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Databricks Apps Adoption Playbook

Field vs Product framework for driving adoption

ICP and Targeting

Owner: Field Ops / Sales Strategy

Ideal Customer Profile for Apps

Dimension Criteria
Platform Maturity Already using Databricks for analytics/ML
Use Case Building new data-intensive applications
Buyer Data team with business unit sponsorship
Tech Stack Open to Python/modern frameworks (not legacy Java/.NET)
AI Interest Wants AI-powered applications

Customer Segmentation: Quality vs Quantity Motion

Apps adoption requires two distinct motions based on customer profile. Matching motion to customer is critical for retention.

flowchart LR
    subgraph QualityMotion["QUALITY MOTION"]
        Q1[Business outcome focus]
        Q2[Few apps, deep impl]
        Q3[PS support required]
        Q4[High stickiness]
        Q5["Metric: Strategic Wins"]
    end
    
    subgraph QuantityMotion["QUANTITY MOTION"]
        N1[Developer experience focus]
        N2[Many apps, lightweight]
        N3[IDE integration critical]
        N4[Dev mindshare governs]
        N5["Metric: Coverage"]
    end
    
    QualityMotion --> Enterprise["Enterprise, Regulated"]
    QuantityMotion --> DN["Digital Natives"]

Motion Selection Guide

Customer Profile Primary Motion Key Success Factors
Enterprise (Regulated) Quality PS engagement, deep use case fit, governance
Enterprise (Non-Regulated) Quality Business value, FE support, ease of use
Digital Native Quantity Developer experience, IDE integration, self-serve

Motion Comparison

Dimension Quality Motion Quantity Motion
Customer Profile Business outcome-oriented Tech/Developer-centric
Typical Segment Enterprise, Regulated Digital Native (DN)
App Count Few (1-5 deep apps) Many (10+ lightweight apps)
Implementation Depth Deep, production-grade Lightweight, often ephemeral
Success Metric Strategic Wins, Retention Coverage, Active Developers
Support Model Professional Services Self-serve, IDE integration
Phase Focus P1 (Prove It) P2-P3 (Scale/Expand)

Disqualifiers (For Now)

Signal Reason
Legacy app migration priority Platform not ready yet
Hyperscaler-first strategy Harder to displace
No existing Databricks footprint Cold start too hard
Pure IT buyer (no business sponsor) Hard to show business value

Lighthouse Account Selection Criteria

Signal Why It Matters
Strat Hunter designation Pre-qualified strategic importance
High platform adoption Data gravity creates Apps opportunity
Strong data gravity More data = more Apps use cases
AI maturity Deep vertical app opportunity
Strong partner ecosystem SI/ISV can accelerate delivery

App Archetype Targeting

Archetype Target Signal Customer Profile Priority
Business Cockpit Unity Catalog adoption Established lakehouse, business stakeholder interest High
Deep Vertical AI adoption signals Strategic lighthouse, needs beachhead, analytical maturity High
Horizontal Small teams OR large business presence Platform productivity, new persona expansion Medium

Targeting Matrix

Signal Cockpit Vertical Horizontal
Unity Catalog adopted ✅    
AI/ML maturity (models in prod)   ✅  
Large business user base ✅   ✅
Deep domain pain point   ✅  
Platform productivity needs     ✅
Strong data gravity ✅ ✅  
Executive sponsorship ✅ ✅  
Small technical team     ✅

Verticals in Scope

Vertical Abbrev Notes Primary Archetype
Manufacturing MFG Supply chain, quality prediction Vertical
Retail RTL Inventory, demand sensing Cockpit
Health and Life Sciences HLS Regulated—security/compliance critical Vertical
Financial Services FSI Regulated—security/compliance critical Cockpit
Digital Natives DN Often more mature, faster adopters Horizontal

Actions for Field Ops

Action Purpose Priority
Finalize lighthouse account list (10-15) Focus strategic wins High
Apply targeting matrix to pipeline Identify Apps opportunities High
Segment accounts by motion type Match approach to customer High
Update SFDC with archetype tags Enable tracking Medium

Last Updated: January 2026

Related: Sales Plays and Patterns Hypotheses