We hear the term “Artificial Intelligence” being used ever-more frequently. A disruptive technology said to revolutionize the way we do things, A.I. is now being applied and leveraged everywhere: From self-driving cars and smart e-commerce to autonomous trading and fraud detection.
As the impact of A.I. makes its way across industries, the space where it has only recently trickled into is market research. Yes, there has been much buzz about the opportunities and challenges A.I. offers the industry, but there is still a lot of fog as to what it really does for us as researchers.
As a market leader in A.I. and smart data, Wizer is committed to helping clients better understand A.I. applications and connect them to real business needs. And here’s the bottom line of what it truly does: A.I. makes us smarter and more useful researchers and marketing people.
Here are a few real-life examples of how Wizer uses A.I. & smart data in its applications:
- – Using smart data to enrich dashboards: from hashtag-based database querying, to global KPI tracking and project management;
- – Using neural network techniques to conduct live quality assurance tests that behave like anti-virus software, by automatically detecting and quarantining invalid survey respondents;
- – Using computer vision to extract metadata from images uploaded by users, to filter out, classify and categorize image-rich libraries (for A&U, concept testing, etc.); and
- – Using natural language processing and machine learning to consolidate multiple data inputs into single-summary slides, that incorporate and analyze closed and open-ended content and product textual insight.
Artificial Intelligence creating a lot of buzz makes sense because it provides not just a new solution, but a solution that actually benefits research providers and consumers at a larger, more impactful scale. The above examples are only some of the many applications of how A.I. helps improve market research, and there’s definitely more.
For an industry marred with challenges for research clients (such as limited budgets, competition increasingly taking up market share and too much data to process) and research vendors (such as siloed insights, slow/long cycles and fragmented data), A.I. might just be the answer. We hope this sheds light on how A.I. can empower and enable us as researchers to do far more greater things than what we are currently capable of.