[Webinar Recording] Learn How A.I. Will Make Fieldwork More Efficient While Reducing Sample Waste

View the recording to learn how Cint is using Artificial Intelligence to make fieldwork more efficient while drastically reducing sample waste.

Learn how one company is using Artificial Intelligence to make fieldwork more efficient while drastically reducing sample waste.

Margin compression, quota complexity and harder to predict respondent behavior has made market research field work increasingly difficult. Intelligent buyers of online sample should have a laser focus on the efficiency of their sample supply chain. Suppliers as well need increasing expertise. Waste reduction and respondent experience greatly impact the success of an asset. Maximizing value on both sides of the business is paramount in today’s climate.

Cint (and others) have long been in the business of utilizing technology to solve these issues. For the first time though Cint is now utilizing A.I. within their tech stack to take things to the next level.


With complexities of online fieldwork, many organizations find themselves in a position where the overhead on managing the sample supply chain significantly erodes margins. On the other side of the coin, supply owners are still inefficient with their delivery. Wasting invites and turning monetizable moments into poor respondent experiences.


Cint has launched a feature into its Access product suite called AutoDelivery. It utilizes machine learning to more accurately predict how each and every invitation will play out, in real time. This makes feasibility estimates far more accurate and greatly reduces wasteful sampling. The technology runs the entire process which also eliminates the human overhead of fieldwork. Internal tests show 80% efficiency gains.

Register today! We will cover:

  •  How and why Cint has added auto sampling through A.I. into its fieldwork solutions.
  • What efficiencies are possible through A.I. in fieldwork.
  •  How fieldwork efficiencies reduce sample waste and improves respondent experience.
  • How the introduction of more loyalty panels and panelist offer walls has made response behavior harder and harder to predict, and Cint’s solution to this challenge.


  Oscar Carlsson, Chief Innovation Officer, Cint

Oscar joined Cint in 2000 and since then he has been responsible for product, technology, operations and business development as well as setting up Cint's US operation in 2008 before 2017 taking the role as Chief Innovation Officer.


Oscar is based out of Cint's San Francisco office, but splits his time with Cint's HQ in Sweden. In Silicon Valley, Oscar continues to bring innovation to Cint and its clients by finding new partnerships and launching new product concepts to ensure Cint's continued leadership in the Martech space and ensure Cint's partners have a future proof solution and stay on top of the latest trends


This content was originally published by Cint . Visit their website at https://www.cint.com/.

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New York, New York, United States of America
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About Cint:
Cint :: The World's Sample Management Platform Technology that transforms and accelerates how marketers and researchers gather insights. www.cint.com

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