESP's pricing tier changes after 50,000 sends. You'll get a map that costs you $5k in overages.

Skip the prompts. Give it your actual CRM data dump and ask it to find the three fields with the highest null rates. That's a real Monday morning task. If it can't handle your messy reality, it's a prop.

Totally feel you on wanting actual tasks, not just summaries. Your list looks solid for testing logic. I'm curious though, when you test the nurture workflow prompt - are you including any constraints like budget or platform limits? The comments here about overage fees have me nervous to try that one without real numbers.

You're right to focus on constraints. The prompt needs to include specific rate limits and tier boundaries from your actual contracts to be a valid test.

For example, a real-world constraint isn't just "budget," it's: "Build this nurture flow assuming our SendGrid Pro plan has a hard cap of 100,000 emails/month and our HubSpot Marketing Hub Starter limits us to 1,000 automated emails. The sequence must trigger for a batch of 5,000 new contacts monthly. Specify where to split the workflow to avoid overages and calculate the projected monthly send volume."

If the output doesn't produce a flow diagram with clear platform handoffs and a volume calculation, it's just generating fiction. Without those guardrails, you're benchmarking its creativity, not its operational utility.

You've nailed the core failure of most marketing automation demos, which is to pretend procurement doesn't exist. Your constraint about the underutilized Braze instance is exactly the type of real-world friction that separates a diagram from a deployable plan.

I'd push your example one step further into vendor-specific logic. Marketo's throughput limits and Braze's channel-based pricing (push vs. email vs. in-app) require fundamentally different branching. A prompt should demand that specificity: "If Braze is used for step 3, calculate the cost difference if 40% of the audience has push notifications disabled, forcing fallback to email within the same platform."

Without that, you're right, it's just a sandbox. You end up with a workflow that's technically correct but financially reckless.

Finally, someone who gets that a vendor contract is part of the architecture. Your push on channel-based pricing is the exact nuance that gets abstracted away.

But even that prompt can be gamed. A tool could spit out a clean cost difference and still miss the real trap: integration debt. If the fallback logic requires a custom field sync from Braze back to Marketo because your reporting dashboard lives there, you've just added 20 hours of dev work that never appears in the 'cost difference'. The financially reckless part isn't just the fees, it's the hidden labor to wire up the escape hatch.

So the test shouldn't just be "calculate the cost difference." It needs to be "calculate the cost difference and list the new API connections or field mappings required to execute this fallback plan." If it can't see the system boundaries, it's designing a liability.

I've been lurking on this thread because I'm in a similar spot, trying to automate some reporting. Your lead scoring prompt is what I'd start with too.

But reading the later comments about garbage fields is worrying. Maybe the test shouldn't be if it builds the system, but if it asks questions about your data first? Like, before drafting the 0-100 rationale, a good prompt might need to include: "Identify which of these fields might be unreliable and suggest one replacement data point for each."

That way you see if it's just executing or actually thinking about the inputs.

Yeah, that's a really smart angle. Making the tool question its own inputs.

But what if it just confidently labels a field as "unreliable" without really knowing? Like, it might flag "time on page" as bad data, but in our case, that's actually the one clean metric we have because it comes straight from the CDN.

Maybe the test needs to include a known trap, like including "social media likes" as a field. If it doesn't flag that, it's definitely just executing.

Hey, this is exactly the kind of list I'd try. I'm in a similar spot trying to automate parts of my weekly check-ins.

For the nurture workflow one, are you planning to include the specific tools you use? Like, "Map a 5-email nurture sequence for abandoned cart in Klaviyo" vs just a generic map. I've found the output changes a lot when you lock it to a real platform's features and limits.