Customer-centric, data-driven decision making has moved well beyond any single source of customer data. Primary research methods such as surveys, focus groups, and in-depth interviews remain vital, but they are now part of a much larger ecosystem of customer-related data sources. 

That ecosystem can include CRM data, ecommerce data, loyalty program data, social sentiment data, and many others. For insights professionals, the challenge is not to become experts in every source. The challenge is to build enough fluency to help clients and stakeholders choose which sources can best answer specific business questions—and when multiple sources should be combined. 

The first step is awareness. To make the landscape easier to navigate, we identified 17 widely available customer data sources and organized them into four broad categories: 

1. Market research data
2. Transactional data
3. Passive data
4. Online behavior data 

Category 1: Market Research Data 

Market research data helps us discover and measure customer attitudes and behaviors. Attitudes may include needs, preferences, pain points, values, perceptions, satisfaction, and decision drivers. Behaviors may include purchase activity, product usage, shopping patterns, information-seeking, and brand interactions. 

This category typically includes custom primary research, but it can also include secondary and syndicated sources. Examples include: 

1. Quantitative research, often including surveys, and also usability testing data, simulated shopping exercises, A/B tests and other experimental methods. 

2. Qualitative methods, such as focus groups, in-depth interviews, dyads, and online qualitative research  

3. Biometrics  

4. Secondary research  

5. Syndicated research  

Quantitative research measures attitudes and behaviors across a defined population of interest (which may be narrow or broad). Survey research is common, but quantitative market research also includes usability testing metrics, simulated shopping exercises, A/B tests and other experimental methods. 

Qualitative methods help us discover and explore attitudes and behaviors in depth. Focus groups, in-depth interviews, dyads/triads, and online qualitative methods allow us to ask questions, probe, and clarify, to learn how people describe a category, product, service, or experience. 

Biometrics can measure attention, emotion, or physiological response, such as eye tracking, facial coding, or heart rate response. These methods are most often used in advertising, packaging, product, and digital experience research when we need to understand responses people may have difficulty self-reporting. 

Secondary research uses existing information from sources such as government agencies (including Census, SEC, and BLS data), industry associations, academic publications, trade publications, analyst reports, and research firms that specialize in published market and category reports (such as Mintel and Euromonitor). Typically sold as reports or subscriptions, it can help us understand market sizing, category trends, competitive context, and broader environmental factors before deciding whether new primary research is needed. 

Syndicated research is data collected and sold by research firms, media measurement companies, or industry-specializing data providers, such as Nielsen, Kantar Media (now Fifty5Blue), MRI-Simmons, and vertical industry specialists. Syndicated research services typically provide data about consumer behavior, brand performance, category trends, competitive benchmarks, and media consumption across channels such as television, streaming, audio, digital, and cross-platform audiences. 

Category 2: Transactional Data 

Transactional data typically comes from customer interactions with a business (a type of first-party data). It is often generated through operational systems and reflects what customers actually did (in contrast relying on self-reporting). 

Examples include: 

6. CRM data  

7. Loyalty program data 

8. POS data 

9. Ecommerce data 

10. Financial profiling data 

CRM data can reveal account history, customer status, touchpoints, sales activity, and service interactions. For B2B and B2C organizations alike, CRM data can help identify customer segments, lifecycle stages, and relationship patterns. 

Loyalty program data is collected by companies that have customer loyalty programs, and it typically includes member-level information such as purchase history, purchase frequency, reward activity, retention, and customer value. It can be used to identify loyal customers, occasional customers, dormant customers, and high-value segments. 

POS data captures actual purchase activity at the point of sale. It can help answer questions about product movement, basket composition, purchase timing, price sensitivity, and promotion response. 

Ecommerce data captures digital shopping behavior, including purchases, abandoned carts, product views, repeat orders, and conversion patterns. It can help teams understand how customers shop online, where they drop off, and which products or experiences are associated with conversion. 

Financial profiling data includes customer-level or household-level financial attributes, such as income proxies (inferred data), risk indicators, or estimated customer value. While not always generated by transactions with the business, financial profiling data is commonly appended to customer records for segmentation and targeting. For example, Experian Marketing Services sells access to thousands of attributes that can be used for segmentation and targeting. As with any customer-level data, it requires careful attention to privacy, regulation, and appropriate use. 

Category 3: Passive Data 

Passive data has a clear appeal for researchers: it can capture actual behaviors, habits, and product usage without relying on human recall. Of course, it applies only to certain topics and contexts—but when it fits, it can be powerful. 

