When Rogers Communications acquired Shaw, I was given an unusual opportunity: create a brand-new role to manage employee attendance and leave during one of the largest telecommunications mergers in Canadian history.
No playbook. No established processes. Just a clear problem: employees were taking too long to return to work, and Team Leads were drowning in administrative tasks that pulled them away from their actual job – leading people.
What I learned during that 15-month transformation fundamentally changed how I think about process improvement. The biggest wins don't come from massive overhauls; they come from identifying the small friction points that create massive drag.
The Pareto Principle in Action
You've probably heard of the 80/20 rule (Pareto Principle): 80% of effects come from 20% of causes. In customer service and operations, I've found this plays out in fascinating ways:
- 80% of customer frustration comes from 20% of process pain points
- 80% of team inefficiency comes from 20% of administrative burdens
- 80% of opportunity lies in fixing 20% of broken processes
The challenge? Identifying which 20% to focus on.
Case Study 1: The Ticket Nightmare
Real-World Example
Reducing Employee Leave Management from 8 Tickets to 2
The Problem: When an employee needed to take medical leave, Team Leads had to create, track, and manage 8 different tickets across multiple systems. The process took anywhere from 3-5 business days per case, and mistakes were common because the workflow was so complex.
The Discovery: I spent my first two weeks shadowing Team Leads, watching them work. I noticed they kept a physical notebook beside their computer, manually tracking which tickets they'd created and which were still pending. That was my first clue that the system was broken.
The 20%: After analyzing dozens of leave cases, I found that 80% of the delays came from just 3 issues:
- Duplicate data entry across multiple systems
- Waiting for approvals from people who weren't actually decision-makers
- Information gaps between HR, Benefits, and Operations that required manual follow-up
The Solution: Instead of trying to replace all eight tickets immediately, I focused on consolidating the three most time-consuming ones. I created a SharePoint database that auto-populated information across systems and established direct communication channels between key stakeholders.
The Impact: Team Leads saved an average of 2 business days per leave case, allowing them to focus on coaching and team development. More importantly, employees returned to work 4 days faster on average because the approval process was streamlined.
Case Study 2: The Win-Back Campaign That Wrote Itself
Real-World Example
Crafting Data-Driven Win-Back Email Campaigns
The Problem: At LinkNow Media, we were losing clients but had no systematic approach to win them back. We'd occasionally send generic "We miss you" emails, but they rarely generated responses.
The Discovery: I analyzed 200 cancellation records in Zoho CRM, looking for patterns. What I found was striking: 80% of cancellations fell into just 4 categories of objections:
- "Not seeing results from SEO" (35%)
- "Too expensive for my small business" (28%)
- "Don't understand what I'm paying for" (12%)
- "Business circumstances changed" (5%)
The 20%: Instead of sending one generic email to all cancelled clients, I created targeted campaigns for each objection category. But here's the key: I didn't try to tackle all four at once.
The Solution: I started with the "Not seeing results" segment – the largest group with the most winnable objections. I created a three-email sequence that:
- Acknowledged their frustration (empathy first)
- Shared recent SEO success stories from similar businesses (social proof)
- Offered a free consultation to review their specific situation (low-friction next step)
Once that campaign proved successful, I replicated the approach for the other objection categories.
The Impact: Win-back rate increased from ~8% to 23% for the targeted campaigns. But the bigger win was that these campaigns ran automatically, freeing up our Client Advocacy team to focus on complex, high-touch situations that required personal attention.
The Process Optimization Framework
Based on these experiences and dozens of other improvement initiatives, I've developed a simple framework for identifying and fixing high-impact process issues:
Step 1: Shadow and Listen (2 Weeks)
Don't assume you know where the problems are. Watch people work. Ask "Why do you do it that way?" at least 100 times. Pay special attention to workarounds – if someone has created their own spreadsheet or tracking system outside the official process, that's a red flag that the official process is broken.
Step 2: Quantify the Pain (1 Week)
Use data to identify the 20%. How much time does each step take? Where do errors happen most frequently? What causes the longest delays? I used PowerBI and Tableau extensively for this, but even a simple Excel spreadsheet with timestamps can reveal patterns.
Step 3: Start Small and Test (2-4 Weeks)
Resist the urge to fix everything at once. Pick the one change that will have the biggest impact with the least disruption. Implement it with a small group first. Get feedback. Iterate.
Step 4: Measure and Scale (Ongoing)
Track before and after metrics religiously. Not just efficiency metrics, but also employee satisfaction and customer impact. If the change works, scale it. If it doesn't, learn why and adjust.
The Common Mistakes to Avoid
In my years of leading process improvement initiatives, I've seen (and made) plenty of mistakes:
Mistake #1: Optimizing for the wrong stakeholder. I once spent two weeks building a beautiful reporting dashboard that management loved but frontline agents found useless. Always start with the people doing the work.
Mistake #2: Confusing activity with progress. Having lots of meetings about process improvement isn't the same as actually improving processes. Bias toward action and experimentation.
Mistake #3: Ignoring the human element. The best process in the world will fail if people don't understand why it changed or how to use it. I learned to spend as much time on change management and training as on the actual process redesign.
Mistake #4: Optimizing too early. Sometimes a process is broken because the underlying system or tool is wrong. No amount of optimization will fix a fundamentally flawed foundation.
The Ripple Effect
Here's what I find most exciting about process optimization: the benefits compound.
When we streamlined the leave management process at Rogers, Team Leads didn't just save 2 business days per case. They gained the mental bandwidth to be more present with their teams. Employee engagement scores in their departments improved. And that engagement translated to better customer service metrics.
When we implemented targeted win-back campaigns at LinkNow Media, we didn't just recover more clients. We learned valuable lessons about objection handling that informed how current clients were supported, reducing cancellations in the first place.
Small changes create ripple effects that extend far beyond their initial scope.
Your Turn: Finding Your 20%
If you're looking to optimize processes in your organization, start by asking:
• What task do your team members complain about most?
• Where do you see people creating unofficial workarounds?
• What process causes the most delays or errors?
• If you could eliminate one administrative task tomorrow, what would free up the most time?
The answers to these questions will point you toward your 20% – the changes that will deliver 80% of your results.
And remember: you don't need perfect data to start. You just need to begin observing, questioning, and experimenting. The insights will reveal themselves if you're paying attention.
Let's Connect
Process optimization and operational efficiency are areas I'm passionate about. If you're facing similar challenges or want to discuss improvement strategies, I'd love to connect.
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