# Cross Selling Opportunities Agent — Deterministic Rules and Locked-Case Evidence

> **FIXED SYNTHETIC SNAPSHOT ONLY.** Use the companion complete source-record file and the exact rules below. Do not browse, enrich, infer missing facts, or substitute live data.

## Deterministic routing

| Operation | Locked request | Required response anchors |
| --- | --- | --- |
| `opportunity_scan` | Scan synthetic customer CUST-001 (Acme Corp) for product gaps, usage signals, buying signals, and budget timing. | `Cross-Sell Opportunity Scan`; `Synthetic Usage Signals`; `Evidence boundary` |
| `product_affinity` | Explain the synthetic product-affinity and benchmark assumptions without treating them as observed conversion performance. | `Product Affinity Matrix`; `Response Assumption`; `Evidence boundary` |
| `recommendation_engine` | Draft engagement strategies, talking points, and an outreach sequence for synthetic customer CUST-001 (Acme Corp) without sending outreach. | `Prioritized Recommendations`; `Draft Engagement Plan`; `Evidence boundary` |
| `revenue_impact` | Show me the revenue impact and conversion timeline for the cross-sell plan, without treating the scenario as committed revenue. | `Synthetic Cross-Sell Value Scenario`; `Portfolio Totals`; `Evidence boundary` |
| `portfolio_scan` | Analyze our top 100 enterprise accounts and identify cross-selling opportunities based on their current product usage. | `Cross-Sell Opportunity Summary`; `Quick Wins`; `Evidence boundary` |
| `top_opportunities` | Show me the top 5 highest-value cross-sell opportunities and a deep dive on the first account. | `Top Cross-Sell Opportunities`; `Deep Dive`; `Evidence boundary` |
| `account_assignments` | Assign the opportunity accounts to reps by expertise and draft this week's action plan. | `Draft Account Assignments`; `This Week's Actions`; `Evidence boundary` |

Only the operations above are supported. Pass `data_source=synthetic` and use only allow-listed identifiers from the companion records. Unknown sources, operations, and identifiers must fail closed.

## Exact computation rules

The following source functions are the authoritative deterministic calculations. They operate only on the bundled records. Preserve their thresholds, ordering, rounding, labels, and formulas exactly.

### `_resolve_customer`

```python
def _resolve_customer(query):
    """Account ID or (part of) an account name; None when nothing matches."""
    if not query:
        return "CUST-001"
    q = str(query).lower().strip()
    for key, cust in _CUSTOMER_OWNERSHIP.items():
        if key.lower() in q or q in cust["name"].lower() or cust["name"].lower() in q:
            return key
    return None
```

### `_money_k`

```python
def _money_k(value):
    """$120K / $1.4M style."""
    if value >= 1000000:
        return f"${value / 1000000:.1f}M"
    return f"${round(value / 1000)}K"
```

### `_potential`

```python
def _potential(cust):
    total = 0
    for rec in cust["recommended"]:
        total += rec["arr"]
    return total
```

### `_recommended_label`

```python
def _recommended_label(cust):
    return " + ".join(rec["product"] for rec in cust["recommended"])
```

### `_portfolio_total`

```python
def _portfolio_total():
    total = 0
    for seg in _PORTFOLIO["segments"]:
        total += seg["potential_arr"]
    return total
```

### `_forecast_total`

```python
def _forecast_total():
    total = 0
    for row in _FORECAST["months"]:
        total += row["arr"]
    return total
```

## Locked operation evidence

Each exact output below is generated by the deterministic source with the corresponding locked-case arguments and appears verbatim within that case's strict-isolation transcript agent log. Use it as the response contract for Copilot Studio.

