cross-sell-target-identifier

# 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 ```