Signal Awards 2027 - Sample Responses

Recognizes teams who have transformed their culture around data. These teams have moved beyond gut instinct, using Quantum Metric as the source of truth to reshape how their organization makes decisions across departments.

1. How has your organization moved to a data-driven way of making decisions? What did the shift away from your previous approach look like?

• Before Quantum Metric, our digital teams made decisions based on quarterly customer satisfaction surveys and support ticket volume, both lagging indicators that told us something went wrong weeks after it happened. Each department also ran its own version of "the truth": care had their ticket tags, digital had their web analytics, and network ops had their own uptime reports, and the three rarely matched. Now our teams work from the same real-time behavioral data from Quantum Metric, so a debate about whether an issue is a UX problem or a network problem gets resolved in the same meeting instead of escalating for weeks. That shift moved us from siloed, reactive damage control to a single, proactive view of the customer that the whole organization plans around.

2. How has Quantum Metric become the source of truth your teams rely on? What role does it play in breaking down silos between departments?

• Our care team used to blame the website for driving call volume, and our digital team used to blame care for poor issue resolution. Quantum Metric ended that argument. Now both teams pull up the same session replay before a meeting even starts, so the conversation moves straight to root cause instead of finger pointing. We formalized this by building a monthly cross-functional review with digital, care, billing, and network ops, where QM data is the only source anyone is allowed to bring to the table. That single rule changed how the room behaves: instead of four departments defending four separate reports, everyone is reacting to the same facts. Billing, network operations, and digital product now reference the same QM dashboards in weekly planning, which has cut cross-team escalations significantly and shortened the time it takes to agree on what to fix next.

3. Describe a moment when you knew the culture had genuinely shifted toward data-driven decision making. What changed, and what prompted it?

• The clearest sign came when our network operations team, who historically never touched a digital analytics tool, started asking for their own Quantum Metric access after a device upgrade flow issue caused a spike in call center volume. They wanted to see the customer impact directly rather than wait for a summary report. Access alone wasn't enough though; a team new to QM still needed someone to build their dashboards and run their queries. That changed once we rolled out Felix Agentic. Network ops could just ask Felix Chat plain questions like "how many customers hit an error during the upgrade flow yesterday" and get an answer directly, no dashboard building, no waiting on the digital team to pull a report. That was the real unlock: teams outside digital didn't just want access, they started using the data on their own because Felix made it easy to find what they needed without any QM expertise. Within two quarters of that shift, the average time between an issue occurring and a cross-functional team acknowledging it dropped from five days to under six hours.

4. Please briefly summarize your submission in your own words, as if you were explaining it to a peer.

• We used to make decisions off delayed surveys, support tickets, and four different versions of the truth across departments. Quantum Metric gave every team a shared real-time view of the customer experience, and we built a recurring cross-functional review to make that shared view the only thing decisions get made from. Felix Agentic took that further by letting teams outside digital ask plain questions and get answers directly, without needing to build a dashboard or wait on another team. That combination of shared visibility and self-serve access ended cross-team blame cycles, pushed network ops to start flagging issues proactively, and cut the average time to acknowledge a cross-functional issue from five days to under six hours. That's the clearest proof the culture actually shifted from siloed reporting to unified, data-driven action.

Recognizes a project where Quantum Metric data drove measurable business impact. These teams can point to a clear goal, the data that shaped their approach, and a specific result, whether that's revenue recovered, conversion gained, or a customer problem solved.

1. Describe a recent project that drove measurable business impact. What were the main metrics you were tracking, and how did Quantum Metric help you monitor progress toward that goal?

• Our merchandising and ecommerce teams had a shared goal this year: reduce cart abandonment during checkout, which had been rising steadily as we added new payment options and promo code fields. The metrics we cared about were checkout completion rate, time to complete checkout, and abandonment rate by payment method. Before this project, merchandising tracked promo performance in one system, ecommerce tracked funnel drop-off in another, and the two teams rarely compared notes. We set up a shared Quantum Metric dashboard so both teams watched the same checkout funnel in real time instead of reconciling two separate reports after the fact, which is what used to happen at the end of every sprint.

2. How did Quantum Metric data shape your approach? What did you learn that changed your strategy or execution?

• Session replays showed us something we hadn't expected: the abandonment wasn't concentrated at payment entry like we assumed, it was happening at the promo code field. Felix Summaries let us know that 34% of customers who abandoned checkout had first attempted to apply an expired promo code from an old marketing email, hit a generic error, and left instead of retrying. That single issue accounted for roughly $620,000 in monthly revenue impact. The insight shifted our fix from a payment page redesign, which is what was originally scoped, to a much smaller change: a clearer error message that told customers the code had expired and pointed them to current promotions.

3. What was the measurable result? Please share specific metrics, whether that's revenue, conversion, cost savings, or another business outcome.

• The updated error messaging shipped within two weeks of identifying the issue. Checkout completion rate increased by 9% and abandonment tied specifically to promo code errors dropped by 41%. That recovered roughly $254,000 of the $620,000 in monthly revenue we'd identified as at risk, and over the following quarter translated into an estimated $1.4 million in recovered revenue that would have otherwise been lost to abandoned carts.

