Klein Insight — Strategic Insights

Part 2: The Approach The customer is not always right

2026-10-31T00:00:00Z

Part 2: The Approach The customer is not always right. The customer experience is always right (to them). Here’s a high-level look at how I’d test three hold experiences to see what makes waiting feel shorter, improves satisfaction, and reduces hang-ups. If this interests you, I’d love to connect. #CustomerExperience #DataAnalytics #DataScience #OperationsResearch #CallCenter #CustomerSuccess #ABTesting #Analytics #BusinessStrategy

The customer is not always right! The customer experience is always...

2026-10-30T00:00:00Z

The customer is not always right! The customer experience is always right! To them... If you've ever worked in customer service, you probably know exactly what I mean. Customers aren't always right about what happened, why it happened, or who's responsible. They can misunderstand something, remember it incorrectly, make assumptions, or come to conclusions that are completely wrong. But that doesn't mean their experience wasn't real to them. And before you start judging those customers, remember that you're also a customer to someone else! I remember sitting in meetings listening to a colleague complain about customers who claimed they'd spent two minutes on hold when our data showed they'd waited only 30 seconds. The irony? I just heard that same colleague complain that their lunch order took 10 minutes when it actually took four. We all do it. Time feels longer when we're frustrated. Expectations influence how we interpret an experience. And sometimes we're so convinced of what happened that we don't even question whether we're remembering it correctly. That's where businesses get themselves into trouble. They forget that their customers are human, just like they are. When someone complains about a bad experience, it's natural to want to explain why they're wrong. Especially when you have the facts to back it up. Maybe they misunderstood your pricing. Maybe they think something took twice as long as it actually did. Maybe they're blaming your business for something completely outside your control. Maybe you prove it. Congratulations. You won the argument. But what did you actually accomplish? Did you change how they felt? Rebuild their trust? Make them more likely to do business with you again? Or did you just make someone who was already frustrated feel like you weren't listening? Now, I'm not suggesting you should agree with every complaint or accept responsibility for things that aren't your fault. There are absolutely situations where correcting a customer is necessary, especially when misinformation could lead to financial harm, safety concerns, or unrealistic expectations. But there's a difference between helping someone understand what happened and trying to convince them they shouldn't feel the way they do. Being factually correct doesn't automatically make you effective. Sometimes explaining the facts helps. Sometimes it makes everything worse. The challenge is knowing the difference. So what do you actually do when your customer's experience doesn't match reality? I'll explain that in the next post! #CustomerExperience #CX #CustomerBehavior #CustomerInsights #CSAT #BusinessStrategy #Data

Are you seeking to maximize the epistemological validity, analytica...

2026-10-27T00:00:00Z

Are you seeking to maximize the epistemological validity, analytical integrity, and actionable utility of consumer-generated qualitative and quantitative feedback? Start asking better questions. Here's a crazy idea.... If you wouldn't ask your neighbor a question that way, why the hell would you ask your customers? Years ago, I was reviewing a customer survey developed by a research vendor in the energy industry. One question went something like this: "In regards to your PV system, what has the higher financial potential: 1 kWh of production for personal use or 1 kWh sent back to the grid?" Got that? Unless you work in the energy industry, probably not. Now imagine you're a homeowner with solar panels trying to answer that question. You might guess, skip it, or just close the survey. And there goes your valuable customer feedback. Here's how I'd approach it. 1. Know what you're trying to learn. Are you measuring what customers know, what they believe, or what they actually do? Those are very different things. 2. Ask it like you're talking to your neighbor. If I wanted to understand what customers believe, I'd ask: "Which do you think saves you more money: using the electricity your solar panels produce in your home, or sending that electricity back to your electric company?" Same general concept. No engineering degree required. 3. Give people permission not to know. Include an "I'm not sure" option. If half your customers don't understand how your product saves them money, that's valuable information! Maybe your messaging needs work. 4. Don't confuse perception with reality. Just because customers think something saves them money doesn't mean it actually does. In this example, the answer depends on electricity rates and how the utility compensates customers. Understanding what customers believe is useful. Treating those beliefs as facts can lead to expensive mistakes. Here's my challenge to you. Pull up the last customer survey or feedback form your business used. Read the questions out loud. Would you actually ask someone those questions in a normal conversation? More importantly, do you know what you're trying to learn, and would you trust the answers enough to make a business decision? If not, rethink your questions. Your customers shouldn't have to work harder to understand your questions than you did writing them. #CustomerExperience #MarketResearch #BusinessStrategy #Survey

