Using small wins to gain trust in analytics

I walked into a room full of frustration. The project was off track, the budget was bleeding, and trust had worn thin. As the new project manager, I had 30 days to rebuild what was broken not just the plan, but the relationships. 💡 Here’s the exact trust-building strategy I used to shift the momentum one conversation, one quick win, and one honest update at a time. ▶ Day 1–5: I started with ears, not answers. 🎧 Active Listening & Empathy Sessions I sat down with stakeholders one by one, department by department. No slides. No status updates. Just questions, empathy, and silence when needed. 💬 I didn’t try to fix anything. I just listened and documented everything they shared. Why it worked: They finally felt heard. That alone opened more doors than any roadmap ever could. ▶ Day 6–10: I called out the elephant in the room. 🔍 Honest Assessment & Transparent Communication I reviewed everything timelines, budgets, blockers, and team dynamics. By day 10, I sent out a clear, no-spin summary of the real issues we were facing. Why it worked: I didn’t sugarcoat it but I didn’t dwell in blame either. Clarity brought calm. Transparency brought trust. ▶ Day 11–15: I delivered results fast. ⚡ Quick Wins & Early Action We fixed a minor automation glitch that had frustrated a key stakeholder for months. It wasn’t massive, but it mattered. Why it worked: One small win → renewed hope → stakeholders leaning in again. ▶ Day 16–20: I gave them a rhythm. 📢 Clear Communication Channels & Cadence We set up weekly pulse updates, real-time dashboards, and clear points of contact. No more guessing who’s doing what, or when. Why it worked: Consistency replaced confusion. The team knew what to expect and when. ▶ Day 21–25: I invited them to the table. 🤝 Collaborative Problem-Solving Instead of pushing fixes, I hosted solution workshops. We mapped risks, brainstormed priorities, and made decisions together. Why it worked: Involvement turned critics into co-owners. People support what they help build. ▶ Day 26–30: I grounded us in reality. 📅 Realistic Expectations & Clear Next Steps No overpromising. I laid out a realistic path forward timelines, budgets, trade-offs, and all. I closed the month by outlining what we’d tackle next together. Why it worked: Honesty created stability. A shared plan gave them control. 💬 In 30 days, we hadn’t fixed everything but we had built something more valuable: trust. And from trust, everything else became possible. Follow Shraddha Sahu for more insights

Want a simple way to earn trust from your stakeholders, analysts? Send them data quality alerts when things go wrong. This is data 101 for engineers, but my team and I are citizen developers. We don't have the same kind of training - things like this simply aren't immediately obvious to us. Here's an example of why you should do this, from just this week: An analysis that we run depends on A LOT of inputs, including some manually uploaded files. Lots of opportunity for things to go wrong. On Monday, I heard from one of the file providers that her upload had been failing for almost 2 weeks. One of my end users spotted the problem at about the same time that I heard from my file provider. Not great being the last one to find out about a data quality problem in an analysis that you're responsible for. I had been working on some data quality alerts, and sure enough, they would have spotted the problem right away. So I'm eager to finalize them and get them into production. Here are some easy things I'm implementing: 1. Record count checks: do today's inputs have roughly the same number of records as yesterday's? This doesn't catch all problems, but it's very easy to implement - it's all I needed to spot the problem I just described. 2. Consistency check: Make sure your inputs "look" the way you expect them to. In this case, the reason the file upload was failing was that one of the columns in the file changed from being numerical to text, and our SQL database didn't like that. 3. Check for null values: You might get the right number of records and the right data types, but the data could all be null. 4. Automated alerts: You don't want to hear from your stakeholders about data quality issues the way that I did. Put in some basic alerts like these with automatic emails when they're triggered. Copy all your stakeholders. This will sound remedial to data engineers, but these are habits that we citizen developers don't always have. There's a lot that we citizen developers can learn from our friends in IT, and simple things like this can go away toward earning our stakeholders' trust. #citizendevelopment #lowcode #nocode #analytics #supplychainanalytics

