Inguo’s innovative Automatic Causal Discovery tool provides tremendous insights in minutes.
As a researcher, we know how demanding your clients can be, especially given the increased pressure to produce ROI-inspired insights that will really drive results. Our breakthrough in automated causal discovery and driver analysis – a superior alternative to correlation or regression – is a result of leading-edge research into advanced algorithms and expertise in machine learning. Whether you are working with primary or secondary data, use Inguo to help you understand how your data is interrelated and what drives key outcomes.
While we have seen a resurgence in the qualitative space, this research method is still costly and slow for understanding why people think and behave the way they do. Additionally, it is impossible for any moderator to interview enough people at scale and understand/size the way people make decisions. This is where Inguo comes in.
Inguo discusses its tool at IIeX North America
Inguo is more efficient, flexible and powerful than even the best traditional methods.
Our platform will help you understand why consumers think and act the way they do. It will help you fill in holes in your dataset after all the bar charts and line graphs have been created. Top-of-the-line approaches to causal like SEM or Bayesian inference are still too limiting. These techniques require the statistician to possess deep domain knowledge to take educated guesses at how the causal model may be structured. This introduces bias into the results. Humans are also limited in the number of variables they can include in their analysis because of the combinatorial explosion that occurs when dealing with more than 4 or 5 variables.
Inguo’s innovative automatic Causal Discovery platform has the following advantages for market researchers seeking answers in causal relationship:
Create a causal graph containing both continuous and categorical data; understand how demographics and scalar data are interrelated
Choose a target (dependent) variable and understand its key drivers in minutes; focus your organization on what will move the needle
Use our simulation feature to understand how changes to one variable ripple throughout the model; use this for conducting what if analyses without the need for A/B testing
Obtain PhD-level insights without a statistics degree; you don’t need to know how to run the numbers to interpret the numbers
"Stain Cleaning" a real eye-opener for detergent client
Understanding the WHY’s embedded in your dataset is important in Product Development, and researchers should be looking to optimize ROI when making recommendations to their engineers and designers. The following causal graph was produced for a laundry detergent client. Discovering “Stain Cleaning” to be the root cause of purchase intent was a real eye-opener – something a simple correlation analysis would have failed to highlight. Following this analysis, the product development team de-prioritized cute little add-ons like fragrances, and focused on what really drove sales – stain cleaning. The marketing team developed new creative that highlighted how their detergent was great at removing a variety of stains, like grass, wine, blood, etc.