Market Research Automation for Choice Modeling

  • July 5, 2016

Market Research Automation for Choice Modeling

Market research automation makes it easier to plan, conduct, and analyze market research studies to uncover market insights. The standardization of processes and data collection simplifies study preparation and also allows for automated solutions for data visualization and analysis.

Automation is taking manual processes and tasks and designing them such that they can be done systematically. It requires a combination of tools, technologies, and processes working together to reduce or eliminate the need for human intervention. And the adoption of automation within the market research industry is growing, according to the latest Greenbook Research Industry Trends (GRIT) Report. Automation is already widely in use or in consideration in several areas, including data analysis and data visualization, and is growing in several other areas, such as project and survey design.

When thinking about market research automation, your tools, technology, and processes must all be aligned to support automation throughout the market research life cycle, including:

  1. Study or project design.
  2. Data collection and formatting.
  3. Data analysis (analysis of survey data).
  4. Data visualization (charting and infographics).

Research studies and data collection must be standardized so that data is collected and formatted in the same way every time. Standard processes and data formats are necessary to build automated solutions that can programmatically perform data analysis and create data visualizations.

Market research automation for choice modeling using Datagame

One popular methodology used in market research is choice modeling. Choice modeling uses carefully designed sets of alternatives to model relative preference of those alternatives in decision making. Preference ranking, conjoint analysis, and maximum-difference scaling (MaxDiff) are all examples of choice modeling. (Read our introduction to MaxDiff for more details.) We also have a preference test game, Prefer!, which produces a rank order for smaller sets.

Datagame is a useful tool in market research automation of choice modeling projects because it automatically:

  1. Standardizes how research is conducted. For example, perform all MaxDiff studies the same using the MaxDiff Rankifier Datagame. As a result, all respondents across all studies will have the same experience for all of your MaxDiff studies.
  2. Creates the experimental design for you. The MaxDiff Rankifier and Prefer! algorithms automatically create well-balanced comparison sets for statistically relevant data. Sure, you could manually design the experiment to ensure well-balanced comparison sets (or use our free MaxDiff Excel Design Template). But then you still have to manually program all of your comparison sets in your survey tool. Why not let Datagame take care of both of these tasks for you, allowing you to focus your time on choosing the options to include in your study?
  3. Outputs data in a consistent and predictable manner. The data export file outputs the same each and every time, making it easy to develop automated data analysis solutions. Connect raw data from individual respondents with other data sources or use it with other analytics tools. In addition, the export file also includes some aggregate calculations for the data set as a whole.
  4. Presents and visualizes data. Datagame creates the most common visualizations. These charts and graphs can provide quick and easy analysis without the effort required to create your own visuals. For a MaxDiff Rankifier Datagame, it automatically creates graphs that depict the net scores and positive and negative counts. These visuals give quick insights into the rank order of the options and which options are most polarizing.

 Try Datagame for market research automation

Sign up for a free Datagame account and put your choice modeling projects on auto-pilot. Datagame helps you to create standardized, well-balanced experimental designs in a fraction of the time. It also makes analysis and interpretation easy with exportable data and clear visualizations.