For decades, volumetric forecasting has been the “gold standard” of concept testing. It layers unit sales and revenue potential onto a product concept —giving decision-makers confidence in whether an innovation will deliver real business impact.
But until recently, there were only two main ways to generate a volumetric forecast:
1. Monadic Concept Testing
This traditional approach asks consumers standardized questions about a concept (e.g., purchase intent, uniqueness, value for money). Results are compared to a large database of past tests, and historical sales are used to convert scores into a volumetric forecast.
- ✅ Strength: Works best in stable categories with strong norms.
- ❌ Limitation: Tests concepts in isolation, ignoring the competitive set. As a result, it cannot estimate where volume comes from. It also requires ongoing database maintenance, making it slow, costly, and less useful in new or fast-changing categories.
2. Virtual Shelf Experiments
This approach creates a digital shopping environment where consumers choose between existing products and the new innovation, sometimes under different prices or promotional conditions. Forecasts are then calibrated against real-world sales.
- ✅ Strength: Provides rich insights, including source of volume, incrementality, and cannibalization.
- ❌ Limitation: Requires extensive inputs, competitive data, and calibration—making it time-intensive (often 2+ months) and expensive.
Both approaches have their place, but in today’s fast-paced innovation environment, insights leaders face a dilemma: choose speed but lose depth, or choose depth but lose speed.
That’s why Dig Insights developed Agile Volumetric Forecasting—a much faster, cost-effective method that delivers forecasts with the same level of accuracy as traditional approaches, without the heavy price tag or months-long timeline.
How Agile Volumetric Forecasting Works
Instead of building complex virtual store environments, our approach uses data from Upsiide’s Idea Screen — a simple, intuitive way consumers share what they like and how they prioritize choices.
- Swipe right/left: Consumers quickly indicate which ideas they like or dislike.
- Head-to-head comparisons: They choose between pairs of ideas, revealing real trade-offs.
- Smart diagnostics: A few additional questions uncover optimization opportunities.
By combining these two types of choices, we can model real-world purchase decisions. Then, we calibrate results against known in-market sales, ensuring forecasts are grounded in reality.
The result? A Market Simulator built into Upsiide. This simulator predicts not just overall sales, but also where volume will come from, helping you understand whether an innovation drives true growth or just steals share from existing products.
Validation in Action: A Client Case Study
One of our clients in the wellness space put the Upsiide Market Simulator to the test. They wanted to understand the true sales potential of two new product SKUs. To help validate Market Simulator, our client provided us with real-world sales data to calibrate our forecasts.
- From swipes to shares: The Upsiide platform projects preference shares based on consumer swipes and choices. These shares tell us which products are most likely to win in-market.
- Converting shares to sales: By using the client’s own category sales data, we transformed those shares into actual category volume—translating projected shares into concrete unit and dollar forecasts.
- Calibrating SKU-level sales data: We align forecasted preference for each SKU to the client’s actual SKU-level sales. This ensured the model didn’t just predict directionally, but reflected real-world velocities and category dynamics.
What they found:
- Our forecasts were within 1% of actual sales across the two products.
- One forecast was 6% high, the other 5% low—a level of accuracy typically only achieved through lengthy, costly virtual-shelf tests.
The client described our agile method as “as good as our gold-standard virtual shelf, at one-quarter of the cost and in one-tenth the time.”
When to Use This Approach
Agile Volumetric Forecasting is ideal when:
- Speed is critical. You need accurate forecasts in days, not weeks.
- Budgets are constrained. Traditional virtual-shelf methods are too costly.
- You’re testing multiple ideas. Quickly filter and prioritize which innovations to move forward.
- You need early confidence. Shift volumetric forecasting earlier in the process to avoid wasting time and money on weak ideas.
What This Means for You
For insights professionals, this is more than a methodological upgrade—it’s a type of agile forecasting never before available at this early stage.
- Faster, evidence-based decisions mean you can respond to the business at the pace they demand.
- Confidence in early-stage forecasts prevents wasted spend on ideas that won’t succeed in-market.
- Agility without compromise ensures you can still deliver the rigor that stakeholders expect.
In short, Agile Volumetric Forecasting puts decision-quality insights in your hands sooner, helping you guide your business toward ideas with true growth potential.
TLDR;
Volumetric forecasting will always be essential. But it doesn’t have to be slow, expensive, and reserved for the final gate. With Dig’s Agile Volumetric Forecasting, you get the best of both worlds: gold-standard accuracy, at the speed and cost today’s innovation landscape demands.
That’s why ESOMAR awarded us Best Use of Data—and why leading clients are making this method their new standard for innovation decision-making.