Examples include: 

11. Sensors/telematics  

12. Geospatial data  

13. Wearables  

Sensors and telematics capture data from connected devices, equipment, or vehicles. Sensor data captures usage activity, environmental conditions, movement, or system performance. Telematics is a related form of sensor-based data used in vehicles and fleets; it may include location, speed, mileage, braking patterns, fuel use, or maintenance needs. These data sources can help the customer insights team understand real-world usage patterns and identify opportunities for product development, service design, pricing strategy, and customer experience improvements. 

Geospatial data uses location signals to measure movement patterns, trade areas, foot traffic, visit frequency, and proximity to competitors or complementary businesses. The data may come from permission-based mobile app location data, GPS, Wi-Fi or Bluetooth signals, or other location-enabled devices and systems. In practice, companies use geospatial data for highly targeted promotions, site selection, trade-area analysis, competitive benchmarking, tourism and event planning, and measuring visits to stores, venues, or other physical locations.  

Wearables data comes from devices such as fitness trackers, smartwatches, smart rings, GPS watches, and other activity-tracking tools. While we often think of these devices as tools for personal monitoring, companies may also use de-identified or permission-based wearables data to analyze activity, sleep, location, or health-related patterns that can inform product development and service design. Of course, privacy concerns, real or hypothetical, can cause customer backlash, so transparency and regulatory adherence is key.  

Category 4: Online Behavior Data 

Online behavior data tracks customer actions across digital channels, including ads, websites, apps, and social platforms. It can show what people clicked, where they bounced, and whether they converted, abandoned, watched, or shared. It it often used for CX and UX research, ecommerce analysis, marketing measurement, and, in some cases, market segmentation. 

Examples include: 

14. Digital ad tracking  

15. Search-behavior data 

16. Social sentiment and activity data  

17. Website clickstream data  

Digital ad tracking captures exposure, engagement, click behavior, and campaign performance. It can help teams with media optimization, audience analysis, and conversion measurement. 

Search-behavior data captures what people search for, how search demand changes over time, and which terms they use when exploring a category, brand, product, issue, or need. It may come from tools such as Google Trends, SEO platforms, site search logs, paid search data, or retail marketplace search data. Search behavior can help customer insights professionals understand emerging demand, category language, comparison behavior, and information needs, but it should be interpreted carefully because searches show expressed interest, not necessarily purchase intent or customer sentiment. 

Social sentiment and activity data examines public online conversations and engagement behaviors to understand how people talk about, react to, and interact with a brand, product, issue, category, or experience. The data may come from online reviews, social media posts, comments, likes, shares, follows, mentions, hashtags, and other public social activity. It is typically accessed through social listening platforms or social media management tools, such as Brandwatch, Brand24, and Talkwalker. 

Because much of this data is unprompted and continuously generated, it can help us identify naturally occurring themes, emotional tone, emerging consumer trends, content engagement, and perceptions of competitive strengths and weaknesses. It is especially useful for monitoring sentiment around a brand, product, campaign, issue, or experience over time. 

However, social data requires careful use. It may not represent the broader customer population of interest, often has limited or unreliable demographic data, and may be degraded by bots, fake accounts, or “coordinated” activity. 

Website clickstream data captures how people move through a website or app, including which pages they visit, what they click, where they exit, and what they do before making—or abandoning—an online purchase. Related tracking tools, such as cookies and pixels, can support website analytics, retargeting, attribution, and conversion measurement. These sources can be useful, but they require careful attention to privacy standards, platform rules, browser changes, and consent requirements. 

Why This Matters for Customer Insights Professionals 

Business decision makers have more customer data available than ever: market research data, transactional data, passive data, online behavior data, and more. They also hear from data-focused advisors, including consultants, vendors, colleagues, and internal teams, many of whom have preferred data sources and methods. This can create confusion about which data source is best suited to a given business question or decision. 

That’s where customer insights professionals come in. Part of the job is to make informed, objective recommendations about which data sources and methods to use, and when, including options that may fall beyond traditional qualitative and quantitative research. 

That broader fluency creates real value. Business decision makers get evidence-based insights they can trust and use, while insights professionals strengthen their role as objective advisors by recommending the best-fit data sources for each decision.

If you lead a research team (whether for Customer Insights, CX, or UX), there is a good chance you have offshored some of your survey research data analysis work over the past several years. And for good reason: for many companies, offshoring has delivered notable cost savings.

