by Robyn Bolton | Nov 12, 2024 | Customer Centricity, Leadership, Stories & Examples
“Now I know why our researchers are so sad.”
Teaching at The Massachusetts College of Art and Design (MassArt) offers a unique perspective. By day, I engage with seasoned business professionals. By night, I interact with budding designers and artists, each group bringing vastly different experiences to the table.
Customer-centricity is the hill I will die on…
In my Product Innovation Lab course, students learn the innovation process and work in small teams to apply those lessons to the products they create.
We spend the first quarter of the course to problem-finding. It’s excruciating for everyone. Like their counterparts in business and engineering, they’re bursting with ideas, and they hate being slowed down. Despite data proving that poor product-market fit a leading cause of start-up failure and that 54% of innovations launched by big companies fail to reach $1M in sales (a paltry number given the scale of surveyed companies), they’re convinced their ideas are flawless.
We spend two weeks exploring Jobs to be Done and practicing interviewing techniques. But their first conversations sound more like interrogations than anything we did in class.
They return from their interviews and share what they learned. After each insight, I ask, “Why is that?” or “Why is that important?
Amazingly, they have answers.
While their first conversations were interrogations, once the nervousness fades, they remember their training, engage in conversations, and discover surprising and wonderful answers.
Yet the still prioritize the answers to “What” over answers to “Why?”
…Because it’s the hill that will kill me.
Every year, this cycle repeats. This year, I finally asked why, after weeks of learning that the answers to What questions are almost always wrong and Why questions are the only path to the right answers (and differentiated solutions with a sustainable competitive advantage), why do they still prioritize the What answers?
The answer was a dagger to my heart.
“That’s what the boss wants to know,” a student explained. “Bosses just want to know what we need to build so they can tell engineering what to make. They don’t care why we should make it or whether it’s different. In fact, it’s better if it’s not different.”
I tried to stay professional, but there was definitely a sarcastic tone when I asked how that was working.
“We haven’t launched anything in 18 months because no one likes what we build. We spend months on prototypes, show them to users, and they hate it. Then, when we ask the researchers to do more research because their last insights were wrong, they get all cra….OOOOHHHHHHHH…..”
(insert clouds parting, beams of sunlight shining down, and a choir of angels here)
“That’s why the researchers are so sad all the time! They always try to tell us the “Whys” behind the “Whats” but no one wants to hear it. We just want to know what to build to get to work. But we could create something people love if we understood why today’s things don’t work!”
Honestly, I didn’t know whether to drop the mic in triumph or flip the table in rage.
Ignorance may be bliss but obsolesce is not
It’s easy to ignore customers.
To send them surveys with pre-approved answers choices that can be quickly analyzed and neatly presented to management. To build exactly what customers tell you to build, even though you’re the expert on what’s possible and they only know what’s needed.
It’s easy to point to the surveys and prototypes and claim you are customer-centric. If only the customers would cooperate.
It’s much harder to listen to customers. To ask questions, listen to answers you don’t want to hear, and repeat those answers to more powerful people who want to hear them even less. To have the courage to share rough prototypes and to take the time to be curious when customers call them ugly.
So, if you want to be happy, keep pretending to care about your customers.
Pretty soon, you won’t have any left to bother you.
by Robyn Bolton | Aug 18, 2024 | Customer Centricity, Innovation, Tips, Tricks, & Tools
AI is everywhere: in our workplaces, homes, schools, art galleries, concert halls, and even neighborhood coffee shops. We can’t seem to escape it. Some hope it will unlock our full potential and usher in an era of creativity, prosperity, and peace. Others worry it will eventually replace us. While both outcomes are extreme, if you’ve ever used AI to conduct research with synthetic users, the idea of being “replaced” isn’t so wild.
For the past month, I’ve beta-tested Crowdwave, an AI research tool that allows you to create surveys, specify segments of respondents, send the survey to synthetic respondents (AI-generated personas), and get results within minutes.
Sound too good to be true?