### CS-01 — `opportunity_scan`

- Persona: Sales Operations Manager
- Locked prompt: Scan synthetic customer CUST-001 (Acme Corp) for product gaps, usage signals, buying signals, and budget timing.
- Arguments: `{"operation": "opportunity_scan", "data_source": "synthetic", "customer_id": "CUST-001"}`
- Exact deterministic output:

**Cross-Sell Opportunity Scan: Acme Corp**

| Field | Detail |
|---|---|
| Account ID | CUST-001 |
| Current Products | CRM, Marketing |
| Current Spend | $85K/year |
| Health Score | 91/100 |
| Champion | VP Marketing (Sarah Chen) |
| Owner | Lisa Chen |

**Synthetic Usage Signals:**
- Heavy data exports (no analytics)

**Synthetic Buying Signals:**
- Requested custom reports last week
- Budget timing assumption: End of quarter budget available

**Recommended Products:**

| Product | ARR Potential |
|---|---|
| Analytics Suite | $120K |
| **Total** | **$120K** |

**Next step:** Schedule analytics demo

Synthetic source model: [CRM + Product Usage + Support Tickets]
Agents: CrossSellingAgent

**Evidence boundary:** Exact account names, products, ARR, scores, percentages, timing, and forecast figures are synthetic planning evidence. Close rates and forecasts are scenario assumptions, not conversion or revenue claims. No outreach was sent and no CRM, pricing, approval, or customer record changed.

### CS-02 — `product_affinity`

- Persona: Enablement Manager
- Locked prompt: Explain the synthetic product-affinity and benchmark assumptions without treating them as observed conversion performance.
- Arguments: `{"operation": "product_affinity", "data_source": "synthetic", "customer_id": "CUST-001"}`
- Exact deterministic output:

**Product Affinity Matrix**

| If Customer Owns | Recommend | Affinity | Synthetic Response Assumption | Synthetic Cycle |
|---|---|---|---|---|
| CRM | Analytics Suite | 94% | 50% | 30d |
| Basic Plan | Enterprise | 88% | 45% | 21d |
| Enterprise | Security | 81% | 48% | 30d |
| Analytics | CRM | 79% | 38% | 45d |
| Marketing | CRM | 76% | 38% | 40d |
| CRM | Integrations | 72% | 45% | 28d |

**Peer benchmark:** 94% of similar companies own both CRM and Analytics.

**Segment Benchmarks:**

| Segment | Response Assumption | Cycle Assumption | Basis |
|---|---|---|---|
| Quick wins | 45% | 21d | historical |
| Medium priority | 38% | 45d | historical |
| High-value | 50% | 60d | strong signals |

Synthetic source model: [Affinity Engine + Historical Data]
Agents: CrossSellingAgent

**Evidence boundary:** Exact account names, products, ARR, scores, percentages, timing, and forecast figures are synthetic planning evidence. Close rates and forecasts are scenario assumptions, not conversion or revenue claims. No outreach was sent and no CRM, pricing, approval, or customer record changed.

### CS-03 — `recommendation_engine`

- Persona: Enablement Manager
- Locked prompt: Draft engagement strategies, talking points, and an outreach sequence for synthetic customer CUST-001 (Acme Corp) without sending outreach.
- Arguments: `{"operation": "recommendation_engine", "data_source": "synthetic", "customer_id": "CUST-001"}`
- Exact deterministic output:

**Prioritized Recommendations: Acme Corp**

Personalized engagement strategies created for each account with specific talking points and timing recommendations.

**Acme Corp Strategy:**
- Approach: Value-led demo showcasing ROI
- Recommended: Analytics Suite ($120K ARR potential)
- Champion: VP Marketing (Sarah Chen)
- Trigger: Requested custom reports last week
- Timing: End of quarter budget available
- Talking Points:
  1. "You're exporting 50K records monthly - Analytics automates this"
  2. "Similar customers saw 340% ROI in 6 months"
  3. "Your competitor TechGiant uses our full suite"

**Draft Engagement Plan (not sent) - Outreach Sequence:**
- Day 1: Personalized email with usage insights
- Day 3: LinkedIn touchpoint
- Day 5: Calendar invite for value demo

Talking points are synthetic examples: verify the ROI benchmark and any competitor reference before use, and confirm contact consent before outreach.

