Keeping Client Expectations Realistic With Data

Should you over-promise your employer/client or set realistic expectations? I run a business where we help finance brands in researching on finance topics to ensure that the content they post is insightful. In the first few assignments, the clients expected us to increase their followers by 'x' % within 'y' months. This made me believe that all the clients in the industry want similar results. I started working with a couple of clients in April this year. During the kick-off call, I gave the targets we'll be trying to achieve by the end of contract term. Both the clients said that they don't want me to stress on engagement results. This will dilute the content quality. Such assignments are sustainable where your KPIs do not involve factors beyond your control, i.e., engagement. Clients that have asked me to focus only on research quality are the ones that have stayed for the longest time frame. When many brands are creating content, community is not built overnight by posting click-baity content. It is built by genuinely delivering value consistently for a very long time frame. Now when I sit in a pitch meeting, I no longer promise engagement results. But we have increased our focus on creating highly-researched quality content. The client who do not understand this are the ones who anyways won't stay on for years. Hence, there's no point in lying to them for short-term gains. The ones who understand will only pressure you for good quality. This is a controllable factor and helps you in maintaining high lifetime value of a customer. Be it your clients, employers/employees, or family. Always speak the truth! Over-promising might give you short term gain but sabotages long-term relationships. What's your view?

Over the past 20 years in market research, many project issues I've seen stem from mismanaging client expectations. Whether you work for a research firm, an agency, a consultancy, or any other business that involves regular client discussions, here are 4 pointers. 1️⃣ Communication—Regularly communicate, candidly ask the client how often they want updates, and never let a week go by without touching base, regardless of the project stage. Anticipate questions and answer them before they ask. A client sending an email asking, "What's the status of...?" is a failure on your end - within reason. Lack of responsiveness leads to mistrust, even more micromanagement, skepticism, and other issues that can be snuffed out by communicating openly. 2️⃣ Be Realistic—We all want to say "yes" to clients, but there are often ways to showcase your experience and expertise by being honest about what can be achieved with a given timeline and budget. The expectation could be a lack of understanding about the process or industry norms. Underpromise and overdeliver versus overpromise and underdeliver. Those honest conversations may appear inflexible, but they're often more about setting expectations and setting up both parties for long-term sustainable success. Saying "no" to this project could be a better long-term decision for the account than saying "yes" and failing with no second chance. 3️⃣ Understand Perspective—Take the time to actively listen to your client's needs, goals, and priorities. It goes beyond listening and includes asking smart (and sometimes bolder) questions to get a complete understanding. What drove the need for research? Why is receiving results within 2 weeks crucial? What happens if you don't receive results in 2 weeks? Understanding what's pushing the decisions behind the scenes can be a game changer. 4️⃣ Solutions Over Problems—Never present a problem or an issue to a client without a path forward. "This happened, but here are 3 things we can do to fix it." You need to be more than someone who relays information, you need to be a true consultant. Be able to justify each recommendation and explain the pros and cons of each path. -------------------------------------- Need MR advice? Message me. 📩 Visit @Drive Research 💻  1400+ articles to help you. ✏️ --------------------------------------

