Author transcript
Sean Nolan: Hello and welcome back to CX Today. My name is Sean Nolan.
Today, we’ll be discussing how businesses can simulate the perspectives of their ideal customers. Joining me to discuss this is Mayank Mehta, CEO and Co-Founder of Gather.
Hi Mayank, thank you for joining me. I wanted to start by asking about what you’re building at Gather. Can you tell me about customer simulations and how the tool works?
Mayank Mehta: Absolutely. It’s a pleasure to be here, Sean. Thank you for having me on.
Briefly, what we’re doing is building a living, breathing understanding of your customer. We’re essentially trying to replicate your ICP, or ideal customer profile, brain so that you can understand what they might say or do across your enterprise and use that to make better decisions.
People have a misconception that simulation happens purely in a lab. Behind the scenes, it starts with real people. We run AI-moderated interviews with real people, your ICP at scale, and understand their relationship with your brand.
That includes people who are customers, people who have left, people who chose a competitor, and people who do not even know your brand. We seek to understand that entire market as it relates to your brand and your ICP.
We then identify the common answers that define the personas you are targeting and enable you to ask those personas questions through simulations. We come back and share a confidence interval, so you can safely ask questions such as: “Will this message resonate?” “What will make someone switch?” or “How might different buyers respond to this price?”
One thing that makes us unique is that we are not dogmatic about simulation or real research. We work backwards from what our clients are looking for.
If you are doing a board presentation or a PR page, you typically do not want to walk into a boardroom saying, “I spoke with 4,000 fake people.” In that case, we will run real interviews. But if you are doing quick message testing and want to understand something more deeply, we will use simulations.
We are really in the business of providing the voice of the customer across the lifecycle, so that businesses can use that to drive not just better insights, but better decisions.
Sean Nolan: That flexibility is valuable. I want to dig into what a traditional marketing team would be doing. They may run surveys manually, collect the data, and have to sort through it themselves.
Where do you see the value in having an easy, convenient simulation tool compared with that traditional approach?
Mayank Mehta: You nailed it exactly.
The traditional approach starts when a business realises it does not know something, or has a decision coming up that it needs to validate. First, it puts together a project, understands the budget and questions it wants to ask, and gets buy-in from stakeholders.
Once that is in place, it works with an agency or external partner to field the research. You do not want just customer input, because that can be somewhat biased depending on the decision or content you are trying to create.
Once the research has been fielded, which can take weeks or months depending on the audience, you receive the data back. Then you are left with a messy CSV file containing a huge amount of quantitative and qualitative data, plus quotes. You need to sort through it.
A data analyst then has to model the information and identify the key areas and insights to highlight. Meanwhile, the content team is busy, the data team is swamped with requests, and the research team is swamped with requests. You have to beg, borrow, and steal time from them to make all of this happen.
The simplest projects we have seen enterprises and customers undertake take anywhere from four weeks, if you are lucky, to upwards of 12 weeks by the time you actually get the answers and can use them to move forward.
In a market that is shifting, launching, and moving customer choices so rapidly, that is no longer an option.
When we come in, it is about making research not just easy, but almost invisible to the customer. It is a bit like doing your taxes. You can use TurboTax and make it easier, but working with a tax adviser makes it easier still. You can say, “Here is the information. You are the expert. Go do it and come back when it is done.”
We work in the same way. How do we make this fully invisible, while allowing customers to jump in where needed? That means a 12- to 16-week timeline can drop to four to seven days.
The expertise, team, and massive budget previously required shift dramatically. It becomes cost-effective, gives time back to team members for higher-velocity projects and internal work, and, most importantly, enables a company to move at the decision speed needed to win market share.
Sean Nolan: From an employee-experience perspective, that must be liberating for marketers. They are interested in ideas, concepts, design, and messaging. They do not necessarily want to be in the CSV file handling data. There is an employee-experience benefit in allowing them to focus on high-value work.
Mayank Mehta: You are right. That is an understated benefit: liberating people to be great at the things they are great at, such as storytelling, brand, and driving decisions as a team, rather than spending their time gathering lifecycle insights across a customer base or market.
A big part of this is giving time back, helping teams move faster, and helping them retain employees.
Sean Nolan: What are some high-value use cases you have seen so far? How might this be used day to day, and where is it most valuable for marketing teams?
Mayank Mehta: I’ll give you a few examples.
The first is B2B SaaS teams, or AI-native teams, selling into a more complex B2B sales cycle. They need to understand not only the CIO or CISO perspective, but also the business champion, procurement, and the entire market dynamic.
