Booster samples are an essential tool in market research, allowing researchers to enhance the representation of specific subgroups within their studies. Our previous blog on this topic has garnered significant attention, demonstrating a strong interest in understanding how booster samples work and when to use them. To provide fresh insights, we sat down with two key players in the process: Dominick, our Senior Project Manager, who strategizes when and why to use booster samples, and Bryon, our Programming Manager, who implements them in data collection.
In this blog, we dive into the collaboration between these teams, the challenges they face, and real-world applications of booster sampling.
What Are Booster Samples? Before we jump into the interview, let’s quickly revisit what booster samples are. In market research, a booster sample is an additional sample added to a study to ensure a specific group is adequately represented. This method is particularly useful when the main sample does not provide enough respondents from a key demographic, such as younger consumers, a particular ethnic group, or a niche professional sector.
For instance, a client may require a minimum of 200 responses from people who have used a specific product in the last 30 days. If the incidence of that user group is low, a booster sample may be added to achieve the needed responses and ensure there is a readable base for meaningful subgroup analysis. We also boost for specific usage groups, such as brand users, for example, people who have consumed a particular product in the past three months, when the main sample doesn’t yield a sufficient base size for reliable analysis. Booster samples allow us to build a readable base size for these key subgroups, ensuring their responses can be analyzed with confidence.
Interview with the Experts: The Project Management and Programming Team
Q&A with Dominick, Senior Project Manager
Q: Can you explain the strategic importance of using booster samples in our research?
A: Booster samples help ensure that the data collected is reflective of all relevant target groups. It’s not just about including the missing population, it’s also about ensuring we have a sufficient number of respondents from key user groups. For example, if a client is running a study about their product, they need enough responses from recent users to evaluate performance or branding. At the same time, they also need enough non-users to assess opportunities for growth and market penetration. Without a readable base, the data collected could fall short of helping the client make informed business decisions.
Q: How do booster samples enhance the accuracy and reliability of study results?
A: Booster samples help ensure that we gather enough responses from specific user groups to allow for meaningful analysis. For example, if a study aims to understand consumer behaviour among recent Coca-Cola drinkers, the general sample may not yield a sufficient number of qualified respondents. By adding a booster sample, we can screen for individuals who have consumed Coca-Cola in the past three months. Those who don’t meet this criterion are screened out and replaced until we reach the required base size. This targeted approach allows researchers to capture relevant insights from niche or low-incidence segments, improving the depth and reliability of the findings.
Q: Could you share an instance where implementing a booster sample significantly impacted research outcomes?
A: Absolutely. In one study, a client needed feedback from people who had purchased a specific type of product, like a salad, from a fast food or QSR (Quick Service Restaurant) in the past three months. Since this is a behaviour that isn’t directly targetable through standard sampling criteria, we started with a general sample but found that very few respondents qualified. To meet the required base, we introduced a booster sample targeting recent purchasers. We screened for those who had bought the product in the past three months and removed any who didn’t qualify. By doing so, we were able to collect the data the client needed from a group that would have otherwise been underrepresented or completely missed in the main sample.
Q&A with Bryon, Programming Manager
Q: What technical challenges arise when setting up studies that include booster samples?
A: One key challenge is ensuring the booster sample is implemented in a way that meets the client’s objectives without compromising the overall structure of the study. For instance, if we’re boosting for calcium supplement users, and that user group skews female, the client may actually want a higher proportion of female respondents to reflect their core audience. In these cases, the focus isn’t on matching demographic proportions to the general population, but rather on achieving a readable base of the relevant user group to support detailed analysis.
From a programming perspective, the main challenge is setting up the logic to ensure that only qualified respondents enter the booster sample. Unlike the main sample, demographic quotas are often relaxed during boost collection because the goal is behaviour-specific targeting. After launch, the programming team also needs to validate incoming data early on to confirm that only the intended respondents, such as verified calcium users are coming in. This helps ensure the integrity of the booster and that the collected data aligns with the client’s target criteria.
Q: Can you describe the steps taken to ensure data integrity when integrating booster samples into the main dataset?
First, we apply strict screening criteria to ensure that booster respondents meet the required profile. Then, we include a variable that clearly identifies whether a participant belongs to the main sample or the booster group. Often reporting on the main sample and the boost separately. Lastly, we run consistency checks to verify that data aligns logically across different segments.
Collaboration Between Project Managers and Programmers
Q: How do we decide on the timing of collecting booster samples—concurrently with the main sample or afterward?
Dominick: It depends on the study and the client’s preferences. We align the setup and timing with several factors, including:
- Client desires and expectations
- The original or revised quote
- Study feasibility
- Timing requirements
Every option has benefits and drawbacks, so we work closely with clients to determine the best approach based on study design, feasibility, and timelines. While booster samples can be collected either during or after the main sample, collecting them afterward can provide added flexibility for adjusting quotas or refining targeting if initial results reveal gaps. Some common timing options include:
- Concurrently with the main sample: Ensuring that the booster sample is coded separately so that the nationally representative sample remains identifiable.
- After the main sample has been collected: Booster sampling begins only once the main sample is fully completed. This approach allows for clear separation between the two groups through group codes and collection dates, helping clients easily distinguish them during their analysis.
By integrating booster samples strategically and using precise data management techniques, research teams can produce insights that truly reflect the target audience. As market landscapes evolve, understanding these techniques will remain essential for researchers looking to capture the most accurate and actionable data.
Q: How do we ensure that booster sampling aligns with client expectations and needs?
Dominick: The first step is always consulting with the client. We discuss their research goals, desired sample composition, and any specific demographic quotas they require. This ensures that booster sampling is not just technically feasible but also strategically aligned with their business objectives.
Bryon: Understanding client requirements helps us implement the right programming logic, quotas, and weighting mechanisms. This ensures the final dataset meets both analytical and strategic needs.
Q: What considerations are made to maintain the demographic representation of the main sample while incorporating booster samples?
Dominick: We carefully determine the number of additional respondents needed without over-representing them.
Bryon: Weighting is a crucial step, if not done properly, the booster sample can skew the results rather than enhance them.
Booster sampling is a powerful technique that, when implemented correctly, ensures accurate and inclusive market research. As we’ve learned from our experts, collaboration between Project Managers and programmers is key to making this method successful.
Interested in learning more about how booster sampling can improve your research? Check out our previous blog here or reach out to our team for a consultation!
