The Problem with Integration Prioritization
Every B2B product eventually faces the integration question: “Which third-party tools should we connect to?” The backlog fills up with requests—Salesforce, HubSpot, Slack, Zapier, Jira, the list never ends.
Without a framework, prioritization becomes political. Whoever has the loudest customer or the most persistent sales rep wins. This leads to a patchwork of integrations, some heavily used, others gathering dust.
I needed a systematic way to evaluate integration requests that balanced customer value against technical cost.
The Framework
Every integration gets scored on three dimensions:
1. Customer Value (0-10)
This isn’t just “how many customers want it”—it’s a weighted assessment:
| Factor | Weight |
|---|---|
| Request frequency | 25% |
| Revenue at risk (churn/expansion) | 30% |
| Segment alignment (ICP fit) | 25% |
| Competitive necessity | 20% |
A Salesforce integration requested by 50 enterprise customers threatening to churn scores higher than a niche tool requested by 5 SMB accounts.
2. Technical Complexity (0-10, inverted)
Lower is better. Factors include:
- API quality: Is the docs good? Is there a sandbox? Rate limits?
- Data model alignment: How well does their object model map to ours?
- Authentication complexity: OAuth 2.0 is straightforward; custom SAML is not.
- Maintenance burden: Will this break every time they update their API?
Score starts at 10 (trivial) and decreases based on complexity factors.
3. Strategic Alignment (0-10)
Does this integration support where we’re going?
- Reinforces our positioning in target segment: +3
- Enables a new use case we’re betting on: +3
- Creates a moat (hard for competitors to replicate): +2
- Partner co-marketing opportunity: +2
An integration that’s technically easy but strategically irrelevant shouldn’t jump the queue.
The Prioritization Score
Priority Score = (Customer Value × 0.4) + (Technical Feasibility × 0.3) + (Strategic Alignment × 0.3)
This weights customer value highest (we’re here to serve customers) while ensuring we don’t ignore technical reality or strategic direction.
Worked Example
Integration Request: HubSpot CRM sync
| Dimension | Score | Reasoning |
|---|---|---|
| Customer Value | 8 | High request volume, 3 enterprise accounts contingent on it, strong ICP fit |
| Technical Complexity | 7 | Good API, decent docs, but complex object model for custom properties |
| Strategic Alignment | 6 | Supports mid-market push, but Salesforce is bigger priority |
Priority Score: (8 × 0.4) + (7 × 0.3) + (6 × 0.3) = 3.2 + 2.1 + 1.8 = 7.1
Compare this to:
Integration Request: Obscure industry-specific CRM
| Dimension | Score | Reasoning |
|---|---|---|
| Customer Value | 3 | Only 2 requests, small accounts |
| Technical Complexity | 4 | Poor API, no sandbox, custom auth |
| Strategic Alignment | 2 | Not in our target segment |
Priority Score: (3 × 0.4) + (4 × 0.3) + (2 × 0.3) = 1.2 + 1.2 + 0.6 = 3.0
HubSpot wins. By a lot. And now you have a rationale to share with the sales rep pushing for the niche CRM.
Using the Framework in Practice
Quarterly Integration Review
Every quarter, we:
- Collect all integration requests from the past 90 days
- Score each one using the framework
- Stack rank by priority score
- Allocate engineering capacity to top N integrations
Saying No (With Data)
The framework makes “no” easier. Instead of “we’re not doing that,” it’s “here’s the score—it didn’t make the cut this quarter, but we’ll re-evaluate as the data changes.”
Tracking Post-Launch
After shipping an integration, we track:
- Adoption rate (% of eligible customers using it)
- Support ticket volume
- Revenue influenced
This feeds back into the model—if our predictions were wrong, we adjust the weighting.
Limitations
No framework is perfect. This one struggles with:
- Truly novel integrations where there’s no historical signal
- Platform bets (like Zapier) that enable many use cases indirectly
- Customer concentration risk where one huge customer skews the data
For these edge cases, judgment still matters. The framework is a starting point, not a replacement for thinking.
The Outcome
Since implementing this framework:
- Integration backlog is transparent and defensible
- Engineering time is allocated to highest-impact work
- Sales has a clear answer for “when are we building X integration”
- We’ve shipped fewer integrations, but they’re more adopted
The goal was never to build more integrations—it was to build the right ones.