Here are the results from my initial test:
- 150 respondents in 3 niche segments (50 respondents each)
- 51 questions, including ten open-ended questions requiring short prose responses
- 1 hour to complete and generate an AI executive summary and full data set of individual responses, enabling further analysis
The Tool is Brilliant
It took just one hour to gather data that traditional survey methods require a month or more to collect, clean, and synthesize. Think of how much time you’ve spent waiting for survey results, checking interim data, and cleaning up messy responses. I certainly did and it made me cry.
The qualitative responses were on-topic, useful, and featured enough quirks to seem somewhat human. I’m pretty sure that has never happened in the history of surveys. Typically, respondents skip open-ended questions or use them to air unrelated opinions.
Every respondent completed the entire survey! There is no need to look for respondents who went too quickly, chose the same option repeatedly, or abandoned the effort altogether. You no longer need to spend hours cleaning data, weeding out partial responses, and hoping you’re left with enough that you can generate statistically significant findings.
The Results are Dangerous
When I presented the results to my client, complete with caveats about AI’s limitations and the tool’s early-stage development, they did what any reasonable person would do – they started making decisions based on the survey results.
STOP!
As humans, we want to solve problems. In business, we are rewarded for solving problems. So, when we see something that looks like a solution, we jump at it.
However, strategic or financially significant decisions should never rely ona single data source. They are too complex, risky, and costly. And they definitely shouldn’t be made based on fake people’s answers to survey questions!
They’re Also Useful.
Although the synthetic respondents’ data may not be true, it is probably directionally correct because it is based on millions and maybe billions of data points. So, while you shouldn’t make pricing decisions based on data showing that 40% of your target consumers are willing to pay a 30%+ premium for your product, it’s reasonable to believe they may be willing to pay more for your product.
The ability to field an absurdly long survey was also valuable. My client is not unusual in their desire to ask everything they may ever need to know for fear that they won’t have another chance to gather quantitative data (and budgets being what they are, they’re usually right). They often ignore warnings that long surveys lead to abandonment and declining response quality. With AI, we could ask all the questions and then identify the most critical ones for follow-up surveys sent to actual humans.
We Aren’t Being Replaced, We’re Being Spared
AI consumer research won’t replace humans. But it will spare us the drudgery of long surveys filled with useless questions, months of waiting for results, and weeks of data cleaning and analysis. It may just free us up to be creative and spend time with other humans. And that is brilliant.
by Robyn Bolton | Apr 27, 2024 | Customer Centricity, Leadership, Stories & Examples
It’s easy to get caught up in the hunt for unique insights that will transform your business, conquer your competition, and put you on an ever-accelerating path to growth. But sometimes, the most valuable insights can come from listening to customers in their natural environment. That’s precisely what happened when I eavesdropped on a conversation at a local pizza joint. What I learned could be worth millions to your business.
A guy walked into a pizza place.
Last Wednesday, I met a friend for lunch. As usual, I was unreasonably early to the local wood-fired pizza joint, so I settled into my chair, content to spend time engaged in one of my favorite activities – watching people and eavesdropping on their conversations.
Although the restaurant is on the main street of one of the wealthier Boston suburbs, it draws an eclectic crowd, so I was surprised when a rather burly man in a paint-stained hoodie flung open the front door. As he stomped to the take-out order window, dust fell from his shoes, and you could hear the clanging of tools in his tool belt. He placed his order and thumped down at the table next to me.
A Multi-Million Dollar Chat
He pulled out his cell phone and made a call. “Hey, yeah, I’m at the pizza place, and they need your help. Yeah, they hate their current system, but they don’t have the time to figure out a new one or how to convert. Yeah, ok, I’ll get his number. Ok if I give him yours. Great. Thanks.”
A few minutes later, his order was ready, and the manager walked over with his pizza.
Hoodie-guy: “Hey, do you have a card?”
Manager: “No, I don’t. Something I can help you with?”
H: “I just called a friend of mine. He runs an IT shop, and I told him you’re using the RST restaurant management system, and you hate it…”
M: “I hate it so much…”
H: “So my buddy’s business can help you change it. He’s helped other restaurants convert away from RST, and he’d love to talk to you or the owner.”
M: “I’m one of the co-owners, and I’d love to stop using RST, but we use it for everything – our website, online ordering, managing our books, everything. I can’t risk changing.”
