Methods & Technologies
ConsumerDNA has identified and vetted the cutting-edge marketing research technologies of our time. These technologies have improved research efficiencies and elevated the value of consumer insights.
Methods
Qualitative Research
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Focus Groups and Depth Interviews
- Online (PC, tablets and handheld devices)
- In-Person
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Ethnographies: In-Situ
- Online (in-home, mobile)
- In-person (at home/work, shopping)
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Mobile: Point of Experience
- While shopping
- While using product or service
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Online Communities
- Category users
- Experienced customers
- Quick tactical feedback on ideas and concepts
Quantitative Research
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Online Surveys
- Respondent Response Through Smartphones, Tablets, or PCs
- Use of Customer Databases for Sampling
- USA and Global Online Panel Sources
- Social Media Panels
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On-site Intercept Surveys
- In-person intercepts with Shoppers
- On Premises, at the “Point of Experience”
- Real-Time Data Uploads
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Customer Panels/Communities
- Voice of the Customer Panels
- Quick, Ongoing Lower Cost Studies
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Mobile Response Surveys
- Direct Response from Consumers
- At Point of Experience
- Via Handheld Device
Telephone Surveys
- Online, Personal Interviews
Mail Surveys
- Traditional Method for Special Target Groups
Research Technologies
Qualitative Platforms
- 20/20 Research
- Go To Meeting and Zoom for depth interviews
Quantitative Platforms
- Decipher for most online surveys
- Qualtrics
- SPARQ for communities management/research
Insights Software Techniques
- Heat Mapping – ads and product concepts
- Applied machine learning – analyzing unstructured respondent data at scale to auto-generating marketing content
- Multi-method, open-end adjuncts
- Sawtooth Software suite – product and line optimization
Statistical Methodologies
- Integrated statistical analyses/modeling and ad hoc, interactive statistical tools
- Conjoint and Choice experiments
- Predictive Modeling using contemporary techniques such as robust regression, logistical regression, multinomial logit, and lexicographic choice models
- Segmentation using CCEA cluster/ensemble analysis, CHAID/CART, and latent class analysis
- Perceptual Mapping with discriminant analysis, multidimensional scaling, and correspondence analysis
- R Programming, machine learning
- Tableaux for analysis and visualization of integrating data sources