Measuring Business Impact

🎡 How To Measure And Show UX Impact. With practical guidelines on how to track and articulate business impact of design work ↓ 🚫 Business rarely sees the value of UX the way designers do. ✅ To many, it shows up merely in good outcomes of A/B tests. ✅ To some, it’s reflected in satisfaction surveys (NPS, CSAT). 🤔 But most UX work goes unnoticed, and so does its impact. ✅ To change that, we can measure and report design success. ✅ Identify 10–12 representative tasks that users must do well. ✅ These tasks must reflect business priorities, get signed off. ✅ Your goal is to achieve 80%+ success rate for these tasks. ✅ Focus on task success rate and task completion times. ✅ You need before/after snapshots to explain your UX impact. ✅ Choose metrics to track impact of your UX changes. ↳ Global KPIs: success for key tasks in a customer journey. ↳ Local KPIs: success for key tasks in a single touchpoint. 🤔 Explain and report your impact with KPI trees/graphs. ✅ Show how your design KPIs reinforce business flywheels. UX work often appears to be disconnected from the heart of the business. As we tirelessly iterate on flows and features, it’s often very hard to make an argument that a design change that we've made recently had a profound impact on key business metrics. The reason for that is that, unlike other departments, we rarely have a set of widely established and regularly reported design KPIs. These KPIs are UX metrics that are tied to business metrics that they are impacting. Design KPIs https://lnkd.in/e5tWimWF Design KPI Trees https://lnkd.in/eTB3wrs9 How To Measure UX and Design Impact, by yours truly https://measure-ux.com Design KPI Graphs, by Ryan Rumsey https://lnkd.in/e5M2G-uu Business flywheels, by Timothy T Tiryaki, PhD https://lnkd.in/eJKuYu3R To visualize UX impact, we often use design KPI trees or design KPI graphs (see above). Both are different ways to visualize how design initiatives help reach business goals, and show the dependencies between them. Another way is to show UX impact within business flywheels — an artefact companies use to explain their business models. Basically they are self-reinforcing cycles of business growth, and design work typically enables these cycles to function. Study where exactly your work fits in those flywheels and attach design KPIs to them to reinforce the value that UX is driving. Surely not all design work is impactful. It depends on the audience it addresses and the value it delivers. But by measuring what matters, we can get a trackable record of the changes we enable over time — and once you shed light on it, it might change how your work is seen much faster than you think. #ux #design

𝗧𝗼𝗱𝗮𝘆, 𝗣𝗠𝗜 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝘀𝘁𝘂𝗱𝘆 𝘄𝗲’𝘃𝗲 𝗲𝘃𝗲𝗿 𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗲𝗱 - 𝗼𝗻 𝗮 𝘁𝗼𝗽𝗶𝗰 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗼 𝗼𝘂𝗿 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘂𝗰𝗰𝗲𝘀𝘀. 📚 Read the report: https://lnkd.in/ekRmSj_h With this report, we are introducing a simple and scalable way to measure project success. A successful project is one that 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝘃𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘁𝗵𝗲 𝗲𝗳𝗳𝗼𝗿𝘁 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗻𝘀𝗲, as perceived by key stakeholders. This clearly represents a shift for our profession, where beyond execution excellence we also feel accountable for doing anything in our power to improve the impact of our work and the value it generates at large. The implications for project professionals can be summarized in a framework for delivering 𝗠𝗢𝗥𝗘 success: 📚𝗠anage Perceptions For a project to be considered successful, the key stakeholders - customers, executives, or others - must perceive that the project’s outcomes provide sufficient value relative to the perceived investment of resources. 📚𝗢wn Project Success beyond Project Management Success Project professionals need to take any opportunity to move beyond literal mandates and feel accountable for improving outcomes while minimizing waste. 📚𝗥elentlessly Reassess Project Parameters Project professionals need to recognize the reality of inevitable and ongoing change, and continuously, in collaboration with stakeholders, reassess the perception of value and adjust plans. 📚𝗘xpand Perspective All projects have impacts beyond just the scope of the project itself. Even if we do not control all parameters, we must consider the broader picture and how the project fits within the larger business, goals, or objectives of the enterprise, and ultimately, our world. I believe executives will be excited about this work. It highlights the value project professionals can bring to their organizations and clarifies the vital role they play in driving transformation, delivering business results, and positively impacting the world. The shift in mindset will encourage project professionals to consider the perceptions of all stakeholders- not just the c-suite, but also customers and communities. To deliver more successful projects, business leaders must create environments that empower project professionals. They need to involve them in defining - and continuously reassessing and challenging - project value. Leverage their expertise. Invest in their work. And hold them accountable for contributing to maximize the perception of project value at all phases of the project - beyond excellence in execution. 📚 Please read the report, reflect on its findings, and share it broadly. And comment! Project Management Institute #ProjectSuccess #PMI #Leadership #ProjectManagementToday

