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2020 was a rollercoaster of a year, and it forced many companies of all sizes to pivot not only in terms of operations, but how they deliver products and services. Digital offerings have become the new norm, and many long-range innovation plans have experienced exponential growth due to increased demand and necessity. One key takeaway from 2020: the collective need for the market research industry to revolutionize itself, and to bring research execution firmly into the 21st century. Now that 2021 is upon us, we’re highlighting 5 tech trends that are poised to dominate the MR space this year:
1. Continued reliance on Artificial Intelligence
We’ve been hearing about the impending arrival of AI for years, and how it will change everything in research. Conferences have seminars dedicated to it, and we’re inundated with webinars, articles and ads touting its importance. For the most part, it’s been more sizzle than steak, but we’re finally starting to see more practical applications being put to use.
AI-driven chatbots for qualitative exercises essentially fill the role of a moderator and can prompt respondents for further explanation based on the answers they provide. AI is also being used to mine historical responses and then extrapolate predictions based on past user behaviour. Early indicators point to a future of less reliance on proprietary ad hoc research and more reliance on historic secondary research, re-packaged for subsequent use.
2. The Internet of Things Plays a Central Role in Tech Trends
As more and more people consume and integrate smart tech into their homes, the opportunity to leverage its use for practical research applications grows. Smart home assistants like Google Assistant and Amazon’s Alexa have created an opportunity to engage consumers on a new level, providing an opportunity to subsidize current methodologies such as online and phone.
Smart tech also provides an opportunity to natively study usage behaviour from everything from lightbulbs to appliances. The average U.S. homeowner has 11 connected smart devices in their home; look for this trend to grow throughout 2021, and with it the ability to leverage these IoT devices for key insights.
3. Usage of Tech to Supplement In-Person
The COVID-19 pandemic has greatly impacted in-person research and has forever changed the way we design and recruit for qualitative projects.
Historically we’ve relied on physical locations to test grocery shelf layouts and to conduct shop-alongs, but the adoption of virtual and augmented reality technologies will allow consumers to take part in the equivalent research digitally from their home. This will in turn allow for greater cost savings and open studies to a larger pool of individuals.
4. Blockchain
Blockchain presents far more possibilities to the research space than just the option to pay out participants in cryptocurrency. It offers the opportunity to build a secure global trust network that is verifiable through the creation of transactional transparency. Imagine a world in which panellists and data can be verified through blockchain technology, going far beyond a simplistic hashing system.
Larger companies such as IBM and Mastercard are currently implementing blockchain as a practical application to reduce fraud and security risk, and we expect similar practical usage in the MR space to grow as well.
5. Enhancing User Responses with 3rd Party Data
In addition to purchased syndicated data and social listening, another way to cut costs and time on fielding a study is to ask a core set of 15 to 20 questions, and then fill in the rest of the data using tie-ins from databases that already contain the answers.
As more and more data sources begin offering customized access to their data through APIs (Application Programming Interface), look for supporting data on studies to become more robust and the profiling and segmentation of respondents to become more granular as a result.
FAQs
Small and medium-sized enterprises (SMEs) can leverage the mentioned tech trends by focusing on scalable and cost-effective solutions that these technologies offer. For instance, AI-driven chatbots and predictive analytics can streamline customer interactions and provide valuable insights without requiring significant investment in proprietary research tools. IoT devices, often already integrated into many homes and businesses, can be utilized to gather real-time usage data, enhancing research efforts without substantial additional costs. Additionally, virtual and augmented reality technologies can facilitate remote qualitative research, reducing the need for physical locations and associated expenses. SMEs can also adopt blockchain for secure data transactions and participant verification, enhancing trust without incurring high costs. Lastly, accessing third-party data through APIs can enrich their research without the need for extensive, costly primary data collection.
The increased use of AI, IoT, and blockchain in market research brings several privacy and security concerns. AI-driven systems can collect and analyze vast amounts of personal data, raising issues about data consent and the potential for misuse. IoT devices, while providing valuable insights, can also be vulnerable to hacking and unauthorized access, putting user data at risk. Blockchain, despite its security features, is not immune to breaches and can introduce complexities related to data transparency and user anonymity. Companies must implement robust data protection measures, ensure compliance with data privacy regulations, and educate users about how their data is being collected and used to mitigate these concerns.
Ensuring the accuracy and reliability of third-party data used to enhance user responses involves several steps. Companies should carefully select reputable data providers with a track record of accuracy and reliability. It’s important to validate the third-party data by cross-referencing it with internal data and other trusted sources. Regular audits and updates of the third-party data sources can help maintain data integrity. Additionally, companies should be transparent with their stakeholders about the use of third-party data and the methodologies employed to ensure its accuracy. Establishing strong data governance practices and continually monitoring the quality and relevance of the third-party data will further ensure its reliability in enhancing user responses in research studies.
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