External AI for Scoring, Profiling, and Daily Follow-Ups
Phase 1 (MVP): External AI for Scoring, Profiling, and Daily Follow-Ups
Goals (what “done” looks like)
Data synced from Zoho CRM (Leads, Contacts, Deals) into your own DB.
Single customer view (deduped Leads/Contacts; company/person merge).
Lead propensity score + buyer profile match score per record.
Daily briefing (email/Slack) with prioritized follow-up proposals for each rep.
Simple web dashboard to see:
Today’s priority leads & rationale
Suggested touch & copy stub
What changed since yesterday
Audit log so reps can mark “accepted/ignored” → model learns.
System design (lean & proven)
Ingestion & sync
Zoho OAuth 2.0; incremental pulls via Modified Time + webhooks for near-real-time updates.
Tables: raw_leads, raw_contacts, raw_deals, plus activities (emails/calls/tasks), and owners.
Warehouse & modeling
PostgreSQL (or BigQuery if you want serverless scale).
dbt for transforms (clean, map picklists, normalize sources).
Identity resolution: email/phone fuzzy match, company domain, last device/IP (if present).
Features (examples)
RFM-style recency/frequency of touches, channel mix (email/call/SMS), reply latencies.
Deal context: stage velocity, average discount, salesperson effect, seasonality (boats have peak months).
Source quality over time, campaign UTM performance, geo × product fit.
Models (start simple, upgradeable)
Lead Propensity (binary classification: close vs no close within N days) — logistic regression / XGBoost.
Time-to-close (regression) to prioritize “soonest wins.”
Profile Match: nearest-neighbor similarity to your “ideal buyer” vectors (product, price band, geo, past wins).
Decisioning layer
For each rep/day: top X leads with reason codes (human-readable “because…”).
Next-best action: call/email/SMS/LinkedIn, with best time window and copy starter.
Delivery
Daily Brief at 8am local: Slack + email.
Web app (FastAPI + small React UI) for drill-downs & marking outcomes.
Learning loop
Reps thumbs-up/down recommendations; log outcomes; retrain weekly.
Security & compliance
Store only what’s needed; at rest encryption (Postgres TDE / disk encryption).
Rotate Zoho refresh tokens, least-privilege OAuth scopes.
PII masking in lower environments; audit logs for access.
Milestones & acceptance criteria
Week 1–2: Data & foundation
OAuth connection; nightly sync + webhook upserts.
dbt models producing clean entity_person, entity_company, deal_facts.
AC: Row counts reconcile ±1% vs Zoho; duplicate rate reported.
Week 3–4: Features & first scores
Feature store built; baseline lead propensity (AUROC ≥ 0.70 on hold-out).
AC: Score for every active Lead; top reasons exposed.
Week 5: Daily brief & dashboard
Slack/email brief with top leads per rep + suggested action/time.
Simple UI with filters and “accept/ignore” buttons.
AC: At least 3 actionable suggestions/rep/day with reason codes.
Week 6: Feedback loop & polish
Capture rep feedback; weekly retrain; performance report.
AC: End-to-end runbook; one-click redeploy; docs delivered.
(We can compress to ~4 weeks if we narrow scope to one business line and skip the web UI in v1, using Slack only.)
Sample daily brief (Slack/email)
Good morning! 12 prioritized leads for Alex
John D. (Web form – Heyday) · Close prob: 0.71 · Best time: 10–11am
Reason: Similar to 8 recent wins (Phoenix, weekend site visits, 2 prior calls, summer season)
Do this: Call with “weekend water test” CTA → calendar link.
Megan S. (Facebook Lead Ad – Barletta) · 0.66 · 2–4pm
Reason: High-engagement email opens; responded to financing pages.
Do this: SMS about 0% for 6 mo pre-qual; link financing form.
…
Reply or after action; I’ll learn from it.
Tech stack (pragmatic picks)
Backend: Python (FastAPI), dbt, pandas/XGBoost (upgrade to LightGBM if needed)
Warehouse: Postgres (RDS) or BigQuery
Jobs: Prefect (flows), or GitHub Actions on a schedule
UI: React (Vite) or simple Streamlit (fastest path)
Messaging: Slack API + SES (or Zoho Mail if preferred)
Infra: AWS (RDS + ECS Fargate) or GCP (Cloud Run + Cloud SQL)
Phase 2 (later): Assisted & Automated Follow-Ups
Cadence engine: templates + guardrails (brand, compliance).
Channel orchestration: call tasks, smart emails/SMS, Zoho task creation.
A/B testing of subject lines, offers, send-times.
Opt-out & compliance: TCPA/TCR applied to SMS; logging & suppression lists.
Human-in-the-loop: reps approve first; then gradually allow auto-send for low-risk tiers.
Upwork Post (Revised to Your New Direction)
Title: Build External AI for Zoho CRM: Lead Scoring, Buyer Profiles & Daily Follow-Up Proposals (Phase 1)
Summary (read first):
DO NOT CONTACT ME OUTSIDE OF UPWORK. We want an external AI (not built inside Zoho) that connects to our Zoho One CRM (Leads, Contacts, Deals), downloads thousands of records, cleans/dedupes, and delivers:
Lead propensity scores & buyer profile match, and
Daily follow-up proposals/reminders to our sales team (Slack/email + simple dashboard).
Phase 2 will add semi-automated outreach.
Scope (Phase 1 MVP):
Secure OAuth integration to Zoho; incremental sync + webhooks.
Data cleaning & identity resolution across Leads/Contacts/Deals.
Feature engineering (recency/frequency, channel mix, seasonality, source quality).
Models: lead close propensity, time-to-close, buyer profile similarity.
Daily 8am brief per rep: top targets, best time to reach, suggested channel & copy stub.
Lightweight web app for reviewing suggestions + logging outcomes.
Feedback loop to improve scores week-over-week.
Docs + handoff.
Deliverables:
Running service (AWS/GCP/Azure) + code repo.
Postgres/BigQuery schema & dbt models.
Model report (metrics, features, reason codes).
Slack/email briefs + minimal dashboard.
Security notes (token rotation, PII handling).
Nice to Have (but not required for MVP):
Zoho task creation from accepted suggestions.
Basic A/B testing framework.
Your Background:
Python (FastAPI), ML (XGBoost/LightGBM), dbt/pandas.
Zoho API experience (or similar CRM: Salesforce/HubSpot) a big plus.
Data warehousing (Postgres/BigQuery), OAuth, webhooks.
Slack/Email integrations; basic React or Streamlit.
Timeline & Budget:
Target 4–6 weeks for MVP. Propose fixed price with milestone breakdown.
How to Apply:
Brief plan (ingestion → features → models → delivery), with risks and mitigations.
Similar projects (lead scoring / CRM AI).
Rough metrics you aim to hit (e.g., AUROC ≥ 0.70).
Tech stack and hosting preference.
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