How to turn customer experience insights into better product

I've learned through trial and error that customer experience is essential for success in any business.

Early lessons in CX

In college, I ran a dropshipping business where I sold products from Amazon to eBay without ever touching them.

Through this dropshipping model, I never had to take in any inventory of my own.

That meant I didn't actually know the quality of the inventory, even though I trusted Amazon to deliver it.

99.9% of the time, things went fine.

But with the volume I was doing, I had customer complaints and cases where I had to get involved.

I quickly learned that folks demand and expect a high level customer experience in every industry these days.

This is even more true in B2B SaaS, where people are even more unforgiving if you can’t meet core expectations.

That's because the field we play in is much more difficult than that of traditional retailers, who often deal with uneducated consumers who are either ignorant or who don't want to bother with a conflict. When you're paying $100,000 for B2B SaaS software, the expectations are higher than they are for the new pair of bath towels you bought from Walmart.

Companies that really thrive on a CX basis do better in the long run.

Many Silicon Valley leaders have emphasized that CX is the secret power to the success of a startup.

Steve Jobs famously talked about starting with CX:

“As we have tried to come up with a strategy and a vision for Apple, it started with “What incredible benefits can we give to the customer? Where can we take the customer?” Not starting with “Let’s sit down with the engineers and figure out what awesome technology we have and then how are we going to market that?” And I think that’s the right path to take.”

What Jobs knew, and what the best product leaders know today as well, is that CX isn't just about CX.

It directly feeds into retention, growth, and so many other factors within your business, making it a critical linchpin of a successful company, not just of your customer experience department.

This is backed up by the numbers as well.

You can see increased retention rate and higher growth because of great customer experience. McKinsey analysis “shows that companies that are leaders of CX achieved more than double the revenue growth of “CX laggards” between 2016 and 2021.”

Companies like Airbnb, Apple, and Stripe have made it a priority to fulfill their customers' every wish and need, both from traditional support levers to internal customer experience.

But how do you actually know how you're performing in customer experience? It's not enough to just have an a decent NPS or CSAT score.

Neither is it enough to collect a bunch of data and let it sit.

You need to have customer experience insights that allow you to make product improvements, better retain users, and grow the business.

One way we’ve seen our customers here at Command AI leverage the platform to create more CX insights is through

1. Micro-survey and “in the moment” feedback collection that offers higher-quality, timely data
2. Personalized CX that leverages AI to make the product faster, more tailored, and more valuable

First, what is CX exactly?

Let's do a quick review of what customer experience exactly is. As opposed to customer success, which is focused on problem-solving customers' complaints, customer experience encompasses everything from the first marketing touchpoint through onboarding, activation, product usage, and eventual upsell or churn. It’s an end-to-end process that captures the entire, well, experience, that your customer has!

Great CX is driven by a few core components.

Ease of use

One of the most important factors related to customer experience is the overall ease of use and smoothness of your product. You might ask yourself:

How well do things integrate and connect from your marketing site into the product and then into your customer support flows and help docs?

Is there a consistent feel of both aesthetic and taxonomy, and also in terms of delivery and communication throughout the experience?

How easy is it to use on a technical level?

Does it take too many clicks and buttons to get through onboarding?

Does it take too much learning to execute a core task?

You want to ensure the product is highly usable and fairly intuitive for users.

Customer success IS part of CX

Customer success =! customer experience, but it is a connected part, and a big one at that.

Your CS team is responsible for handling customer complaints and dealing with issues.

Doing this well is crucial to ensuring your customer experience is great.

Personalization

More and more personalization is expected, maybe even required, to find success.

In the early days of SaaS, a nice-looking app with decent functionality was impressive.

Now, with AI-powered technologies and more segmentation and data at folks' fingertips, there's an expectation for personalization.

This means more than just having the user's name and account number on the app. It involves proactive and helpful advice based on the user's context, ensuring alignment with what they're trying to do at the moment + delivering targeted and nuanced messaging at the right time and in the right context. This is hard to do, but it's important.

How do we measure CX and the accuracy of your insights?

Let's quickly run through a couple of key ways that folks have traditionally measured customer experience.

Most people have thought about a customer satisfaction survey, like a CSAT. This is a great way to get a high-level understanding of customer satisfaction.

You can run this on a quarterly or monthly basis to create coherent pools that allow you to see trends over time and different segments of performance.

Net promoter score (NOS) is another popular one. With this 1 to 10 scale, you can see who's a promoter (8, 9, or 10) of your business and who has a poor experience (1, 2, or 3.)

Customer effort score is another great one.

Ensuring that your customer effort score is great means you have an easy-to-use product that hits those intuitive and easy boxes we talked about earlier.

A world with only surveys is an incomplete one

All of these metrics have a place and can be helpful in specific contexts.

But even with proper segmentation, which is key to the success of these metrics, you're only getting a limited view of your customer experience.

You're getting an aggregate metric.

We all know how humans are — fickle, with short memories.

Think about all the times you've had a good interaction with a product, only to have it shattered by a poor interaction with a support agent or a broken feature later.

If you survey that person at different times, you'll get a misguided picture overall.

