Most RGM breakdowns are not caused by a lack of insight. They happen when the organization cannot translate analysis into the routines where pricing, promotion, and innovation calls are actually made because data is fragmented, work becomes one-off, and decision owners keep moving while analytics catches up.
This session is a candid look at what it takes to make RGM decision-ready in a highly promotional, margin-sensitive category where speed matters, data perfection is rarely realistic, and adoption depends as much on sponsorship and cadence as it does on models.
What You’ll Learn
- Stop over-building data for decisions that only need direction, and over-simplifying the ones that run the business. Apply different data standards based on the decision at stake, so speed and governance are intentionally balanced rather than accidentally compromised.
- Turn recurring “same question” requests into always-on outputs before chasing new sophistication. Identify repeat commercial asks and convert them into automated, standardized deliverables that remove friction from weekly and monthly routines.
- Earn your seat in innovation and commercial routines by co-owning decision gates, not by delivering better decks. Influence stage-gate, pricing, and RTM processes directly so RGM inputs are embedded before decisions are finalized.
- Use scrappy automation to protect capacity while long-term data foundations are being built.
Deploy interim pipelines and lightweight tools that reduce manual effort and burnout, even if they are not permanent solutions.
- Prioritize like an operator by focusing on fewer than five core moves at a time.
Tie capability build to business impact, data availability, and organizational readiness, and adjust as demand shifts.
Meet the Speaker
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Sunny Yurasek
Head of Advanced Analytics & RGM at Wells Enterprises
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Sunny Yurasek is Head of Advanced Analytics & RGM at Wells Enterprises, leading data science, business intelligence, and AI-driven revenue growth initiatives across pricing, forecasting, segmentation, and trade optimization.
Session Highlights
Data-rich does not mean decision-ready, and “insights poor” is an operating model problem.
Even mature organizations struggle to bring multiple data sources into one usable place; the bigger issue is whether that information reliably shows up in the meetings where decisions are made. [04:31]
Not all analytics opportunities are equal, so stop treating data quality like a universal requirement.
Sunny lays out a practical trade-off: RTM decisions demand governance and precision, while strategic work like directional guidance for new product development can move with less structured data if it accelerates action. [07:11]
If the pipeline is not ready, the question does not stop, so build interim automation before you burn out the team.
From manual store-level scan checks every two weeks to experimenting with automation tools, the point is simple: a throwaway scrappy solution that saves 20 to 50% effort can keep the organization moving while harmonization catches up. [12:12]
RGM influence starts with sponsorship, and technology will not matter if decisions are made without you.
Sunny is explicit: if RGM is not treated as a COE with senior sponsorship, the business can have great data and tools and still bypass insights in the cadence. Changing routines requires being invited into the process first. [16:11]
Adoption is built through publishing and education, not just better analysis.
She describes an educator mindset: cadenced coaching, office hours, monitoring usage decline, and iterating outputs so stakeholders know when and how to engage RGM consistently. [28:41]
Build the core, then pilot to learn, and do not confuse experimentation with foundations.
Wells prioritized core capabilities such as promotion effectiveness, price-pack architecture, executive reporting, innovation benchmark, and elasticity while running pilots like GenAI proof-of-concepts to learn fast and later enrich the core. [38:26]
Q&A
Where does RGM decision-making most often break in practice, and what do you do when the pipeline is not ready?
Sunny points to a familiar constraint: decisions move faster than data pipelines, especially when harmonization is still underway. Her approach is to run foundation work in parallel with interim automation, using scrappy solutions that reduce manual effort and keep the cadence moving. [11:16]
How do you shift from one-off analysis to always-on insights without building an unrealistic operating model?
Start by identifying the questions the business asks repeatedly. If ten people ask the same thing, it is a candidate for automation. Then decide whether the need is reporting or predictive work, and convert the repeatable workflow into a standardized output that is easy to self-serve. [31:12]
What is the hardest organizational change when integrating RGM and analytics, and how do you overcome it?
She argues the hardest change is assuming stakeholders already understand what RGM is and how to use it. The fix is continuous education with live, business-specific examples, and being explicit about what RGM is not so the function is not dragged into unrelated work. [22:18]
If most teams still work in Excel, what is a practical way to move toward scalable decision support?
Sunny does not dismiss Excel. She acknowledges it can run serious simulations, but challenges leaders to translate recurring Excel work into automated dashboards and visualizations. Recording the exact steps of a weekly report can be enough to hand off automation and free capacity for more strategic work. [36:45]
Was there anything you deliberately chose not to build in year one, even though the business wanted it? Why?
She intentionally deprioritized marketing mix modelling early on. The trade-off was to first build consumption driver-based forecasting as a more foundational layer, then use that strength to enable a simpler, more reliable path into MMM later. [41:40]
In volatile market conditions, how do you balance strategic vs tactical RGM, and keep the function strategically relevant?
Sunny frames it as a constant trade-off: competition and market shifts can force tactical reactions, but strategy stays anchored in brand health, portfolio roles, and the tier you choose to win in. RGM maintains strategic importance by continuously grounding pricing and portfolio calls in data points, monitoring marketplace response, and repeatedly building confidence through evidence rather than assumption. [53:05]
