Platform Evaluation Scorecard
Evaluate competing platforms across six dimensions of operational fit. Each dimension includes evaluation criteria and a scoring rubric grounded in advancement technology practice. Adjust weights, score each platform, and generate a comparative analysis.
Platforms Under Evaluation
Name two to four platforms to compare. These can be specific products, vendor proposals, or architectural alternatives.
Evaluation Context
These inputs shape the generated analysis. The evaluation criteria and scoring rubrics remain the same regardless of context; the analysis applies your context to interpret the scores.
Score the platform's native data model against your constituent record structure, gift processing requirements, and relationship tracking needs. A CRM with a weak advancement data model requires custom development that compounds through every subsequent phase. Evaluate against your actual operational workflows, not the vendor's demo script.
Evaluation criteria
- Constituent record structure: individual, organization, and household relationships with proper cardinality
- Gift processing model: pledges, pledge payments, recurring gifts, planned giving vehicles, gift-in-kind
- Soft credit and matching gift handling with accurate aggregation logic
- Fund, campaign, appeal, and designation hierarchies with rollup reporting
- Relationship tracking across overlapping affiliations: board, volunteer, alumni, parent, donor, patient
Scoring rubric
- 1
- Core constituent and gift objects require custom development; no native advancement model
- 2
- Basic contact and opportunity model; gift processing requires significant extension; limited relationship types
- 3
- Standard advancement model covering individual giving, pledge management, and basic fund structures; customization needed for complex giving vehicles
- 4
- Comprehensive model with planned giving instruments, multi-entity soft credits, and hierarchical fund/campaign structures built in
- 5
- Purpose-built advancement data model with stewardship tracking, prospect management, committee/volunteer tracking, and extensible entity framework
Common evaluation mistakes
- Rating "customizable" the same as "already built." A platform that can be configured to handle planned giving is not equivalent to one that ships with planned giving objects, workflows, and reporting.
- Evaluating the data model in isolation from reporting. A data model that stores gift data correctly but cannot aggregate it for campaign progress reporting without custom views is incomplete.
- Ignoring the household model. Organizations that report on household giving need a platform that models households natively, not one that requires manual relationship management.
Integration architecture determines how the platform communicates with every other system in the advancement technology stack: student information, events, online giving, wealth screening, marketing automation. Weak integration capabilities mean manual data movement between systems and ongoing maintenance of point-to-point connections.
Evaluation criteria
- API coverage: what percentage of platform functionality is accessible via API, not just record CRUD
- Bulk data operations: can the API handle initial data loads and large batch updates efficiently
- Change notification mechanisms: webhooks, change data capture, platform events
- Authentication and authorization: OAuth 2.0, SAML, field-level API security
- Pre-built connectors and middleware compatibility for common advancement systems
Scoring rubric
- 1
- No public API or limited to basic record CRUD with severe rate limits
- 2
- REST API available; limited to single-record operations; no change notification mechanism
- 3
- Full REST API with bulk operations; webhook support for object-level changes; OAuth 2.0 authentication
- 4
- Comprehensive API surface including bulk, streaming, and metadata APIs; native change data capture; published rate limits with reasonable thresholds
- 5
- Full API parity with the UI; native integration platform with pre-built connectors for common advancement systems; SDKs in multiple languages
Common evaluation mistakes
- Accepting "we have an API" at face value. API existence and API adequacy are different things. Ask for endpoint coverage documentation and rate limit specifications.
- Ignoring the bulk data path. Every platform needs an initial data load. If the only mechanism is single-record REST calls, a 500K-record migration becomes a multi-week throttled operation.
- Testing integration in one direction only. Your warehouse needs to read from the platform; the platform needs to receive updates from external systems. Evaluate both paths.
Calculate the five-year sum of every cost the platform creates: license fees, implementation, customization, training, ongoing administration, and the opportunity cost of staff time spent on platform maintenance.
Evaluation criteria
- License and subscription structure: per-user, per-record, or flat; annual escalation terms and caps
- Implementation partner costs: system integrator fees, not just software
- Training and change management: initial onboarding and ongoing skill development
- Ongoing administration FTE requirement: how many people does the platform need to run
- Customization and development costs: initial build plus accumulated technical debt from platform upgrades
- Data migration costs: extract, transform, validate, load, and parallel operation
Scoring rubric
- 1
- Five-year TCO exceeds budget by more than 40%; licensing structure penalizes growth
- 2
- TCO within 20–40% above baseline; significant hidden costs in implementation or customization
- 3
- TCO within budget range; licensing scales predictably; implementation costs are within market norms
- 4
- TCO competitive with alternatives; transparent pricing with no surprise line items; implementation scope well-defined
- 5
- TCO meaningfully below alternatives at equivalent capability; licensing incentivizes growth; vendor absorbs platform upgrade costs
Common evaluation mistakes
- Comparing license fees without implementation costs. The platform with the lowest annual license frequently has the highest implementation cost because it requires more custom development to reach functional parity.
- Treating implementation as a one-time cost. Platforms that require custom development create ongoing maintenance costs that compound annually. Factor in the developer FTE needed to maintain custom code through platform upgrades.
- Ignoring administration overhead. A platform that requires 1.5 FTE to administer costs $120K–$180K per year in personnel before a single license dollar is counted.
