How to use big data in market research

Author: Will Webster

Subject Matter Expert: Mark Hamilton

What is big data?

Big Data refers to the vast volume of information generated every second from digital interactions, devices and systems.

For businesses, Big Data — when combined with expertise and stringent guidelines — is a powerful and highly effective tool for gaining a competitive edge. With it, you can identify patterns, predict trends and unlock personalization strategies for the benefit of your customers.

Get more ROI out of every research study using big data

The three Vs of dig data 

It’s often thought that the ‘Big’ in Big Data comes from the sheer volume of data points gathered, but there’s more to it than that. In fact, Big Data is defined by three key characteristics — the three Vs — that set it apart from traditional datasets:

• Volume: The amount of data
• Velocity: The speed at which the data is generated and processed in real time
• Variety: The diversity in the types of data

When we combine these three Vs we can see what separates Big Data from traditional datasets: you’re capturing vast amounts of real-time information from diverse sources, offering insights that are immediate, granular and highly dynamic.

However, with the growth of Big Data itself and its adoption, we’ve seen the three Vs become four Vs, five Vs and even six Vs. Here are the helpful additions that have been suggested over time to create a more comprehensive definition of Big Data:

• Veracity: Refers to the accuracy and reliability of the data — even when it may include inconsistencies or uncertainties
• Value: Focusing on getting useful and actionable insights from the data, rather than just collecting large amounts of it
• Variability: Highlighting that the data can be inconsistent or unpredictable

Merging structured and unstructured data

Much of Big Data’s significance lies in its ability to integrate and analyze both structured data and unstructured data, providing market researchers and data scientists with comprehensive insights that go beyond what either type could reveal alone.

• Structured data is highly organized and easily searchable within databases, such as customer information in spreadsheets, transaction records or survey responses.
• Unstructured data is unformatted data that’s, as a result, more complex to analyze. It includes sources like social media posts, video content, audio files or text communications.

The role of big data in modern market research

Big Data has transformed market research by enabling the analysis of vast, real-time datasets that offer a deeper understanding of consumers. Unlike traditional methods, Big Data captures trends and behaviors as they happen and at scale, empowering businesses to swiftly respond to changing market conditions.

It’s an enabler of granular audience segmentation and unmatched personalization, empowering companies to go beyond basic demographics to design campaigns tailored to specific behaviors or individual preferences. And when combined with Artificial Intelligence and Machine Learning, it can also be an enabler of predictive power for companies — using historical patterns to forecast trends and guide proactive decision-making.

By leveraging this real-time, comprehensive view of their market, businesses can better anticipate needs, refine their offerings and maintain a competitive edge in an increasingly fast-paced, data-driven environment.

Key advantages of integrating big data with traditional research methods

Rather than replacing traditional research, Big Data complements it.

Combining Big Data with traditional market research tools and methods creates a powerful opportunity for deeper insights and better decision-making. Traditional methods, like surveys and focus groups, provide context and emotional depth, while Big Data offers scale, speed and granularity.

One major advantage is validation. Big Data can confirm or challenge findings from smaller, qualitative studies, ensuring insights are robust and representative. It can also fill gaps by highlighting behaviors or trends that may otherwise have gone unnoticed in traditional research.

Compile insights into a collection to use as a starting point for your next research project

Integrating Big Data with traditional research methods can also enhance forecasting. Big Data identifies patterns and segments at scale, while traditional methods explain the “why” behind those patterns. Together, they improve the accuracy of predictions and the relevance of marketing strategies.

By blending the two, businesses can ultimately build a far more comprehensive view of their audience — balancing quantitative precision with the nuanced understanding essential for informed decision-making.

Best practices for combining big data and market research

Successfully integrating Big Data into your market research requires more than just access to it — it demands strategy, quality control, the right tools and strong ethical considerations.

Let’s look at how to get there.