Specifying expected performance for an AI product can be a struggle for product managers. Setting the right acceptance criteria is important for development of every AI product. In many cases, if the performance is below a certain threshold, the product may lack sufficient value for customers (e.g., defect detection in manufacturing fails too often, skin tone analyzer recommends a wrong foundation shade, or a LLM-powered chatbot fails to give correct information). On the other hand, leaving the team of researchers to do their best may lead to endless improvements, excessive cost, and prolonged time to market. Finding the balance is challenging, even in more mature industries in which safety and regulatory contexts, or competitors already set some boundaries. Here are some possible approaches which I've seen can work in practice: 1️⃣ 𝐔𝐬𝐞 𝐜𝐨𝐧𝐜𝐫𝐞𝐭𝐞 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬 Just as machine learning models need training examples for learning, development teams can benefit from having concrete examples. For instance, instead of specifying that the chatbot should handle user inquiries about products in the eCommerce platform, product managers can provide 10-15 examples of acceptable conversations to clarify the scope and the intended use. For image analysis products, besides the intended key performance indicators, product managers should provide sample images with manually annotated desired outputs to help engineers evaluate feasibility and guide development. Initially, their collection and annotation can be improvised, following a practice that may not scale to collecting the full training datasets. 2️⃣ 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 𝐭𝐢𝐠𝐡𝐭𝐥𝐲 𝐰𝐢𝐭𝐡 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 Target KPIs are influenced by business context (customers expectations, competitors, regulatory framework, etc.) as much as by feasibility. Therefore, setting realistic KPIs is a joint effort between PMs and engineers, requiring shared responsibility and collaboration. If you use scrum, regular refinement meetings are a good place to start. 3️⃣ 𝐓𝐞𝐬𝐭 𝐡𝐞𝐚𝐯𝐢𝐥𝐲 Test even the earlier versions which still do not satisfy the acceptance criteria. This will help you adjust the direction of development (maybe refine the data collection plans, or revisit some technical decisions), but also to align the acceptance criteria itself with the new reality. Testing by domain experts and non-expert users is equally important. Experts can help understand which aspects of performance are more relevant, and can also inspire finding shortcuts. For example, to evaluate the quality of our face landmarks for makeup try-on, we work together with an external makeup artist and a group of beauty experts whose perception is often more valuable than objective KPIs. ➡️ As AI applications proliferate, product managers increasingly embrace example-driven product specifications, closer collaboration with engineers, and both expert and non-expert testing to deliver impactful products.

I’ve never hesitated to say no to a client when something doesn’t feel right. That clarity has shaped S2W Media since day one. We’d rather lose a deal than destroy trust. When we say "no," it’s usually one of these scenarios: 1. The audience doesn't exist "We need 1,000 leads from CTOs in 50–100 employee fintech companies using X technology.” Sometimes that audience is 200 people. Globally. We’ll tell you that. We’ll show you the data. We’ll suggest alternatives. But we won’t promise 1,000 leads when we know it’s impossible. 2. The timeline is unrealistic “We need 500 qualified leads by end of quarter. It’s November 25th.” Quality data takes time. Proper validation takes time. Building first-party audiences takes time. We push back on timelines that force us to compromise quality. Bad data delivered fast hurts everyone. 3. The expectations don't match reality “We expect every lead to close in 30 days.” Lead ≠ Deal. Leads are opportunities. They need nurturing, follow-up, and a sales process. If someone expects a 100% close rate, we’re not the right fit. When we say no, it’s not rejection. It’s responsibility. It’s respecting your goals enough to be honest about what’s achievable. That mindset has helped us build partnerships that last well beyond a single campaign or quarter. We’ve been doing this for 13 years, and many of our clients stay with us for 7+ years. That only happens when you refuse to burn people in the first place. What expectations do you want aligned before your next campaign starts? #B2BMarketing #DemandGeneration #Data

I wrapped up a pretty eye-opening project that I wanted to share with you. A few months ago, I helped a client untangle their project profitability data, and let me tell you—it was a bit of a beast. 😵💫 Like many digital agencies I see, they had a TON of data... ...but it none of it was woven together. We pulled in data from all over the place: Harvest time tracking, QuickBooks, Gusto Payroll —you name it. It took some serious mapping and visualization work, but we got there. And here’s where it got interesting. 👀 The data revealed something unexpected: 📉 a downward trend in gross margins for one of their key clients. Naturally, that led us to dig deeper. We found out that this client had become increasingly indecisive and unpredictable with their retainer engagement. My client was bending over backward to keep them happy, often without billing for all the extra time. This was a case of good intentions leading to bad margins. 😬 But here’s the good news: armed with this data, my client was able to have a tough but necessary conversation with their client. Expectations were reset, and we’re already seeing those margins start to climb again. Moral of the story? 👉 Data doesn’t lie 👈 When something feels off, there’s usually a reason—be it people, processes, or tech. Having the right data helps you ask the right questions, which leads to better conversations and, ultimately, better results.