But recently, some researchers have been finding that once successful offshoring arrangements have become more challenging. Here are three common sources of pain I have been hearing about from my clients:

Here are three pain points I keep hearing from clients.

• Offshore data analysts overlooking patterns that should have been escalated immediately. In one case, a client’s offshore team failed to flag survey response combinations that were clearly internally inconsistent, and thus missed flagging a clear data-quality problem, simply because they were not taking the time to read the data before sending in their deliverable. 

• High turnover disrupting workflows and compromising data quality. High turnover at offshore firms is common. One client saw four different analysts cycle through their account in under six months before they decided to find a better option. 

• Knowledge transfer promises unfulfilled. In these cases, the offshoring vendor promise a collaborative relationship between their data analysts and the clients’ survey research teams, but it never happened. The offshoring partner’s business model (understandably) priortizies efficiency and automation, so data analysts aren’t given the time or incentive to collaborate more.  

The net effect is not simply operational inefficiency. It can be a reduction in survey research data quality. When data analysis is performed inconsistently or without attention to context, the researcher writing the survey report ends up frustrated in their goal of delivering high quality customer insights.  

US-based Contractors for Skills and Quality

There is another option: getting U.S.-based data analysis support from survey research specialists who understand both the methods and the research context. 

At Research Rockstar, we have 40+ data analytics contractors in our staffing pool who can offer you: 

• Data analysis support ranging from basic descriptives and banner tables to multivariate analysis such as factor and cluster, regression, discrete choice/conjoint, and MaxDiff. 

• Work completed in your preferred tools such as SPSS, Q, R, and Sawtooth. 

• Integration of multiple data sources (for those projects that may require combining survey data with 1st party or other data). 

• Client-facing presentations of more technical analysis, acting as a virtual team member. 

• Deep vertical industry value-add, such as past experience analyzing survey data for auto, CPG, financial services, hospitality, medical device, online gaming, pharma, retail, and more. Rather than have your survey data analyzed generically, you can have it analyzed by someone who can spot red flags and make meaningful recommendations for how to treat specific variables. 

If offshored data analysis help has started to become problematic, now may be the time to consider a US-based option: working with contractors who have the skills and knowledge to boost your team’s work. 

Budget Scenarios for On-Demand Market Researchers

Through our Rent-a-Researcher Service, engagements can be scoped for as small or large as you need. Here are some examples (all varying by number of variables and specific requirements): 

Here are typical examples (all varying by number of variables and specific requirements):

• Smaller projects — 10 to 20 hours of data cleaning, weighting, and banner tables — typically fall in the $1,400 to $3,000 range.
• Mid-sized projects — design and analysis for pricing studies, discrete choice/conjoint, or MaxDiff projects, typically requiring 20 to 30 hours — generally land in the $3,000 to $5,500 range.
• Larger projects — 50 to 80 hours of advanced multivariate or modeling work, including support on presentations and workshops of the results — often fall in the $6,500 to $14,000 range.

If you need broader short-term market research staffing beyond data analysis alone, we can scope that too.

When to Switch from Offshore to US-based Survey Data Analysis

A few signals that it’s time to make the move:

• QA is eating your team’s time. Researchers spend too many hours checking—and requesting corrections for—banner tables, open-ended coding, and other analyst deliverables.
• Supplier turnover is eroding continuity. Enough experienced staff have left that established service level agreements and deliverable requirements are starting to slip.
• Complex methods get rote treatment. Conjoint, MaxDiff, and price-sensitivity studies require judgment, not just procedure. When analysts follow the steps without engaging with the study’s context or its variable-level decisions, the work comes back technically complete but off the mark.
• Stakeholders are pushing back. Internal partners are asking tougher questions about data quality and methodological rigor.

If two or more of these sound familiar, a US-based contract market researcher is probably the right next conversation.

Ready to See Candidate Profiles?

If offshoring challenges are creating rework, delays, or uncertainty, now may be the right time to consider a US-based partner who can support both quality and continuity.

Contact us at RAR@ResearchRockstar.com or request a freelance market research analyst profile here.

We are excited to invite you to our upcoming, instructor-led Live Events on important topics for all of us who work in Market Research & Insights. Conducted via Zoom, these events offer a unique chance to expand your knowledge, interact with expert instructors, and engage with peers, all from the comfort of your own space. Scheduled for 55 minutes each, to save you 5 minutes before your next meeting. Click Here to Sign Up.