Synthetic source model: [Sales Playbook + Customer Intelligence]
Agents: CrossSellingAgent

Want to see the revenue impact forecast?

**Evidence boundary:** Exact account names, products, ARR, scores, percentages, timing, and forecast figures are synthetic planning evidence. Close rates and forecasts are scenario assumptions, not conversion or revenue claims. No outreach was sent and no CRM, pricing, approval, or customer record changed.

### CS-04 — `revenue_impact`

- Persona: Sales Leader
- Locked prompt: Show me the revenue impact and conversion timeline for the cross-sell plan, without treating the scenario as committed revenue.
- Arguments: `{"operation": "revenue_impact", "data_source": "synthetic", "customer_id": "CUST-001"}`
- Exact deterministic output:

**Synthetic Cross-Sell Value Scenario**

Revenue model shows $2.1M realizable ARR this quarter with a staged conversion timeline.

**Quarterly Revenue Forecast**

| Month | Opportunities | Expected Closes | ARR Impact |
|---|---|---|---|
| Month 1 | 32 quick wins | 12-15 deals | $540K |
| Month 2 | 20 medium | 8-10 deals | $720K |
| Month 3 | 12 high-value | 5-6 deals | $840K |

**Conversion Assumptions:**
- Quick wins: 45% close rate (historical)
- Medium priority: 38% close rate
- High-value: 50% close rate (strong signals)

**Portfolio Totals - Pipeline Impact:**
- Current expansion pipeline: $1.8M
- After this analysis: $5.0M (+178%)
- Quota coverage: 2.8x (vs 1.2x before)

**Resource Needs:**
- 12 accounts need SE support for demos
- 8 accounts need executive sponsor intro

Synthetic source model: [Revenue Analytics + Historical Conversion]
Agents: CrossSellingAgent

Want me to assign accounts and create the action plan?

**Evidence boundary:** Exact account names, products, ARR, scores, percentages, timing, and forecast figures are synthetic planning evidence. Close rates and forecasts are scenario assumptions, not conversion or revenue claims. No outreach was sent and no CRM, pricing, approval, or customer record changed.

### CS-05 — `portfolio_scan`

- Persona: Sales Leader
- Locked prompt: Analyze our top 100 enterprise accounts and identify cross-selling opportunities based on their current product usage.
- Arguments: `{"operation": "portfolio_scan", "data_source": "synthetic"}`
- Exact deterministic output:

I've analyzed all 100 enterprise accounts against usage patterns, peer comparisons, and buying signals. Total expansion opportunity: $3.2M ARR.

**Cross-Sell Opportunity Summary**

| Segment | Accounts | Potential ARR | Avg Deal |
|---|---|---|---|
| High Priority | 12 | $1.4M | $117K |
| Medium Priority | 20 | $1.2M | $60K |
| Nurture | 68 | $0.6M | $9K |

**Quick Wins (Ready for Outreach):**
- 32 accounts showing active buying signals
- 8 accounts exceeded usage limits this quarter
- 5 accounts requested features in products they don't own

**Top Signal:** 12 accounts using CRM but not Analytics - peer data shows 94% of similar companies have both.

Synthetic source model: [CRM + Usage Analytics + Peer Benchmarks]
Agents: CrossSellingAgent

Want to see the top 5 highest-value opportunities?

**Evidence boundary:** Exact account names, products, ARR, scores, percentages, timing, and forecast figures are synthetic planning evidence. Close rates and forecasts are scenario assumptions, not conversion or revenue claims. No outreach was sent and no CRM, pricing, approval, or customer record changed.

### CS-06 — `top_opportunities`

- Persona: Sales Leader
- Locked prompt: Show me the top 5 highest-value cross-sell opportunities and a deep dive on the first account.
- Arguments: `{"operation": "top_opportunities", "data_source": "synthetic"}`
- Exact deterministic output:

Top 5 opportunities represent $547K in potential ARR. All have strong buying signals and account health.

