cross-sell-target-identifier

name	cross-sell-target-identifier
description	Analyzes successful product customers to identify patterns, then finds similar accounts that are good cross-sell candidates with fit scores and reasoning. Use when user asks "who should I pitch this product to", "find cross-sell opportunities", "which customers should buy Product X", "identify upsell targets", "product expansion candidates", or "who else would buy this".
metadata	{"author":"Dataverse","version":"1.0.0","category":"sales-analytics"}

Cross-Sell Target Identifier

When launching a new product or looking to expand product adoption, sales teams need to identify which existing customers are most likely to purchase. This skill analyzes the characteristics of successful customers for a given product, finds similar customers who don't own it yet, and provides prioritized recommendations with justification.

Instructions

Step 1: Identify the Target Product

When user asks "Which customers should I pitch Product X to?":

1. Identify the Product:

SELECT productid, name, description, producttypecode, productstructure
FROM product
WHERE name LIKE '%[product name]%'
AND statecode = 0

2. Confirm with user if multiple matches

Step 2: Analyze Successful Product X Customers

2.1 Find Customers Who Own Product X Query won opportunities that included the target product:

SELECT op.opportunityid, op.customerid, op.accountid, op.actualvalue,
       op.actualclosedate, op.salesstage
FROM opportunity op
JOIN opportunityproduct opp ON op.opportunityid = opp.opportunityid
WHERE opp.productid = '[target_product_id]'
AND op.statecode = 1

Note: A product may appear in multiple opportunityproduct rows per opportunity. Deduplicate opportunityids programmatically after fetching results.

2.2 Build Success Profile from Winning Accounts For each winning account, gather firmographic data:

SELECT accountid, name, industrycode, numberofemployees, revenue,
       customertypecode, address1_stateorprovince, address1_country,
       ownershipcode, createdon
FROM account
WHERE accountid IN ([list of winning account ids])

2.3 Analyze Success Patterns

Firmographic Analysis:

Calculate distribution across successful customers:
- Industry breakdown (industrycode): Which industries buy most?
- Company size (numberofemployees): What's the typical range?
- Revenue range: What's the typical revenue bracket?
- Geography (address1_stateorprovince/country): Regional concentrations?
- Customer type (customertypecode): Are they customers, partners, etc.?

Existing Product Ownership:

For each successful customer, identify other products owned:
SELECT a.accountid, a.name, p.name as product_name
FROM account a
JOIN opportunity o ON a.accountid = o.accountid
JOIN opportunityproduct op ON o.opportunityid = op.opportunityid
JOIN product p ON op.productid = p.productid
WHERE o.statecode = 1
AND a.accountid IN ([winning account ids])

Buying Pattern Analysis:

Identify patterns in successful deals:
- Average deal size for Product X
- Common bundled products
- Typical sales cycle length
- Time since becoming customer before purchasing Product X

Activity Pattern Analysis:

Review activities preceding successful deals:
- Types of engagement (calls, meetings, emails)
- Number of touchpoints before close
- Content/resources shared

Step 3: Generate Ideal Customer Profile (ICP)

Based on the analysis, create an Ideal Customer Profile:

IDEAL CUSTOMER PROFILE FOR [PRODUCT X]
════════════════════════════════════════════════════

FIRMOGRAPHIC CHARACTERISTICS:
- Industry: [Top 3 industries, e.g., "Financial Services (35%), Healthcare (28%), Technology (22%)"]
- Company Size: [Employee range, e.g., "100-500 employees (sweet spot)"]
- Revenue: [Revenue range, e.g., "$10M-$100M annual revenue"]
- Geography: [Regional patterns, e.g., "Primarily US, expanding to UK/EU"]

BEHAVIORAL INDICATORS:
- Already Owns: [Products commonly owned first, e.g., "80% have Product Y"]
- Customer Tenure: [Time as customer, e.g., "Typically 6-18 months as customer"]
- Recent Activity: [Engagement patterns, e.g., "High engagement with support/success"]
- Expansion Signals: [Growth indicators, e.g., "Recent hiring, new funding"]

