Promises, Pitfalls and Proof of Concept
MODERATOR:
Carly Shira, Highlight
PANELISTS:
Tim Hall, Simporter
Cory Lommel, Cargill
Gabriel Dorosz, New York Times Advertising
“I do think it is important to set parameters around [risk and rigor], but it is, it’s hard to put hard and fast rules in place. You do have to rely on intuition at the end of the day.”
This intriguing panel explores the evolving landscape of DIY research, highlighting its benefits, challenges, and future potential. DIY research emphasizes control and speed, blending internal efforts with external tools and partnerships. Key drivers include agility, cost-efficiency, and insight ownership, though challenges exist in scalability, data quality, and time investment. Strong vendor partnerships and a “risk vs. rigor” framework are crucial for success. AI offers further potential for automation and efficiency, while a hybrid approach can optimize resource allocation. Starting with a prototype project helps ensure smooth adoption and maximize impact.
10 KEY TAKEAWAYS
- DIY research is not always “do it alone”: It often involves a blend of internal efforts and leveraging external tools and partner support. The emphasis is on control and speed, not necessarily complete self-sufficiency.
- Key drivers for DIY adoption: Speed and agility, cost-efficiency, increased ownership of insights, and the need to address time-sensitive opportunities.
- Challenges of DIY adoption: Scalability across the organization, ensuring data quality, the time investment required, and the need for experienced staff to maximize the value of DIY tools.
- Risk vs. Rigor Framework: Matching the level of research rigor to the risk associated with the decision being made. High-risk decisions often necessitate more rigorous, potentially full-service research, while low-risk decisions can benefit from the speed and efficiency of DIY.
- Importance of Partnerships: Strong partnerships with vendors are crucial for successful DIY implementation. Look for partners who understand your needs, provide adequate support and training, and offer flexible service models.
- The Human Element: Don’t underestimate the importance of people and expertise. Even with the best tools, skilled researchers are needed to design studies, analyze data, and translate findings into actionable insights.
- AI’s Impact on DIY: AI has the potential to further enhance DIY research by automating routine tasks, improving efficiency, and potentially increasing objectivity. However, caution is warranted, particularly with emerging technologies like synthetic data.
- Internalization and Empathy: DIY research can foster a deeper understanding of the data and increase engagement with the insights generated. It can also be valuable for qualitative research, enabling quick access to consumer feedback.
- Hybrid Approach: A hybrid approach, combining DIY efforts with external partner support, can be a valuable strategy, offering flexibility and potentially reducing costs.
- Prototype Mindset: When adopting new DIY tools, it’s beneficial to start small with a prototype project to test the tool’s capabilities and assess its fit within the organization.
10 key takeaways with further detail:
1. DIY Research is Not Always “Do It Alone”:
The term “DIY research” can be misleading. It doesn’t necessarily imply that organizations are conducting research entirely independently. Instead, DIY research is more accurately defined by the level of control and speed it offers. While some aspects of the research process may be handled internally, DIY often involves leveraging external tools, platforms, and even partner support. The key differentiator is the ability to quickly design, field, and analyze research studies, allowing organizations to respond to time-sensitive opportunities and gain rapid insights. This often necessitates a collaborative approach, where internal teams work closely with vendors who provide the necessary technology and expertise. Vendors may offer training, technical support, or even analytical services, effectively acting as a “scaffolding” for DIY efforts. The focus shifts from complete self-sufficiency to strategic control and agility, enabling organizations to conduct research more efficiently and effectively. This collaborative approach allows organizations to tap into the specialized skills and resources of external partners while retaining ownership of the research process and the resulting insights.
2. Key Drivers for DIY Adoption:
Several factors are driving the increasing adoption of DIY research methodologies. Perhaps the most compelling is the need for speed and agility in today’s rapidly changing business environment. Organizations need to be able to quickly gather consumer feedback, test new ideas, and adapt to market trends. DIY research enables them to do just that, providing the ability to field studies rapidly and obtain results in a matter of days, rather than weeks or months. Cost-efficiency is another significant driver. DIY research can often be conducted at a fraction of the cost of traditional full-service research, making it an attractive option for organizations with limited budgets. Furthermore, DIY research fosters a greater sense of ownership over the insights generated. When internal teams are directly involved in the research process, they are more likely to engage with the data, understand the nuances, and translate findings into actionable strategies. Finally, DIY research can be particularly valuable for addressing time-sensitive opportunities. When organizations need to gather information quickly to inform a critical decision, DIY methodologies provide the speed and flexibility required.
3. Challenges of DIY Adoption:
Despite the numerous benefits of DIY research, organizations also face several challenges when implementing these methodologies. Scalability can be a significant hurdle. While it may be relatively easy to implement DIY research within a small team or department, expanding its use across the entire organization can be more complex. Ensuring data quality is another critical challenge. Without proper training and oversight, DIY research can be susceptible to biases and methodological flaws, leading to unreliable results. The time investment required for DIY research is often underestimated. Designing studies, programming surveys, and analyzing data can be time-consuming tasks, particularly for those who are new to the process. Finally, successful DIY research requires experienced staff who are comfortable with research methodologies, data analysis, and the use of relevant software tools. Organizations may need to invest in training and development to equip their teams with the necessary skills.