4. Please briefly summarize your submission in your own words, as if you were explaining it to a peer.

• Merchandising and ecommerce used to track checkout performance in separate systems and only compared notes after a problem had already cost us conversions. Quantum Metric gave both teams the same real-time view of the checkout funnel, and Felix Summaries surfaced that 34% of abandoning customers were hitting an expired promo code error, worth $620,000 a month in impact, an issue we hadn't scoped to fix. That insight redirected us from a planned payment page redesign to a much smaller, faster change: clearer error messaging. The result was a 9% increase in checkout completion and $1.4 million in recovered revenue in a single quarter.

Recognizes a team who made a key discovery powered by AI. These teams used Felix Agentic, Felix Copilot, Felix Summaries or Quantum MCP to surface insights that would have taken hours of manual analysis to find, and turned that discovery into a clear action.

1. What business question were you trying to answer? How did you use AI to find the answer in Quantum Metric?

• Our revenue management team wanted to understand why booking conversion on our luxury suite category had been declining for two consecutive quarters, despite pricing staying competitive and overall site traffic holding steady. The question seemed simple, “why are fewer people booking suites?”, but nobody could point to a clear cause, and the usual suspects like pricing and availability had already been ruled out. Instead of building a custom funnel report or waiting on an analyst to pull session data, our team asked Felix Agentic directly: "why are users abandoning the suite booking flow before payment?". Felix surfaced a pattern we hadn't considered, a significant share of users were opening the suite comparison table, switching between two specific room types repeatedly, and then leaving without booking either one. Felix Chat let us follow up in plain language, asking which specific amenities users hovered over most before abandoning, and the answer pointed to confusion around whether suites included resort fee waivers, a detail that was listed inconsistently between the comparison table and the individual room pages.

2. How would you have approached this problem without AI, and how long would it have taken?

• Without Felix, this would have started with a request to our analytics team to build a custom event funnel isolating suite bookings specifically, then a separate request for session replay sampling to find qualitative patterns, then a manual review of dozens of replays to spot commonalities. Based on similar requests in the past, that process typically took two to three weeks from request to actionable insight, and it depended entirely on an analyst's availability and their guess about where to start looking.

3. What action did you take based on this discovery, and what was the measurable outcome?

• We corrected the resort fee messaging so it matched exactly between the comparison table and individual room pages, and added a small clarifying note directly in the comparison table itself. Within three weeks of the fix, suite booking conversion increased by 17%, and the specific back-and-forth comparison behavior Felix had flagged dropped by nearly half, indicating customers were reaching a booking decision faster and with more confidence.

4. How has Felix Agentic/Felix AI changed your overall strategy or approach to finding and solving problems?

• We no longer default to filing an analytics request when something looks off. Revenue management and digital product teams now use Felix Chat as the first step in almost any investigation, reserving analyst time for the more complex, multi-step analyses that genuinely need it. That shift has cut our average time to insight on issues like this from weeks to days, and it's made data investigation something individual teams can start on their own instead of waiting in a queue.

5. Please briefly summarize your submission in your own words, as if you were explaining it to a peer.

• A quarter over quarter decline in suite bookings had no obvious cause until we asked Felix Agentic directly why customers were abandoning that specific flow. Felix Summaries and Felix Chat pointed us to inconsistent resort fee messaging between our comparison table and room pages, something that would have taken two to three weeks to uncover through a traditional analytics request. Fixing the messaging drove a 17% increase in suite conversion within three weeks. That experience shifted how our teams work: Felix Chat is now the default first step for any investigation, freeing up analyst time and cutting our time to insight from weeks to days.

Recognizes teams delivering a standout mobile app or native experience. These teams use Quantum Metric to diagnose friction in their mobile experience and translate those diagnostics into a broader mobile strategy, not just a one-off fix.

1. How do you use Quantum Metric to monitor your mobile app or native experience day to day? What does that monitoring process look like for your team?

• As a Quick Service Restaurant, over 60% of our orders now come through our app. Mobile isn't a secondary channel for us, it's how most customers actually interact with our brand. Our monitoring approach is built around real-time alerts on our order, payment, and loyalty flows, so the moment something unusual happens, the right team knows immediately instead of finding out days later through a support ticket trend. Alongside that, a shared dashboard tracks order completion rate, crash frequency, and average order time, and both our Product and Engineering are held to those same three numbers so mobile performance is a joint responsibility rather than something only one team watches. Together, the alerts catch the moment something breaks and the dashboard tells us whether it's an isolated blip or a pattern worth digging into.

2. Describe a specific issue you diagnosed using this data. How did you identify it, and how did it affect your customers?

• Recently, an alert flagged a gradual rise in abandonment at the "Place Order" step over a two week period. Because the alert gave us the exact timeframe and flow where the shift was happening, we could go straight into session replays for that specific moment rather than digging blind, and quickly saw that customers were getting silently logged out mid-checkout with zero explanation on screen, so they naturally assumed the app had crashed and left instead of trying again. Felix Summaries confirmed how widespread the problem actually was, showing that 28% of customers hitting this error had no idea their cart had just emptied out from under them. Once we quantified it, the scale was significant: roughly 4,200 orders and $63,000 in revenue were disappearing every single week.