H: “That’s the thing, my friend does it all for you. He’ll help you pick the new system, set it up, migrate you from the other system, and ensure everything runs smoothly. You have nothing to worry about.”
M: “That would be amazing. Here’s my direct line. Have him give me a call. And if he’s good, I can guarantee you that every other restaurant on this street will change, too. We all use RST, and we all hate it. We even talked about working together to find something better, but no one had time to figure everything out.”
They exchanged numbers, and the hoodie guy walked out with his pizza. The manager/owner walked back to the open kitchen, told his staff about the conversation, and they cheered. Cheered!
Are You Listening?
In just a few minutes of eavesdropping, I uncovered a potential goldmine for a B2B business – 15 frustrated customers, all desperate to switch from a system they hate but unable to do so due to time and resource constraints. The implications are staggering – an entire local market worth tens of millions of dollars ripe for the taking simply by being willing to listen and offer a solution.
As a B2B leader, the question is: are you truly tapping into the insights right in front of you? When was the last time you left your desk, observed your customers in their natural habitat, and listened to their unvarnished feedback? If you’re not doing that, you’re missing out on opportunities that could transform your business.
The choice is yours. Will you stay in your office and rely on well-worn tools, or venture into the wild and listen to your customers? Your answer could be worth millions.
by Robyn Bolton | Mar 5, 2024 | Customer Centricity, Tips, Tricks, & Tools
AI is NOT a substitute for person-to-person discovery conversations or Jobs to be Done interviews.
But it is a freakin’ fantastic place to start…if you do the work before you start.
Get smart about what’s possible
When ChatGPT debuted, I had a lot of fun playing with it, but never once worried that it would replace qualitative research. Deep insights, social and emotional Jobs to be Done, and game-changing surprises only ever emerge through personal conversation. No matter how good the Large Language Model (LLM) is, it can’t tell you how feelings, aspirations, and motivations drive their decisions.
Then I watched JTBD Untangled’s video with Evan Shore, WalMart’s Senior Director of Product for Health & Wellness, sharing the tests, prompts, and results his team used to compare insights from AI and traditional research approaches.
In a few hours, he generated 80% of the insights that took nine months to gather using traditional methods.
Get clear about what you want and need.
Before getting sucked into the latest shiny AI tools, get clear about what you expect the tool to do for you. For example:
- Provide a starting point for research: I used the free version of ChatGPT to build JTBD Canvas 2.0 for four distinct consumer personas. The results weren’t great, but they provided a helpful starting point. I also like Perplexity because even the free version links to sources.
- Conduct qualitative research for me: I haven’t used it yet, but a trusted colleague recommended Outset.ai, a service that promises to get to the Why behind the What because of its ability to “conduct and synthesize video, audio, and text conversations.”
- Synthesize my research and identify insights: An AI platform built explicitly for Jobs to be Done Research? Yes, please! That’s precisely what JobLens claims to be, and while I haven’t used it in a live research project, I’ve been impressed by the results of my experiments. For non-JTBD research, Otter.ai is the original and still my favorite tool for recording, live transcription, and AI-generated summaries and key takeaways.
- Visualize insights: Mural, Miro, and FigJam are the most widely known and used collaborative whiteboards, all offering hundreds of pre-formatted templates for personas, journey maps, and other consumer research templates. Another colleague recently sang the praises of theydo, an AI tool designed specifically for customer journey mapping.
Practice your prompts
“Garbage in. Garbage out.” Has never been truer than with AI. Your prompts determine the accuracy and richness of the insights you’ll get, so don’t wait until you’ve started researching to hone them. If you want to start from scratch, you can learn how to write super-effective prompts here and here. If you’d rather build on someone else’s work, Brian at JobsLens has great prompt resources.
Spend time testing and refining your prompts by using a previous project as a starting point. Because you know what the output should be (or at least the output you got), you can keep refining until you get a prompt that returns what you expect. It can take hours, days, or even weeks to craft effective prompts, but once you have them, you can re-use them for future projects.
Defend your budget
Using AI for customer research will save you time and money, but it is not free. It’s also not just the cost of the subscription or license for your chosen tool(s).
Remember the 80% of insights that AI surfaced in the JTBD Untangled video? The other 20% of insights came solely from in-person conversations but comprised almost 100% of the insights that inspired innovative products and services.