BUYING PATTERNS:
- Average Deal Size: [$X]
- Typical Bundle: [Product X + Y + Z]
- Sales Cycle: [X days average]
- Common Champion: [Job title patterns]

SUCCESS INDICATORS FROM NOTES:
- Pain Points: [Common challenges mentioned]
- Use Cases: [How they use the product]
- Trigger Events: [What prompted purchase]

Step 4: Query Customer Base for Matches

4.1 Find Non-Owners of Product X

Note: Dataverse SQL does not support subqueries. Run two separate queries and exclude owners programmatically.

First, get all accounts that already own Product X (from Step 2.1 results — collect their accountids into a list).

Then query all active accounts:

SELECT a.accountid, a.name, a.industrycode, a.numberofemployees, a.revenue,
       a.customertypecode, a.address1_stateorprovince, a.createdon
FROM account a
WHERE a.statecode = 0

Filter out accounts whose accountid appears in the owner list programmatically after fetching.

4.2 Score Each Potential Target

For each non-owner account, calculate fit score:

Firmographic Fit (40%):

Factor	Points	Scoring Logic
Industry Match	0-15	Exact match to top ICP industry = 15, Adjacent = 10, Other = 0
Size Match	0-15	Within ICP range = 15, Close = 10, Outside = 5, Way off = 0
Revenue Match	0-10	Within ICP range = 10, Close = 5, Outside = 0

Behavioral Fit (35%):

Factor	Points	Scoring Logic
Owns Prerequisite Products	0-15	Has common prerequisite = 15, Related product = 10
Customer Tenure	0-10	In ICP tenure range = 10, Close = 5
Recent Engagement	0-10	High recent activity = 10, Moderate = 5, Low = 0

Buying Signals (25%):

Factor	Points	Scoring Logic
Recent Purchases	0-10	Bought something in last 6 months = 10
Expansion Behavior	0-10	Added users, upgraded = 10, Stable = 5
Strategic Initiative Signals	0-5	Mentioned in notes/activities = 5

Step 5: Analyze Buying Signals

Important: Dataverse SQL Limitations Dataverse SQL does NOT support: subqueries, DATEADD(), GETUTCDATE(), HAVING, DISTINCT, UNION, CASE statements, AVG on sentiment. Calculate date filters programmatically before querying (e.g., '2025-09-01' for 6 months ago).

5.1 Recent Purchase Activity

SELECT a.accountid, a.name, o.opportunityid, o.name, o.actualclosedate, o.actualvalue
FROM account a
JOIN opportunity o ON a.accountid = o.accountid
WHERE o.statecode = 1
AND o.actualclosedate > '2025-09-01'

5.2 Expansion Indicators Look for signals in activities and notes:

SELECT annotationid, objectid, subject, notetext, createdon
FROM annotation
WHERE objecttypecode = 'account'
AND createdon > '2025-09-01'

Keywords to Detect:

• Expansion: "growing", "scaling", "expanding", "new offices", "hiring"
• Strategic: "initiative", "project", "transformation", "migration"
• Pain: "struggling", "challenge", "problem", "need"
• Competition: "evaluating", "considering", "looking at"

5.3 Recent Cases (Support Indicators) Query cases per account, then aggregate programmatically:

SELECT incidentid, customerid, createdon, prioritycode, msdyn_casesentiment
FROM incident
WHERE createdon >= '[6_months_ago]'

Group and count by customerid programmatically after fetching results.