For customers, we often help them understand through simulation what that buying cycle might look like and how they can get ahead of it to make sure they are prepared. What are the top questions coming up now? As you lean into AI-native, how do you get more prepared and ready for that? That is an important use case.
Another concrete example is content creation. B2B companies have to continually drive their brand halo and stay top of mind for AEO and SEO.
By running AI-moderated interviews with real humans, we can extract insights from their ICP very quickly and convert them into branded content that they can publish as thought leadership.
The next time a B2B company reaches out to you, it can say: “Hey Sean, we spoke with 400 of your peers and this is what we discovered about how they are leveraging AI as it relates to CX, media, publication, or video creation.”
That is much more interesting than simply asking, “Would you like a sales call with us?” The opportunity to learn from your peer group, benchmark yourself, and use that to move forward is very valuable.
Content creation, and using it to drive top-of-funnel activity and brand halo, is another strong use case.
For B2C companies, it is often about market movement and making sure they can stay ahead of it. In the food and beverage world, GLP is an obvious example. If GLP happens, how does that shift your breakfast strategy? What are you selling, and how does consumption change over time?
There is no way to understand that other than asking the market. By the time it hits your revenue, it is too late.
You want to get ahead of that and ask: “How are you shifting your habits? What are you starting to do now versus earlier?” You can use that to make downstream changes before it hits the bottom line.
Businesses can test messaging, positioning, pricing, menu changes, and updates quickly, and move at the speed the market is moving.
The final use case that is exciting for us is brand health and competitive intelligence. How much should I be spending on top-of-funnel activity? Whether you are B2B or B2C, you need to understand that.
We can quickly get a sense, across the geographies that matter for the ICP being targeted, of how many people know about a brand, do not know about it, and where those pockets are. That means brand-building budgets can be allocated appropriately and their impact measured quickly.
Those are some of the dominant use cases across the enterprises we are serving now.
Sean Nolan: Marketing can be a huge ship to turn. I saw a video production for a Christmas advertisement being created in September or October. It shows how much effort and time goes into the marketing engine.
By the time it comes out at Christmas, if the messaging is not right or it is not speaking to the ideal customer effectively, it is too late. It is an important proof point for getting it right the first time rather than getting it wrong too late.
Mayank Mehta: Absolutely. Even for simulations, we have found that to be an interesting part of the workflow.
One of the things we are starting to do is embed ourselves in the go-to-market motion. Before you put an ad out, you can test how your ICPs would react and avoid putting something out that is going to underperform.
The same applies to content. You might be about to publish something or send emails to all your prospects. Run a quick simulation and determine whether it will land before you waste airtime with customers and prospects.
Being able to do that quickly, and A/B test things internally before you ship them, is where research becomes more invisible but also more crucial as part of the go-to-market motion we are helping marketers optimise.
Sean Nolan: You were involved in running Pulse and Gartner Peer Insights. What did you learn from that time about managing a customer-facing platform, and about customer reviews, feedback, and perceptions?
Mayank Mehta: I had a wonderful time at Gartner. There were three major things I took away from having the privilege to be part of that platform.
The first is that real insights from real people matter a lot. The trust you provide as part of the platform is crucial. You need the right gates, funnels, and security procedures to ensure that whatever is published is truly something you can stand behind, and something users will appreciate.
This is not just about fraud. It is also about how reviews are sourced, what incentives are involved, whether things are overstated, and whether there is potential direction behind the scenes about how something should be published.
Making sure you are truly proud of what is published is crucial. To me, brand and trust are the same. The minute you lose trust, you lose your brand and its halo.
The second is: how do we make this easy to read through for a decision-maker? You may be about to make a major software purchase. How do you make it so you do not have to read every review?
Star ratings are a helpful top-level signal, but how do you quickly get to what matters to you? It is similar to buying a house. There is no such thing as a perfect house. Depending on how many children you have, how close you want to be to school, what your commute looks like, and whether you spend more time at home or outdoors, you define what makes the best house for you by working backwards from success.
A star rating is fine, but you need to get quickly to the insights that matter for you and personalise that information. That extends trust.
How do we craft a UI and UX that can be generic at a top-down level but also get very specific when needed, allowing us to work backwards from success for the individual customer?
The third thing is how we work backwards, from a growth perspective, from the questions people are asking. Not just forwards from what we are publishing, or the insights people want to write about, but backwards from the questions that lead people to Peer Insights and, in the past, Pulse.
