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Quantitative Research

When it comes to quantitative research, we are leaders in delivering the most effective questionnaires and ensuring the most accurate and valid sample data – netting you the insights you need to make better business decisions.

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Our Toolkit

Machine Learning: using predictive modeling to estimate how KPIs are likely to change under different conditions, and how best to respond.
Example: Airline passenger count predictions under different COVID environments.
Data Integration Strategy: to fuel robust, predictive analytics models.
Example: Evaluating existing observed customer behavioral data (web behavior), primary quant research, expenditure data (e.g. Experian), watch data (e.g. Nielsen); and determining what to use and how to integrate it for predictive models.
Driver Analysis: Identify critical factors that impact business success by uncovering underlying motivators of consumer attitudes and behaviors – such as satisfaction, purchase intent, and likelihood to recommend.
Maximum Difference Scaling (MaxDiff): Obtain relative importance or preference scores for features, benefits, claims or offerings through a robust trade-off exercise. This method provides enhanced measurement and discrimination in results to help clients prioritize areas of focus.
Discrete Choice Modeling (DCM) / Conjoint : Optimize product offerings by evaluating consumers’ preferences and willingness to pay. This rigorous approach evaluates consumer purchase decisions in the context of actual buying scenarios through head-to-head trade-offs.
Reach/Line Optimization : Identify the optimal combination of features and benefits that appeal to and reach the largest number of consumers. Using powerful statistical techniques like Total Unduplicated Reach and Frequency (TURF) or Shapley Value, insights help inform product development, rationalize decisions about specific product lines, and enhance marketing planning.
Segmentation: Uncover key consumer groups for targeting and shaping marketing strategy by deploying advanced multivariate clustering and classification algorithms. Segments are created based on what most differentiates consumers and drives their perceptions, attitudes, needs, and behavior.
Need State Identification: Identify consumers’ needs and occasions that align with, or can be created to align with, a product or service. We often deploy factor and cluster analysis to look at the intersection of consumer segments to help clients prioritize focus areas, innovation activities, and marketing strategies.
Market Sizing : Estimate the potential opportunity within a specific market category or subcategory. This analysis allows clients to verify if a new product or service opportunity has financial validity or if there is value in repositioning an existing product.
Pricing Analysis: Evaluate price sensitivity and determine the optimal price point for a product or service by assessing consumer expectations against the perceived value of the offering. We deploy a variety of methods to identify the optimal pricing, including Discrete Choice Modeling, Van Westendorp, and Gabor Granger.
Dial Testing: Collect real-time feedback on video/advertising content online or in-person by allowing consumers to adjust a “dial” based on their reactions to key performance metrics such as believability, motivation, and appeal.

During the pandemic, many clients recognized they cannot rely on behavioral data. They also began to recognize they need robust, actionable consumer insights more – and faster than ever. The video below highlights thought leadership from a webinar on conducting effective research.

What challenges are you facing today?

We’re ready to deliver product optimization and development insights and move your organization forward.