Measuring the Impact of Agile Strategy Changes

"You can’t manage what you don’t measure." Yet, when it comes to change management, most leaders focus on what was implemented rather than what actually changed. Early in my career, I rolled out a company-wide process improvement initiative. On paper, everything looked great - we met deadlines, trained employees, and ticked every box. But six months later, nothing had actually changed. The old ways crept back, employees reverted to previous habits, and leadership questioned why results didn’t match expectations. The problem? We measured completion, not adoption. 𝗖𝗼𝗻𝗰𝗲𝗿𝗻: Many organizations struggle to gauge whether change efforts truly make an impact because they rely on surface-level indicators: → Completion rates instead of adoption rates → Project timelines instead of performance improvements → Implementation checklists instead of employee sentiment This approach creates a dangerous illusion of progress while real behaviors remain unchanged. 𝗖𝗮𝘂𝘀𝗲: Why does this happen? Because leaders focus on execution instead of outcomes. Common pitfalls include: → Lack of accountability – No one tracks whether new processes are being followed. → Insufficient feedback loops – Employees don’t have a voice in measuring what works. → Over-reliance on compliance – Just because something is mandatory doesn’t mean it’s effective. If we want real, measurable change, we need to rethink what success looks like. 𝗖𝗼𝘂𝗻𝘁𝗲𝗿𝗺𝗲𝗮𝘀𝘂𝗿𝗲: The solution? Focus on three key change management success metrics: → 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 – How many employees are actively using the new system or process? → 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 – How has efficiency, quality, or productivity changed? → 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 – Do employees feel the change has made their work easier or harder? By shifting from "Did we implement the change?" to "Is the change delivering results?", we turn short-term projects into long-term transformation. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Organizations that measure change effectively see: → Higher engagement – Employees feel heard, leading to stronger buy-in. → Stronger accountability – Leaders track impact, not just completion. → Sustained improvement – Change becomes embedded in the culture, not just a temporary initiative. "Change isn’t a box to check—it’s a shift to sustain. Measure adoption, not just action, and you’ll see the impact last." How does your organization measure the success of change initiatives? If you’ve used adoption rate, performance impact, or user satisfaction, which one made the biggest difference for you? Wishing you a productive, insightful, and rewarding Tuesday! Chris Clevenger #ChangeManagement #Leadership #ContinuousImprovement #Innovation #Accountability

Collecting insights is easy. Measuring their impact on strategy, clinicians, and patients? That’s the real challenge. Most teams are logging notes. Tagging KITs and KIQs. Tracking MSL activity. But if you asked, “What changed as a result of those insights?” The answer is often unclear. Here’s how we’re changing that 👇 Step 1: Start with a focused strategy Before you ask “What did we learn?” Define what you want to learn. That means: KITs = Key Insight Themes (ex: Safety vs. Competitors) KIQs = Key Insight Questions (ex: “Are HCPs confident in Drug A’s tolerability?”) Embed these into your CRM, onboarding, and field coaching so everyone’s aligned on what signals to capture. Step 2: Define what impact actually looks like You need metrics that move beyond volume: 1/ Business impact: Did we adjust content, launch a new IIT, improve cross-functional alignment? 2/ Clinician impact: Did HCP sentiment or behavior shift? 3/ Patient impact: Are more patients being identified or diagnosed earlier? The best teams track activity → sentiment → adoption, with dashboards, briefs, and executive decks tailored by audience. Step 3: Audit your data and sources Insights don’t only live in CRM. They’re hiding in med info, IME outcomes, ad board transcripts, and congress debriefs. Map what exists. Spot the gaps. Enable tagging and structure where it's missing. Step 4: Build a coaching loop around insight quality Most insights fall flat because they’re too vague. Example: ❌ “HCP had a question about safety.” ✅ “Dr. Smith expressed concern over neutropenia in older patients and asked for real-world data.” Start peer reviews. Create a simple rubric. Reward specificity and strategic alignment. Step 5: Operationalize it Make insights part of your team’s muscle memory. Dashboards that show KIT trends, sentiment shifts, and top themes Monthly briefs to highlight what's changing Quarterly reports that tie insights to business and clinical outcomes Every insight should be traceable to: → What was heard → What action was taken → What impact it had Bottom line: Insights aren’t valuable because they’re collected. They’re valuable when they drive action. And when that action changes strategy, improves HCP confidence, or accelerates patient care? That’s when insights stop being noise and start becoming leverage. If you want access to a more detailed version of this playbook with more examples and a plan for how to operationalize insights within your team, comment below or DM me!

Measuring the revenue impact of an experimentation program isn't difficult. However, having worked with various companies, I’ve noticed they often struggle with something far more complex: 'measuring the impact of the insights gained from those experiments.' It's easy to get lost in the numbers when running experiments. But the value is in how effectively the insights lead to action and measurable change. So, how do we measure that impact of insights? My recommendations... 1. KPIs: Start with the basics—conversion rates, revenue, churn reduction. These are your North Stars. Did the insight move the needle? 2. Hypothesis Validation: Did the experiment validate your hypothesis? Insights that clarify assumptions allow us to streamline strategy and make targeted optimizations. 3. ROC: Is the insight generating a positive return compared to the resources you’re pouring into it? If your experimentation isn’t paying off, it’s time to reconsider the process. 4. User Behavior: Did the insights impact behavior? Lower drop-offs, higher engagement, and more feature adoption—are all indicators that you're on the right track. 5. Efficiency: Faster iteration cycles, targeted testing—if your insights are driving efficiency, that’s impact right there. 6. Learning Rate: Not every experiment leads to immediate wins. However, building a knowledge base over time increases your long-term success rate.