Role-and-Stack Personalization Workflow
Turn customer role and technology data into concise automated recommendations
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 95%
The Role-and-Stack Personalization Workflow begins with operational customer context—especially a recipient's job function and the software they use—and converts it into relevant recommendations. In a manual AI playground, prompt the model to identify a small number of high-value workflows, explain why each matters, and return no unnecessary information. Refine the output for brevity, tone, and conversational quality before automating it. Next, expand the strongest message into a short drip campaign, making each successive follow-up shorter and less repetitive. Only after the prompt performs reliably should it be integrated through an API or automation platform. The mechanism creates substantive personalization because the recommended use cases change with the recipient's role and stack, rather than merely inserting their name into generic copy.
Origin
Extracted from Marketing Against the Grain during Kieran Flanigan's demonstration of generating Zapier workflow recommendations and a personalized email sequence from role and technology-stack data.
Core principles
- 01Personalize from useful operational data, not cosmetic tokens
- 02Translate customer context into relevant use cases
- 03Perfect the prompt manually before automating it
- 04Shorten follow-ups as recipient engagement declines
How to run it
- 1
Capture useful context
Collect the recipient's role, company type, and relevant technology stack through consented first-party data.
Pro tip Use only fields that materially change the recommendation.
Watch out Inaccurate enrichment can make personalization feel invasive or absurd.
- 2
Generate relevant workflows
Ask the AI for a small number of automations or use cases suited to the role and stack.
Pro tip Limit the output to the top three recommendations with brief reasons.
Watch out Do not recommend integrations that the product cannot actually support.
- 3
Refine the core email
Transform the recommendations into a concise message with an appropriate conversational tone and clear next step.
Pro tip Specify the intended feeling, such as discovering something unexpectedly useful.
Watch out Overly playful language can undermine trust in serious contexts.
- 4
Design the sequence
Create a short campaign with intentional delays and distinct follow-up purposes.
Pro tip Make later emails progressively shorter because the recipient has already seen the context.
Watch out Do not repeat the same long pitch in every message.
- 5
Validate manually
Test examples across different roles and stacks before connecting the prompt to production systems.
Pro tip Create edge cases for incomplete or unusual technology stacks.
Watch out Automating an untested prompt multiplies errors.
- 6
Integrate and monitor
Connect the validated workflow to CRM or lifecycle automation, then track engagement and recommendation quality.
Pro tip Retain logs linking generated claims to the input data and supported product capabilities.
Watch out Respect consent, privacy, suppression lists, and email regulations.
In the wild
Given a SaaS marketer using tools such as Typeform, HubSpot, Hootsuite, Asana, Hotjar, and Unbounce, the model proposed lead nurturing, social monitoring, and conversion-rate workflows. Kieran then asked it to make the email shorter, punchier, and more conversational before expanding it into a three-email sequence.
→ Customer data became a tailored set of automation ideas and a progressively shorter nurture campaign.
Common mistakes
Automating before refining
A prompt should first be perfected and tested manually; otherwise production automation scales weak or incorrect recommendations.
Using superficial personalization
Names and company fields do not add much value unless the recommendations genuinely change with customer context.
Writing every follow-up at full length
Later messages should acknowledge prior contact and become more abbreviated instead of restating the entire pitch.
Is it for you?
Best for
SaaS companies with reliable role and technology data that can recommend concrete workflows or use cases.
Not ideal for
Businesses with sparse, inaccurate, sensitive, or weakly predictive customer data.
From the transcript
“you should try to perfect it here in terms of the prompt and then you can start to automate it through zap your and other…”
“this is me just trying to figure out can I pull from your Tech stack and your role automations that are actually applicable to you”
“it understands to shorten each email because that person the user Behavior”
From the episode
I Used ChatGPT To Create A Marketing Plan In 30 Minutes