Using Post-interaction AI, the company has increased its call-to-sale conversion, improved customer satisfaction, and reduced operating costs.
Super-Sod is a leading supplier of turfgrass sod, compost, and other landscaping products to homeowners, businesses, schools, golf courses and sports facilities across the southeast United States. Demand for these products soars during peak seasons such as spring planting. Swamped with customer inquiries, store staff often struggled to keep up, leading to unanswered calls and missed sales opportunities.
“We were abandoning close to 50% of incoming calls during busy seasons,” says Customer Support Manager Mike Mayak. “That’s obviously not good for sales.”
In 2020, Super-Sod decided to open a contact center in Charlotte, North Carolina. Bringing its customer support together under one roof and adopting a conversation intelligence platform gave the company visibility into every customer call. Mayak’s team can now quickly identify what’s working, what’s not, where agents need support, and how to better serve customers, driving stronger sales from more consistent service.
Team leaders at the new contact center initially performed QA manually. They would listen to calls and score them to guide agent coaching. But with less than 1%of total calls reviewed, it was difficult to identify training gaps and missed opportunities.
This practice changed with the rollout of the Observe.AI Post-interaction solution. Team leaders went from listening to a tiny fraction of calls to being able to gain insights from 100% of them. “It was a huge leap,” as Mayak notes.
One of the biggest benefits of this change was visibility into where agents were missing key sales opportunities, particularly in offering secondary products such as compost and fertilizer.
“In most of our geography, the soil is red clay, which isn’t good for growing sod,” explains Mayak. “Our black compost is a big seller because it helps with that. Observe.AI allowed us to see where agents weren’t offering the product when they should have been.”
By flagging missed opportunities, team leaders have been able to deliver timely coaching and ensure product pitches are included in every call. As a result, agent performance has improved significantly. Sales dollars per call rose by 13%—an increase of about $13 per call—and the call-to-sale conversion rate grew by 8.4% following the implementation of Observe.AI.
According to Mayak, agents have become more proactive and consistent now that every customer interaction can be reviewed. “When you know someone’s paying attention, you stay more focused. It has reinforced the right habits to the point where offering the right products has become automatic.”
Before adopting Observe.AI, coaching at Super-Sod involved listening to full-length calls to find a moment worth discussing. Now, team leaders can go directly to the most important 30-second snippet, a major change from the previous average of 7 minutes per call, including time to review. With calls now averaging 4.5 minutes, this improvement means team leaders can review and coach in a fraction of the time.
The coaching forms developed with Observe.AI have also made feedback more specific and easier to act on. Agents can regularly review their performance and learn from each other.
“They can listen to their own calls and reflect—‘Oh, I should’ve said this.’ It’s a powerful learning experience,” shares Mayak.
Better visibility into agent performance has also helped Super-Sod reduce its total wage costs by almost 20%. By identifying and retaining top-performing agents, the company has scaled back its customer support team from close to 50 members to 31 today.
The company has also streamlined its QA process by eliminating the need for manual reviews. Team leaders spend less time on administrative work and more time coaching agents and improving performance.
As Mayak adds, “We’re way more efficient now with Observe.AI than we were a couple of years ago. We’re doing more with less.”
Notably, Super-Sod’s customer satisfaction, as measured through net promoter score, has increased since implementing Observe.AI.
“Our busiest and most challenging month is May—we sometimes even run out of product,” says Mayak. “Last May, our NPS was 47. This May, we reached 73.2, the highest we’ve ever recorded.”
And the improvement wasn’t just a spike. For the first time since Super-Sod began tracking NPS in 2020, the company has maintained a score above 70 every month in 2025.
“For us to get through our busiest time of the year, where customers tell us we’re doing an excellent job, that’s awesome.”
With quick access to performance and customer sentiment data, Super-Sod now leverages its Observe.AI platform to support decisions outside its customer support operations.
Sales teams, for example, utilize Observe.AI data to track regional trends, while management uses it to understand changes in customer sentiment.
Before Observe.AI, the sales team had little visibility into regional performance trends. Now, they can track these insights daily, allowing them to quickly adjust strategies by location, tailor messaging, and seize new opportunities in real time. As a result, they’ve become more agile and data-driven, driving faster decision-making and boosting regional sales performance.
These insights now play a critical role in long-term planning—something Super-Sod couldn’t do effectively before due to a lack of forecasting data.
As both grower and retailer, understanding demand is essential to their business. Previously, decisions on where and what to grow were based on limited historical data or anecdotal feedback. Now, with real-time call data, Super-Sod can spot emerging trends across regions. For example, when customers in other states began asking about a sod variety sold only in Texas, the data pointed clearly to where demand was growing, helping the company confidently decide where to expand production next.
Insights from Observe.AI have also helped Super-Sod respond more effectively in times of crisis. “We were able to get an idea of how many customers mentioned Hurricane Helene in Georgia and North Carolina last year when the hurricane hit, impacting our customers,” shares Mayak. “That helped us know who to follow up with and where to send supplies. We even used our trucks to get things up to those customers, not just sod, but anything they needed.”
This visibility also enabled new levels of cross-functional collaboration, with teams such as marketing and logistics stepping in to support affected customers.
Likewise, the marketing team uses data from Observe.AI to track how well promotions resonate with customers and whether they’re worth running again.
For example, during a recent early bird compost sale, customer mentions of the product rose from about 5% of calls to 20%. The team broke down customer interest by sod type to guide future campaigns.
Previously, there was no collaboration between the contact center and marketing. That has changed as the team now shares insights that improve service and messaging.
The data has revealed other issues as well, such as customers having trouble locating a store due to incorrect Google directions. “By listening to calls, I quickly saw the problem and told marketing,” says Mayak. “They were able to fix it with Google Ads changes.” This reduced unnecessary calls to the contact center and helped create a better experience for customers.
Mayak’s team continues to find new ways to use Observe.AI. In particular, it is looking at how to reduce low-value call volume so top salespeople can spend more time closing deals. That includes rerouting calls about store directions or delivery times, which don’t require a live agent.
As they plan these next steps, the team continues working with Observe.AI to find ways to further streamline calls and boost sales performance.
“The Observe.AI team has done a great job understanding our business and often steps in with solutions before we even ask,” says Mayak. “That really goes a long way.”
.png)
.png)
.jpg)
.jpg)
.png)
.png)
.png)
