import os, requests, time
MP_SA = os.environ["MIXPANEL_SERVICE_ACCOUNT"]
MP_SECRET = os.environ["MIXPANEL_SECRET"]
MP_PROJECT = os.environ["MIXPANEL_PROJECT_ID"]
MV = os.environ["MAVERA_API_KEY"]
mp_auth = (MP_SA, MP_SECRET)
def query_engage_segment(filter_expr, props):
r = requests.post("https://mixpanel.com/api/query/engage", auth=mp_auth,
json={
"project_id": MP_PROJECT,
"filter_by_cohort": filter_expr,
"output_properties": props,
"page": 0,
})
if r.status_code == 429: time.sleep(60)
r.raise_for_status()
return r.json().get("results", [])
PROFILE_PROPS = [
"plan", "role", "company_size", "signup_source",
"total_events", "sessions_count", "features_used",
"days_active_last_30", "last_seen",
]
retained = query_engage_segment(
'(properties["days_active_last_30"]) >= 10', PROFILE_PROPS
)
time.sleep(2)
churned = query_engage_segment(
'(properties["days_active_last_30"]) <= 1 and (properties["sessions_count"]) >= 5', PROFILE_PROPS
)
def summarize(users, label):
if not users:
return f"{label}: No users found"
props_list = [u.get("$properties", {}) for u in users]
avg_events = sum(int(p.get("total_events", 0) or 0) for p in props_list) / len(props_list)
avg_sessions = sum(int(p.get("sessions_count", 0) or 0) for p in props_list) / len(props_list)
avg_days = sum(int(p.get("days_active_last_30", 0) or 0) for p in props_list) / len(props_list)
from collections import Counter
plans = Counter(p.get("plan") for p in props_list if p.get("plan")).most_common(3)
roles = Counter(p.get("role") for p in props_list if p.get("role")).most_common(5)
sources = Counter(p.get("signup_source") for p in props_list if p.get("signup_source")).most_common(3)
features = Counter(
f for p in props_list
for f in (p.get("features_used", "").split(",") if isinstance(p.get("features_used"), str) else [])
if f.strip()
).most_common(10)
return (
f"**{label}** ({len(users)} users)\n"
f" Avg events: {avg_events:.0f} | Avg sessions: {avg_sessions:.0f} | Avg days active (30d): {avg_days:.1f}\n"
f" Plans: {', '.join(f'{p[0]} ({p[1]})' for p in plans)}\n"
f" Roles: {', '.join(f'{r[0]} ({r[1]})' for r in roles)}\n"
f" Sources: {', '.join(f'{s[0]} ({s[1]})' for s in sources)}\n"
f" Top features: {', '.join(f'{f[0]} ({f[1]})' for f in features)}"
)
retained_summary = summarize(retained, "Retained (10+ days active/30d)")
churned_summary = summarize(churned, "Churned (≤1 day active, had 5+ sessions)")
mave = requests.post(
"https://app.mavera.io/api/v1/mave/chat",
headers={"Authorization": f"Bearer {MV}", "Content-Type": "application/json"},
json={"message": f"""Research strategies to convert churning user behaviors into retained user behaviors.
RETAINED USERS:
{retained_summary}
CHURNED USERS:
{churned_summary}
Analyze:
1. Key behavioral differences between retained and churned
2. Which features correlate with retention? Which are missing from churned?
3. Content strategies to drive feature adoption among at-risk users
4. Onboarding improvements to increase early activation
5. Re-engagement campaign ideas for users showing churn signals
6. Messaging recommendations: what to say at each stage of the retention curve"""},
).json()
print("--- Cohort Content Strategy ---")
print(mave.get("content", "")[:3000])