Those questions might include: “How are you making AI decisions?” “What does the budget look like?” “What is the highest-impact ROI you have seen for AI within your enterprise?” and “How do you shift your security practices, if at all?”
Those are the things you need to stay ahead of. It was initially counterintuitive to me: you start with the questions before you get to the insights.
A big part of it is understanding what people are thinking about and talking about, then using that as your top of funnel to fill in the rest of the content, rather than filling in content and expecting people to come.
The key takeaways were building trust, building context, and providing value quickly for the questions that matter. We have applied that to Gather too: how do we build trust, make sure people can lean on the insights to answer questions that matter quickly, and provide a layer of confidence in the data and insights we present, even as we make research invisible?
Sean Nolan: You mentioned the UI and UX angle. With Gather, you have a natural-language system integrated into Slack, allowing people to ask questions and engage with Gather’s insights there.
Is that connected to what you learned about quickly giving people the insights they are looking for through a natural-language interface that people are increasingly used to with AI?
Mayank Mehta: Yes, absolutely. A big part of this is integrating with people’s workflows and working where they work, rather than trying to create a new habit or destination.
It provides multiple benefits.
First, engagement. I do not have to think about Gather because it is where I already am.
Second, you do not have to be a research expert. Like the tax adviser analogy, you can say, “Go make this happen,” and trust that an expert is on the other side making it work.
The same applies here. You do not have to be a research expert. Communicate in English, without having to learn UI or UX along the way, and we will make it happen.
Third, you can see what other people are asking in Slack and leverage their answers and insights. You might see that a product manager asked a great question, then look deeper because it will help with your own positioning and messaging.
All three are major benefits. One of the big learnings coming from Gartner was exactly that.
Sean Nolan: Finally, looking ahead, what do you think the workflow of a future CMO or marketing team will look like? How will AI, intelligence tools, and customer intelligence change marketing workflows?
Mayank Mehta: I love playing Oracle, so I will try. We asked something similar of our marketing advisers and received some great insights, so I might borrow some of them.
I would divide it into two parts.
The first is what will not change. This may seem self-serving, but it is not. Teams that are closest to their customers and understand them really well will continue to win.
The minute you move away from that, culturally, technologically, or otherwise, you start to move away from the pulse. Especially right now, it is crucial to have a pulse on the market.
Regardless of what the CMO role, workflow, or organisation looks like in the future, the one truth that will not change is this: do we understand our customer well across the entire lifecycle in which they interact with us as a brand? If the answer is yes, you will win.
In terms of the workflows I am excited about, a lot of marketing is currently thought of as reactive to the market. You react to a competitor’s pricing, positioning, or roadmap update. You react to a dramatic market change that caught you unaware.
That will shift into something proactive and almost predictive. Marketers will have a pulse on what is going to happen before it does and use that to steer the boat. In many ways, they are the conduit between the brand and the overall market, so having that pulse is crucial.
Marketers will move away from being reactive into being predictive and use that to shape their storytelling downstream.
The second shift is that there will be smaller teams and tighter budgets that are expected to do a lot more. This is not simply board-level pressure for its own sake. Every board meeting CMOs walk into now includes, “Have you seen this Cursor team that did crazy revenue with two people?”
We joke about CMOs getting a check-in at the beginning, middle, and end of the board meeting asking, “What is your ARR now?” Things are moving so fast, but that pressure is real.
Workflow automation, and working with fewer vendors that can do more across workflows, will be crucial. For us, that means thinking about how to conduct entire lifecycle research rather than only focusing on one closed-lost area or something else.
More broadly, budget pressure will lead to fewer vendors that do more, are deeply integrated into workflows, and do not simply drive output but drive outcomes they sign up for with the CMO.
The third point is that CMOs and their teams will still be responsible for creative judgement, ambition, thinking big, and owning the decisions that follow. AI will make everything else much easier, but those things will not change.
How do you think big? How do you fast-forward five years and think about what this could be? How do you tell stories and craft narratives around that? I think that will be the difference between a win and a loss, even as everything else is quickly commoditised.
Sean Nolan: Marketing teams will be feeling pressure from budget tightening, while balancing it with ambition and the belief that they do not need a huge team to achieve something spectacular. That will be an important tension for marketers moving forward.
Mayank, thank you for your time today. It has been great speaking with you.
Mayank Mehta: Thank you, Sean. I really appreciate you having me and asking all the questions.
Sean Nolan: Thank you for watching. Stay tuned for more insights into customer experience and AI on CX Today.