Attribution is overrated. Incrementality is what actually matters Every new-age brand wants to know what’s working. Meta ROAS is looking good. CAC is steady. Revenue is growing But here’s the truth: Your Meta ad might get the conversion. But did it cause the conversion? That’s the difference between attribution and incrementality. Most dashboards, attribution tools, and agency reports stop at attribution. But if you’re a brand selling across Amazon, Flipkart, GT, MT, Q-com, and D2C—pure attribution will always lie to you Because the sale might happen on Amazon. But it might have been nudged by a Meta video or a YouTube bumper ad 4 days ago. You don’t need a full-blown Marketing Mix Model to get started. There are simpler, street-smart ways to directionally understand what’s working—and what’s not. Here are 4 that have worked for us at Atomberg: 1. Geo Split Testing Pick two similar markets. Run campaigns in one. Don’t run in the other. Then track: • Branded search volume • Sell-through on marketplaces • Secondary sales from GT counters If the test market moves faster than the control, you’re seeing true lift. That’s incrementality. 2. First-Time Buyer Growth vs Returning Buyer Growth Track whether your growth is coming from first-time buyers or repeats. If your campaigns are just bringing back old customers—you’re not creating net new demand. But if there’s a spike in new buyers across Amazon, Flipkart, D2C—your campaigns are likely working at an incremental level 3. Paid Traffic vs Organic Trend Lines If paid traffic, clicks and spends are going up—but your organic sales or branded search isn’t moving—you’re likely just harvesting demand that already existed. But if organic lifts alongside paid—your ads are creating interest. Not just closing it. Directionally, this is one of the simplest sanity checks most teams ignore. 4. Channel Crossover + Offline Signal Mapping Your Meta ad may not show up in last-click attribution. But it might have nudged the consumer to visit your store or buy on Amazon. You can detect this through: • Post-purchase surveys (Where did you first hear about us?) • Branded search + store footfall spikes in campaign-active cities • And most powerfully—offline signals passed back to Meta At Atomberg, we pass back data from installations and warranty registrations—including pincode and purchase timelines Sometimes, we’re even able to identify this at a unique customer level through their cookies for warranty registration This has helped us understand true incrementality of perf marketing campaigns even for offline sales If you’re only measuring ROAS, you might scale what’s only taking credit for sale about to happen anyway If you chase incrementality, you’ll scale what’s working. For more details, read the full post- link in first comment.

