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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