Businesses and market research agencies are looking for multiple sources of samples in order to perform their surveys faster, to make them more affordable, and to fill hard to achieve quotas. Machines and APIs have now made this integration of data workable, because considering the human errors and costs of such an operation it has not been practical to do this manually. Programmatic sampling is an automated solution for sampling operations such as pricing, bidding, and matchmaking of sample sellers and buyers.

While programmatic sampling offers substantial benefits, this new technique comes with its own set of challenges for experts in the market research industry. Two of the most common challenges in the sampling industry are lack of standards and user fraud.

  1. Lack of standards in data structures
    Every company stores data within its own structures, and there is no global standard. This makes data merging a laborious procedure, because usually every company stores data differently and there are a variety of APIs for different interfaces. For this reason we are constantly working on our APIs (sample providers and buyers’ common language) to connect them efficiently into smart sampling hubs of ROMs. 
  2. User fraud
    The second challenge is related to the validation of the respondents’ data. Offers are presented to users to provide motivation, and these incentives lead to some scammers using bots to carry out ad fraud. Validation of these data when dealing with real-time data is more challenging, so we are constantly using machine learning to master the fraud patterns and clean the data offered to clients. ROM fraud detection algorithms ensure that survey respondents are qualified by calculating a fraud score with the help of IPs and alternative service providers. These scores can also reduce the losses incurred in paying scammers. 
  3. Quality control
    Dealing with big data in real time makes quality control a more serious matter. As everything is happening so fast, the quality check systems also need to perform faster, which creates the need for a quality score to check whether the respondents are attentive to their answers and if their answers are accurate. Besides fraud scores, we constantly upgrade our quality score to ensure that the data we provide has the best quality. 

Conclusion

Sampling automation is evolving, and it is time for market research players to welcome this trend for a better understanding of the market. Machine learning, AI and computers give us the power to automate and scale sampling procedures, and it is important for businesses to look past these challenges and to use the advantages instead.

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