We spent the last 3 months researching how PE firms create value 🌱 The result: “The Private Equity Value Creation Report” — one of the most in-depth studies on the topic, based on the data from over 10,000 PE entries and exits globally. 𝟳 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1️⃣ Revenue growth is the largest driver of PE value creation On average, it contributes to 54% of value creation. Recently, revenue growth has become an even more critical driver of success (as multiples have come down), contributing to ~65-70% of value creation in the last 2 years. 2️⃣ Margin expansion plays a smaller role at 15% Margin expansion is most impactful when PE firms target operationally challenged businesses rather than already-efficient businesses. 78% of deals with negative EBITDA margins achieved margin expansion (median +1250bps), while businesses with high EBITDA margins (>30%) typically saw margin contraction. 3️⃣ Multiple expansion contributes significantly at 32% For the top quartile deals, its contribution is even higher at 40%.  By sector, TMT, Science & Health, and Services see the largest multiple expansion. Consumer and Industrials see the least. By size, multiple expansion is the highest for smaller deals under $100M EV. 4️⃣ Growth amplifies all other PE value creation drivers Growing companies benefit from operating leverage and are more likely to achieve margin expansion. 58% of growing firms expand margins compared to 44% of those with negative growth. Higher-growth companies also typically command 30–50% higher multiples at exit. 5️⃣ Top and bottom-performing deals are held the longest Investors hold onto the best-performing assets for greater upside but also hold the worst, trying to fix the business. Assets held in the 3-6 year range tend to cluster around more predictable, moderate returns. 6️⃣ Buy-and-build is central to PE value creation When done right, buy-and-build bolsters all three value creation drivers: revenue growth, margin expansion, and multiple expansion. Buy-and-build works at any size, but the uplift is strongest in small platforms. The multiple arbitrage strategy still works with add-ons trading at a 20% discount to platforms. 7️⃣ Larger deals drive more margin expansion Large businesses ($1bn+ EV) and public-to-private deals, on average, deliver more margin expansion. Smaller businesses, on the other hand, rely more on growth and multiple expansion to drive returns. Given the smaller size, returns on average, are also higher for family-to-sponsor deals. _______ 𝗙𝘂𝗹𝗹 𝗥𝗲𝗽𝗼𝗿𝘁 Don’t miss out on insights: 💡 By Sector 💡 By Deal Type and Size 💡 MOICs and Loss rates + 5 case studies and 43 charts. Get it here ➡️ https://lnkd.in/d9Z3kubU (E-mail required) #ValueCreation #Growth #PrivateEquity

By now, the "95% failure rate" of GenAI financial returns (ref MIT's Project NANDA) is part of all consulting decks. The report blames the incorrect approach as the primary reason, rather than model maturity, etc. The key is to understand what #ROI metrics are used to determine the financial returns. I asked #Copilot on this, and here's what it told me: --- Here are three examples of ROI frameworks that enterprises are using to evaluate and scale GenAI adoption effectively: 1. Business Outcome-Based ROI Framework (Gartner) Summary: Gartner recommends aligning GenAI initiatives with measurable business outcomes such as cost reduction, revenue growth, or productivity gains. For example, a retail company using GenAI for automated product descriptions tracked a 22% increase in conversion rates and a 15% reduction in content creation costs. The framework emphasizes setting baseline metrics before deployment and tracking improvements post-implementation. 🔗 https://lnkd.in/dER7cTeF 2. Time-to-Value and Efficiency Metrics (BCG) Summary: Boston Consulting Group suggests using time-to-value (TTV) and operational efficiency as key ROI indicators. In one case, a logistics firm used GenAI to optimize routing, reducing delivery times by 18% and fuel costs by 12%. BCG’s framework includes pre/post comparisons, automation impact, and employee productivity metrics to quantify GenAI’s contribution. 🔗 https://lnkd.in/da2zcSfW 3. Model Performance vs. Business KPIs (McKinsey) Summary: McKinsey advocates for linking GenAI model performance directly to business KPIs. For instance, a financial services firm used GenAI for customer support automation and tracked resolution time, customer satisfaction scores, and call deflection rates. The framework includes continuous monitoring of model accuracy, relevance, and business impact. 🔗 https://lnkd.in/dA6zEGuS 🔑 Key Message Summary Effective GenAI ROI frameworks combine technical performance metrics with business impact indicators. Leading approaches include tracking cost savings, productivity gains, time-to-value, and alignment with strategic KPIs. Enterprises that define success upfront and monitor outcomes continuously are more likely to scale GenAI successfully. --- The direction taken seems to be well-intentioned. However, the measure of success is not quite what might lead to real solid business outcomes! Individual productivity improvements are just that! They don't scale across the organization unless "vertically scaled" top-to-down an entire process delivering bottomline improvements, which then need to be further "horizontally scaled" end-to-end across the entire value chain of the firm to deliver topline value! My forthcoming book on Cognitive Chasm provides actionable guidance to practitioners on this.