Day 6 | Hollywood has an AI problem

2026-10-26T00:00:00Z

Day 6 | Hollywood has an AI problem. What happens when fans start making better content than the studios? Go on YouTube right now. You'll find AI generated movie trailers featuring actors fans have always wanted in certain roles, along with short films that would probably never survive a Hollywood pitch meeting. Some of it is rough. Some of it is scarily good. Hollywood has survived plenty of disruptions, including COVID. But COVID changed how people watched movies. AI could change who gets to make them. And that's where things get interesting from a data perspective. Studios have decades of audience research, box office results, and streaming data. But nearly all of it comes from a world where audiences depended on studios to create their entertainment. How do you predict consumer behavior when that fundamental relationship starts changing? This reminds me of one of my biggest frustrations working with electric vehicle adoption forecasts. Nearly every model relied heavily on historical data from early adopters to predict future demand. You see the problem, right? Early adopters tended to be wealthier, more enthusiastic about the technology, and willing to tolerate inconveniences that mainstream consumers might not accept. Yet we were using their behavior to predict what an entirely different group of consumers would do. The data wasn't necessarily wrong. Using that data to create 5 year EV adoption forecasts was b****t in my opinion and I'll gladly die on that hill. And I think Hollywood could be facing a similar blind spot. If I were analyzing this market, I wouldn't be particularly interested in how many people are experimenting with AI-generated videos. I'd want to know whether people keep coming back to watch more, whether that content is replacing traditional entertainment, and whether there's a viable business model behind it. Because there's a huge difference between a technology people enjoy experimenting with and one that actually changes consumer behavior. Now, AI could just as easily make studios more valuable. Established franchises, recognizable characters, and professional storytelling might become even more important when anyone can create content. I don't know which way this goes. And anyone claiming to know exactly how it plays out is guessing. But after more than a decade in market research, I've learned that recognizing when your assumptions might be wrong is just as important as understanding what your data says. And that's something every business owner should think about. Your historical data might be completely accurate. But will the conditions that produced it still exist five years from now? So tell me I'm wrong. Sometimes having good data that gives you confidence in an assumption you never thought to question is worse than bad data. Please enjoy what Mike Tyson would look like as Gandalf that I made on ChatGPT in under 15 seconds. #AI #MarketResearch #CX #BusinessStrategy #EV #Data

Day 5 | Can Republicans rely on GDP growth to win this November? We...

2026-10-25T00:00:00Z

Day 5 | Can Republicans rely on GDP growth to win this November? We all know GDP isn't a measure of Americans' financial well-being. But come election season, suddenly it's the country's favorite report card! And the numbers look pretty good. Real GDP grew 2.2% (annualized) in Q2, and inflation-adjusted consumer spending rose 0.6% in August. Not bad. Until you look at consumer sentiment. The University of Michigan's October Consumer Sentiment Index came in at 48.0, the second-lowest reading in 74 years. The lowest? 44.8, just five months earlier in May. So the economy is growing, Americans are spending more, and consumers are still deeply pessimistic. But here's what caught my attention when I dug into the survey. Consumer sentiment improved among both Republicans and Democrats in October, but declined among independents. Interesting, considering we're heading into the midterms. Republicans can point to GDP growth. Critics can point to historically low consumer sentiment. And neither group is necessarily wrong, but neither dataset directly measures voting intentions. Tell me I'm wrong. People love using data to prove they're right. Far fewer use it to find out whether they're wrong. The numbers aren't lying. We're just asking them to answer questions they weren't designed to answer. While both parties argue over which economic numbers tell the better story, is either one actually listening to independents? Sources: Bureau of Economic Analysis; University of Michigan Surveys of Consumers, October 2026. #MarketResearch #ConsumerBehavior #Economics #Elections #DataAnalytics

Day 4 | Tesla’s next big distraction will be announcing a SpaceX/Te...