I believe it is more important to gather real-time, in-context feedback than it is to gather high-level metrics. That said, I recommend doing both. Let's talk more about how you can get more clear user intent data that reveals customer experience insights.

User intent data

Most of us are familiar with the idea of user intent. It reveals what users want to do at a specific moment.

For a long time, understanding user intent has been the pinnacle of customer experience success.

If you could understand the exact user intent and fulfill it in the moment, you could deliver an amazing CX.

But that’s been nearly impossible to do at scale, until these last few years.

With new AI technology, it's now easier to gather true user intent data.

User intent can be many things: a specific action they're trying to take, a specific goal they're trying to achieve, or anything else experienced in the product.

To collect user intent data and drive more CX insights, you need to do things more granularly, combining both qualitative and quantitative methods.

The quantitative side of things

Analyzing user interactions and behavior en masse within your product has been possible for a while with tools like Google Analytics and Hotjar.

As that data gets more expansive and better, it's easier to understand what all this data—from clicks to session recordings to user funnel interactions—actually means.

That said, it was obviously impossible to go through on a user by user basis, and even the aggregate collected metrics with proper segmentation still didn't necessarily have the actionable insight you needed to make changes

User intent has gone a step further with the introduction of generative AI chat interfaces within products. By turning that chat data into insight and making product improvements and changes to your customer success teams and more, you can drive and improve customer experience instead of sitting on this gold mine.

One of the most exciting things we've seen with our Copilot product, which sits as a user assistant within your product, is its value not only in reducing the number of tickets through deflection and supplementing the agents + bringing them up to handle more complex edge cases, but also in creating a ton of easily digestible user intent data.

For the first time, teams can see what users are asking about en masse. Even if they've done this before with customer support tickets, they can now see how different answers, prompts, nudges, and tours that launch from Copilot address that intent.

Does it require fallback?

Do folks hit a dead end and fall off?

Where do our help docs fall short?

Where do agents usually need to step in? Why?

Does that lead to eventual churn when you connect it to broader customer service?

All of this data is really exciting and interesting. It's important to ensure that user intent data is involved in all of this.

Contextual qualitative data

Gathering customer experience insights is most effective when the data is as contextual as possible.

This means having the right targeting at the right time for the right people, then creating insights segmented from that.

Let's dig into this more.

Imagine you want to understand how easy a product feature is to use. Running CES or CSAT scores or NPS might reveal users are generally satisfied with your product as a whole.

It's up to you to turn that data point into action to fulfill your goals.

The issue? Without more context, you can't really do that.

The more real-time feedback you can gather, and the more detail you can glean, the better.

Instead of broad, after-the-fact surveys that trigger once a month or a week after an action, or even an hour after an action, you query users as they complete flows, reach a-ha moments, or run into roadblocks.

That real-time feedback captures the actual user sentiment much more accurately.

This data is 10 times more relevant and accurate than any data you collect from a follow-on survey, email, phone, or other methods.

This helps you immediately identify issues while they are salient and top of mind, not relying on users to have a super memory that jogs back to what they were trying to do when they ran into an issue.

We've seen our CB users do this. This can be as simple as a small nudge, unobtrusively asking folks for an emoji response to how they're interacting with a particular feature. A user can read, engage, respond, and move on in under three or four seconds, which is much faster compared to a big takeover and intrusive CSAT or CES survey.

With a CES survey targeted for use after a specific product interaction, you can start to get feedback from users.

Maybe it's a five, and folks find it easy to use. Maybe it's a two, and there are huge issues. Most of the time, you'll have a mixed bag.

But the point is that your data will be much more accurate because it's more contextually and timely and provides much more raw feedback.

Don’t just find the negative

You can also use microsurveys this at positive moments, like an aha or wow moment. Once you've created these with the instructions we gave in our other article, you can run surveys after them to understand exactly why these are being triggered. Or, if you don't want feedback but rather want to push upsell or revenue, you can introduce those as well. For example, if someone has a wow moment, you ask them about the interaction. If it's a 10 out of 10, you can follow up immediately or sometime after, suggesting they sign up or upgrade, particularly with a discount, because they're at a high level of positive sentiment.

Microsurveys aren't the end-all, be-all. When combined with traditional survey methods and quantitative user data, you get a much clearer picture of your customer experience through varied and multi-level insights.

Customer experience insights are more accessible than ever — so what?

You can have all these different methods for collecting customer feedback, but it doesn't mean anything if you can't properly analyze it and implement it.

Ensure you're not only collecting this data but creating a system and direct responsible individuals for analyzing it and turning it into actionable insight.

This is a common question folks have when we are talking them about our Copilot product: who’s in charge of it?

Some teams have customer support folks look through Copilot.

Others have a product member on it.

Others use a cross-functional approach.

There's no one right way to structure it, but what has become clear is that the best customer experience-focused organizations turn all this qualitative and quantitative data into a positive feedback loop.

Understanding what feedback is actually important and actionable is crucial.

Repeated complaints are easy to act on, but most of the time, product engineering and CS people have to triage decisions.

Be critical and decisive in understanding what your customers want.

Communicate changes to them and augment automated user insight data with actual user interviews, surveys, and live conversations.

Knowing your customers will do more than all this data combined.