Identifying relevant data sources

To effectively combine Big Data with market research, start by pinpointing data sources that align with your research goals.

A great starting point should be sources offering actionable insights into your target audience, such as sales data, website analytics and social media platforms. For instance, if you’re a retail business aiming to understand seasonal purchase behavior, combining your e-commerce sales data with social media trends could illuminate customer preferences.

By choosing relevant sources you can avoid data overload and ensure your analysis stays focused. Aligning sources with your objectives will give you a strong foundation for meaningful insights and impactful decision-making.

Ensuring data quality and reliability

Big Data is only valuable if the data is high-quality and reliable — especially if you’re searching for meaningful insights. Poor data leads to skewed results and misinformed decisions.

But what does that look like? When combining Big Data with traditional research, your key priorities should be accuracy, completeness and consistency — and of course, regular audits.

Continuing with the retail example, you should invest time to ensure that your sales data is up-to-date and free from duplicate or erroneous entries. And the same applies to your social media data — verify that it’s sourced from credible platforms and sufficiently reflects your target demographic.

Leveraging advanced analytics techniques

Advanced analytics techniques are how we unlock the full potential of Big Data in market research: Machine Learning, predictive modeling and sentiment analysis can reveal patterns and insights that traditional methods might miss.

And the good news is that their accessibility has skyrocketed thanks to today’s AI revolution, helping businesses turn complex data into clear, actionable strategies en masse.

Extract in-depth insights from all of your research, instantly surfacing key themes and actionable intelligence for quick action

In our retail example, you could use predictive modeling to forecast seasonal purchase trends based on past sales data. Combine this with a sentiment analysis of social media conversations, and you’re left with a far deeper understanding of how customers feel about specific products.

These techniques can not only enhance the depth of your insights but also help you anticipate future behaviors.

Maintaining ethical standards and data privacy

Ethics and privacy are non-negotiable when combining Big Data with market research.

In reality, any business capturing, storing and leveraging data has to prioritize the two Cs: compliance with data protection laws, like GDPR or CCPA, and consent for data collection.

In the retail example once again, all of the customer data you extract from e-commerce transactions should be anonymized and securely stored. And when using social media data, understanding and respecting platform policies and user privacy is equally key.

How your company respects data can often have a huge bearing on its public trust and reputation — in both directions. Here, ethical practices not only protect consumers but also create a foundation for sustainable, trustworthy market research that benefits both businesses and their audiences.

Real-world applications of big data in market research

So, how are the world’s leading businesses using Big Data to innovate and grow?

Across examples from Netflix, Amazon, Coca-Cola and Peloton, here we explore Big Data’s role in segmentation, prediction, sentiment analysis and product development.

Customer segmentation and personalization

Customer centricity has become a key tactic for marketers and, in turn, an expectation for audiences.  Today, customer segmentation and personalization are at the heart of many of the most effective marketing strategies.

Big Data provides the foundation for all of this, enabling businesses to create detailed audience profiles and deliver tailored experiences that resonate with individual preferences.

Netflix has worked its way into millions of homes by leveraging this approach. Using Big Data analytics across user viewing habits, search behavior and on-platform ratings, Netflix identifies audience segments and recommends content based on individual tastes in real time — while producing more of the same content in the background for the most engaged segments.

This strategy not only improves user satisfaction but also boosts engagement and retention. It’s the perfect case study to demonstrate how Big Data can drive deeper customer connections by aligning offerings with unique consumer needs.

Predictive analytics for consumer behavior

Predictive analytics uses Big Data to forecast future customer actions, enabling businesses to anticipate needs and make proactive decisions.

The aim is to turn historical data, patterns and trends into insights that can refine future strategies and improve outcomes. So, it’s probably no surprise that Amazon is a leader in this domain.

Amazon leverages purchase history, user browsing behavior and demographic data to suggest new products customers are likely to buy next. For the user, this creates a more seamless, efficient shopping experience; for Amazon, it increases sales.