4. Risk vs. Rigor Framework:
The “risk vs. rigor” framework is a valuable tool for determining the appropriate level of research rigor for a given project. This framework recognizes that not all research questions require the same level of scrutiny. High-risk decisions, such as major product launches or significant marketing investments, typically necessitate more rigorous research methodologies to ensure the reliability and validity of the findings. In these cases, organizations may opt for full-service research partners who have the expertise and resources to conduct complex studies. On the other hand, low-risk decisions, such as testing a new advertising slogan or gathering preliminary feedback on a product concept, may be adequately addressed through less rigorous, DIY research methods. By carefully evaluating the potential risks and consequences associated with a decision, organizations can determine the appropriate balance between research rigor and cost-effectiveness.
5. Importance of Partnerships:
Strong partnerships with vendors are essential for successful DIY research implementation. Vendors provide the necessary tools, platforms, and support that enable organizations to conduct research efficiently and effectively. However, the value of a partnership goes beyond simply providing access to technology. Effective vendors act as true partners, working closely with their clients to understand their specific needs, challenges, and research objectives. They offer training, technical support, and expert guidance, helping organizations to maximize the value of their DIY research efforts. Furthermore, good partners are transparent about their capabilities and limitations, ensuring that clients have realistic expectations about what can be achieved through DIY research. They are also willing to adapt their services and support models to meet the evolving needs of their clients. Ultimately, successful DIY research relies on a collaborative partnership between organizations and their vendors, where both parties are invested in the success of the research program.
6. The Human Element:
While technology plays a crucial role in DIY research, it’s important not to overlook the human element. Even with the most sophisticated tools, skilled researchers are needed to design effective studies, interpret data accurately, and translate findings into actionable insights. Researchers bring their expertise in research methodologies, data analysis, and critical thinking to the table, ensuring that DIY research is conducted rigorously and ethically. They also play a vital role in communicating the findings to stakeholders, helping them to understand the implications of the research and make informed decisions. In addition, the human element is essential for building strong relationships with research participants. Researchers who are skilled in interviewing and focus group moderation can gather rich qualitative data that provides valuable context and depth to quantitative findings. Ultimately, successful DIY research relies on a combination of technology and human expertise, where both are equally valued and integrated.
7. AI’s Impact on DIY:
Artificial intelligence (AI) is poised to transform the landscape of DIY research, offering the potential to further enhance efficiency, improve data quality, and unlock new insights. AI-powered tools can automate many routine tasks, such as questionnaire design, data cleaning, and basic statistical analysis, freeing up researchers to focus on more strategic aspects of the research process. AI algorithms can also analyze large datasets to identify patterns and trends that might be missed by human analysts, providing deeper insights into consumer behavior. Furthermore, AI has the potential to reduce bias in research by providing more objective and data-driven analyses. However, it’s important to approach AI with a degree of caution. Organizations need to carefully evaluate the capabilities and limitations of different AI tools and ensure that they are used ethically and responsibly. The black box nature of some AI algorithms can also be a concern, making it difficult to understand how the results were generated. Despite these challenges, AI has the potential to be a powerful tool for DIY researchers, enabling them to conduct more efficient, insightful, and impactful research.
8. Internalization and Empathy:
DIY research can foster a deeper sense of ownership and engagement with the insights generated. When internal teams are directly involved in the research process, they are more likely to internalize the findings and understand their implications for the business. This increased ownership can lead to greater buy-in from stakeholders and a stronger commitment to acting on the research results. DIY research can also be a valuable tool for developing empathy with consumers. By conducting interviews and focus groups themselves, researchers can gain firsthand insights into the lives, needs, and motivations of their target audience. This direct exposure to consumer perspectives can lead to a deeper understanding of their challenges and opportunities, informing the development of more effective products, services, and marketing campaigns.
9. Hybrid Approach:
The hybrid approach, combining DIY efforts with external partner support, offers a flexible and potentially cost-effective way to conduct research. This model allows organizations to leverage the speed and efficiency of DIY methods for certain aspects of the research process while still relying on the expertise of external partners for more complex or specialized tasks. For example, an organization might choose to design and field a survey internally using a DIY platform but then partner with a specialized analytics firm to conduct advanced statistical modeling or qualitative data analysis. This approach can be particularly beneficial for organizations with limited internal resources or expertise. By strategically allocating tasks between internal teams and external partners, organizations can optimize their research budget and ensure that they have access to the necessary skills and resources for each stage of the research process. The hybrid model also allows for greater flexibility and control over the research process, enabling organizations to tailor their approach to the specific needs of each project.
10. Prototype Mindset:
When adopting new DIY tools or methodologies, it’s beneficial to approach the process with a “prototype mindset.” This involves starting small with a pilot project or test case to evaluate the tool’s capabilities, assess its fit within the organization’s workflow, and identify any potential challenges. By focusing on a small-scale project, organizations can minimize the risks associated with adopting a new tool and gain valuable experience before scaling up their efforts. The prototype phase allows for experimentation and iterative improvement, enabling organizations to refine their approach and optimize the use of the DIY tool. It also provides an opportunity to gather feedback from internal stakeholders and identify any training or support needs. A successful prototype can serve as a compelling demonstration of the value of the DIY tool, facilitating wider adoption across the organization. Furthermore, the prototype mindset encourages a culture of continuous learning and adaptation, recognizing that the research landscape is constantly evolving and that organizations need to be willing to experiment and adapt their methodologies to stay ahead of the curve.