What specific actions separate top-performing sales reps from the rest — and how can we coach others to replicate that success?
Map My Customers partnered with Jasper Engines to answer exactly that question. By analyzing data from over 150 field sales representatives, pairing app usage data with actual sales results, we uncovered the specific actions that drive real revenue growth.
What we found can help any sales leader, coach, or rep understand what really drives revenue — and what doesn’t.
Three Types of Reps Emerged
Using clustering analysis, the study identified three distinct groups based on behavioral patterns and sales outcomes:
| Cluster | Nickname | Description | Avg. Quarterly Sales Growth |
|---|---|---|---|
| Cluster 0 (C0) | Low Performers | Low tool usage, few check-ins, minimal account engagement | $48,160 |
| Cluster 1 (C1) | Rising Performers | Moderate tool usage, growing engagement | $66,658 |
| Cluster 2 (C2) | High Performers | Strategic, high-depth engagement with fewer accounts, high check-in and planning activity | $83,463 |
The Big Difference?
Top reps weren’t successful because they were busy — they were successful because they were intentional. They used their tools wisely, prioritized meaningful account touches, and focused their energy where it mattered.
Cluster 2 reps worked smarter rather than simply logging more activity. They invested in deeper, strategic relationships — even with smaller account portfolios.
It’s Not Just About Working Hard. It’s About Working Smart.
High performers didn’t just do more — they did the right things more often:
- Used tools like route planning and global search to optimize their days before hitting the road
- Had slightly fewer accounts but visited them 2.5x more and logged nearly 3x the activity, proving that depth beats volume
- Logged GPS-verified check-ins, revealing they were consistently showing up in person
The result? These reps grew quarterly sales by $83,000, compared to $48,000 for the lowest-performing group.
AI Can Predict Who Will Win (And Why)
Using an XGBoost machine learning model, the research team predicted whether a rep’s sales would rise or fall with nearly 79% accuracy — based on behavioral patterns alone.
What behaviors made the biggest impact?
- Frequent use of global search
- Active route planning
- Regular GPS-verified check-ins
A SHAP transparency tool revealed which actions helped or hurt performance, providing clear, actionable coaching insights for managers.
What Sales Teams Can Do Today
1. Set Smarter KPIs
Base rep goals on what top reps in similar territories are already achieving — not gut instincts. When you know exactly what high performers do differently, you can set targets that actually matter.
2. Ditch the Territory Excuse
It’s not just where you sell — it’s how. Rep behavior had a bigger impact on sales outcomes than territory size. A smaller territory worked by an intentional rep outperforms a large territory worked passively.
3. Coach with Clarity
Focus on what matters: check-ins, planning activity, and deep account engagement. Don’t give vague motivational advice — give reps specific, measurable actions to model.
4. Demystify Success
The best reps aren’t magical — they follow structured, repeatable routines. Share and scale those habits across the team so rising performers can model what works.
5. Use Tech to Plan Better Days
Tools like routing, proximity-based prospecting, and CRM filters help reps turn chaotic field days into strategic wins. The tool itself isn’t magic — but how top reps use it is.
Bottom Line: Sales Success is Measurable — and Coachable!
This isn’t just research; it’s a playbook for building better reps. With proper data, tools, and coaching, field sales leaders can shift from guesswork to precision.
By learning from real behaviors and applying explainable AI, teams can understand top rep habits and drive performance gains organization-wide.
Source: Rai, Sumedha and Sniff, Matthew, Intelligent Field Operations: Applying Machine Learning to Drive Business Growth (July 18, 2025). Available at SSRN: https://ssrn.com/abstract=5360311