Upcoming Events & Details:

Asynchronous Qualitative Methods: What’s Hot Now

When: Tuesday, June 9 @ 1:00 PM ET.
Instructor: Marni Martens
Topic: As advanced technologies continue to reshape the market research industry, researchers are rethinking the role of qualitative research—especially the role asynchronous methods can play alongside quantitative and live approaches. This session explores the what, why, and how of asynchronous qualitative research, highlighting emerging 2026 trends and the increasing importance of human judgment in delivering high-quality insights.
Learning Objectives: By the end of this session, attendees will be able to:

• Identify key trends shaping asynchronous qualitative research and their implications for study design and execution.
• Distinguish how asynchronous methods fit within today’s qualitative research toolkit, including when and where they are most effective.
• Evaluate how emerging trends and AI-assisted tools are reshaping the role of the researcher and the growing importance of human judgment in ensuring high-quality insights.

Level: Any—Suitable for all levels.
Format: Hot Topics.

Designing Surveys for UX Research

When: Thursday, June 25 @ 3:00 PM ET.
Instructor: Marni Martens
Topic: Have you ever been asked to design a UX-focused survey when your background is rooted in traditional market research? As UX plays an increasingly prominent role in business strategy and decision-making, the lines between UX research and market research have blurred, and many market researchers are responsible for more UX/experience-focused work. This session explores the key principles of designing surveys for UX research, contrasting how this differs and doesn’t in intent from traditional market research/goals, with real-world examples and practical tips to help you successfully navigate your next UX survey request.
Learning Objectives: By the end of this session, attendees will be able to:

• Apply key principles for designing surveys that support experience-based research goals and business strategy.
• Recognize where UX and market research objectives overlap—and where they diverge.
• How to avoid common pitfalls when designing surveys for projects with mixed market and experience-focused objectives.

Level: 2—For those with basic market research knowledge, typically 2-5 years in the field.
Format: How-to.

Click here to Sign Up.

We are excited to invite you to our upcoming, instructor-led Live Events on important topics for all of us who work in Market Research & Insights. Conducted via Zoom, these events offer a unique chance to expand your knowledge, interact with expert instructors, and engage with peers, all from the comfort of your own space. Scheduled for 55 minutes each, to save you 5 minutes before your next meeting. Click Here to Sign Up.

Upcoming Events & Details:

How to Do Survey Data Analysis in Excel with Copilot

When: Tuesday, April 14 @ 1:00 PM ET.
Instructor: Jeff McKenna
Topic: How can researchers use Excel with Copilot to efficiently analyze survey data without specialized statistical software? This session will demonstrate practical workflows for producing frequencies, means, medians, and subgroup comparisons using Excel’s built-in tools enhanced by Copilot. Attendees will see how to translate common analysis questions into prompts and outputs that support faster, more confident survey reporting.
Learning Objective: After this Live Event, participants will be able to analyze survey data in Excel using Copilot to generate descriptive statistics and subgroup comparisons that support clear, defensible insights.

Level: 2—For those with basic market research knowledge, typically 2-5 years in the field.
Format: How-to.

Mixed-Method at Scale: Qual + Quant Hybrid Designs

When: Thursday, April 23 @ 3:00 PM ET.
Instructor: Kathryn Korostoff & Guests
Topic: Mixed-methods research at scale requires intentional integration of qualitative and quantitative approaches to strengthen survey design and interpretation. This session will illustrate how qualitative inputs—such as in-depth interviews, online communities, and social media listening—can inform questionnaire development, segmentation, and interpretation of quantitative findings. Attendees will see how hybrid designs improve validity, context, and decision confidence when executed with clear research objectives.
Learning Objectives: Those attending will leave ready to make evidence-based methodology recommendations for mixed-methods designs that integrate qualitative insights to improve large-scale quantitative research outcomes.

Level: Any—Suitable for all levels.
Format: Application.

Understanding Data Reliability & Confidence Intervals

When: Tuesday, May 12 @ 1:00 PM ET.
Instructor: Julie Worwa
Topic: Interpreting survey results requires a clear understanding of data reliability, confidence intervals, and variability. This session will explain what confidence intervals represent, why they are often misunderstood, and how variability affects the stability and precision of survey estimates. Attendees will gain practical guidance for interpreting small versus large confidence intervals when evaluating differences, trends, and business implications.
Learning Objective: Upon completion, attendees will be able to confidently analyze confidence intervals and variability in survey data to assess reliability and avoid over-interpreting unstable results.

Level: Any—Suitable for all levels.  
Format: Application.

