Using Customer Insights to Build Enterprise Products | UserTesting and SurveyMonkey

Awesome, thanks so much, and thanks everyone for joining us today for our session on using customer insight to build enterprise products. As mentioned, my name is Janelle Estes. I'm the Chief Insights Officer at UserTesting, and I'm honored to be joined today by one of our fantastic customers, Erika Jader, VP of Product Design and Research at SurveyMonkey. So let's jump into it. In terms of our agenda today, we're going to be covering why great customer experience is good for business, how companies traditionally measure the customer experience, what's missing from CX measurement, and how human insight and authentic customer perspectives can help you gain a holistic view of your customers to make both incremental and radical improvements. So we've all likely heard about the importance of providing a great customer experience. It's important in the B2C world and has become increasingly important in the enterprise and B2B world as customer experiences and expectations around them continue to rise. You know, with every great experience that a customer has, whether it's B2C or B2B, they expect every company they interact with to follow suit. And the interesting thing about providing great customer experience is that it's not just a nice thing to do, it has massive business impact. This is some data of stock market returns of companies that are considered to be CX leaders versus CX laggards against the S&P 500. And as you can see, CX leaders show stock, stock market return almost 3x higher than CX laggards. So to cut through the noise here, it's no longer optional to have a great customer experience. You need it to be a market leader. So how do companies today actually keep tabs on the customer experience they provide? Here are some common metrics. They're probably familiar to you all. First, there's NPS, which is a question we've probably all answered at some time or another from a company we've done business with, which is, how likely are you to recommend us to a friend or colleague? And you provide a response on a scale of 0 to 10. There's also CSAT and customer effort score, which is typically measured after a specific interaction with a customer. And of course, we have customer retention, which we all know and love. The challenge is that these are lagging indicators, and they put you, your team, and your company in sort of the state of reactiveness. Now, of course, these measures aren't the only way that we're providing and capturing information about our customers and the experience that we provide. NPS, CSAT, and these other measures typically sit alongside behavioral metrics, such as analytics and what people are doing in your product or experience. And then, of course, we have CRM and other databases that tell us who our customers our customers are, which to be fair, is a very company-centric view of our customers. All of this to say that while we have loads of data about our customers, what we're doing, what they're doing with us, how they're rating our experience, we have a couple challenges. First is the data sets us up to be super reactive. It trickles in day after day, and we see something we don't like, or more importantly, something our customers are giving us feedback about that they don't like. We have to figure out how to fix it. And that's after the customer has a poor experience or after we see behavior trending in the wrong direction. And the second challenge with this data is that it's not actionable. It doesn't give us any indication of how to fix the problem. Sure, sometimes you have written feedback on surveys like NPS or CSAT, but that only takes you so far. Imagine this was your dashboard. You can see the NPS here is not great. It's, it's It's not terrible, but it's not great. And imagine you were responsible for lifting the NPS score.

What would you do?

What would you change? How would you make the experience better, which then would make the Net Promoter Score better? There's almost no direction here. And don't just take my word for it. Erika from SurveyMonkey is going to share a bit about this challenge that she and her team manage as well.

Yes, thanks, Janelle. And here at SurveyMonkey, of course, we pride ourselves on our ability to listen to customers, as you would expect from a company that builds tools to really enable listening everywhere. Some of our key voice of customer listening channels internally for our own product include our, you know, semi-annual relational NPS survey that we send out, various transactional NPS places that we collect feedback, like in the support process or in customer success. We have a mountain of insights coming from our support queue and support cases. Obviously, you know, in enterprise, very traditional to get feature requests and feedback from sales. We're running in-product surveys all the time. So, you know, a lot of listening channels, but of course, as many of you know, in order to be really truly customer-centric in the enterprise, listening to customers is just not enough. You really need to empower people to be able to take action on that feedback. And in the case of my world, which is really focused on product and design, that action is really happening at the head end of the product development lifecycle. So what we found is that these traditional VOC listening channels, they're necessary, but they're not sufficient. We'll find that the insights are either too specific for fueling innovation or too generic to be really actionable in our product. And so here's an example of something that we might hear from our customers. So an example of something too generic may be, we may see ourselves ranking highly on brand attributes like ease of use, for example. But then NPS feedback has us all over the map with promoters and detractors, both citing examples related to usability and ease of use. Really hard to be actionable. Another example where we might get some feedback that's too specific is through our in-product feedback surveys or mining the feedback in our customer support cases. We might hear about the confusion with a specific affordance, say, in our WYSIWYG editor that we have for authoring, or maybe somebody's frustrated with an upgrade trigger that we were testing in a recent growth experiment. So in these cases, we are, of course, listening to customers. We know what they are unhappy about, but only after they are unhappy. As Janelle was mentioning, we are just really being reactive. Truly customer-centric companies in the enterprise know their customers so well that they can anticipate those future issues, or better yet, the holy grail, solve the problems that the customers didn't even know they had. Back over to you, Janelle, to talk a little bit more about how you do that.