Score migration complexity based on the actual difficulty of moving your data: whether historical records survive intact, whether operational continuity holds during cutover, and whether the institution can revert if the new platform fails to perform. Use your data volume and schema complexity as inputs, not the vendor's migration estimates.
Evaluation criteria
- Schema mapping difficulty: structural alignment between source and target data models
- Historical record fidelity: transaction-level giving history, audit trails, original receipt dates
- Parallel operation requirements: how long both systems must run simultaneously and at what cost
- Rollback planning: can you revert to the prior platform if migration fails, and how long does reversal take
- Timeline risk: realistic elapsed time from kickoff to full decommission of the prior system
Scoring rubric
- 1
- Fundamental schema mismatch; historical data requires lossy transformation; no viable rollback path
- 2
- Significant mapping gaps requiring custom ETL; limited historical fidelity; rollback requires full re-implementation
- 3
- Standard mapping with known transformations; historical giving data preserves amounts, dates, and fund coding; parallel operation plan exists
- 4
- Strong schema alignment; historical data maps with minimal transformation; automated validation compares source and target; rollback tested
- 5
- Platform provides migration tooling with pre-built source connectors; automated data validation; phased cutover with per-module rollback
Common evaluation mistakes
- Trusting the vendor's migration timeline. A vendor quoting "12 weeks" has not examined your custom fields, your historical data quality, or your parallel operation requirements. Validate against your actual data complexity.
- Treating migration as purely technical. The people who use the data must validate it. Build validation cycles into the timeline: gift officers confirming portfolio data, development services confirming tax receipt accuracy, finance confirming reconciliation totals.
- Skipping parallel operation. Running both systems simultaneously for a full gift processing cycle (typically 30–60 days) catches errors that no automated validation can find.
Evaluate the surrounding ecosystem: availability of implementation expertise, strength of the user community, the vendor's financial trajectory, and the degree to which the vendor invests specifically in the advancement market rather than treating it as a vertical afterthought.
Evaluation criteria
- Implementation partner availability: number and depth of advancement-specialized partners, not just general CRM partners
- User community: active user groups, conferences, peer-to-peer knowledge sharing
- Product roadmap transparency: published roadmap, customer influence on priorities, track record of delivery
- Customer retention and satisfaction: churn rate, reference customers in your institutional segment
- Vendor financial stability and sustained commitment to the advancement market specifically
Scoring rubric
- 1
- Sole-source vendor with no independent implementation partners; no user community; no published roadmap
- 2
- Limited partner network; small or inactive user community; roadmap exists but is not shared; mixed customer satisfaction
- 3
- Established partner network with advancement experience; active user community; annual roadmap updates; stable customer base
- 4
- Multiple specialized advancement partners; strong user community with peer-to-peer knowledge sharing; roadmap influenced by customer advisory board; high retention
- 5
- Deep partner network with competitive specialization; thriving user community with conferences; transparent, customer-influenced roadmap; very high retention; sustained advancement-specific investment
Common evaluation mistakes
- Confusing general CRM partners with advancement expertise. An implementation partner with enterprise sales experience but no advancement background is not a qualified advancement partner. Ask for advancement-specific client references.
- Weighting the roadmap over the shipping record. Features on a roadmap are promises; features in production are capabilities. Score what exists today, discount what is planned.
- Ignoring concentration risk. If one partner handles 80% of the vendor's advancement clients, that partner's capacity, pricing, and quality control your experience regardless of the vendor's platform strengths.
Reporting is the primary way most advancement staff interact with platform data. Evaluate the full analytical path: how data gets into reporting tools, the query and visualization layer, and how results reach stakeholders. Weak built-in reporting means the institution builds and maintains a parallel analytical infrastructure.
Evaluation criteria
- Built-in reporting tools: complexity of reports buildable by advancement staff without code or IT involvement
- Data warehouse and analytical platform connectivity: native connectors, data replication, export mechanisms
- Real-time dashboard capability: operational dashboards for giving day, campaign progress, event registration
- Ad hoc query flexibility: can non-technical users answer questions the standard reports do not cover
- Metric standardization: can the platform enforce consistent definitions of "total raised," "donor count," and "retention rate" across all reporting
Scoring rubric
- 1
- Reporting limited to pre-built templates with no modification capability; no data export mechanism
- 2
- Basic report builder with limited joins; CSV export only; no real-time capability; no external connectivity
- 3
- Functional report builder with cross-object queries; scheduled export or replication; basic dashboards; some ad hoc capability
- 4
- Strong reporting with complex filters, grouping, and cross-object joins; native warehouse connectivity; real-time dashboards; user-accessible query interface
- 5
- Full-featured analytical platform with role-based dashboards, embedded analytics, native data warehouse sync, semantic layer for metric definitions, and API-accessible reporting
Common evaluation mistakes
- Evaluating the demo dashboard instead of the report builder. Every vendor demo features a polished dashboard with curated data. Ask to build a report from scratch: "Show me donors who gave over $1,000 last year but have not given this year, grouped by gift officer, with last gift date and lifetime giving."
- Ignoring the analytical off-ramp. Most institutions will eventually outgrow built-in reporting and need a data warehouse. A platform that makes data extraction difficult or expensive creates a long-term bottleneck.
- Treating reporting as separate from data architecture. The reporting tool is only as good as the data model underneath. If the platform cannot model household giving correctly, no reporting tool can produce accurate household giving reports.
Comparative Analysis
Based on your scores and evaluation context.