2026-10-24T00:00:00Z

Day 4 | Tesla’s next big distraction will be announcing a SpaceX/Tesla merger. Tell me I'm wrong. The company is officially trapped in a game of corporate one upmanship with itself. Every single time they hit an operational wall, they pull a shiny new object out of the hat to completely reset the valuation clock. Just look at the receipts: • 2017: The Roadster is promised for 2020 to distract from Model 3 “production hell.” • 2019: With the Roadster nowhere near ready, the Cybertruck is suddenly unveiled. • 2021: Cybertruck faces massive delays? Enter a guy dancing in a spandex suit as the “Tesla Bot.” • 2024: The $25k mass-market car gets quietly shelved, and hours later, the Cybercab hype machine drops. • 2026: Facing flat deliveries and major earnings pressure, Tesla stages an over-the-top, totally unbuyable “flying” Roadster stunt at a SpaceX facility—while quietly pushing production back to 2028. Seriously, once you’ve weaponized rocket thrusters on a sports car to make people forget about a 9yr production delay, where else can you possibly go? You merge for now. The massive integration of SpaceX engineers and facilities for that recent Roadster air show wasn’t just a cool stunt. It was a strategic narrative pilot. By teasing a merger, Musk can completely decouple Tesla’s stock from the automotive growth plateau it’s currently facing. It throws the ultimate narrative shield over the brand, forcing Wall Street to treat it like a trillion dollar business on the level of Nvidia and Apple. All while completely burying the reality that the Roadster still won't be ready in 2028 when he announces the merger late next year. And no, this is not the end. There's still an endless horizon of AI networks and planetary humanoid labor fleets to pivot to when the run into there next inevitable production delay. But right now, a SpaceX merger is big enough for their next distraction in 2027. This is no disrespect to Elon Musk, the man is the best there ever was at this in my opinion. He understands human psychology and market mechanics better than any CEO in history. The real shame here belongs to us. As consumers, investors, and media outlets, we willingly let him off the hook every single time. We get so intoxicated every time he promises the next sci-fi shiny object that we completely forget about the trail of broken promises left in the rearview mirror. Are we ever going to hold him accountable, or are we all just waiting in line to buy whatever he hypes next? Let me know your thoughts! #Tesla #SpaceX #CorporateStrategy #MandA #Valuation #Leadership #Marketing

Tell Me I'm Wrong | Day 3: Zooming Doesn't Make It Bigger

2026-10-23T00:00:00Z

Tell Me I'm Wrong | Day 3: Zooming Doesn't Make It Bigger. Didn't get the results you expected from that expensive marketing campaign you spearheaded? Don't panic. Just zoom in! Six months ago, awareness was sitting comfortably at 55%, and you confidently told the execs, "Given how targeted this campaign is, we feel a 10pt increase to 65% in product awareness is more than achievable!" They approved your budget and 6 months later, the campaign is over, and awareness only went up to 59%. Well, shit... The executive meeting is in a few days, and you're staring at Excel wondering how you're going to explain spending half the company's annual marketing budget to deliver less than half the improvement you projected. Don't worry! I'll show you how to get out of that meeting with your head still attached! First, let's just hope nobody remembers that 10pt commitment from six months ago. Next, throw those results on a simple bar graph. "But Joe, this looks terrible!" Hold on, I haven't finished yet! Right now it's not exactly the visual you're hoping to put in front of execs, and I know that. So go to the chart settings, start that Y axis at 52% and cap it at 60%. BOOM! Your 7.3% relative increase in awareness now looks more than TWICE as tall as before the campaign! Same numbers. Same results. Completely different visual story. Now get your story straight and say "Our strategic marketing initiative generated significant positive momentum in top-of-funnel awareness, validating our targeted go-to-market approach and strengthening the foundation for customer acquisition!" Beautiful! Your missed target now sounds like a strategic victory! Don't pause though! Move quickly to the next slide, highlight some positive customer feedback, and wrap things up before anyone starts asking uncomfortable questions. Oh, this is very important! Make sure you request that all questions be held until the end. And just like that, I've saved your presentation! You're welcome. All sarcasm aside, I've spent enough time in corporate meetings to wonder whether people genuinely don't notice how data is presented or simply prefer the story they're being told. When you're more concerned with making your results LOOK successful than understanding why you missed your target, you lose out on everything that data could actually teach you. That's the data worth looking into before spending another dollar. Instead, everyone's admiring a pretty graph. That's the power of data visualization, and exactly why we need to be careful with it. You may not know you are being misled and honestly, many people don't realize they're being misleading. Excel can automatically truncate the Y-axis. So, tell me I'm wrong. I believe graphs showing percentages should almost always start at 0 and end at 100. Your audience shouldn't have to recalibrate their eyes every time someone changes the Y-axis. If you need to zoom in to make a change visible, maybe you're using the wrong graph.

Tell Me I'm Wrong | Day 1: The easiest way to lie with data is to t...