Mastering AI Prompts for Research Report Writing

When: Thursday, May 28 @ 3:00 PM ET.
Instructor: Kathryn Korostoff & Guests
Topic: How can researchers design AI prompts that support accurate, disciplined research report writing without introducing outside data or unsupported assumptions? This session will demonstrate structured prompting techniques—including prompt chaining and reflective prompting—to improve clarity, traceability, and analytical rigor in AI-assisted reporting. Attendees will see how to guide AI outputs to summarize findings, draft narratives, and maintain methodological integrity.
Learning Objective: After this Live Event, researchers will be ready to confidently use advanced prompting strategies to draft research reports with AI while controlling for accuracy, scope, and overgeneralization.

Level: Any—Suitable for all levels. 
Format: Tool Demo.

Click here to Sign Up.

We are excited to invite you to our upcoming, instructor-led Live Events on important topics for all of us who work in Market Research & Insights. Conducted via Zoom, these events offer a unique chance to expand your knowledge, interact with expert instructors, and engage with peers, all from the comfort of your own space. Scheduled for 55 minutes each, to save you 5 minutes before your next meeting. Click Here to Sign Up.

Upcoming Events & Details:

AI Tools That Cut Qualitative Analysis Time in Half

When: Tuesday, March 10 @ 1:00 PM ET.
Instructor: Kathryn Korostoff & Guests
Topic: How can researchers use AI tools to accelerate qualitative analysis for IDIs and focus group transcripts without compromising rigor or insights quality? This session will demonstrate multiple AI-assisted approaches for coding, summarization, and theme development, while showing where human judgment is still required to avoid misinterpretation, overgeneralization, and missed nuance. Attendees will see practical examples of how to integrate AI into existing qualitative research workflows and evaluate tool outputs critically.
Learning Objective: After this Live Event, researchers will be ready to confidently apply multiple AI tools to speed qualitative analysis for interview and focus group transcripts, assess output quality, and identify common risks when relying on AI-generated insights.

Level: 2—For those with basic market research knowledge, typically 2-5 years in the field.
Format: Tool Demo.

Managing Up for Researchers: Demonstrating Impact and Earning Trust

When: Thursday, March 26 @ 3:00 PM ET.
Instructor: Jeff McKenna
Topic: Managing up as a researcher requires clearly demonstrating impact, aligning insights to business priorities, and building credibility with senior stakeholders. This session will translate proven management-up principles into practical strategies researchers can use to communicate value, anticipate executive concerns, and position research as a decision-making asset rather than a cost center. Attendees will leave with a structured, time-bound approach for strengthening trust and influence with leadership, informed by perspectives commonly discussed in Harvard Business Review.
Learning Objectives: Participants will leave equipped to create and implement a practical 60-day plan for managing up, demonstrating research impact, and strengthening trust with senior business stakeholders.

Level: Any—Suitable for all levels.
Format: Hot Topics.

How to Do Survey Data Analysis in Excel with Copilot

When: Tuesday, April 14 @ 1:00 PM ET.
Instructor: Jeff McKenna
Topic: How can researchers use Excel with Copilot to efficiently analyze survey data without specialized statistical software? This session will demonstrate practical workflows for producing frequencies, means, medians, and subgroup comparisons using Excel’s built-in tools enhanced by Copilot. Attendees will see how to translate common analysis questions into prompts and outputs that support faster, more confident survey reporting.
Learning Objective: After this Live Event, participants will be able to analyze survey data in Excel using Copilot to generate descriptive statistics and subgroup comparisons that support clear, defensible insights.

Level: 2—For those with basic market research knowledge, typically 2-5 years in the field.  
Format: How-to.

Mixed-Methods at Scale: Qual + Quant Hybrid Designs

When: Thursday, April 23 @ 3:00 PM ET.
Instructor: Kathryn Korostoff & Guests
Topic: Mixed-methods research at scale requires intentional integration of qualitative and quantitative approaches to strengthen survey design and interpretation. This session will illustrate how qualitative inputs—such as in-depth interviews, online communities, and social media listening—can inform questionnaire development, segmentation, and interpretation of quantitative findings. Attendees will see how hybrid designs improve validity, context, and decision confidence when executed with clear research objectives.
Learning Objective: Those attending will leave ready to make evidence-based methodology recommendations for mixed-methods designs that integrate qualitative insights to improve large-scale quantitative research outcomes.

Level: Any—Suitable for all levels. 
Format: Application.

Click here to Sign Up.