There’s a lot of excitement about generative AI in pharmaceutical R&D — and rightly so. But alongside the enthusiasm, there’s still real uncertainty about where & how it’s delivering value today, and what it will actually take to scale impact. A recent paper from the DISRUPT–Data Science Industry Roundtable — a cross-industry consortium I co-founded — offers a useful, data-based snapshot of the current state of play. The analysis is grounded in aggregated, anonymized benchmarking across multiple large pharma companies, which makes the insights especially practical. What I appreciate most about the paper is that it doesn’t oversell where the industry is. Where GenAI is actively being used today: ◆ Knowledge search, summarization, and synthesis across biological, chemical, and clinical data ◆ Drafting & supporting research, clinical, and regulatory-adjacent documents ◆ Assistive productivity tools for scientists, statisticians, and data teams ◆ Early traction in molecule design, though maturity & consistency remain mixed across organizations These use cases are real and, in many organizations, already moving into routine use — particularly for knowledge workflows & document generation. Where proof points are still emerging: ◆ Clear, reproducible examples of GenAI directly driving clinical-stage success ◆ Demonstrated impact beyond efficiency gains into durable biological insight ◆ Scaled, end-to-end integration into core R&D decision-making processes Much of today’s GenAI value in pharma remains assistive rather than decisive — an important & often overlooked distinction. What continues to limit scale: ◆ Data quality & availability: models amplify what they’re trained on, and biology remains noisy & incomplete ◆ Process integration: embedding GenAI into existing R&D workflows is as hard as building the models themselves ◆ Trust, robustness, and oversight: human-in-the-loop remains essential in regulated environments ◆ Organizational readiness: governance, operating models, and adoption matter as much as algorithms The takeaway for me isn’t skepticism — it’s focus. Early wins matter because they build confidence & momentum. But the step-change impact many hope for will come from doing the harder work: investing in high-quality data foundations, tightly coupling AI systems to experimental feedback, and building operating models that earn trust over time. That’s where efforts like DISRUPT are valuable — not because they provide all the answers, but because they create shared visibility. They help the industry separate real progress from noise & have more honest conversations about what it will actually take to scale impact responsibly. We’re entering a more mature phase of generative AI in medicine — one where optimism is paired with discipline, and success is measured not by what’s possible, but by what’s proven. Read more:  https://lnkd.in/e5ZZkWRE #GenerativeAI #AIinMedicine #DrugDiscovery

How do we know if we’re actually becoming an AI-first company? That’s the question two customers asked me this week—and it’s a really fair one. AI buzz is everywhere, but how do you know if you’re making real progress? Here are 5 metrics every company should track to measure whether they’re truly on the path to becoming AI-first: 1. Revenue per Employee (Lagging Indicator) The ultimate test of success with AI: are you generating more value for every employee you hire? AI should amplify output, not just automate tasks. When each person drives more revenue, you know productivity is compounding. 👉 It's the north star, but it takes time to move. 2. Customer Satisfaction (CSAT) (Lagging Indicator) AI-driven productivity is meaningless if customer experience suffers. CSAT should hold steady—or better yet, improve—as AI delivers faster, smarter, more personalized service. 👉 If it drops, your AI strategy is likely misaligned with customer needs. 3. % of Teams with Access to AI Tools (Leading Indicator) You can’t be AI-first if your teams aren’t equipped. Measure how many employees have easy access to approved AI tools and whether those tools are embedded in their daily workflow. 👉 Access is the foundation. No access, no adoption. 4. Active AI Usage (Daily/Weekly) by Team (Leading Indicator) This is where the rubber meets the road. Track actual usage. Who’s using AI every day or week? What teams are lagging behind? 👉 To be AI-first, every team should be using AI every week—if not every day. 5. % of Work Carried Out by Agents (by Function) (Leading Indicator) This is the most transformational shift. What % of your team’s output is now driven by agents or AI copilots? In marketing, it could be content drafting. In sales, meeting booking. In support, ticket resolution. 👉 When agents do the work, your people focus on higher-leverage thinking—and the flywheel starts turning. Bottom line: Becoming AI-first isn't about buying tools, it’s about changing how work gets done. When you combine these 5 metrics, you get a clear picture of progress—and the compounding path toward higher productivity, better outcomes, and real transformation. What would you add to the list?