High case volume could indicate:

• Active usage (good for expansion)
• Frustration (may need resolution first)
• Product limitations (potential for upsell to better solution)

Step 6: Rank and Present Target Accounts

Output Format:

CROSS-SELL TARGETS FOR [PRODUCT X]
════════════════════════════════════════════════════
Analysis Date: [Date]
Methodology: Compared against [N] successful Product X customers

SUMMARY:
- Total Eligible Accounts: [N]
- High Fit (Score 80+): [N] accounts
- Medium Fit (Score 60-79): [N] accounts
- Low Fit (Score <60): [N] accounts

════════════════════════════════════════════════════
TOP 10 CROSS-SELL TARGETS
════════════════════════════════════════════════════

1. CONTOSO CORPORATION
   Fit Score: 92/100
   ────────────────────────────────────────────────
   
   WHY THEY'RE A FIT:
   ✓ Industry: Financial Services (top ICP industry)
   ✓ Size: 350 employees (in sweet spot 100-500)
   ✓ Already Owns: Product Y, Product Z (common prerequisite)
   ✓ Customer Since: 14 months (optimal tenure range)
   ✓ Recent Activity: 8 touchpoints in last 30 days
   
   BUYING SIGNALS DETECTED:
   • Mentioned "scaling operations" in recent meeting notes
   • Purchased add-on licenses last month (expansion behavior)
   • Attended Product X webinar 2 weeks ago
   
   RECOMMENDED APPROACH:
   • Lead with [specific value prop based on industry]
   • Reference success story from [similar customer]
   • Contact: [Primary contact name and role]
   
   ESTIMATED DEAL SIZE: $45,000 (based on similar deals)

2. FABRIKAM INDUSTRIES
   Fit Score: 87/100
   ────────────────────────────────────────────────
   
   WHY THEY'RE A FIT:
   ✓ Industry: Manufacturing (adjacent to ICP)
   ✓ Size: 800 employees (slightly above sweet spot)
   ✓ Already Owns: Product Y
   ✓ High engagement with Customer Success
   
   BUYING SIGNALS DETECTED:
   • New CTO joined 3 months ago (leadership change)
   • Mentioned "digital transformation" in discovery call
   
   POTENTIAL CONCERNS:
   ⚠ Above typical company size - may need enterprise approach
   ⚠ No activity with Sales in last 60 days
   
   RECOMMENDED APPROACH:
   • Re-engage through Customer Success warm intro
   • Position as part of transformation initiative
   • Consider executive sponsor engagement

[Continue for top 10...]

Step 7: Create Action Items

Generate Follow-up Tasks:

For each top target, offer to create:

Use create_record with tablename: task
{
  "subject": "Cross-sell outreach: [Product X] to [Account Name]",
  "description": "Target identified as high fit for [Product X].\n\nFit Score: [X]/100\n\nKey talking points:\n- [Point 1]\n- [Point 2]\n\nContact: [Recommended contact]",
  "regardingobjectid": "[accountid]",
  "scheduledend": "[appropriate date]",
  "prioritycode": [based on fit score]
}

Update Account with Cross-Sell Flag:

Consider adding notes to account:

Use create_record with tablename: annotation
{
  "subject": "Cross-sell opportunity identified: [Product X]",
  "notetext": "[Summary of why they're a fit and recommended approach]",
  "objectid": "[accountid]",
  "objecttypecode": "account"
}

Dataverse Tables Used

Table	Purpose
product	Identify target product
opportunity	Find won deals with target product
opportunityproduct	Link opportunities to products
account	Customer firmographic data
contact	Stakeholder information
activitypointer	Engagement history
annotation	Notes containing buying signals
incident	Support case patterns
task	Create follow-up tasks

Key Fields Reference

product:

• productid (GUID) - Unique identifier
• name (NVARCHAR) - Product name
• productnumber (NVARCHAR) - SKU/product number
• producttypecode (CHOICE) - Sales Inventory(1), Misc Charges(2), Services(3), Flat Fees(4)
• productstructure (CHOICE) - Product(1), Family(2), Bundle(3)
• statecode (STATE) - Active(0), Retired(1), Draft(2), Under Revision(3)

opportunity:

