Roundtable discussions are among the most engaging sessions at our Elevate Client Conferences. At the Elevate NYC conference in May 2026, attendees broke into small groups to workshop three topics central to trade promotion management and revenue growth:
Here’s a summary of the key themes and takeaways from each topic.
Measuring whether a promotion actually worked is a core exercise in trade—and it’s often more complicated than it sounds. The group identified some key data challenges and practical approaches to address them.
One fundamental challenge was establishing a true baseline—the first piece in calculating incrementality, and by extension, ROI.
But among those who felt confident in their promotion performance calculations, there was a bigger issue. Some participants shared concerning findings from their own experience: roughly 70% of promotions don’t generate economic value, while only about 15% deliver strong returns.
Beyond that, the group identified several compounding challenges:
Teams are at different stages of addressing these. Some evaluate effectiveness entirely within their planning platform; others pull promotion timing from the system and compare against POS data externally. Those with dedicated RGM functions are further along — actively cleaning historical data, running monthly channel reviews, and moving toward real-time ROI visibility as actuals load. The shared aspiration is a process where every promotion has a defined objective, results feed back into planning guidelines, and budget shifts toward the customers and events that actually move the needle.
Even for those teams that find most of their promotions are unprofitable, there’s still a path to ROI with better data. Identifying and protecting those high-performing events — and reducing overlap between promotions that dilute each other — is where the biggest opportunity lies.
Real-time visibility into trade spend remains out of reach for most teams, and the gap between planned and actual spend was a major discussion point across the group.
Participants brought up various culprits of misaligned data, including lags in reporting for finance vs. account managers depending on when a promotion is closed. Uncleared deductions, spend discrepancies, and in-year pricing changes also make annual budgets unreliable.
A few specific pain points from the group:
Participants also discussed a few change management strategies that have worked within their organizations. For instance, one attendee mentioned how they’ve helped their sales teams adjust to new reporting processes, including tactics like daily email summaries, mobile-friendly reporting, and recruiting internal advocates. Tying sales compensation to budget variance accuracy was also flagged as a meaningful lever for improving forecast discipline and reducing hedging behavior.
The shared vision is a unified view of actual versus projected spend, with budget controls that surface overspend before it happens. Several participants brought up the initiative of integrating consumption data from NielsenIQ and Circana to enable accurate actualization.
Forecasting generated the broadest conversation of the three topics, covering process, governance, data, and the emerging role of AI. Throughout the discussions, attendees highlighted a gap between the forecasts teams are working with versus the ones they actually need.
The dynamic in forecasting is shaped by bias, with different teams’ inputs influencing their priorities. Some participants brought up that sales leans conservative, which can make joint forecasting difficult.
The base-versus-incremental question came up repeatedly — separating expected baseline volume from promo-driven uplift is harder to execute but far more meaningful for understanding true promotional impact and ROI. For teams navigating significant pricing changes or competitive disruption, historical data has also become a less reliable anchor, complicating year-over-year comparisons.
What teams are doing today varies considerably:
Participants also shared how they use AI in their day-to-day work. Current practical applications are grounded — generating statistical baselines, analyzing historical promotion data, and using AI to write data prep scripts that reduce manual processing time.
Near-term value is most likely in data cleansing and anomaly detection: catching outliers, surfacing variance, and identifying where inputs look off before they distort the forecast. Longer-term possibilities like agentic AI for automated promotion planning generated conversation, but the more immediate blockers are practical — change-resistant sales teams, training gaps, and limited visibility into how users are actually engaging with planning tools.
The group’s consensus: AI needs clean data and well-configured systems to deliver real value. Without that foundation, it’s garbage in, garbage out.
The best-in-class vision combines sell-in, sell-out, and POS data with a statistical baseline as the foundation. For most teams in the room, the near-term goal is more modest: moving from a process driven by subjective inputs to one anchored in data, with accountability mechanisms that make forecast quality visible and improvable over time.
