Are surveys reliable? The big truth from data collected via surveys and how AI can help?
In the past, businesses often relied on surveys as a means to gather insight into how their customers behave and make decisions. To a large extent, this practice continues with some improvements such as advanced softwares which help develop and design surveys be it mobile surveys, online surveys, or plain old-fashioned paper surveys.
Why does data from surveys fail to deliver?
Surveys are often considered a cost-effective means of gathering the required info. But the data collected from surveys may not always be reliable enough due to various factors such as lack of inputs from the right respondents, insignificant sample size, impatient or dishonest respondents, et al. Sometimes, the right type of respondents are not accessible due to the very mode or nature of the survey i.e. online or mobile. These respondents can only be reached through traditional ways such as door-to-door surveys and such due to them having no access to modern technology such as computers or mobile phones.
he question of bias: Besides, the respondents to many surveys may not provide true/honest answers especially if the answers make them appear in a bad light. Some surveys are also filled up by respondents in the lure of incentives. Moreover, surveys oftentimes tend to be extremely lengthy, prompting respondents to quickly answer the survey without giving much thought to the questions and thus resulting in incorrect data. At times, respondents even answer questions in a certain manner to please the surveyors. A respondent may agree to pay a big sum for a luxury product to hide his or her paying capacity while he or she may actually negotiate for a hefty discount while buying that product. In such cases the surveys may present hypothetical bias.
Something that is lost in translation is the interpretation of the questions on the part of the respondents, especially vague multiple option answers such as “somewhat interested”. Thus the structure of the survey itself should be well-researched. If there are open-ended questions then they are kind of invalid vis-a-vis the other questions on the survey. If surveys are important for a business they must go all out and use multi-mode surveys for their research including paper surveys and kiosk surveys for better results.
It remains to be seen if such loopholes in conducting surveys can be plugged. Researchers and businesses can turn to AI for meticulously planned surveys which are created by feeding the algorithm with a wide range of surveys to create the right mix of questions for a particular survey targeted at a particular audience in the future. AI can step in by merging machine learning with organic sampling to enhance data quality and mitigate the existing barriers in survey outreach and inauthentic answers.
Detecting dishonest answers: AI can help detect dishonesty in the overall survey by getting rid of questions/answer options which may be troublesome. It can also detect if a single user is attempting to take the same survey more than once by checking for duplicate IDs etc.
Better outreach: AI -based tools can also get you a better outreach and a higher response rate by reaching consumers globally through advanced targeting and even by using mobile apps.
Better outreach: AI -based tools can also get you a better outreach and a higher response rate by reaching consumers globally through adUsing NLP in augmenting data from surveys: AI tools are trained by feeding them data collected over the years based on several years of open-ended customer feedback. Additionally the researchers physically go through a lot of answers to understand the customer voice and integrate them in the final outcome.vanced targeting and even by using mobile apps.
Incorporate user experience research methods: When user experience researchers work in tandem with product managers to arrive at studies from customer behaviours new features can be introduced to an existing product. Besides, keeping a constant tab on user metrics can provide additional data to plug any loopholes.
Behavioural analytics | One size does not fit all | Customized data: The pandemic too had an impact on surveys and customer feedback. The unpredictable nature of the pandemic has posed additional challenges to how we collect and analyse survey data. One size does not fit all any more. This is where inferred feedback comes into play. It helps plug loopholes by tracking any changes in customer behaviour. But a customer behaves differently with different companies and products and inferred data of one company or discipline cannot be applied to the other. It must therefore be customized. Inferred data can be used in combination with data from other sources. For example, a bank can analyze if the customers are able to handle the new advanced ATM machine by understanding if they were able to withdraw money or do other transactions or not.
Bringing in AI can help with the perfect mix of data from surveys and other modes of gathering information of customer behaviour is therefore required across disciplines.
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It’s most obvious in the digital media space, from click buys to personalized web experiences. For marketing, the AI journey has just kick-started, while in the tech sector it has been applied for a while now. We are still at an early stage where inroads are being made into AI content via chatbots and even some explanatory content creation but what will make anyone jump up and embrace it is when we will start seeing a lot of mainstream content being created by AI.
Prior to joining Infinite Analytics, Richard served as the CFO of CrowdFlower, COO and CFO of Phoenix Technologies, as a member of the board of directors and chairman of the Audit Committee at Intellisync, and previously as CFO and executive vice president strategy and corporate development at Charles Schwab.
Pravin Gandhi has over 50 years of entrepreneurial operational and investing experience in the IT industry in India. He was a founding partner of the first early stage fund India - INFINITY. Subsequently a founding partner in Seedfund I & II. With over 18 years of investing experience, he is extensively well networked in investment and entrepreneurial scene and is an active early stage angel investor in tech & impact space. Pravin holds a BS in Industrial Engineering from Cornell University, and serves on the board of several private corporations in India. He is on the board of SINE, IIT Mumbai Incubator.
Puru has his Masters in Engineering and Management from MIT. Prior to MIT, he worked with Fidelity Investments building electronic trading products and high volume market data processing applications. He has completed his BE from VJTI, Mumbai.
Deb Roy is Professor of Media Arts and Sciences at MIT where he directs the MIT Center for Constructive Communication, and a Visiting Professor at Harvard Law School. He leads research in applied machine learning and human-machine interaction with applications in designing systems for learning and constructive dialogue, and for mapping and analyzing large scale media ecosystems. Deb is also co-founder and Chair of Cortico, a nonprofit social technology company that develops and operates the Local Voices Network to surface underheard voices and bridge divides.
Roy served as Executive Director of the MIT Media Lab from 2019-2021. He was co-founder and CEO of Bluefin Labs, a media analytics company that analyzed the interactions between television and social media at scale. Bluefin was acquired by Twitter in 2013, Twitter’s largest acquisition of the time. From 2013-2017 Roy served as Twitter’s Chief Media Scientist.
Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He also is the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), Professor by Courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a Research Associate at the National Bureau of Economic Research (NBER).
Akash co-founded IA while studying for his MBA from MIT. Prior to MIT Sloan, he co-founded Zoonga. Before this, Akash was an engineer with Oracle in Silicon Valley. He has completed his M.S from University of Cincinnati and B.E from the College of Engineering, Pune.