AI can only tell you what everyone already knows. You need to discover what no one knows, but everyone feels. That still takes time, money, and the ability to connect with humans.
Run small experiments before making big promises
People react to change differently. Some will love the idea of using AI for customer research, while others will resist with. Everyone, however, will pounce on any evidence that they’re right. So be prepared. Take advantage of free trials to play with tools. Test tools on friends, family, and colleagues. Then underpromise and overdeliver.
AI is a starting point. It is not the ending point.
I’m curious, have you tried using AI for customer research? What tools have you tried? Which ones do you recommend?
by Robyn Bolton | Feb 27, 2024 | Customer Centricity, Tips, Tricks, & Tools
Unintended consequences often catch us off guard despite their predictability. The moment they occur, we gasp in shock, shake our heads, and look at each other in wide-eyed horror at this thing that just happened that we could never ever ever have anticipated.
Yet, when (if) we do an After-Action Review, we often realize that these consequences were not entirely unforeseeable. In fact, had we anticipated them, we might have made different decisions.
The Unintended Consequences of Spreadsheets
In 1800 BCE, ancient Babylonians started recording data by scratching grids and columns onto clay tablets, and the spreadsheet was born. Over the millennia, we went from clay tablets to papyrus to parchment and then paper.
Fast forward to 1963 when R. Brian Walsh of Marquette University ported the Business Computer Language (BCL) program to an IBM 7040, and electronic spreadsheets became a reality. The introduction of VisiCalc by Apple in 1979 revolutionized spreadsheet capabilities, followed by Lotus 123 and Microsoft Excel. Today, spreadsheets are ubiquitous in education, business operations, financial markets, budgeting, and even personal inventories.
Unintended yet predictable consequences
While spreadsheets have undoubtedly enhanced efficiency and accuracy compared to traditional methods like clay tablets or hand-drawn tables on parchment, their ease of use has inadvertently led to complacency.
We stopped engaging in a multi-millennial habit of discussing, debating, and deciding before making a spreadsheet. We started flippantly asking people to create spreadsheets and providing little, if any, guidance because “it’s easy to make changes and run scenarios.”
This shift resulted in a reliance on automated models and a lack of shared assumptions or analytical rigor in decision-making processes.
Of course, these behaviors were never intended. They were, however, very predictable.
Research spanning disciplines as varied as network scientists, anthropology, neuropsychology, and paleontology shines a light on how truly predictable we are.
Here are some examples:
Emotions before Reason: Ask someone if they make decisions based on their motivations, aspirations, and fears and use data to justify the decisions, and they’ll tell you no. Ask them the last time someone else made a decision that “made no sense,” and you’ll listen to a long list of examples.
Small gains now are better than big gains later: Thoughtfully planning before using solutions like spreadsheets, word processing, email, and instant messaging could save us time at work and help us get home 30 minutes earlier or work a few hours less on the weekend. But saving a few seconds now by brain-dumping into Word, setting up a “flexible” spreadsheet, and firing off a text feels much better.
Confidence > Realism: We’ve all been in meetings where the loudest voice or the most senior person’s opinion carried the day. As we follow their lead, we ignore signs that we’re wrong and explain away unexpected and foreboding outcomes until we either wake up to our mistakes or adjust to our new circumstances.
Predict the 93%. Create for the 7%
Acknowledging the predictability of human behavior is not an endorsement of stereotypes but a recognition of our innate cognitive processes. By incorporating this understanding into design, innovation, and decision-making processes, we better anticipate potential outcomes and mitigate unintended consequences.
While 93% of human behavior may follow predictable patterns rooted in evolutionary instincts, focusing on the remaining 7% allows for the exploration of unique behaviors and novel solutions. By embracing both aspects of human nature, we can navigate challenges more effectively and anticipate a broader range of outcomes in our endeavors, leading to informed decision-making and value creation.
Now, if I could only get Excel to stop auto-converting numbers into date/time format.
by Robyn Bolton | Jan 31, 2024 | Customer Centricity, Innovation, Tips, Tricks, & Tools
Most customer research efforts waste time and money because they don’t produce insights that fuel innovation. Well-meaning businesspeople say they want to “learn what customers want,” yet they ask questions better suited to confirming their own ideas or settling internal debates. Meanwhile, eager consumers dutifully provide answers despite the nagging belief that they’re being asked the wrong questions.