Awesome. Yeah, thanks, Erica. So I mean, based on what we just covered in the story that Erica just told us, it's pretty clear that we're missing a critical piece of customer understanding. We've got all this data that tells us about our customers, but we don't have the ability to actually see what it's like to be a customer. And this is where the power of human insight, or the ability to see your own experience that you provide, through your customer's eyes in real time as they interact with any part of your experience, talk about their perspectives and expectations, and you can even watch their facial expressions and body language. And the power of this type of insight is that it gives you first and foremost a super authentic and deep understanding of what it's like to be your customer, but also it gives you perspective on what isn't working so you can pinpoint exactly what you need to fix. It, it makes the, the data really actionable. And not enough teams are doing this. Erica is going to tell you a little bit about how she and her team do it. But according to the Harvard Business Review, product managers on average spend about 7% of their time with customers or interacting with them, and CEOs spend just about 3% of their time. So it's certainly an area where we're not spending enough time. So Erica is going to walk you through how her team at SurveyMonkey uses these types of perspectives to influence and inform other CX measures like NPS.

Thanks. So yeah, I'm just going to take you through a quick example of how we've used tools like user testing and get feedback to springboard into more actionable insights and to really push that customer centricity into the head end of the product development lifecycle. I just put a screenshot here, something that probably looks nothing like the SurveyMonkey that you're used to. It's, it's certainly conceptual, but really meant to illustrate this example. So, you know, we're constantly changing things in our survey authoring experience. We're developing new features, running growth experiments, thinking about the future of authoring. So even if we were able to take some of those general dissatisfaction or even specific complaints and tie them to an area of the product, it would be really hard to tell which features or experiments are driving those the changes on those metrics. So we recently set up a benchmarking program that would help us discover and establish a baseline for where the largest points of friction are in the experience. So by running a regular task-based unmoderated usability study using user testing, we're able to establish baseline measures for things like happiness, engagement, effectiveness, and efficiency. Once the baselines are established, we can test those design changes and understand the impact more directly. This benchmark study gives us then some deeper insight into this theme that we had an example of earlier around ease of use that we're hearing in other listening channels. So perhaps it's actually the case that our higher value customers are running into more issues associated with implementing logic in their survey, a feature that they're more likely to use. This then takes us back to this virtual qual and quant lifecycle for product development where tools like GetFeedback and user testing can tell us what customers are doing, thinking, feeling, and, and also give us a sense of why. So if you take a traditional example like this with a product design team like I lead, that benchmarking study may take us down two paths. So we may take that and do some optimization experiments, changing things like copy, button placement, etc., and we'll see directly if those things are working through quantitative measures like survey deploys or even surveys with, with logic deployed. But we also want to take those insights that are much, much richer in the human understanding and use those to create a vision for where we want to go with our authoring experience. And we'll call that like a North Star to really drive the innovation. So imagine a future where actually there is no logic to implement and the authoring experience is automated and you have a much more visual metaphor for seeing the experience of the survey. Something like that, we would measure success through qualitative testing at the head end of the cycle. And then, you know, hopefully further down the road, once it's implemented, you would actually see the success reflected in your VOC metrics. So in this one example, you can see that by shifting our listening channels to the head end of the product development lifecycle, we're able to improve our usability benchmarks and key engagement metrics. We're fueling new product innovation that helps us create a competitive advantage. And as I mentioned, hopefully, we're seeing that impact out on the other end and those more rear-facing VOC measures.

Thanks, Erica. What I really love about your example is that you're using human insight to see the world through your customers' eyes to not only make incremental improvements, but also to build kind of your innovative North Star vision for the future. And honestly, that's the name of the game if you want to stay relevant in the enterprise space. And in order to do this, companies have to, as Erica mentioned before, almost anticipate these needs and solve problems before your customers even realize they need those problems solved. And you have to move fast. So if you look at Gong, for example, it's a conversation intelligence platform that is used mostly by sales and marketing teams. They were founded in 2015, and they're currently at, uh, the last time I checked, a little over $2 billion in valuation. And this platform wasn't something that people were asking for in things like NPS or CSAT. It was created to solve a real pain, which was that most companies don't understand why their sales teams aren't meeting their targets. And how did they identify this pain? They did this by empathizing with and understanding those teams and leaders so they could find the right problem to solve. And you do this by seeing the world through the eyes of your potential customers as human beings, not data points. So as we wrap the session, here are our key takeaways. First, providing a great CX is no longer optional. If you want to be a market leader, you have to provide an exceptional customer experience. Second, in order to build great experiences and optimize existing ones, you need to understand what it's actually like to be a customer. Currently, most teams don't have a holistic understanding of their customers, and they're relying on data exhaust that puts them in a reactive state. And finally, in order to make both incremental and radical improvements, as we've seen through the SurveyMonkey story today, you have to pull human insight into your process. Thanks so much for joining us, and at this point we'd like to move over to the Q&A portion of the session.

