Quantitative Research

Actionable Insights to Build Your Brand

By collecting and analyzing numerical data from large sample sizes, companies can accurately assess market demand, patient demographics, and prescribing behaviors. This process helps identify unmet medical needs and quantify the potential market size for new drugs or therapies.

Moreover, quantitative research enables firms to …

  • Evaluate competitive landscapes

  • Track drug adoption rates

  • Measure the effectiveness of marketing campaigns with statistical precision

  • Supports evidence-based forecasting and budgeting by highlighting trends and patterns in healthcare provider preferences and patient outcomes

  • Ultimately, these insights reduce risk, optimize resource allocation, and accelerate time-to-market for pharmaceutical innovations, fostering improved patient access and business growth.

Our Approach

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    OLG Research has the expertise at the types of quantitative research that allow our clients to objectively measure the topic at hand and gives them the conclusive answers to the research questions that they need.

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    We manage every aspect of your quantitative insight needs and have a broad range of methodologies that we can employ. 

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    Our experience suggests that QUAL and QUANT go hand-in-hand, so we can package these methodologies to get the most robust trends with the feedback to support what the data has told us.

Research Methods

ATU - Awareness, Trial, Usage

Completing Awareness, Trial, and Usage (ATU) studies in quantitative market research is critical for effectively measuring the impact of new disease awareness campaigns or promotional platforms. ATU studies systematically track shifts in customer attitudes and beliefs over time, providing valuable insights into how marketing efforts influence product perception and engagement.

The rationale behind ATU studies lies in their ability to capture market dynamics as they evolve, allowing businesses to assess the effectiveness of messaging, promotional activities, and sales strategies. By quantifying changes in awareness, trial rates, and usage patterns, companies gain actionable data that informs future marketing decisions, optimizes resource allocation, and enhances overall campaign efficacy.

In a healthcare context, understanding patient and provider attitudes toward a disease or treatment through ATU research ensures that communication strategies resonate and drive desired behavior changes. This continuous feedback loop supports more adaptive and responsive marketing approaches, ultimately improving patient outcomes and commercial success.

Engaging in ATU research enables a precise evaluation of your marketing initiatives’ return on investment and helps identify areas for improvement or adjustment during the campaign lifecycle. For these reasons, ATU studies are indispensable tools in the quantitative market research arsenal.

Choice Based Conjoint

The Choice-Based Conjoint (CBC) method is a powerful analytical approach used to understand consumer preferences by simulating real-world purchasing decisions. The rationale behind CBC lies in its ability to present respondents with realistic choice scenarios where they select their preferred option from a set of products or services defined by varying attributes and levels. Unlike traditional rating or ranking conjoint methods, CBC mimics actual market situations, thereby capturing trade-offs consumers naturally make.

The importance of the CBC method includes:

  1. Realistic Decision-Making Context: By forcing respondents to choose among competing alternatives, CBC more accurately reflects the decision-making process consumers face, providing insights that are more predictive of actual market behavior.

  2. Measurement of Attribute Importance and Part-Worth Utilities: CBC quantifies the relative importance of product attributes and estimates the utility values of different attribute levels, facilitating targeted product design and segmentation.

  3. Market Simulation and Forecasting: The data derived from CBC can be used to simulate market scenarios, forecast market share for new product concepts, and evaluate the competitive landscape.

  4. Flexibility and Robustness: CBC can analyze a wide range of products and services, accommodating complex attributes and interactions, making it suitable for diverse industries and research objectives.

  5. Enhanced Respondent Engagement: Presenting choices instead of direct ratings keeps respondents engaged and reduces bias, resulting in more reliable data.

In summary, the Choice-Based Conjoint method is essential for deriving actionable insights into consumer preferences, enabling businesses to optimize product features, pricing strategies, and market positioning in a data-driven manner.

TURF Analysis

TURF (Total Unduplicated Reach and Frequency) analysis is a critical tool in market research used to optimize product or service offerings by identifying the combination that maximizes reach within a target market. The rationale behind TURF analysis lies in its ability to evaluate multiple options simultaneously, determining which set of features, products, or messages reaches the largest unique audience without overlap.

The importance of TURF analysis includes:

  1. Maximizing Market Coverage: TURF helps businesses understand how to select a portfolio of products or features that collectively appeal to the broadest possible customer base, thereby enhancing market penetration.

  2. Optimizing Resource Allocation: By highlighting the most effective combination of offerings, TURF analysis enables companies to allocate marketing and development resources efficiently, avoiding redundant efforts on overlapping products.

  3. Informed Product Development: It guides product managers in designing product lines or feature sets that complement each other, maximizing appeal rather than cannibalizing sales.

  4. Strategic Decision-Making: TURF provides quantitative insights into consumer preferences and reach, supporting strategic planning around product launches, market segmentation, and promotional campaigns.

In summary, TURF analysis is essential for businesses seeking data-driven strategies to maximize unique customer reach, optimize product mix, and improve overall market effectiveness.

Maximum-Difference

The maximum-difference (MaxDiff) method in quantitative market research addresses the limitations of simple scaling techniques, which often suffer from respondent bias and analytical inaccuracies. Traditional rating or ranking methods can lead to overstated or inconsistent importance levels due to tendencies such as scale use bias or central tendency bias.

In contrast, MaxDiff requires respondents to make explicit trade-offs by selecting the most and least important (or most and least preferred) attributes within a set. This forced-choice approach significantly enhances the reliability of the data by reducing ambiguity and measurement error. It delivers clearer differentiation among attributes, allowing researchers to identify true preference structures with greater precision.

Consequently, MaxDiff supports more accurate prioritization and optimization decisions in product development, marketing strategy, and customer experience design. Embracing MaxDiff methods ensures that insights reflect genuine consumer preferences, facilitating more effective business actions and resource allocation.

Forecast Modeling

Forecast modeling in quantitative market research serves as a critical tool for predicting future market trends, consumer behavior, and sales performance based on historical data and statistical techniques. The rationale behind employing forecast modeling lies in its ability to transform raw numerical data into actionable insights that support strategic decision-making.

By leveraging forecast models, businesses can anticipate market demand fluctuations, optimize resource allocation, and mitigate risks associated with uncertainty. This predictive capability enables companies to develop more accurate budgeting, inventory management, and marketing strategies. Furthermore, forecast modeling aids in identifying growth opportunities and potential market disruptions before they occur, providing a competitive edge.

The importance of forecast modeling extends to improving the precision and reliability of market research outputs. It enhances the validity of quantitative findings by validating assumptions through empirical evidence. In a dynamic business environment, such models ensure that organizations remain agile and responsive, aligning their strategies with projected market realities rather than relying on intuition or anecdotal evidence alone.

In summary, forecast modeling is indispensable in quantitative market research because it facilitates informed planning, reduces uncertainty, and strengthens the ability to make data-driven decisions that drive sustainable business success.