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Is having “Ex-SAP, Ex-Google, Ex-Microsoft” in your headline a good strategy? Here’s what the data shows👇 1️⃣ Engagement: Profiles with specific, value-driven headlines—like “Helping SaaS companies scale to $10M ARR”—get significantly more views and messages than those focused on past employers. Recruiters are searching for skills and impact, not just logos. 2️⃣ Recruiter Behavior: Recruiters spend 90% of their time looking at your headline, title, and summary. A headline that highlights what you bring now is more likely to show up in searches and spark interest. 3️⃣ Credibility: Yes, big names add credibility. But relying solely on them in your headline may come across as clinging to past prestige. Instead, let those names shine in your experience section and use your headline to tell people why they should reach out to you today. 4️⃣ Focus on Outcomes: A LinkedIn study found profiles highlighting results and impact (e.g., “Driving $100M in enterprise sales”) get 6x more profile views than vague headlines. So, what’s the better approach? Blend your past credibility with a clear value proposition: • “Ex-Google | Driving AI adoption for Fortune 500 companies” • “Ex-SAP | Building scalable cloud solutions for mid-sized businesses” Your headline is the first thing people see—make it count. Highlight who you are today and the impact you’re creating. What does your headline say about you? Let me know your thoughts below! Image by Karsten Kotter

I stopped obsessing over publishing… and that’s when my research career took off. (Here’s what I discovered) Four years ago, I believed publishing was the only path to academic success. My inbox? Empty. My collaborations? Stagnant. My impact? Limited to footnotes in other people’s papers. Then, I did something radical: I shared my work outside journals. A blog post about the LCA work I did for a partner. A LinkedIn post breaking down techno-economic assessment and process design methods. A webinar sharing my research outputs. Crickets. For weeks. Until a founder DM’ed: "Liked the recording of your webinar. Can you do something like this for us to verify our TEA?" A week later, an academic mentor slid into my DMs: "Saw your recent work on carbon capture. Can we co-write a research bid?" I wasn't sure what to do. This wasn’t “real” academic work. I’d been pre-conditioned to share my work only in scholarly journals and conferences. But suddenly, my research was solving problems, not merely gathering dust. So I leaned in. I built a simple system:  1. Every paper made available as PDF with posts on problem, method, outputs  2. Conference slides became PDFs shared with key takeaways  3. Complex science converted into trade magazines and blog posts The response? A FTSE 250 energy company invited me to perform a market study for their new direct air capture business strategy. Learned societies invited me as a keynote speaker for their events. And yes, citations keep coming, but now tied to real-world impact. Here’s what academia won’t tell you: Visibility isn’t vanity. It’s the bridge between your work and the problems it can solve. You don’t need 100 papers to make a difference. You need the right people to see your work. Now? I teach researchers to build this bridge. Because your career shouldn’t hinge on how many journals you’ve cracked. It should hinge on how many minds you’ve changed. Start there. Let the papers follow. P.S. What is the most significant challenge that pre ents you getting your research seen by others? #science #scientist #research #publishing #phd #postdoctoral #professor #academia #highereducation