Understanding your customers is the lifeblood of any successful business. But with an overwhelming amount of data and analysis techniques available today, it can be challenging to determine the best approach.

Should you invest in traditional Market research or dive into the world of big data analytics? Are they mutually exclusive or can they work together?

In this post, we’ll explore the key differences, strengths and limitations of market research versus big data. While both can provide invaluable customer Insights, they tend to serve different purposes.

Market research offers a more focused qualitative view through surveys, interviews and focus groups. Big data analytics provides a broader quantitative perspective by identifying patterns in large datasets. Think of them as different lenses to view your customers. Used together, they can bring your customers into full focus.

We’re building Zensus Technologies, a startup that lies between market research and big data. Through our Zensus app, we’re collecting data through short video polls.

Market Research: Getting Up Close and Personal with Customers

Market research is a structured approach to learning directly from customers. By asking carefully crafted questions through surveys, interviews, focus groups, and other interactions, researchers gain first-hand insights into customer beliefs, motivations, pain points and needs.

Market research tends to be more qualitative and descriptive. It dives deep into the “why” behind consumer behavior vs. just the “what.” Some key capabilities include:

Laser-Focused Discovery

While big data casts a wide net, market research zeroes in on precise topics of interest. Researchers design studies to answer specific questions, for example:

• Why did customers choose a competitor’s product over ours?
• How do customers experience our website?
• What new features would most excite customers?

Approaching customers directly means you can clarify responses and extract richer insights.

Human Connection

Unlike broad data analytics, market research establishes personal connections. Interviews and focus groups put a human face on target audiences. This fosters empathy and deeper understanding.

Observing emotional reactions and body language during interviews offers insights that data alone can’t provide. For example, a frustrated facial expression may reveal issues not brought up verbally.

Storytelling

Market research presents data through compelling narratives. Quotes, anecdotes, photos, and videos breathe life into findings. This makes the information more vivid, memorable and actionable.

For example, a quote from a customer explaining how a product solved a frustrating problem makes the value tangible. Vivid qualitative insights like this inspire empathy and drive better decision making.

Agility

Surveys and interviews can be designed, conducted and analyzed rapidly. This enables greater adaptability to changing business needs.

For example, unexpected results from one market research phase can inform the next. Follow-up questions pursue new angles uncovered mid-study. This flexibility is difficult with broad quantitative data.

Big Data: Spotting Trends from Vast Volumes of Information

Big data refers to extremely large, complex datasets compiled from various digital sources. Sophisticated analytics tools process these vast volumes of structured and unstructured data to uncover patterns, correlations and insights.

The goal is a bird’s-eye view of broad trends vs. a worm’s-eye view into individual customer perspectives. Big data capabilities include:

Holistic View

By combining data from CRM systems, social media, web analytics, and more, big data analysis sees things no single source can. Connecting data dots reveals powerful insights otherwise missed.

For example, loyalty program data may show declining purchases among top customers. Integrating this with social media listening could link the drop to complaints about a new product line.

Predictive Power

Statistical algorithms applied to big data can identify probable future outcomes. Models can forecast sales, detect fraud, anticipate churn risk, and more.

For example, weather data, historic sales patterns and event info could predict busy days at each store location. Models then automatically adjust staff schedules in advance to meet demand.

Automation

With the right infrastructure, big data analysis runs 24/7 on auto-pilot. New data is continuously incorporated to refine models and output updated insights.

For example, clicks and scroll rates on website content get ingested daily. Automated analysis then reveals top-performing content to guide marketing. Human analysts just monitor outputs.

Innovation

Big data often reveals unexpected insights that inspire product innovation. Predictive analytics might detect an unmet customer need before competitors.

For example, analyzing in-car GPS data, acceleration rates and braking patterns could reveal demand for a smoother ride. This could inspire an automaker to develop advanced suspension technology years ahead of rivals.

Agility

The always-on nature of big data analytics enables rapid response to emerging trends. Models ingesting live streams of social media conversations, for example, can detect customer complaints in real-time.

