For one of the world’s largest medical and dental distributors, quoting speed played an outsized role in winning new business. Each time a sales rep received a list of products that a prospect was purchasing from a competitor, a race began. The goal was simple: convert that list into an accurate quote faster than anyone else. But the reality inside the organization was anything but simple.
Large bids routinely took three to four weeks to process. Accuracy hovered around 70–80%, meaning teams were often reviewing and correcting items manually. Customer frustration grew as delays mounted.
As one sales support leader put it, customers were increasingly direct: if competitors could respond in days, why should they switch?
The distributor understood that the delay was more than an operational inconvenience. It created uncertainty with prospects, slowed the sales cycle, and put the company at a disadvantage in competitive takeaways.
The sales support team managed thousands of SKUs, product variants, and manufacturer details across multiple divisions. Their internal, custom-built tool provided a starting point, but it still required extensive human verification. Each competitive list had to be parsed, normalized, compared against internal data, and validated line by line.
Even small discrepancies—an alternate manufacturer name, a truncated descriptor, a missing unit of measure—could derail matching accuracy and extend review cycles. Much of this friction stemmed from product data living in different systems, following different rules, and being maintained with varying levels of rigor. It’s a pattern we see across many distributors and one reason our data strategy work often sits behind successful automation efforts: clean, consistent data makes accurate matching possible.
Complicating matters further, the distributor’s two main divisions prioritized data differently. One division relied heavily on product descriptions as the source of truth, while the other placed greater weight on manufacturer details. A rule that improved accuracy for one group often introduced errors for the other, making consistency nearly impossible.
As volumes grew and competitive bids increased, backlogs became routine. The distributor needed a solution that respected the nuances of each division’s workflows while driving speed and accuracy across the entire organization.
The distributor partnered with ProfitOptics to design and build a conversion automation engine aligned to how their teams actually worked. The goal was to accelerate quoting dramatically without sacrificing accuracy. The system needed to ingest competitor lists, extract detailed product data, apply matching logic that accounted for division-level differences, and generate quotes reliable enough to move quickly through approval.
Designing the engine required deep collaboration. ProfitOptics facilitated working sessions with both divisions, mapping out their processes, identifying where rules aligned and diverged, and creating a shared, transparent structure that both groups could trust.
These sessions became the backbone of the eventual solution, ensuring decisions were made with clarity and consensus.
Midway through development, a wave of new sales reps joined the distributor’s dental division. Each rep brought a book of business, and each book brought immediate quoting demands. The backlog grew dramatically, and the pressure on the support team intensified. Some new reps wondered if they had made the right move.
When the conversion automation engine went live, it cleared hundreds of bids within two weeks.
The shift was immediate and unmistakable:
- Quotes that once took days or weeks were now completed in under an hour.
- Accuracy rose above 90%, and the support team regained control over their workload.
- Reps saw faster turnaround times, cleaner outputs, and improved confidence in the quoting process.
As momentum built, teams that had been cautious began asking when they could start using the tool. What began as a necessary fix quickly became a model for how automation could support growth across the organization.
The distributor achieved nearly double the output with fewer resources, reduced the strain on its teams, and positioned itself as a faster, more reliable partner for its customers.
With nearly double the monthly output, faster cycle times, and more accurate matches, the distributor not only reduced internal strain—it strengthened its competitive position.
Sales reps gained confidence.
Customers noticed the faster response times.
And the organization had a quoting engine built for the pace of the market, not the pace of manual review.
It’s the kind of shift every distributor wants: less friction, more accuracy, and quoting workflows that actually keep up with the opportunities in front of them.
If you’re reading this because your quoting process feels a lot like the “before” column, you’re in good company. Every team we speak with is facing the same pressures—profit demands, complex SKUs, scattered data, and reps who need answers faster than their systems allow.
If you’d ever like to compare notes or talk through what this kind of conversion automation could look like in your world, let’s talk.