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.
The 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.
Using 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.
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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