It doesn’t have to be this way. In fact, you can get profound revelations into consumers’ psyche, motivations, and behaviors if you do one thing – channel your inner Elmo.
First, a confession
I find Elmo deeply annoying. I grew up watching Sesame Street, and I still get an astounding amount of joy watching Big Bird, Mr. Snuffleupagus, Cookie Monster, Bert and Ernie, Grover, and Oscar the Grouch (especially when Oscar channels his inner Taylor Swift).
Elmo moved to Sesame Street in 1985, and it hasn’t been the same since. He’s designed to reflect the mental, emotional, and intellectual capabilities of a 3.5-year-old, and, in that aspect, his creators were wildly successful. I fully acknowledge that Elmo plays a vital role in the mission of Sesame Street and that people of all ages love Elmo. But Elmo makes my ears bleed, and I will never be ok with the fact that Elmo refers to himself in the third person.
This is why my recommendation to channel your inner Elmo is shocking and extremely serious.
Next, an explanation
On Monday, Elmo posted on X (yes, the minimum age limit is 13, but his mom and dad help him run the account, so it’s apparently okay), “Elmo is just checking in! How is everybody doing?”
180 million views, 120,000 likes, and 13,000 comments later, it was clear that no one was okay.
And lest you think this was Gen Z trauma dumping on their ol’ pal Elmo, Dionne Warwick, T-Pain, and Today Show anchor Craig Melvin responded with their struggles. Comments ranged from, “Mondays are hard” to “Elmo I’m gonna be real I am at my f—ing limit,’ to “Elmo each day the abyss we stare into grows a unique horror. one that was previously unfathomable in nature. our inevitable doom which once accelerated in years, or months, now accelerates in hours, even minutes. however I did have a good grapefruit earlier, thank you for asking.”
Wow. Thank goodness for that grapefruit.
There are a lot of theories about why Elmo’s post touched a nerve – it’s January and we’re tired, it’s easier to share our struggles online than in person, or we still enjoy “that wholesome and sincere bond from childhood that makes us want to share.”
I’m sure all those are true, and I think it’s something more, something we can all learn and do.
Now, the secret
Elmo may be a red, hairy, 3.5-year-old muppet. Still, he nailed the behaviors required to get people to open up and share their inner worlds – the very thoughts, beliefs, and motivations that enable others to create and offer impactful and innovative solutions.
Here’s what Elmo did (and you should, too):
- Show that you’re genuinely curious: Elmo didn’t open with the standard “How are you?” that if answered with anything other than the socially acceptable “Fine,” results in awkward silence and inner panic. Elmo opened by declaring his intent – checking in – and then asked a question. Because of that, we understood his motivation was genuine, and he wanted an honest answer.
- Ask open-ended questions: Elmo didn’t ask a closed question that can be answered with yes or no. He asked a question that allowed people to share as much or as little as they wanted and that could act as a springboard to a deeper conversation.
- Listen silently and without judgment: Elmo didn’t follow up his original tweet with options like “Are you doing ok, or not ok, or are you happy, or sad, or mad, or…” Elmo asked a question and then listened (read the responses) without jumping back into the conversation or firing off follow-up questions.
- Acknowledge and thank the person sharing: On Tuesday, Elmo responded but not by skipping off to the next scheduled post. He acknowledged the response by opening with, “Wow! Elmo is glad he asked!” He didn’t share his opinion or immediately ask another question. Instead, he thanked people for sharing, acknowledged that he heard their responses, and was grateful.
- Do something with what was shared: Even if you do #4, it’s tempting to move on to the next question. Don’t. Elmo didn’t. Instead, he wrote that he “learned that it is important to ask a friend how they are doing.” He also wrote that he “will check in again soon, friends! Elmo loves you.” You don’t have to profess your love but do respond with what you learned and what it makes you wonder.
People can’t tell you what to create because they don’t know what you know. But they can tell you the problems they have. If you’re willing to listen (just don’t talk about yourself in the third person, you’re not a muppet).
