Market Research
Opportunities start with facts. That's why we use quantitative market research and advanced statistics to understand market trends and consumer behaviour.
designing market research
Getting an accurate understanding of human behaviour is critical to decision-making. To ensure our market research studies are accurate we design them with construct, predictive, and discriminant validity in mind, drawing upon validated measures for factual accuracy and making sure that findings correspond with real-world behaviours (Nosek et al., 2005; Haynes et al., 2019).
We also like to consider the user experience in our market research, paying careful attention to design features and aesthetics that make participating in our studies is easy, intuitive, and in line with how information is processed on a neurocognitive level (Mahon-Haft & Dillman, 2010; Tee & Taylor, 2018).
In terms of sampling, we are proponents of a flexible approach to data collection. Our market research will often use population sampling techniques such as stratification to find target markets and audiences in nationally representative studies (Bethlehem, 2009; de Keeuw et al., 2008). However, we also employ alternative sampling techniques like river sampling when attempting to understand unique customer or user groups (Baltar & Brunet, 2012; Chandler & Shapiro, 2016).
Analyzing Market Research
We believe that data should be used to tell a story, unlocking hidden information that can be transformed into something important and meaningful (Monette et al., 2013). That’s why our market research analysis process will often employ advanced statistical methods that have been adopted in social science and medical literature, including implicit association testing, factor analysis, cluster analysis, regression analysis, and effect size testing (Costa & McCrae, 2008; Greenwald et al., 1998; Morisky et al., 2008; Bartholomew, 2011; Cohen et al., 2013; Wasserstein et al., 2019).
When it comes to presenting findings, we realize the importance of moving research into action. That’s why we use data visualization and infographics to help ensure that market research findings are understood by our clients (Kosara & Mackinlay 2013). We also know that storytelling can help facilitate understanding, which is why we like to craft narratives on human behaviour that are driven by data (Segel & Heer, 2010).
Through this combination of academic rigor and creative flexibly, our market research has made valuable contributions to topics including social values, health behaviour, eating and exercise patterns, and online activity (Oliffe et al., 2016; Flannigen et al., 2019; Oliffe et al., in press).
FINDING MARKET FACTS:
- Who are our current customers?
- What motivations drive market behaviour?
- How are people using our products?
- How large is the market opportunity?
FINDING BRAND FACTS:
- Who is aware of our marketing?
- What is our brand associated with?
- How are we influencing customer behaviour?
- How large is the brand opportunity?
REFERENCES
- Baltar, F., & Brunet, I. (2012). Social research 2.0: Virtual snowball sampling method using Facebook. Internet Research, 22(1), 57-74. DOI: 10.1108/10662241211199960
- Bartholomew, D. J. (2011). Analysis of multivariate social science data (2nd ed.). Boca Raton, FL: CRC Press. ISBN: 9781584889618
- Bethlehem, J. G. (2009). Applied survey methods: A statistical perspective (1st ed.). Hoboken, N.J: Wiley. ISBN: 0470494980
- Chandler, J., & Shapiro, D. (2016). Conducting clinical research using crowdsourced convenience samples. Annual Review of Clinical Psychology, 12(1), 53-81. DOI: 10.1146/annurev-clinpsy-021815-093623
- Cohen, J., Cohen, P., West, S.G., Aiken, L.S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, N.J: L. Erlbaum Associates. ISBN: 1134800940
- Costa, P. T., Jr., & McCrae, R. R. (2008). The Revised NEO Personality Inventory (NEO-PI-R). In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The SAGE handbook of personality theory and assessment, Vol. 2. Personality measurement and testing (pp. 179-198). Sage Publications, Inc. DOI: 10.4135/9781849200479.n9
- de Leeuw, E. D., Hox, J. J., & Dillman, D. A. (Eds.). (2008). International handbook of survey methodology. New York: Lawrence Erlbaum Associates. ISBN: 113691062X
- Flannigan, R. K., Oliffe, J. L., McCreary, D. R., Punjani, N., Kasabwala, K., Black, N., Goldenberg, L. S. (2019). Composite health behaviour classifier as the basis for targeted interventions and global comparisons in men’s health. Canadian Urological Association Journal, 13(4), 125-132. DOI: 10.5489/cuaj.5454
- Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464-1480. DOI: 10.1037//0022-3514.74.6.1464
- Haynes, S. N., Smith, G. T., & Hunsley, J. D. (2018). Validity of clinical assessment measures. In S. N. Haynes, G. T. Smith, & J. D. Hunsley (Eds), Scientific foundations of clinical assessment (2nd ed., pp. 68-93). New York, NY: Routledge. ISBN: 1351210548
- Kosara, R., & Mackinlay, J. (2013). Storytelling: The next step for visualization. Computer, 46(5), 44-50. DOI: 10.1109/MC.2013.36
- Mahon-Haft, T. A., & Dillman, D. A. (2010). Does visual appeal matter? Effects of web survey aesthetics on survey quality. Survey Research Methods, 4(1), 43-59. DOI: 10.18148/srm/2010.v4i1.2264
- Monette, D. R., Sullivan, T. J., & DeJong, C. R. (2013). Applied social research: A tool for the human services (9th ed.). Belmont, CA: Brooks/Cole. ISBN: 9781111792473
- Morisky, D. E., Ang, A., Krousel‐Wood, M., & Ward, H. J. (2008). Predictive validity of a medication adherence measure in an outpatient setting. The Journal of Clinical Hypertension, 10(5), 348-354. DOI: 10.1111/j.1751-7176.2008.07572.x
- Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the implicit association test: II. method variables and construct validity. Personality and Social Psychology Bulletin, 31(2), 166-180. DOI: 10.1177/0146167204271418
- Oliffe, J. L., Black, N., Yiu, J., Flannigan, R., McCreary, D. R., & Goldenberg, S. L. (in press). Mapping Canadian men’s recent and intended health behavior changes through the Don’t Change Much electronic health program. Journal of Medical Internet Research.
- Oliffe, J. L., Ogrodniczuk, J. S., Gordon, S. J., Creighton, G., Kelly, M. T., Black, N., & Mackenzie, C. (2016). Stigma in male depression and suicide: A Canadian sex comparison study. Community Mental Health Journal, 52(3), 302-310. DOI: 10.1007/s10597-015-9986-x
- Segel, E., & Heer, J. (2010). Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139-1148. DOI: 10.1109/TVCG.2010.179
- Tee, J., & Taylor, D. P. (2018). Is information in the brain represented in continuous or discrete form? arXiv:1805.01631 [q-bio.NC].
- Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond "p < 0.05". The American Statistician, 73(Suppl. 1), 1-19. DOI: 10.1080/00031305.2019.1583913