I remember the first time I landed a meeting with a $50M+ brand. No big connections, no fancy portfolio—just a clear focus on delivering value. But it wasn’t about flashy pitches or overpromising. Here’s what really worked: → Trust takes time Big brands don’t rush into partnerships. It started with small wins—a few insights, valuable recommendations. Small, but enough to build trust. → Exhilarating growth Going from $10M to $50M+ was a rush. The secret? Once you nail the winning angle and messaging, growth compounds exponentially—not linearly. Every insight fuels the next, and the momentum takes off. → Deliver results, every time I treated every project, no matter the size, like it was the most important. Meeting expectations wasn’t enough—I made sure to exceed them. → Offer value first, ask later I never led with a hard sell. My focus was on helping—whether through strategy, insights, or simply a fresh perspective. When you offer real value upfront, the partnership comes naturally. → Build relationships, not transactions I didn’t view it as “winning a client.” It was about building a relationship. That mindset shift made all the difference. Fast forward to today, and those same brands trust me with projects that directly impact their growth. It didn’t happen overnight—it was about showing up consistently and focusing on their needs first. If you’re aiming to work with bigger brands: → Build trust. → Deliver results. → Offer value. The rest will follow.

A few months ago, we worked with a retail brand that was hesitant to adopt Marketing Mix Modeling (MMM). So, we convinced them to do a pilot project with us. The pilot project revealed that: - Their baseline sales were high, and - Paid marketing wasn’t contributing as much as the platforms had claimed. The results took them by surprise. I’m confident many large brands would have a similar reaction if they ran a pilot MMM. That’s why it amazes me that as we approach 2025, Marketing Mix Modeling (MMM) and Incrementality Testing still aren’t as mainstream as they should be. I know these tools might seem complex... But they’re essential for uncovering which channels are truly delivering results. ---------------------------------- Here’s how to make them a part of your strategy in 2025: 1. Educate Stakeholders → Resistance often comes from a lack of understanding. So, host workshops and presentations to break down MMM into simple, actionable insights. → Show stakeholders how it differs from existing methods and the powerful insights it can deliver. 2. Start with a Pilot Project → Run MMM alongside your current framework to establish a baseline. 3. Demonstrate Quick Wins → Begin with small optimizations, like tweaking the media mix in select geographies. (When stakeholders see tangible results, you build trust with them. That same retail brand scaled our recommendations nationwide after seeing early success.) 4. Leverage the Right Tools → If you lack a marketing science team, don’t worry. Tools like Lifesight make MMM accessible with user-friendly dashboards and robust analytics. We simplify the process and guide your next steps without the complexity. ---------------------------------- What’s stopping you from adopting MMM in 2025? Let’s talk in the comments.

You're the new data lead for a $100M brand. Here's your 90-day playbook. I used to think the first 90 days was about proving technical expertise. But it's not about the tools. Instead, It's about building trust. Days 0-30: Get a lay of the land. Start with people, not platforms. Schedule coffee chats with every sr. leader across: - Finance - Marketing - Merchandising - Operations - Customer Service Ask them: - What data challenges keep you up at night? - Which KPIs drive your team's success? - Where are your biggest blind spots? While you're doing this, go figure out what data and tools are available. Get a list every tool and report. If this doesn't exist, creating it is an easy win. Get access to everything. Start digging in and exploring. Days 31-60: Quick wins. By now you've got a list of pain points. Pick 3 high-impact, low-complexity problems like: - GA4 cleanup - CAC payback analysis - Return reason analysis - Run an incrementality test - Identify the most profitable promos - Basic customer analytics (RFM, LTV) - Post-purchase survey implementation - Profitability analysis by product/category - Marketing spend dashboard consolidation Pro tip: Make the data accessible while you're at it. - Set up a basic data warehouse (BigQuery/Snowflake) - Start using no-code ETL tools like Fivetran - Focus on commonly used data sources first Days 61-90: Building Momentum By now, you've gained trust... ...Now scale it. - Keep those leadership conversations going - Automate manual reporting processes - Make data self-service where possible - Train teams on analysis best practices - Start plotting your long-term roadmap Most new data leads try to fix everything at once. But true success comes from: - Building trust with leadership (and your peers) - Solving tangible problems quickly - Making data accessible to everyone - Having a clear vision for what's next What would you add to this 90-day plan? What quick wins worked for you? ♻️ Share this with a data lead who needs it 🔔 Follow me for more rants on data + marketing