**Top Cross-Sell Opportunities**

| Account | Current Products | Recommended | ARR Potential |
|---|---|---|---|
| Acme Corp | CRM, Marketing | Analytics Suite | $120K |
| TechCo Industries | Basic Plan | Enterprise + Security | $115K |
| GlobalRetail Inc | CRM | Full Platform | $108K |
| Meridian Finance | Analytics | CRM + Integrations | $104K |
| Apex Manufacturing | Marketing | CRM + Analytics | $100K |

**#1 Acme Corp - Deep Dive:**
- Current spend: $85K/year
- Usage: Heavy data exports (no analytics)
- Champion: VP Marketing (strong relationship)
- Trigger: Requested custom reports last week
- Next step: Schedule analytics demo

Synthetic source model: [CRM + Product Usage + Support Tickets]
Agents: CrossSellingAgent

Should I create engagement strategies for each?

**Evidence boundary:** Exact account names, products, ARR, scores, percentages, timing, and forecast figures are synthetic planning evidence. Close rates and forecasts are scenario assumptions, not conversion or revenue claims. No outreach was sent and no CRM, pricing, approval, or customer record changed.

### CS-07 — `account_assignments`

- Persona: Sales Operations Manager
- Locked prompt: Assign the opportunity accounts to reps by expertise and draft this week's action plan.
- Arguments: `{"operation": "account_assignments", "data_source": "synthetic"}`
- Exact deterministic output:

**Draft Account Assignments**

Accounts matched to reps by expertise and existing relationships; the action plan below is a draft for your review.

| Rep | Accounts | Total ARR | Specialty Match |
|---|---|---|---|
| James Wilson | 8 | $680K | Enterprise/Security |
| Lisa Chen | 7 | $520K | Analytics/Data |
| Mike Torres | 9 | $490K | Marketing/CRM |
| Sarah Kim | 8 | $410K | Manufacturing |
| **Total** | **32** | **$2.1M** | |

**Top 5 owners:**
- Acme Corp -> Lisa Chen
- TechCo Industries -> James Wilson
- GlobalRetail Inc -> Mike Torres
- Meridian Finance -> James Wilson
- Apex Manufacturing -> Sarah Kim

**This Week's Actions (drafts ready for you to send):**
- Today: 32 quick-win outreach emails (templates ready, not sent)
- Tomorrow: Schedule 12 SE demos
- Friday: Executive intro requests to leadership

**Recommended Triggers (not enabled):**
- Alerts when accounts log in
- Email notifications on feature requests
- Weekly pipeline review dashboard

**Team Briefing:** proposed for tomorrow 9 AM with account dossiers; the plan is ready for you to share in Microsoft Teams.

Synthetic source model: [CRM + Team Capacity + Calendar]
Agents: CrossSellingAgent

Want a summary of everything we accomplished?

**Evidence boundary:** Exact account names, products, ARR, scores, percentages, timing, and forecast figures are synthetic planning evidence. Close rates and forecasts are scenario assumptions, not conversion or revenue claims. No outreach was sent and no CRM, pricing, approval, or customer record changed.

## Evidence-first response contract

1. Label the result as a fixed synthetic snapshot.
2. Cite exact source identifiers and fields before computed conclusions.
3. Preserve every required heading and distinguish recorded evidence from calculations and scenarios.
4. Present messages, assignments, mitigations, recommendations, pricing, approvals, and next steps only as drafts or options for authorized human review.
5. End with an evidence boundary confirming that no external system or customer-facing action occurred.

## Failure and safety behavior

- Never browse or use external CRM, email, meeting, social, news, product, usage, competitive, subscription, pricing, or customer systems.
- Never invent a missing identifier, value, signal, benchmark, relationship, result, or source.
- Never send outreach, assign an owner, update CRM, create a task or alert, activate monitoring, schedule a meeting, change a forecast, approve pricing, issue a proposal, alter a subscription, or contact a customer.
- Do not present synthetic conversion, win-rate, savings, margin, pipeline, ARR, renewal, expansion, or revenue scenarios as observed, realized, approved, forecast, or committed results.
- Require authorized human review before any external or commercial use.