Going Beyond the Survey: How Meta Reality Labs Operationalized CX

The CX team at Meta Reality Labs share their best practices for improving the customer experience — going beyond surveys to collect the right data to drive action.

Advanced customer experience (CX) professionals know that gathering customer feedback through surveys is just one tactic of many that can be used as a broader, more sophisticated strategy for improving the customer experience. Voice of the customer surveys can have a greater impact when they’re conducted alongside other approaches that achieve better visibility into the customer experience, prioritize actions that enhance CX, and activate continuous improvements that build loyalty and strengthen the brand. 

And the team responsible for customer experience at Meta’s virtual reality and augmented reality division, Meta Reality Labs, is leading the way — combining surveys with a wide range of customer signals. 

I had the pleasure of learning from two CX leaders at Meta Reality Labs — Pegah Valeh, Head of Global Customer Experience, and Stephen Lopez, CX Lead — about how they’re capturing more customer experience signals beyond traditional surveys, incorporating AI into their efforts, unlocking more actionable insights, prioritizing areas for improving the customer experience, and enabling data-driven action at every level across the organization in our recent webinar, Beyond the Survey: How Comprehensive Experience Programs Enable Action. 

Here are some of the key highlights from our discussion on how Meta is building customer loyalty with confidence, and some bonus answers to questions we received from our webinar audience members.*

Q: In just a few short years, your team at Meta Reality Labs has evolved from conducting simple surveys to having the right data to drive real action across the business. Can you tell us about your journey?

Pegah Valeh: We didn’t have a customer experience program when I joined Meta. We only had one single survey that people weren’t really using for analytics or decision-making. What we did was establish a CX program by first onboarding Medallia and establishing our first survey through Medallia for people to take after interacting with our agents.

We started there because we were actually embedded in the customer support team, and this was the area that actually needed more insights, and we already had buy-in. Then we expanded our program to look at end-to-end customer experiences, all of the interactions throughout the customer journey.

We started gradually expanding our program from customer support to the purchase experience, delivery experience, product listening, and then the return experience.

We also wanted to get a diverse set of feedback and insights and not just rely on surveys, so we started expanding into capturing indirect feedback, such as product reviews, social media, and operational data, like inferred data, to complement our surveys in terms of increasing our understanding of our customers.

Q: What made you realize that other types of feedback were needed for improving your customer experience program? 

PV: That was our intention since the beginning — our vision was to build a 360-degree view of the customer experience across everything that we do. We also realized that it’s important to have a diverse set of information, because surveys are absolutely great, but they might have blind spots and there also might be other ways that are quicker than waiting for survey results to come back.

That’s why we started expanding into the other types of feedback, including direct feedback, indirect feedback, and data sources that help us infer, based on customer behaviors and on operational data, if the customer had a good experience or a bad experience without even having to ask.

Stephen Lopez: Our team sits within a support organization, so it’s natural for us to take advantage of operational data, support tickets, and digital data from our help center to understand self-service experiences. Then as our program grew, we identified — and started using — additional types of feedback based on where we were listening. Throughout this whole entire process, we prioritized understanding what would complement our survey data to help us better understand our experiences for our customers.

For every new strategy that we launch, we use the insights collected through that program and bring them back to the rest of the partners across the organization to show the value of the effort, and then expand accordingly.

– Pegah Valeh, Head of Global CX, Meta Reality Labs

Q: Can you tell us about how you got buy-in from stakeholders?

PV: We started by looking at key moments of truth, the ones that have an exceptional impact on customer experience. The other thing that we took into consideration were areas that we already had some level of buy-in, the low-hanging fruit.

It either had to be a key moment of truth for us to go and fight for it, or something we could quickly get buy-in for by showing the value of insights. 

For every new strategy that we launch, we use the insights collected through that program and bring them back to the rest of the partners across the organization to show the value of the effort, and then expand accordingly.

Q: What was most important to you as you developed your CX program design?

PV: Our analytics. It’s pretty easy to collect feedback. But most companies struggle with how to translate all of this feedback into action. So that was the main design principle behind our program — leveraging analytics to more effectively translate insights.

The act of asking for feedback creates the expectation for customers that you’re going to do something with it. And if you can’t, you better not ask. That’s why we designed our program to make sure that analytics and learning take precedence and priority over even listening.

Q: What kinds of data have helped you unlock more actionable insights?

SL: On the support side, there’s the tickets themselves, but also the volume of specific issues and the transcripts of those tickets, where we can actually learn what’s happening from a customer’s point of view. We can then pair that with other behavioral data. So, for example, how do customers engage with our help center? Are they experiencing difficulties with navigation? That helps us paint a more holistic picture of our overall support experiences. 

Another example is within our order, delivery, and return data. We monitor on-time delivery, which we know through previous analyses correlates with both experience and business outcomes. So while we still survey customers during their delivery and return experiences, we can also leverage on-time delivery to make inferences about these experiences for all of our customers, not just the responders.

We also pair indirect feedback, things like product reviews and social media posts, with our operational data for a more holistic picture. 

Q: How do you prioritize which areas to focus on for improving the customer experience?

SL: It comes down to severity and prevalence. Is something negatively impacting our customers? And, then combined with that, how prevalent is the issue? 

For example, if we’re seeing something heavily impact NPS® or other experience metrics, and we can also assess the prevalence and severity from support volume, product reviews, behavioral data, etc., then that’s likely an area worth digging into. 

Q: Can you share examples of actions you’ve taken to improve the customer experience based on data?

SL: When we launch a product, we leverage our existing listening efforts to monitor and quickly identify and address issues that come up. With a recent product launch, we understood from our purchase survey that there was some confusion with the purchase and delivery experience. We validated that the feedback was showing up across multiple sources and brought that back to our internal teams to help improve that purchase flow and marketing materials to set clearer expectations for our customers and ultimately improve their experience.