3. What did you do to fix it, and what was the measurable outcome? Please include specific percentage increases or decreases in the behaviors you were tracking.

• My team dug into the session logs and traced the root cause to a session token that was expiring too early during longer ordering sessions, which disproportionately affected customers customizing large group orders. These were exactly the higher value orders we cared most about protecting. We extended the token lifespan and added a clear on-screen message telling customers their session had expired and inviting them to log back in, rather than leaving them to assume the app had simply broken. The results were immediate: abandoned orders at that step dropped 52% within a week of shipping the fix, recovering roughly $33,000 of the $63,000 in weekly revenue we'd identified as at risk. That fast, measurable win is what gave us the case we needed to go to leadership and propose something bigger than a one-time fix: expanding our alerting strategy so it could catch this type of gradual, silent shift earlier next time.

4. How has this experience shaped your broader mobile strategy going forward?

• This experience changed what we build alerts for. We used to set thresholds for sudden spikes, the kind of break that's obvious the moment it happens. Now we also build alerts for slow, gradual drift, changes that build up over days or weeks and add up to real revenue loss without ever looking dramatic enough to trip a normal alert. Since making that change, we've caught two similar issues in our loyalty redemption flow early, before either one grew into a problem the size of the checkout issue.

5. Please briefly summarize your submission in your own words, as if you were explaining it to a peer.

• Our alerting strategy is built to catch both the obvious breaks and the slower, quieter shifts that are easy to miss. An alert on gradual abandonment growth led us straight to a checkout logout bug affecting 28% of customers and costing us $63,000 a week. Fixing it recovered $33,000 in weekly revenue, and the bigger win was using that case to build a new category of alerts tuned to catch slow moving issues, which has already caught two more problems in our loyalty flow before they became bigger. That shift, from only catching hard breaks to also catching gradual drift, is now central to our mobile strategy.

Recognizes the individual whose influence made Quantum Metric stick. These advocates turned skeptics into believers, expanded how their organization puts data to work, and turned that adoption into real progress towards their business goals.

1. Describe your role in driving Quantum Metric adoption within your organization. How did you first become an advocate for the platform?

• I lead digital experience for our online and mobile banking channels, and my path to becoming an advocate started with a problem that had frustrated our product team for over a year: a persistent drop-off in new account applications that nobody could fully explain. We had theories, maybe the form was too long, maybe customers were price shopping and comparing rates elsewhere, but no real evidence either way. When I got into Quantum Metric for the first time, I watched a replay where a customer got stuck on a document upload step during identity verification, retried it four times, and quietly closed the tab. That one replay told us more in five minutes than a year of speculation had. From there, I stopped thinking of QM as a tool the web team used to check page performance and started thinking of it as the way our entire digital product organization should understand what customers actually experience, not what we assumed they experienced.

2. Tell us about a moment when you had to convince a skeptical team or leader to get on board. What was the objection, and how did you address it?

• Our mobile product team was the hardest group to bring in. They had their own backlog, their own roadmap, and their own theory that our mobile account opening flow underperformed simply because customers preferred to open accounts on desktop. It was a reasonable assumption, and it meant mobile improvements kept losing priority to other features. I asked their product lead for fifteen minutes to walk through replays of mobile-only account applications. What we found was that customers weren't choosing desktop, they were being forced there. The mobile camera upload for identity documents was failing silently on a specific range of Android devices, and customers were abandoning entirely rather than switching devices. That reframed the entire conversation. It wasn't a channel preference problem, it was a broken experience on a device we hadn't been testing carefully enough. Mobile account opening became a top priority on their roadmap within the month.

3. How have you helped expand the way your organization uses Quantum Metric, whether that's bringing on new teams, unlocking new use cases, or deepening adoption within teams that already had access?

• Since that conversation, I've helped bring QM into the hands of three additional teams within digital product: mobile engineering, our design systems team, and most recently our small business banking product team, none of whom had direct access eighteen months ago. For teams that had access but were underusing it, I pushed adoption of Felix Agentic specifically, since most product managers had never gone beyond a handful of saved dashboards and didn't realize they could ask direct, specific questions instead of requesting a custom report every time. Our mobile team now uses Felix Chat during sprint planning to check device-specific failure rates before committing to a fix, which has become a standard part of how they scope mobile work rather than a one-off investigation.

4. Please briefly summarize your submission in your own words, as if you were explaining it to a peer.

• A year-long mystery around account application drop-off turned into the moment I became a Quantum Metric advocate, once a single session replay showed us more than a year of guessing had. The bigger shift came when I used that same approach to help our mobile product team, who assumed customers simply preferred desktop, and instead we found a device-specific camera failure that had been quietly driving people away. Since then, I've helped bring QM to three additional product teams and pushed adoption of Felix Agentic so product managers could get direct answers during sprint planning instead of waiting on custom reports. What started as one team's tool has become how our entire digital product organization builds and prioritizes based on what customers are actually experiencing, not what we assume.