A leading real-world application of how Big Data can be used to influence consumer behavior, Amazon has showcased how predictive analytics can turn data into actionable insights — and help build one of the most successful businesses of all time.

Social media sentiment analysis

Social media sentiment analysis helps businesses understand public opinion and consumer attitudes by analyzing social media conversations, often in real time. Armed with these insights, brands can gauge sentiment around products, industry trends, major events and the impact of their own campaigns.

Often running several marketing campaigns at once across geographies, Coca-Cola is one of many brands that leverages sentiment analysis to monitor how its audiences are perceiving its campaigns. In Coca-Cola’s application, it’s not only about identifying what’s resonating and what isn’t — it’s often equally about using these insights to refine their strategy in real time.

Big Data from social media can be overlooked by companies in favour of simpler, cleaner data sources, but social media often provides the most candid and actionable user insights. With the right skills and tools, it can be leveraged to help brands respond to customer needs, enhance messaging and strengthen their market position.

Product development and innovation

Big Data’s ability to uncover unmet customer needs and emerging trends also makes it a powerful tool for steering and fueling product development and innovation.

With the huge popularity for fitness trackers and wearable devices, the health and fitness space is littered with companies taking this data-driven approach to product development.

Peloton is a standout example here. Peloton tracks and analyzes all the data from its user workouts, preferences and engagement patterns — all first-party or zero-party data — to inform its product updates. This includes everything from the production of new workout content, to fitness categories and even hardware innovations.

Overcoming challenges in big data market research

Big Data comes with potential in market research — alongside its own unique set of challenges.

From integrating diverse sources to bridging skills gaps, here we highlight the common obstacles to overcome — and actionable strategies to do so.

Data integration and management

One of the biggest Big Data challenges is simply integrating a huge volume of data from multiple sources and generating insights from it all — and all while maintaining consistency and accuracy. On top of that, the data is often arriving in disparate formats, and from sources and technologies that don’t understand each other.

For example, a global retailer working across e-commerce sales, in-store transactions and customer loyalty data — all across different geographies — must integrate these sources to build a single unified view of its customer and sales trends. That view is essential to the business gaining a holistic understanding of its audience and basing its strategy on informed, data-driven decisions.

The data businesses collect has grown in abundance and complexity. But at the same time, so have the Big Data technologies helping them make sense of it all. In our example, combining standardized processes and advanced data integration platforms can streamline the retailer’s data integration.

Skills gap and talent acquisition

Big Data’s complexity has created huge demand in virtually every industry for data professionals to analyze, interpret and apply insights. With the demand for talent high and the supply not yet catching up to that demand, finding and retaining talent with the right expertise is a challenge for businesses.

Returning to the same example, our retailer can’t really rely on upskilling existing employees when dealing with such a high volume of complex data. Instead, hiring a trained Data Scientist and/or partnering with analytics firms is essential to extracting the most value from that data — while upskilling existing teams in the meantime to bridge the gap.

It’s no surprise that Harvard Business Review famously labelled Data Scientist the ‘sexiest job of the 21st Century’.

Balancing quantitative and qualitative insights

With the sheer potential of Big Data, it’s easy to obsess over its quantitative insights and overlook the emotional context and deeper motivations behind consumer behavior (i.e., qualitative methods and insights). Instead, maintaining a healthy balance between the two is key to ensuring a complete understanding.

Putting this approach into action, our global retail brand would combine an analysis of sales and loyalty data with focus groups or customer interviews to uncover the “why” behind certain patterns — like why a product resonates or why certain customers are churning.

Balancing quantitative and qualitative insights enriches decision-making, creating strategies that are both data-driven and empathetic.

The future of big data in market research

As is often the case with rapidly-evolving technologies, one of the big questions to answer in Big Data is simply, ‘what’s next?’

From AI-driven insights to revolutionary new Big Data tools, the possibilities are as vast as the datasets themselves. Let’s see where the future might take us — and how businesses can stay ahead of the curve.

