> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mavera.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Lead Scoring Validation
> Pull scored leads from each tier, create a persona per score bucket, then run a focus group to validate whether your scoring model aligns with buyer reality
## Scenario
Your lead scoring model assigns hot/warm/cold tiers — but does the scoring actually predict engagement? This job pulls scored leads from each tier, creates a persona per score bucket, then runs a focus group asking "Would you respond to this outreach?" with your real email templates as stimulus. The output validates whether your scoring model aligns with buyer reality.
## Architecture
```mermaid theme={"dark"}
flowchart LR
A["Salesforce Leads (by score tier)"] --> B["POST /api/v1/personas (hot/warm/cold)"] --> C["POST /api/v1/focus-groups (email stimulus)"] --> D[Accuracy Report]
```
## Code
```python Python theme={"dark"}
import os, requests
SF = os.environ["SALESFORCE_INSTANCE"]
SF_T = os.environ["SALESFORCE_ACCESS_TOKEN"]
MV_K = os.environ["MAVERA_API_KEY"]
SF_H = {"Authorization": f"Bearer {SF_T}"}
MV_H = {"Authorization": f"Bearer {MV_K}", "Content-Type": "application/json"}
TIERS = {"hot": "Rating = 'Hot'", "warm": "Rating = 'Warm'", "cold": "Rating = 'Cold'"}
EMAIL_TEMPLATE = (
"Subject: Quick call this week?\n\n"
"Hi {{Name}}, given your team's growth, I'd love 15 minutes to show "
"how we've helped similar companies cut onboarding time by 40%."
)
persona_ids = []
for tier, where in TIERS.items():
leads = requests.get(
f"https://{SF}/services/data/v66.0/query", headers=SF_H,
params={"q": f"SELECT Name, Title, Company, Industry FROM Lead WHERE {where} LIMIT 20"},
).json()["records"]
titles = [l.get("Title", "Manager") for l in leads]
top = max(set(titles), key=titles.count) if titles else "Manager"
p = requests.post(
"https://app.mavera.io/api/v1/personas", headers=MV_H,
json={
"name": f"{tier.title()} Lead — {top}",
"description": f"'{tier}' scored leads. Typical role: {top}. Based on {len(leads)} leads.",
},
).json()
persona_ids.append(p["id"])
fg = requests.post(
"https://app.mavera.io/api/v1/focus-groups", headers=MV_H,
json={
"persona_ids": persona_ids,
"questions": [
{"type": "nps", "text": "You receive this email. How likely are you to respond (0-10)?"},
{"type": "open_ended", "text": f"Here is the outreach email:\n\n{EMAIL_TEMPLATE}\n\nWould you open, reply, or ignore? Why?"},
{"type": "open_ended", "text": "What would make you more likely to engage with cold outreach?"},
{"type": "open_ended", "text": "Describe the last vendor email you actually replied to. What stood out?"},
],
},
).json()
print(f"Focus Group: {fg['id']}")
for r in fg.get("responses", []):
print(f"\n[{r['persona_name']}] Q: {r['question'][:60]}...")
print(f" A: {r['response']}")
```
```javascript JavaScript theme={"dark"}
const SF = process.env.SALESFORCE_INSTANCE;
const SF_T = process.env.SALESFORCE_ACCESS_TOKEN;
const MV_K = process.env.MAVERA_API_KEY;
const mvH = { Authorization: `Bearer ${MV_K}`, "Content-Type": "application/json" };
const TIERS = { hot: "Rating = 'Hot'", warm: "Rating = 'Warm'", cold: "Rating = 'Cold'" };
const EMAIL_TEMPLATE =
"Subject: Quick call this week?\n\nHi {{Name}}, given your team's growth, " +
"I'd love 15 minutes to show how we've helped similar companies cut onboarding time by 40%.";
async function sfQuery(soql) {
return (await fetch(
`https://${SF}/services/data/v66.0/query?q=${encodeURIComponent(soql)}`,
{ headers: { Authorization: `Bearer ${SF_T}` } }
).then((r) => r.json())).records;
}
const personaIds = [];
for (const [tier, where] of Object.entries(TIERS)) {
const leads = await sfQuery(`SELECT Name, Title, Company, Industry FROM Lead WHERE ${where} LIMIT 20`);
const titles = leads.map((l) => l.Title || "Manager");
const top = [...new Set(titles)].sort(
(a, b) => titles.filter((t) => t === b).length - titles.filter((t) => t === a).length
)[0];
const p = await fetch("https://app.mavera.io/api/v1/personas", {
method: "POST", headers: mvH,
body: JSON.stringify({
name: `${tier.charAt(0).toUpperCase() + tier.slice(1)} Lead — ${top}`,
description: `'${tier}' scored leads. Typical role: ${top}. Based on ${leads.length} leads.`,
}),
}).then((r) => r.json());
personaIds.push(p.id);
}
const fg = await fetch("https://app.mavera.io/api/v1/focus-groups", {
method: "POST", headers: mvH,
body: JSON.stringify({
persona_ids: personaIds,
questions: [
{ type: "nps", text: "You receive this email. How likely are you to respond (0-10)?" },
{ type: "open_ended", text: `Here is the outreach email:\n\n${EMAIL_TEMPLATE}\n\nWould you open, reply, or ignore? Why?` },
{ type: "open_ended", text: "What would make you more likely to engage with cold outreach?" },
{ type: "open_ended", text: "Describe the last vendor email you actually replied to. What stood out?" },
],
}),
}).then((r) => r.json());
console.log(`Focus Group: ${fg.id}`);
(fg.responses || []).forEach((r) => {
console.log(`\n[${r.persona_name}] Q: ${r.question.slice(0, 60)}...`);
console.log(` A: ${r.response}`);
});
```
## Example Output
```json theme={"dark"}
{
"id": "fg_9wT2r",
"summary": { "hot_nps_avg": 8.2, "warm_nps_avg": 5.4, "cold_nps_avg": 2.1 },
"responses": [
{
"persona_name": "Hot Lead — VP Engineering",
"question": "Would you open, reply, or ignore? Why?",
"response": "I'd reply. The 40% stat is specific enough to be credible, and the ask is low-commitment."
},
{
"persona_name": "Cold Lead — Manager",
"question": "Would you open, reply, or ignore? Why?",
"response": "Archive. Nothing here tells me you know anything about my company or my problems."
}
]
}
```
If cold personas give high NPS scores, your model is too conservative — you're leaving deals on the table. If hot personas score low, your model over-weights firmographic fit without considering timing or intent.
***
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