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

Focus Groups and Depth Interviews

  • Online (PC, tablets and handheld devices)
  • In-Person

Ethnographies: In-Situ

  • Online (in-home, mobile)
  • In-person (at home/work, shopping)

Mobile: Point of Experience

  • While shopping
  • While using product or service

Online Communities

  • Category users
  • Experienced customers
  • Quick tactical feedback on ideas and concepts

Quantitative Research

Online Surveys

  • Respondent Response Through Smartphones, Tablets, or PCs
  • Use of Customer Databases for Sampling
  • USA and Global Online Panel Sources
  • Social Media Panels

Onsite Intercept Surveys

  • In-person intercepts with Shoppers
  • On Premises, at the “Point of Experience”
  • Real-Time Data Uploads

Customer Panels/Communities

  • Voice of the Customer Panels
  • Quick, Ongoing Lower Cost Studies

Mobile Response Surveys

  • Direct Response from Consumers
  • At Point of Experience
  • Via Hand-Held Device

Telephone Surveys

  • Online, Personal Interviews

Mail Surveys

  • Traditional Method for Special Target Groups

Research Technologies

Qualitative Platforms

  • 20/20 for focus groups, chat boards, and collaborative online sessions
  • 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