Free eBook: This year’s global market research trends report

Emerging technologies and their impact

It will be no surprise that AI is set to dominate the future of Big Data in market research.

Already transforming how businesses gather, analyze and act on insights, AI-driven tools can enable hyper-personalized campaigns, real-time predictive analytics and adaptive strategies that evolve with consumer behavior.

Looking ahead, advancements in Machine Learning could make insights even more precise, while generative AI could automate tasks like survey design and data visualization. Meanwhile, technologies like Blockchain may revolutionize data security and transparency, and the continued adoption of IoT (Internet of Things) technologies will exponentially increase the volume and diversity of data streams.

Evolving consumer expectations and data usage

Consumers today expect personalized, seamless experiences. The industry leaders like Netflix and Amazon have reset expectations, and audiences are demanding tailored recommendations and real-time adjustments.

People want businesses to use their data in more innovative ways — but all while staying ethical, transparent and safe when collecting, storing and using that data.

Looking to the future, these expectations will only grow. Consumers will demand even greater levels of personalization, such as hyper-targeted recommendations driven by predictive analytics, and interactions powered by AI that feel genuinely intuitive. Transparent data-sharing models, where users have more control over their data, could too become the norm.

The future lies in using data not just to meet expectations but to anticipate them, delivering experiences that are both innovative and respectful of user rights.

Comprehensive data integration capabilities

Seamless data integration is fundamental to the successful use of Big Data, where data sources that are currently very separate and speak very different languages can be combined into a unified system.

Ongoing advances in data integration platforms will allow businesses to merge structured and unstructured data — from CRM systems, social media, IoT devices and much more — into a single, actionable view. AI-powered systems will automate this entire process, ensuring data flows smoothly and insights are instantly accessible.

This evolution will enable researchers to uncover richer insights and make decisions with unparalleled precision and speed.

Advanced analytics and visualization tools

The future of Big Data in market research is about much more than crunching more and more numbers — it’s also about making insights impossible to ignore.

Here, advanced analytics and visualization tools are transforming complex datasets into visually compelling, actionable narratives that anyone can understand and act on.

AI-powered platforms will lead the charge, offering intuitive dashboards that make interpreting data faster and more engaging. For example, imagine augmented reality (AR) visualizations that bring predictive models to life, giving researchers a way to immerse themselves into trends and scenarios on a deeper level than ever before.

In the not too distant future, new age analytics and visualization tools will help researchers identify patterns more efficiently, communicate insights more effectively and drive smarter, data-informed strategies.

Ensuring data security and compliance

As data usage grows, so will the need to ensure security and compliance. Make no mistake, this is fundamental to the future success of Big Data in market research.

With the impact of regulations like GDPR and CCPA — and rising consumer empowerment in data awareness and data rights — businesses have no option but to build solid foundations for transparent, ethical data practices into the future.

Of course, technology plays a key role here once again. Emerging technologies like Blockchain have the potential to revolutionize data security, offering immutable records and ensuring data integrity, while AI will play a crucial role in real-time compliance monitoring and identifying vulnerabilities.

For market researchers, aligning with evolving privacy standards and proactively safeguarding data will not only prevent legal risks — it will also strengthen consumer relationships and help build long-term success in an increasingly data-conscious world.

Harnessing the power of big data for market research success

Leveraging Big Data in your work can be a seriously big task — but it doesn’t have to be.

The Qualtrics® Research Hub enables researchers to organize and analyze large, complex datasets — including customer feedback, brand studies and market data — in a single AI-powered platform.

A centralized repository to store and manage all of your research — not only projects conducted on Qualtrics — it combs through thousands of past studies and millions of data points to maximize ROI from every study.

Access all your research in seconds, not hours. Plus, by understanding your intent and the contextual meaning behind your search query, Qualtrics AI provides you with better insights, faster.