Improving the habit of “over-promising and under-delivering” in the FMCG (Fast-Moving Consumer Goods) business environment in India requires a structured, cultural, and operational shift. This behavior is often rooted in aggressive competition, pressure to meet short-term targets, or poor internal alignment. Here’s a practical roadmap to address this habit: 🔧 Step-by-Step Strategy to Improve “Over-Promise, Under-Deliver”: 1. Root Cause Diagnosis • Why it happens: • Sales pressure to hit unrealistic targets • Misalignment between sales, supply chain, and marketing • Lack of real-time data visibility • Cultural belief that saying “no” means weakness • What to do: • Conduct internal audits and team interviews to understand where promises break. • Analyze top 10 cases of missed delivery vs. committed promises. 2. Set Clear, Realistic KPIs • Replace “targets at any cost” with “accuracy of commitments” as a performance metric. • Examples of KPIs: • Promise Accuracy Score (% of commitments met on time) • Forecast vs. Actual Variance • Service Level Agreement (SLA) adherence 3. Build Cross-Functional Alignment • Create a Sales–Supply Chain–Operations bridge. • Weekly alignment meetings to review: • Stock availability • In-transit logistics • Market demand trends • Encourage “truth-telling” culture: enable teams to flag risks without fear of blame. 4. Customer Expectation Management • Train sales teams to set realistic expectations, even if it means risking a tough conversation. • Use tiered delivery models: • Tier 1: Fast-track SKUs with consistent availability • Tier 2: Variable lead time products • Tier 3: Seasonal/Special packs with limited promise scope 5. Create Feedback Loops • After-action reviews: every failed commitment should have a feedback loop. • Engage with distributors/retailers to recalibrate expectations regularly. • Reward honesty and transparency in sales discussions. 6. Use Tech & Tools • Implement real-time tracking dashboards for: • Inventory levels • Order-to-delivery timelines • Promises made vs. actual deliveries • Adopt demand planning and S&OP (Sales and Operations Planning) tools suited for Indian FMCG nuances. 🚀 Culture Shift: “Under-Promise, Over-Deliver” Train leadership and front-line managers in “promise discipline”: • Internalize that reputation > short-term sale • Celebrate stories where accurate forecasting improved client trust • Encourage sales to say, “Let me check and get back” rather than committing instantly Indian FMCG-Specific Insights: • Trust and relationship-based business: Be honest and consistent rather than enthusiastic and unreliable. • Rural and semi-urban markets: Delivery reliability builds long-term loyalty more than flashy promises. • Retailer loyalty is hard-won and quickly lost—missed deliveries cost more than a lost order. #Leadership #Process #Planning #KPIs #Review #Execution

Another agency promised them $300k in 30 days. I promised them nothing. Guess who's still their agency? While other agencies pitch shiny forecasts and guaranteed returns, I tell prospects the truth: Give me 90 days. We'll know if this is working. No crystal ball. No made-up numbers. Just honest expectations. Here's why this approach works: TikTok Shop success depends on variables no one can predict upfront. Product-market fit on the platform. Creator response rates. Content performance patterns. I can't tell you if you'll hit $50K or $500K in month three. I can tell you exactly what we'll test, track, and optimize to find out. We treat those first 90 days like discovery research. High-volume creator testing. Content optimization. Funnel analysis. By day 90, the data tells us everything we need to know about scalability. The agencies promising specific revenue targets are either lying or setting everyone up for disappointment. I'd rather underpromise and overdeliver than create unrealistic expectations that damage the relationship from day one.