Excellent. Thank you so much, Janelle. All right, everybody, time for Q&A. If you have a question, please go ahead, pop into the chat. We will be able to take your question there. Just to kick us off, Janelle, Erika, I'm curious, how do frontline teams help enable this? What can they do tactically to help really feed that feedback as well?

Erika, do you want to take that one first? And I can kind of pile on.

Well, I think that the frontline teams play a really critical role in surfacing the voice of the customer. And I think the intent here is to communicate that that information that they're able to give is necessary, but not sufficient. I think if you think about the qual and quant lifecycle, that information that the frontline teams are surfacing while rear-facing gives us a sense of where to go investigate and do that deeper dive into the human understanding and invent that the sort of next generation of products. So I think it's really frontline teams, you know, my experience do a fantastic job of surfacing insights and opportunities to improve the current product, which are really valuable both in terms of optimizing the current product, but also giving us a sense of where to go look for that next evolution of the product.

Yeah, I wholeheartedly agree with that. I think if you look at the entire kind of, I guess, customer experience or customer journey that you provide for your customers, thinking about before people are— before people actually come on to be customers and what the field team and sales team and marketing teams might gather, I find that input extremely valuable for, as Erica mentioned, kind of planning for innovation and sort of the next evolution of your product or offering. And I see more of that feedback coming in at that part of the process than once you have people on board. I typically see the, the feedback that you get from folks that are on board is much more incremental. It doesn't mean you can't pull more innovative kind of feedback out of that, but it tends to be a lot more about feature functionality. Can you change this? Can you switch this around? Versus, hey, have you thought about this whole new offering, which is really where you, you get that sort of direction from when you're talking to people who are not quite customers yet.

The other thing I'd add that is really critical for frontline teams and something that I really appreciate with our team is the frontline teams are building the relationship with the people that we want to talk to, to get that human understanding, right. And so owning those relationships, you know, our research team is constantly working with our our customer success and customer support folks to get customers that meet a certain demographic or certain plan type or those kinds of things to talk to for our research. So it's sort of like they are also a recruiting source for these head-end research activities that we're talking about.

Yeah, that's a great, great point. And I actually, just to pop into another question here, I don't want to lose it, but How do you ensure you're building for the masses and not for one or two larger enterprises? How do you balance those things in a data-driven way?

Well, my first response is we don't want to build for the masses. We want— because you build for the masses, you end up building for nobody. And I think that's a really critical point in enterprise, which is that we We want to build for a very specific customer segment and use case and opportunity. That being said, I think your, your point is spot on, which is you don't just want to build for one or two customers who are, you know, specifically asking for a feature. And I think that is again where the qual-quant cycle comes in, because what we tend to do is we tend to use qualitative research to really unpack those insights and human needs and then validate them quantitatively. So it's like we'll pick up on a theme, you know, around a certain point, and then we'll run a survey to see like, well, how, you know, how common is this? So it's really like that's where the qual and quant really need to come together. But, you know, really, but I think great products are really, you hone in on who your specific customer is and what their needs are and build for a small group of people, not the masses. I don't know, Janelle, if you have anything you want to add there.

Yeah, no, I think you're, I think you're spot on, Erica. And I think one, I guess, takeaway is for this is always be kind of listening, even if you're hearing it from some of your top customers, one or two customers, they, you're likely going to be hearing similar themes or trends from other customers, but maybe not just stated in the same way. And so I think that's part of the listening and sort of perspective that you have to gather as part of this squishy area of getting feedback from real people versus just looking at survey results or data points is that sometimes people will describe things to you in a way where they might be asking for a solution to solve a problem, but that is actually the same problem that you heard from a handful of other customers. And so making sure that you have a way to sort of parse and synthesize what you're hearing to look for themes across your customer base. Because like I mentioned, sometimes people are asking for things that they might not necessarily— might not necessarily be the right solution, but they still have the same problem that they're trying to solve. So having a team that can really kind of dig into that and, and look for those trends is really powerful to avoid building for just a couple specific customers with very specific asks.

Yes, and to your point, anecdotes are dangerous, right? I think this is where Well, qualitative research is small sample. It is not single data point, right? I think the, the, you know, effective qualitative research, you're synthesizing the data and you're looking at trends and anomalies. So a single data point is never something that you would want to make decisions on. And I do think, you know, it's something that I see is really dangerous with customer-centric companies is we want to bring that culture customer centricity, and part of that culture customer centricity is sharing stories about customers, which is really important for motivating the team and connecting the work to the impact it's having, but dangerous when you hear a story about customer at all hands and then start making decisions based on that one story. So I think that's exactly right.

Anecdotes are dangerous. I love that. Okay, well, this has been an absolute great session. Thank you, Janelle. Thank you, Erica, for all of that insight. We have still more great content to come for SaaS Enterprise, so hope to see you all in the next couple of sessions. Thanks, everybody.