Redesigning networking around human motivation
10KC is a $75M B2B employee development platform used by enterprise organizations, including Amazon, Uber, and PayPal.
After acquiring another company, we evaluated whether to migrate the existing networking experience to the new platform or rethink it entirely. Rather than carry forward a model that wasn’t working, we chose to rebuild networking from the ground up.
My role
Lead Product Designer: Owned problem definition, UX strategy, interaction design, and validation
Company
B2B SaaS HR platform
Duration
2 months
Team
Two Software Engineers
The Problem
Only 2 out of 10 networking matches resulted in a meeting.
Engagement in networking programs was dropping. Employees were passively assigned matches with no context, no agency, and no clear reason to engage. Once lively networking communities were becoming stale and riddled with ghosting, leading to client churn when the value and ROI of the platform was called into question.
The Solution
We redesigned networking around how people actually build relationships. Instead of a recurring networking program where users were passively assigned a match every few weeks, the new experience gives employees agency over who they meet, why they meet, and when they engage. AI is applied strategically at key moments to reduce friction by helping users find relevant connections and have more meaningful conversations.
The Process
User Journey Pain Points
To uncover the core problem areas, I analyzed support tickets, networking analytics, and interviews. I spoke to four super users and four people who disengaged and stopped using the tool.
ACTIONS
Receives a match notification
Skims profile quickly
PAIN POINTS
Match feels randomly assigned
No context for why this person was chosen
No control over timing or criteria
“
I know what I want best. I should have a say in who I get to meet.
— Sarah, Program Participant
ACTIONS
Debates reaching out
Sends a message (or doesn't)
Waits for a response
PAIN POINTS
Unclear value exchange
Fear of being ignored
High emotional effort to initiate
“
Doesn't feel good when you're always the person reaching out.
— Marty, Program Participant
ACTIONS
Skips future matches
Stops responding
Silently disengages
PAIN POINTS
Matches feel too frequent
No clear way to pause or reset
“
I get matched wayyy too frequently. It feels like a chore.
— Jessica, Former Active User
Root causes of networking drop off
Most people network with a specific intent: Once that goal is met, the motivation drops sharply.
There’s no intrinsic “pull” without a reason. More networking matches ≠ more value.
Networking has a high emotional cost. If a match isn’t clearly relevant, it's “not worth the effort.”
Every networking interaction carries friction: social anxiety, fear of awkwardness, unclear value exchange, time commitment.
When users are forced into a networking program, they don’t feel invested.
They don’t understand why they are being matched with specific people and they don’t feel responsible for outcomes.
The "Aha" moment
Previous networking programs struggled with low engagement because they were designed around how we thought people should network, rather than how they actually behave.
Networking isn’t a daily habit—people do it occasionally, driven by specific needs or goals. They know best when and why they want to connect, so they should be in the driver’s seat. To succeed, we needed to focus on relevance by helping people make the right connections at the right time, rather than trying to maximize long-term participation.
Design principles
Based on these findings, I created principles to ensure that networking stays relevant and motivating.
User-Driven, Not Admin-Controlled
Design around employee needs, not admin configuration. Give users meaningful agency over their experience, supported by clear guardrails.
Guided personalization
Use AI to create more personalized, relevant experiences where it matters most.
Brainstorming solutions
With our principles in place, we explored a variety of approaches to tackle two core problems: relevance and emotional friction.
How might we create matches that users feel intrinsically motivated to act on?
Original issue: Match relevance
Explored & Rejected
More frequent matching
Increased fatigue
Admins dictate who meets
Misaligned with user needs
Chosen Direction
AI recommendations based on user goals
Final Solution
Users define their goals, and AI translates that into match criteria
2 personailzed match recommendations are provided, clearly explaining "why you were matched"
How might we reduce the emotional effort so that users will engage with their matches?
Original issue: Awkwardness and anxiety
Explored & Rejected
Forced introductions
Removed consent, increased anxiety
Scripted icebreakers
Felt artificial
Chosen Direction
Automate the most stressful parts of networking
Final Solution
Private match selection to ease rejection anxiety
Personalized AI generated conversation topics to alleviate anxiety
Pause / opt-out options are offered to avoid pressure to engage
The biggest learning from testing with users
I tested the design early and often, iterating with feedback at each stage. The moderated prototype walkthrough included four participants—two super users and two with no prior experience—to capture both expert and fresh perspectives.
Before
Members found the “Send Match Request” language intimidating, causing rejection anxiety.
After
Members could privately express interest in meeting with someone by marking them as a “preferred match,” signaling the algorithm to prioritize the pairing.
The Result
AI-Driven Match Personalization
Original issue: Non relevant matches
Users describe what they are looking for in natural language, and AI translates this intent into structured matching signals that directly influence their recommendations. This allows matches to reflect real, evolving goals rather than static profile data, giving users ownership over outcomes while increasing relevance without increasing match volume.
AI-Generated Conversation Starters
Original issue: Awkward conversations
AI generates personalized conversation starters using shared context, goals, and profile data. By reducing the emotional effort required to initiate contact, users are more likely to reach out with confidence and follow through on meaningful conversations.
One-Click Compliments to Reinforce Momentum
Original issue: Dwindling engagement
Users can send pre-written, one-click compliments that acknowledge effort, insight, or shared interests. These lightweight moments of positive reinforcement sustain momentum, encourage reciprocity, and keep interactions active between meetings.
Pause or Opt-Out of Networking
Original issue: Ghosting
Users can pause participation or opt out entirely. After extended inactivity, the system gently nudges users before automatically removing dormant accounts. This ensures active participants benefit, respects autonomy, and reduces guilt-driven disengagement, building long-term trust.
Reflections
The Biggest Challenge Along the Way
Managing bias in AI-driven matching
CONSTRAINT
Using AI to improve match relevance introduced the risk of algorithmic bias, including over-indexing on similarity, profile verbosity, or existing power dynamics.
DECISION
We constrained AI inputs, avoided bias-amplifying signals, and kept humans in the loop by letting users choose between recommended matches. We monitored outcomes through surveys and match distribution analytics to validate fairness over time.
Key Learnings
Designing for emotion is designing for outcomes
Reducing anxiety and uncertainty had a greater impact on engagement than any algorithmic optimization alone.
Navigating the "Migration Mindset"
Users often cling to "bloated" features simply because they are familiar. Change is easier to sell to our clients (the program admins) when the "New" is demonstrably more effective than the "Old."