Why Engine stopped paying for features they didn't need

When you need more than a recording tool

Mollie Bodensteiner oversees all operations at Engine: revenue operations, business systems, client operations, and supplier operations. When you're running a travel management company (flights, hotels, rental cars) with over 200 sellers and call goals , the numbers get overwhelming fast.

Engine had Gong, but Gong was built for a different sales motion. 

"We're contractless, consumption-based," Mollie explains. "A lot of the features built within Gong around forecasting and traditional SaaS value propositions just don't apply to our business. We were paying for features that we did not need and were not  providing value."

The search for a better fit led to Attention.

The three things that mattered

• AI-first automation. CRM write-back that actually works. Workflows that eliminate admin work without requiring reps to think about it.
• Real coaching that went beyond recordings. The ability to quality-manage and performance-manage systematically. AI-generated scores that help managers prioritize where to spend their time instead of randomly picking calls to review.
• Speed and simplicity. A tool that did not require a steep learning curve when onboarding reps at scale.

What the old world looked like

Before Attention, the coaching workflow was simple but ineffective: "Go review 10 calls today."

Sounds reasonable. Except when you ask: Which 10 calls? From what pool?

The answer: Thousands. Literally thousands of calls are happening weekly across Engine's sales team.

So managers had to pick randomly. Or they picked based on hunches. Maybe they reviewed the same high performers because those calls were easier to watch. Or maybe they reviewed their lowest performers and missed opportunities with the folks in the middle of the pack. Meanwhile, the reps who actually needed coaching weren't getting it, and the patterns that mattered stayed hidden in the noise.

What changed

Now? Daily alerts go to managers and reps: here were your best calls, here were your worst calls, and here's why.

The why is important because it separates the idea of random guesswork from active support. Systematic quality management that surfaces the calls worth reviewing.

The result was that managers could spend their time where it matters. Reps get feedback on the moments that actually need coaching. The signal-to-noise ratio flips.

Time to value (and why in-person training worked)

Engine moved fast. They brought Attention's team in person to train the sales teams.

"We had the Attention team come in person to do training," Mollie says. "We have in-person sales teams, which made a huge difference. Let's get people in the room, let's get them aligned, let's get it done."

The tool itself wasn't hard to figure out and the learning curve wasn't steep. But the in-person kickoff accelerated adoption. When you're rolling out to 200 outbound reps, you can't afford slow adoption.

What reps would miss most if Attention disappeared tomorrow

Mollie doesn't hesitate: "The CRM write-back and the email follow-ups."

Why? Convenience. Accuracy. Customer experience.

"I'm not having to go and type all my notes and reference this again and manage this," she explains. "The accuracy of the information goes a long way too. Productivity goes up if reps can be on more calls, more conversations, but admin time is going down."

There's a second effect: customer experience. When a rep has a meeting and doesn't follow up for two days, that's frustrating. Attention removes that friction. The follow-up happens automatically, accurately, immediately.

The workflows that run the operation

1. Call scoring that drives coaching decisions

Engine tracks week-over-week call score performance by team. If the average demo call is a 2.5, that's a signal. Where do they need enablement? Where should coaching focus?

The scores aren't just numbers. They're diagnostic. They point to exactly where the most opportunity exists to drive change.

2. Churn risk detection that catches problems early

Strong negative sentiment on a call? Attention flags it to a dedicated channel with the call summary. Account management and support teams see potential churn issues before they become actual churn.

"We have all of our churn signaling," Mollie says. "If there's a call where we hear really strong sentiment like this might be a churn issue or a product issue, we have a channel that flags those calls and the summaries directly."

The goal: get the full picture of what's really going on with a customer by connecting signals across sales, support, and account management.

3. Product feedback loops that don't require manual work

The product team used to ask Mollie to query their Snowflake database for keywords. Now? They use Helper.

"Product managers were like, can you tell me the signals? And I was like, yeah, now you can go self-serve," Mollie says. "I used to have to go query our Snowflake database for keywords. Now I don't have to do that, which is great. That's what I want them to use this for, and I want to personally not use it."

Marketing uses it. Product uses it. Everyone's finding fast, easy insights they can actually act on.

4. High/low call summaries that surface what matters

Mollie gets daily summaries of high-scoring and low-scoring calls. She typically starts in Helper, looking at something specific, then clicks through to listen to the relevant call.

The workflow removes the guesswork. You're not hunting through thousands of calls hoping to find something useful. The system tells you where to look.

The Numbers

• Rep Time: 500 calls a week - saving them 30 minutes a call - that is 250 hours per week
• Manager/Director Time: Saving 2 hours a week on call reviews per manager
• Key data is 80% more accurate now
• Engine Helper channel has over 80 avg messages a month
• Over 2K signals are surfaced each month via Attention

What actually improved

• Quality went up. Not just close rates (though those matter), but the measurable quality of calls. The scoring system makes it visible.
• Onboarding accelerated. New reps get coached faster with better signals sooner. "We've been able to onboard reps faster, coaching them faster, getting better signals sooner," Mollie says. Game film sessions can happen quickly because Attention recommends which five calls to review.
• Churn reduction. By catching issues earlier through sentiment analysis and systematic call review, Engine identifies problems before they escalate.
• Productivity gains. When admin time drops and call volume can increase, the math is simple. More time on actual selling activities, less time on data entry.
• Performance management that works. "It's definitely helped performance management a ton," Mollie says. The systematic approach means coaching happens where it's needed, not randomly.

The organic expansion

Engine started with AEs and AMs on the travel side. Then expanded to supplier sales teams. Now member support is coming on board.

The expansion wasn't pushed from the top. Teams saw what other teams were doing and asked for access.

"We really have almost everybody on Attention," Mollie says.

What Mollie actually thinks about day-to-day

She doesn't think about Attention. And that's the point.

"The hardest part of my day has nothing to do with Attention," she says. "That's the least of my worries."

When you're overseeing all operations (revenue, business systems, client, supplier operations) the tool that runs quietly in the background and just works is the one you want.

How Engine talks about it internally

"We talk about it as a coaching tool," Mollie explains. "We're not just recording the calls to record the calls. We're recording the calls to take action and drive outcomes and improve behaviors based on our voice of the customer and the sentiment we're hearing."

That framing matters. Transparency, not tracking or surveillance.

Was there nudging at the beginning? Sure. But not now. "People are pretty excited about using it. It's just part of their workflow."

The Bottom Line

When you have 200+ reps making calls, traditional conversation intelligence breaks down. Engine needed AI-first automation, systematic coaching, and speed. Attention delivered all three. Quality improved. Onboarding accelerated. Churn risk got caught earlier. And now it's expanding across the entire organization. Not because operations mandated it, but because teams saw the value and asked for access.

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