With every product launch, we actively rely on our real-time insights to intervene as fast as we can to address issues. On an ongoing basis, we work with teams on continuous improvement opportunities.

Going back to severity and prevalence, we’re consistently evaluating issues that have the greatest impact on our customers, identifying the relevant teams that own addressing these issues, and then bringing these teams together to brainstorm how to fix things. It’s really important for us to tie these experience issues back to their goals, whether that’s reducing the cost to serve, churn, case volume, etc.

We’re trying to mature our analytics program, and part of that is leveraging AI and predictive and prescriptive analytics to predict things like churn, customer intent, and predicting responses of non-responders, aka “the silent majority” that doesn’t take surveys, to eventually decrease our dependency on surveys.

– Stephen Lopez, CX Lead, Meta Reality Labs

Q: How are you thinking about using AI for improving the customer experience from an analytics standpoint?

SL: We’re trying to be very intentional with where we invest in AI for our programs, and that includes reviewing AI applications and making sure we’re taking advantage of solutions that not only add value for our program, but ultimately our customers.

Some of our analyses require a heavy lift from our analysts or involve a bunch of low-effort tasks, so we’re starting to explore how we can bring in AI to help reduce the time to insight, make our analysts’ jobs easier, and be more efficient.

Building on top of that, we’re focused on scaling our insights — using AI solutions for self-service insights and reporting. 

Shifting to the types of analytics themselves, we’re trying to mature our analytics program, and part of that is leveraging AI and predictive and prescriptive analytics to predict things like churn, customer intent, and predicting responses of non-responders, aka “the silent majority” that doesn’t take surveys, to eventually decrease our dependency on surveys.

Ultimately, we want to use all of this to inform experience design and enable real-time orchestration. 

It’s important to look at this from a “crawl, walk, run” approach with AI. We’re really dedicating the time to understanding how AI can help us within our “listen and learn” programs before we jump right into taking action.

Q: Where do you see trends in AI and CX evolving over the next 12 months?

PV: The advancement of AI is going to completely change the customer experience industry. It’s going to accelerate our progress towards providing better customer experiences. It’s going to help us not only increase the speed of time to insight, but it’s going to give us the scale and depth that it’s been so hard to achieve. In the future of CX with the use of AI, we’re going to be able to quickly and more accurately get to a better understanding of our customers’ pain points and a better understanding of the root cause of these issues, and then be able to orchestrate actions. 

There are a lot of companies that are doing a good job of using predictive and prescriptive analytics and experience orchestration, but they’re underutilized right now. With AI, building personalized experiences and proactive experiences is going to be table stakes. That’s why we’re exploring these opportunities as early as possible, because we want to make sure that we can leverage the power of AI to provide a better customer experience, reduce churn, reduce costs, and eventually improve customer experiences through more personalization, through more predictive capabilities, and by making sure we stay ahead and proactively address and orchestrate actions.

Q: What’s next for your team as you continue on this journey?

PV: The use of predictive analytics and prescriptive analytics is really important for us and is an area that we’re investing in. This is eventually going to help us orchestrate action and build personalized experiences, which is another goal of our organization. The byproduct is that it also helps us reduce the time to insights, which is another goal of ours. We want to make sure that we don’t spend a lot of time manipulating data, cleaning data, or making sense of it, so we’re also focusing on self-service capabilities for our analysts and our partners, so they don’t have to be completely reliant on us. 

Lastly, none of these priorities matter if we don’t have the right culture of customer-centricity within our organization, so that’s another area we’re investing in. 

Audience Questions: Meta’s Responses to Improving the Customer Experience

There was so much interest in and engagement with this webinar topic that we were flooded with questions from our audience. The team at Meta shared these additional learnings in response to some of the questions that came up:

Q: How are you able to ensure accountability and action on the insights that are found?

PV: What has worked for us has been establishing shared goals, metrics, and KPIs that represent customer experience with our stakeholders. This has helped us to stay aligned and create accountability. 

We also prioritize educating stakeholders about the impact on customers — that is, what a lack of action means for customers — and then connect these impacts to the goals and metrics stakeholders care about (e.g., impact on future sales if we don’t take action on this issue). 

We also share industry or internal research showing the importance of customer-centricity and acting on customer feedback on overall business success. We have created a deck with all these types of insights using mostly external research on ROI and impact of CX on the businesses, and we’ve used every opportunity to educate our organization about it to increase awareness.

Q: When focusing on KPIs, which do you prioritize — NPS® or short-term profitability? What’s the sweet spot for you?

PV: A healthy balance of the two is key, but I personally try to lean towards long-term loyalty gains since this growth is more sustainable. It’s hard to give you the exact sweet spot as it depends on the touchpoint and the type of NPS® (and methodology of the survey). 

Q: What are some best practices for getting more customers to respond to surveys? What can companies do to receive diverse feedback?

PV: We have a relatively healthy response rate, but here are some strategies I have used previously. Send out survey reminders (this helped us improve our response rate by +5% to 6%). Ask within the same channel/interaction (e.g. send surveys via chat if customers interacted with you via chat or right after the interaction is over for other types of channels).

Be sure to ask questions that really matter to customers, from their point of view — ask about actions they’ve taken, items they’ve purchased, experiences they’ve had, and why you’re interested in their unique point of view. Keep your surveys simple, short, and to the point and ask for feedback via customers’ preferred channels, where they’re actively engaging with your brand. 

For even more CX best practices from Meta Reality Labs, check out the full webinar recording here: Beyond the Survey: How Comprehensive Experience Programs Enable Action.

*This conversation has been edited for length and clarity.