• accountid (LOOKUP → account) - Related account
• statecode (STATE) - Open(0), Won(1), Lost(2)
• statuscode (STATUS) - In Progress(1), On Hold(2) [Open]; Won(3) [Won]; Canceled(4), Out-Sold(5) [Lost]
• actualvalue (MONEY) - Won deal value
• actualclosedate (DATE) - When deal closed
• originatingleadid (LOOKUP → lead) - Source lead

opportunityproduct:

• opportunityid (LOOKUP → opportunity) - Parent opportunity
• productid (LOOKUP → product) - Product in the deal
• quantity (DECIMAL) - Units sold
• priceperunit (MONEY) - Unit price
• extendedamount (MONEY) - Line total (calculated)
• manualdiscountamount (MONEY) - Line discount

account:

• industrycode (CHOICE) - Accounting(1), Agriculture(2), Broadcasting(3), Brokers(4), Building Supply(5), Business Services(6), Consulting(7), Consumer Services(8), etc.
• numberofemployees (INT) - Company size
• revenue (MONEY) - Annual revenue
• customertypecode (CHOICE) - Customer classification
• openrevenue (MONEY) - Total open pipeline value (rollup field; availability depends on org configuration — query opportunity table directly if not present)
• opendeals (INT) - Number of open opportunities (rollup field; availability depends on org configuration)

Cross-Sell Best Practices

1. Start with success: Always analyze existing successful customers first
2. Multi-factor matching: Don't rely on single criteria for fit
3. Watch for timing: Recent purchases indicate budget availability
4. Leverage relationships: Warm introductions beat cold outreach
5. Segment recommendations: Different approaches for different segments
6. Track results: Monitor conversion rates to refine the model

Examples

Example 1: Find Cross-Sell Targets for New Product

User says: "Who should I pitch our new Analytics Pro product to?"

Actions:

1. Search product table for "Analytics Pro"
2. Find accounts that have purchased Analytics Pro
3. Build ideal customer profile from successful accounts
4. Query non-owners matching the profile
5. Rank by fit score and provide recommendations

Result:

IDEAL CUSTOMER PROFILE FOR ANALYTICS PRO:
- Industry: Financial Services (45%), Healthcare (30%)
- Size: 200-1000 employees
- Already owns: Platform Basic (80% correlation)

TOP 10 CROSS-SELL TARGETS:
1. Northwind Bank (92% fit) - Financial Services, 450 employees, owns Platform Basic
2. Alpine Health (87% fit) - Healthcare, 800 employees, recent support engagement

Example 2: Upsell Existing Customers

User says: "Which customers should upgrade to Enterprise tier?"

Actions:

1. Identify customers on lower tiers
2. Analyze Enterprise customers for common traits
3. Find Standard tier customers matching Enterprise profile
4. Factor in engagement and growth signals

Result:

UPGRADE CANDIDATES (Standard → Enterprise):
1. Contoso Ltd - Growing usage, added 50 users last quarter
2. Fabrikam Inc - Multiple support cases about feature limits
3. Tailspin Toys - Recent funding, headcount doubling

Example 3: Product Bundle Opportunity

User says: "Who bought Product A but not Product B?"

Actions:

1. Find accounts with Product A in won opportunities
2. Exclude accounts with Product B in won opportunities
3. Rank by recency and relationship strength

Result:

PRODUCT A CUSTOMERS WITHOUT PRODUCT B:
- 23 accounts identified
- Average fit score: 78%
- Top 5 recommendations with outreach scripts provided

Troubleshooting

Error: Product not found

Cause: Product name doesn't match exactly or product is inactive Solution:

• Search with partial name match (LIKE '%name%')
• Check statecode to include active products only
• List available products for user to select

Error: No customers found for target product

Cause: New product with no sales history yet Solution:

• Use similar product's customer base as proxy
• Define ideal customer profile manually
• Start with industry/size matching only

Error: Too few differentiation signals

Cause: Winning customers too diverse for clear pattern Solution:

• Increase sample size (longer date range)
• Focus on top-performing accounts only
• Add firmographic filters (industry, size)