Case Study · Research & Product Design Strategy

Activation Fee Waiver Strategy

Turned a recurring revenue + customer trust conflict into a decision system leadership could govern — starting from buyflow behavior, waiver patterns, and operational reality.

Spectrum Mobile
Strategy-led
Journey + Governance
Decision Framework
Fee
$20 per line
Where
Buyflow during purchase
Type
Research strategy-led
Deliverables
Opportunity map, decision framework, experiment plan
↓ Waiver spend
Target
Reduce “uncontrolled” waivers by tightening eligibility + improving expectation-setting.
↓ Contacts
Operational
Fewer escalations driven by “surprise fee” perception.
↑ Trust
Customer
Shift from fee debate to clarity + consistent handling.

The real problem (not the fee)

Leadership was stuck in “waive vs enforce.” That’s bad framing. Waivers were a signal of expectation failure + inconsistent guardrails. This created a governable system: classify → apply guardrails → decide → learn → fix upstream.

Context

Spectrum Mobile charges a $20 activation fee per line during buyflow when customers purchase new lines. Over time, the fee became a repeated trigger for distrust, escalations, and inconsistent waivers.

Buyflow screen showing $20 activation fee during mobile line purchase
Buyflow moment where the $20 activation fee is introduced during purchase.

What leadership believed

“We must enforce to protect revenue” vs “We must waive to protect CX.”

What the system was actually saying

Waivers were a recurring signal of expectation failure + inconsistent operational guardrails.

Problem

Waiver decisions varied across agents and channels. Customers often felt the fee was “added” after their decision, even though it was present in buyflow. This created contacts and made waiver spend unpredictable.

Business

Revenue leakage + higher cost-to-serve.

Customer

Trust break + “surprise fee” perception.

Operations

Discretion without guardrails; waivers used to de-escalate ambiguity.

Root cause

Policy debate disguised a journey failure: expectations weren’t set at the right moment.

What I did

I led a research-only strategy effort to move the conversation from “waive vs enforce” to “what conditions justify a waiver and what conditions demand a system fix.”

Diagnosed the system

Mapped disclosure moments, downstream contact points, and expectation gaps.

Found repeat patterns

Partnered with CX + frontline to identify clusters and escalation triggers.

Built governance logic

Created a decision framework + experiment plan leaders could operationalize.

Designed for learning

Defined metrics + guardrails to reduce waivers without harming conversion.

Insights

Expectation gaps drove waivers

Customers were less upset about $20 and more upset about feeling misled.

Waivers became conflict-resolution

When policy was unclear, waiving was the fastest path to resolution.

Revenue vs CX was false binary

Inconsistent enforcement damaged both; the fix is rules + expectation-setting.

Hiring-manager takeaway

This is decision design: policy + ops + journey stitched into a system leaders can run.

Activation waiver trends chart
Activation waiver trends + emerging signals (Jan–Sept 2024).

Opportunity map

Opportunities organized by where they occur in the journey and how directly they reduce distrust and escalation.

Image placeholder: Opportunity Map (from Figma)

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Opportunity map visual
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Decision framework

Reframed the question from “Should we waive?” to: “When does a waiver protect long-term value, and when should it trigger a system fix?”

1) Classify the trigger

Disclosure issue · System error · Policy exception · Misunderstanding

2) Apply guardrails

Tenure/LTV · Channel · Evidence of expectation gap · Severity of friction

3) Decide + close the loop

Waive, don’t waive, or trigger a fix — then feed learnings back into governance.

Why this matters

Turns inconsistent discretion into a repeatable decision process leadership can govern.

Image placeholder: Decision Matrix / Flow (from Figma)

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Decision matrix / decision tree
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Experiment plan

Experiments designed to reduce waivers without harming conversion — learn safely, generate evidence leadership trusts.

Experiment
Hypothesis
Primary metric
Guardrail
Buyflow disclosure test
Earlier + clearer fee copy reduces “surprise fee” perception.
Waiver rate, contacts
Conversion rate
Confirmation message
Reinforcing expectations post-purchase reduces first-bill shock.
First-bill contacts
Complaint rate
Waiver reason tagging
Structured reasons turn waivers into insight signals.
Driver clarity
Agent time
Automation pilot
Automating high-confidence scenarios reduces inconsistency.
Escalations
Revenue leakage

Impact

Replaced opinion-driven waiver debate with a governable system: classify → apply guardrails → decide → learn.

Business

Reduced arbitrary waiver behavior and created inputs for scalable automation.

Customer

Shifted focus upstream to expectation-setting to prevent trust breaks.

Operations

Gave teams consistent guardrails and shared decision language.

Hiring manager takeaway

You’re seeing product thinking: policy + ops + journey stitched into a system leadership can run.

Next steps

If extending into execution, I would embed this framework into governance and product:

Improve disclosure across channels

Make expectation-setting consistent: web, app, retail, and scripts.

Require reason tagging

Turn every waiver into structured insight that informs product fixes.

Rule-based automation

Automate high-confidence scenarios to reduce inconsistency + time-to-resolve.

Monthly dashboard

Waiver drivers + contact volume + conversion + guardrails.

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