Your marketing partnership is failing before it even starts. The problem isn't budget or strategy. It's that nobody told you the truth about what to expect. I've watched this play out dozens of times. Agencies pitch with glossy case studies. Client signs contract expecting those same results immediately. Three months later, relationship is dead because reality didn't match the promise. The Math Nobody Wants to Show You Here's what honest communication looks like: Before we launch anything, we need to talk about your current conversion volume. If you're only getting 10 qualified conversions per month, the algorithms don't have enough data to optimize effectively. That's not an opinion, that's just how these platforms work. Sometimes it means we need to start with a higher-funnel conversion action just to generate enough learning data. Sometimes your budget needs to be 3x what you initially planned. Sometimes it means accepting that the first phase is about data collection, not immediate scale. What Actually Works The best client relationships I've had all started the same way. We mapped out the reality using actual numbers. We aligned on what success looks like at each phase. We built a clear process for communication and iteration. When you set those expectations early, clients don't panic when results aren't instant. They understand we're building infrastructure. They know what we're tracking and why. But when you skip that foundation and just promise results, nobody's aligned on what success actually means. The Real Questions to Ask Your tracking is broken? Everything downstream will be wrong until we fix it. Your budget can support testing but not scaling yet? Let's be clear about what we can learn in that phase. If someone is selling you guaranteed results without first understanding your current state and having an honest conversation about what's realistic, run. At Understory we provide expert service for a lot of platforms, but we never try to pitch add-on services that we know don't make sense for the business. The agencies who last are the ones who set proper expectations upfront. Who show you the math. Who explain their process clearly. Who tell you when something won't work before you waste budget on it. Marketing should be a business partnership, not a gamble. But that only works when both sides are honest about what it takes to win. 🌲 If you're looking to partner with an agency that will tell you the honest truth and help you define what marketing success can look like for you, shoot me a DM or hit my Calendly button and let's chat.

Early in my career, I thought more content meant more success. I’d assure clients that posting 20-30 times a month would skyrocket their revenue. But soon, I realized it wasn’t just about quantity—it was about clear communication and strategy. One time, a client was frustrated. Despite all the posts, they weren’t seeing the "$ millions $" they expected. That’s when it hit me: we hadn’t set clear expectations. Now, I make sure every client understands the strategy from day one. Here’s my approach to avoid any miscommunication: 1️⃣ Clear Expectations from the Start: I sit down with clients and lay out what social media can realistically achieve. Posting frequently won’t automatically translate to millions. It’s about quality, engagement, and targeted efforts. 2️⃣ Detailed Strategy: I provide a clear, detailed strategy tailored to their goals. This includes defining what success looks like and the metrics we’ll use to measure it. No vague promises—just concrete plans. 3️⃣ Regular Check-Ins: To ensure everyone’s on the same page, I schedule regular check-ins. We review progress, adjust strategies if needed, and address any concerns. Open communication is key. Setting these expectations has transformed client relationships. They understand the value of a strategic approach and appreciate the transparency. No more unrealistic expectations—just clear, achievable goals. In my latest video, I share tips on how to set clear expectations with your clients to ensure smooth, successful collaborations. Check it out! #ClientCommunication #MarketingStrategy #ClearExpectations #DigitalMarketing #BusinessSuccess

In a room full of vendors promising the moon, be the one who delivers next Tuesday. Your biggest competitive advantage in health tech isn't your AI, your algorithms, or your innovation. It's being the one vendor who doesn't overpromise. I've sat through several dozen vendor showcases in my career. However, last month's showcase was not typical (mainly because it was onsite which I rarely get to do anymore). However, the pitches were more of the same. Every company claimed they would "transform healthcare delivery" and "revolutionize patient outcomes." The winner? The company that said: "We'll reduce your lab turnaround time by 23 minutes and save you $47 per test. Here's exactly how." Why underpromise-overdeliver wins in healthcare: ▶️ Scenario A (The Overpromiser): "Our AI will revolutionize your clinical workflows!" Reality: 6-month implementation, marginal improvements, lots of training and adoption issues with the end users ▶️ Scenario B (The Realist): "We'll cut your documentation time by 12 minutes per patient encounter" Reality: 8-minute savings achieved in month 2, plus unexpected workflow benefits Guess who gets the renewal? The Reality-Based Messaging Framework: ✓ Specific metrics instead of broad promises ✓ "Typically" and "on average" instead of "always" ✓ Implementation timelines with buffer built in ✓ Clear scope limitations upfront The counterintuitive result: When you set realistic expectations, buyers trust you with bigger opportunities. In a market drowning in AI vaporware and "revolutionary" claims, being the vendor who tells the truth—even when it's less sexy—is refreshingly sellable. Your credibility is your moat. Protect it fiercely.