2026-10-21T00:00:00Z

Tell Me I'm Wrong | Day 1: The easiest way to lie with data is to tell the truth. You don't have to make up numbers to mislead people. Sometimes all you have to do is leave out the ones that don't support your argument. I've spent over a decade in market research, and few things frustrate me more than watching good research get twisted to justify decisions someone already made. Now imagine a company testing a new product. Research shows that 88% of respondents would consider buying it, but only 17% say they're "very likely" to purchase it at the proposed price. Guess which number makes it into the executive presentation? "88% OF CUSTOMERS WOULD CONSIDER BUYING OUR NEW PRODUCT!" Technically true! Sounds like a winner! Never mind that only 17% expressed strong purchase intent at that price, or that production costs might force the price even higher. The research did its job. The presentation didn't. And lets not pretend this only happens in business. We all cherry-pick information to support our arguments, conveniently leaving out details that don’t “work” for us. Looking at data isnt the same as understanding what it actually tells you. An accurate statistic can still support a completely misleading narrative. So, tell me Im wrong. At what point does selectively presenting the truth become lying?

My beautiful daughter is nearing 9 months old and becoming a parent...

2026-05-17T00:00:00Z

My beautiful daughter is nearing 9 months old and becoming a parent has been one of the most profound periods of personal growth in my life. It has changed how I think about time, responsibility, ambition, and the kind of future I want to build. The past few years have brought a lot of transition and challenges, but becoming a father has given me a clearer sense of direction. It has made me more intentional about the work I pursue, the people I learn from, and the kind of impact I want to have. That clarity has also changed how I think about my career. I still care deeply about doing thoughtful, useful work. I still enjoy solving messy problems, understanding people, and helping teams make better decisions through research, customer insight, and strategy. But now, the reason behind that work feels sharper. I want to build stability. I want to contribute to work that matters. I want to keep growing into the kind of professional, partner, and father my daughter can look up to. This next chapter feels less about proving myself and more about building something meaningful.

Market research doesn’t always reveal the truth about your customer...

2025-06-26T00:00:00Z

Market research doesn’t always reveal the truth about your customers’ virtues Let’s be honest. No one wants to look dumb in front of their peers. We nod along when someone brings up a topic we don’t understand. We laugh at jokes we don’t get because everyone else is laughing. We say “of course” when we’re unsure — because it’s easier than admitting we’re lost. This happens in market research too. People take surveys with the best intentions, but they still shape their answers to look smart, responsible, or virtuous. They say a message was clear when it wasn’t. They say they support sustainable products, even if they rarely buy them. They say they bought an EV to save the planet — but it was really to save money. I’ve spent years running research in the EV space, including conjoint studies to understand what actually drives adoption and financials consistently rises to the top. Lower fuel costs. Lower maintenance. Federal and state incentives. Time-of-use charging savings. HOV lane access. Environmental values are good for storytelling. But financial value is what changes behavior. This gap between stated values and actual behavior shows up everywhere — not just in EVs. And it’s measurable. A meta-analysis of 28 stated preference studies found that people overstate their willingness to pay by a median factor of 1.35 (Little & Berrens, 2004). When market research gives people space to protect their self-image, it’s no longer revealing behavior. It’s capturing aspiration. If you’re building strategy based on stated intent, without validating it through behavioral data or tradeoff modeling, you’re not grounded. This is where research becomes strategy. And this is what I help companies do — connect what people say with what they actually do. If you’re working through that gap, let’s connect.

So much market research is badly designed, and no one realizes it u...

2025-06-18T00:00:00Z

So much market research is badly designed, and no one realizes it until it’s too late Too many companies trust that their market research is solid because they hired a vendor to run it. But I’ve seen too many surveys that look fine on the surface and are garbage underneath. Poorly framed questions Biased answer options No logic checks No exclusivity No internal consistency It happens at utilities, government agencies, nonprofits, startups. Everywhere. And the worst part is no one realizes it until they’ve already made decisions that affect budgets, operations, or public trust. Here’s a small but personal example. When I lived in Sacramento, I got a survey from the local parks department. They were planning $50,000 in updates to two neighborhood parks I used almost daily. I wanted to give feedback. The survey was a disaster. They used government park names that no one locally used. You could say you didn’t own a dog and then later say you brought your dog to the park every day. They let you select multiple activities and still choose “none of the above.” No one who reviewed that survey thought through what the data would look like. And I knew they’d just roll it up, report the totals, and build a plan around bad inputs. That money was going to be wasted and this kind of thing happens more than people realize. It is not about whether you used Qualtrics or had a pretty dashboard. If the research design is broken, your insights are worthless. Collecting bad data is worse than collecting no data. It leads to confidence in the wrong direction. This is why it matters who designs your research. Good survey design takes experience, curiosity, and the ability to spot problems before they become outcomes. I work with teams to get this part right because everything that follows depends on it. If this resonates with you or your team is navigating a similar challenge, feel free to connect. I am always open to trading stories and helping where I can.