Table of Contents
What is consumer survey data?
Consumer survey data provides a collection of answers and survey results given by consumers on a wide range of topics. Several market research companies carry out interview surveys to understand consumer preferences. Companies use this data to gain consumer insights, measure consumer satisfaction, and perform audience research.
Where does consumer the data come from?
Companies sometimes create their own surveys for existing customers to get feedback on customer experience and measure customer satisfaction. They also create surveys for the general population to understand demographic characteristics, lifestyle preferences, and to gauge why some customers discontinued.
These surveys are distributed in the stores, on the website, by email or SMS, and on social media. Companies also use questionnaires designed by third parties or hire market research agencies to conduct the surveys. Companies may choose a target audience based on general surveys and use focus groups to target more specific questions.
What types of attributes should I expect?
Consumer surveys typically provide three types of data: customer satisfaction, customer experience, and the voice of the customer.
The attributes vary according to the type and usually contain demographic information, including the geographic location. Companies often divide respondents by demographics, geographic location, or custom segments, such as existing/new/returning customers.
Responses to customer satisfaction are commonly on the 3 or 5-point scale, such as very positive, positive, neutral, negative, or very negative. The survey responses can also include comments with additional information. Customer experience surveys have similar attributes and may focus on specific features of the products or services.
The voice-of-customer surveys provide a large number of attributes containing lifestyle and psychographic responses. They include lifestyle choices, habits, interests, desired features of a product or service, customer budget, who makes most purchasing decisions in the household, and other attributes.
How should I test the quality of the data?
If you are creating and managing your own surveys, you can begin research on formulating questions and sampling the audience. The European Commission: The Joint Harmonised EU programme of Business and Consumer Surveys is a good resource with details of weights for survey responses. Improving the quality of data collected from surveys needs data cleansing and NLP algorithms to extract insights from the responses.
For data that is available from sources such as U.S. Census Bureau, or purchased from other vendors, testing data accuracy and timeliness is critical to ensure the reliability of the insights. If the surveys are outdated, the analysis will not generate accurate insights. As the types and attributes of consumer survey data cover a wide range, you can also test if the data matches your requirements.
To test the quality of the data:
- Validate the data for accuracy, consistency, and completeness.
- Verify that the data provided was collected recently.
- Ensure that the data type, demographics, and attributes match your requirements.
- Ascertain that the data is privacy compliant, as it contains personal or personally identifiable information (PII).
Who uses consumer survey data?
Marketers and company executives use this type of data primarily to improve customer satisfaction, deliver a better experience, and strategize growth. They also use the data to conduct market research. Voice-of-customer research helps companies plan for audience targeting, content distribution, and identifying dissatisfied or former customers to win them back.
What are the common challenges when buying the data?
Challenges of consumer survey data depend on the method of data collection. If you are conducting your own survey, the most critical challenge is designing a questionnaire appropriate for your objectives. Sampling the audience is also a challenge, and using reliable sources for conducting the surveys is a good idea.
If you are buying the data from a vendor, the biggest challenge is selecting the datasets aligned with your objectives. Data accuracy and recency are common concerns in both cases, along with privacy compliance.
- Data accuracy: Conducting your own surveys need considerable resources to get a good response rate, aggregate the responses and microdata, and ensure that data accuracy does not get compromised. The volume and variety of data also make it challenging to assure accuracy. Data cleansing and quality improvement measures are essential. Vendor data needs to be tested for accuracy, especially for the insights extracted with NLP methodologies. With the volume of data being large, vendors may lose focus on accuracy while attempting to deliver data quickly.
- Data recency: It takes time to collect survey data from the sample audience. If your own surveys or vendor datasets deliver outdated information, the resulting insights may not be relevant. Data recency is a big challenge, as companies use this information and the generated insights to drive their strategies. When buying from vendors, ensure that the data collected is recent and has not lost its relevance.
- Data consistency: Consumer survey data is collected from several respondents from a carefully selected sample. If the survey is conducted in person, standard errors may occur in terms of data entry, which could lead to contradictory information. Online surveys also tend to deliver mismatches in the selection of options across questions. Data consistency is critical to ensure trusted insights.
- Privacy compliance: Consumer survey data includes personal information and personally identifiable information (PII). It must comply with the privacy regulations applicable to the region and industry.
What are similar data types?
Consumer survey data is similar to audience data, consumer lifestyle data, eCommerce data, retail data, and other related data categories used in marketing and advertising.
You can find a variety of examples of consumer data in the Explorium Data Gallery.
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What are the most common use cases of consumer survey data?
The most common use cases for consumer survey data are consumer intelligence, customer lifetime value, audience segmentation, and promotion planning. This data is also used for consumer data enrichment, price planning, and churn prediction.
- Consumer Intelligence: It provides insights into consumer preferences and choices. These insights are used to understand consumer behavior and market requirements. Marketers, product designers, and business leaders use these insights for improving products (such as personal care products) and services to fuel their company growth. Business leaders and investors leverage these insights to launch new products and achieve better ROI on their ventures.
- Customer Lifetime Value (CLTV): It is the value that individual customers bring to a company over the course of their lifetime. CLTV focuses on long-term customer engagement to build mutually beneficial and profitable relationships. Insights gained with consumer survey data help companies build a good model for CLTV.
- Audience Segmentation: Consumer survey data offers insights into consumer interests, choices, and preferences, which helps divide the target audience into smaller specific groups. Companies use these smaller audience segments to distribute relevant content, provide a customized experience, and deliver targeted promotions. Audience segmentation also helps assess the viability of new products and services targeted at specific segments.
- Promotion Planning: Consumer product companies devise optimized promotions to meet their sales targets. A well-planned promotional campaign reaches the target audience, delivers relevant content, and offers attractive deals. Businesses use a variety of data categories to design their promotions, and consumer survey data contributes insights into consumer preferences. Today, companies often use sophisticated ML tools for promotional planning to maximize their resources.
Which industries commonly use this type of data?
Consumer products industries commonly use this data for driving their marketing and product design strategies. They include retail, CPG, tourism, sports, entertainment, travel, hospitality, leisure, non-profit, healthcare, financial service providers, insurance providers, and banking.
How can you judge the quality the data vendors?
If you buy data from vendors, assuring the quality and reliability of vendors can be a key challenge. You can use a variety of methods to judge the vendor quality, beginning with the information provided on their websites, analyzing case studies and comparing with your projects, watching demos, and interacting with their reps to resolve your specific queries.
- Customer reviews and testimonials: The majority of vendors make customer reviews and ratings available on their websites. While the ratings indicate the overall quality, reviews can provide deeper insights into how the vendor engages with the customers and delivers quality datasets. Customer testimonials not only highlight the vendor’s strengths but also recommend specific products. They can be a valuable indication of vendor quality.
- Case studies: Case studies typically describe customer challenges and how the vendor helped solve them. You can assess the vendor engagement and ability to deliver suitable datasets to shortlist vendors for further analysis. You can also get a good estimate of vendor knowledgeability and commitment from case studies.
- Demo: Viewing vendor data in action is an opportunity to evaluate the vendor suitability for your industry, projects, and dataset requirements. Many vendors provide recorded demos on their websites. Some of them arrange a live demo similar to your projects. A live demo can also be a good time to discuss your requirements.
- Interacting with vendor reps: The easiest way of judging the vendor quality is directly interacting with vendor reps. You can take this opportunity to explain your requirements and see if the vendor can provide suitable datasets. You can also discuss the ease of integration and availability of custom datasets.