Strengths
- Strong automation capabilities
- Low-friction data collection
- Clear visibility into engagement activity
- Fast onboarding experience
Product Design Case Study · Enterprise SaaS
Altify Relationship Map Automation
Designing an automated relationship mapping experience for enterprise sales teams - turning a static diagram into a living strategic intelligence system.
/ Problem to Solve
Enterprise sales teams struggle to maintain accurate relationship maps. In complex B2B environments, sellers need to understand who influences decisions, political alignment, buyer roles, champions and blockers, relationship strength, missing stakeholders and executive coverage.
The previous experience was very clicky. A single relationship map could end up with 200+ contacts, and before this project every one of them had to be added and connected by hand. Maps were built manually, became outdated quickly, hid missing influencers, became visually overwhelming on large accounts and rarely surfaced relationship risks to leadership.
The challenge
Design an automated relationship mapping experience that reduces manual work, identifies stakeholders, detects relationship gaps, surfaces political risks and supports strategic account planning - while keeping users in control of the final map. The long-term vision: turn relationship mapping from a static diagram into a living strategic intelligence system.
/ Competitor Analysis
Focused on how enterprise revenue platforms approached relationship intelligence, stakeholder discovery, AI-driven account insights, CRM enrichment and strategic account visibility.
People AI
People AI demonstrated strong relationship intelligence capabilities through automated contact capture, relationship scoring, communication tracking, CRM enrichment and stakeholder visibility.
Weaknesses
- Focused heavily on operational tracking
- Limited strategic account planning support
- Minimal political relationship mapping
- Limited collaborative workflows
/ User Interviews
Understanding how sellers currently build and maintain relationship maps.
Participants
- Strategic Account Managers
- Enterprise Sellers
- Sales Leaders
- Customer Success Teams
- Revenue Operations
Research questions
- How do users currently create relationship maps?
- Which sources do they use for stakeholder information?
- What makes relationship maps difficult to maintain?
- How do sellers identify political influence?
- How do users validate stakeholder relationships?
Key findings
1. Relationship maps were rarely updated
Most sellers updated relationship maps only during deal reviews, strategic planning sessions, leadership escalations and quarterly business reviews. After creation, maps quickly became outdated.
2. Stakeholder discovery was highly manual
Users relied heavily on memory, meeting notes, email conversations, CRM comments and internal discussions - increasing the risk of missing key decision-makers.
3. Enterprise relationship maps became hard to scale
Users managing strategic accounts reported visual overload, relationship confusion, difficulty understanding hierarchy and challenges identifying influence paths - highlighting the need for scalable visualisation patterns.
/ User Flows
The flow was reorganised around a single root - Manage Contacts - branching into four parallel paths so sellers could add, organise, import or remove stakeholders without losing context.
Manage Contacts
Add
Create New
Search
Suggestions
Attribute Toggles
Personas
Select Persona
If Persona Already Added
Contact Details
Import Maps
Search
Suggested Maps
Remove
List of Contacts on the Map
/ Ideation - Low Fidelity Designs
The low-fidelity phase helped validate interaction patterns before moving into visual design or development.
Key insights
Drag & drop
Contacts could be dragged and dropped from the panel directly onto the map.
Bulk selection & removal
Multiple contacts could be selected at once to speed up adding them to - or removing them from - the map in a single action.
Import attributes
A toggle to import a contact's attributes alongside the contact itself.
Auto reporting lines
A toggle to automatically import who each contact reports to.
Import from related maps
Contacts could be reused by importing them from other related relationship maps.
Recently added highlight
Contacts recently added to the map were highlighted so users could quickly spot what just changed.
/ High Fidelity Designs
Balancing strategic depth, scalability, clarity, trust and AI transparency.
Final experience highlights
AI-assisted relationship creation
Users could generate relationship maps from:
- Meeting transcripts
- CRM activity
- Opportunity data
- Email interactions
Relationship intelligence layer
The UI surfaced:
- Missing stakeholders
- Weak relationships
- Political risks
- Influence gaps
- Engagement summaries
Strategic visualisation
The final experience emphasised:
- Clear hierarchy structures
- Simplified stakeholder grouping
- Political visibility
- Buyer role clarity
- Relationship strength indicators
Human validation experience
To improve trust, the designs included:
- Editable AI suggestions
- Confidence indicators
- Source visibility
- Suggested corrections
- Manual override controls
The experience positioned AI as a strategic assistant instead of a fully autonomous system.
/ User Testings
Validating usability, trust, scalability and strategic clarity.
Participants
- Enterprise sellers
- Strategic account managers
- Sales leaders
- Customer success representatives
Testing goals
- How quickly users could build relationship maps
- Whether users trusted AI-generated suggestions
- If users understood political structures faster
- How scalable the experience felt for enterprise accounts
- Whether relationship risks were easier to identify
These findings directly informed future roadmap discussions - the test was very successful with both internal and external stakeholders.
They suggested the map centring itself once a new contact is added so users can see that their work has been done, and also adding a toast to double-confirm it.
/ Conclusion
This project transformed relationship mapping from a static manual workflow into an automated strategic intelligence experience.
The final solution helped
- Reduce manual maintenance
- Improve stakeholder visibility
- Surface relationship risks earlier
- Accelerate strategic account understanding
Reinforced product design principles
- Transparency is essential for enterprise trust
- Strategic workflows require scalability and clarity
- Automation should reduce friction without increasing complexity
This case study became an important step toward a broader vision: Automated revenue execution inside Altify.