Key Differences Between Market Research and Big Data

While both deliver customer insights, some core differences determine the best application:

Think of big data casting a wide net across oceans of information, while market research strategically drops hooks only where the fish are biting.

When Market Research Shines

Market research is ideal when you need rich qualitative insights directly from customers. Key applications include:

Discovering Customer Needs

Open-ended questions elicit detailed feedback on wants, problems and desired solutions. Surveys and interviews fit customer voices, emotions and stories into strategic plans.

Evaluating Concepts

Focus groups and online panels test reactions to early-stage ideas long before sales data exists. This provides directional input to refine concepts that resonate and fix those that don’t.

Understanding “Why”

Big data shows what customers do. Market research reveals why. Uncovering motivations, thought processes and emotions informs messaging and product design.

Launching Products

Pre-launch market research mitigates risk of failure. Testing concepts, pricing models and positioning helps launch with customer expectations met.

Gauging Satisfaction

Surveys measuring satisfaction, likelihood to recommend and net promoter scores provide early warning signs. This allows proactive responses to prevent churn.

Qualifying Leads

Lead scoring models and sales call analysis help prioritize the hottest prospects worth chasing vs. those likely to fizzle out.

Improving Experiences

User testing identifies friction points in customer interactions that data alone can’t uncover. Watching users navigate a website, for example, reveals pain points.

Segmenting Audiences

Cluster analysis and factor analysis group customers into segments with common needs and behaviors. Personas help tailor messaging and experiences.

When Big Data Rules

While market research zooms in, big data analytics zooms out to identify patterns across massive datasets. Use cases include:

Customer Targeting

Combining behavioral, demographic and purchase data hones targeting. Propensity models identify the best customer profiles to pursue.

Predictive Analytics

Statistical models forecast sales, inventory needs, hiring demand, credit risk, churn probability, machine failures and more. This enables planning and optimization.

Rapid Experimentation

A/B and multivariate testing leverages analytics to instantly measure outcomes from website, offer, email, display ad variations. The best performers then scale up.

Recommendation Engines

Customer browsing history, purchases, product views and survey responses feed algorithms behind product suggestions, content recommendations and promotions.

Sentiment Analysis

Natural language processing of social media conversations, reviews, call center logs and emails interprets millions of text sources to gauge emotional satisfaction.

Clickstream Analysis

Following how site visitors navigate pages and interact reveals drop-off points. Sites then optimize user flow, navigation, content, offers and messaging to reduce exits.

Location Analytics

Point-of-sale data, mobile locations and geo-tagged social media are analyzed to score locations, optimize sites and tailor local marketing.

Risk Scoring

Hundreds of data variables feed models detecting transaction fraud in real time. High-risk interactions trigger alerts to intervene.

Better Together: Blend These Approaches for Deeper Insights

While both market research and big data analytics offer distinct advantages, combining them multiplies their power. Each approach alone has blind spots. Together they provide complete customer visibility.

Here are just a few examples of blended research and data programs:

Research-Enhanced Data Mining

Start with open-ended interviews to identify metrics for statistical modeling. This reveals key data points associated with outcomes like customer satisfaction or retention. Models based on metrics customers actually care about are far more insightful.

Research Data Feedback Loops

Use market research to continually validate and improve big data models. Surveying customers flagged by propensity models as high-value prospects tests if predictions match reality. Discrepancies inform model tweaks until calibrated.

Qualitative Insights to Interpret Data

Back dry quantitative insights with rich market research data that clarifies meaning and significance. For example, associate a sudden drop in page views with focus group quotes about difficult navigation. Combining data and research creates an “Aha!” moment.

Personalized Analytics

Blend broad demographic and behavioral data with market research-derived customer personas. This tailors dashboards, KPIs and reports to align with each decision-maker’s role and concerns.

Choose the Right Research and Data Mix

With so many options today, there is no shortage of ways to learn about customers. But this brings the risk of analysis paralysis. Finding the optimal balance is key to avoid over-investing. Consider this framework for picking tools:

Business Questions

Start by defining the exact customer issues that require insights to drive decisions and strategy. Get very specific. Vague questions yield vague results.

Data Requirements

Determine what blend of quantitative behavioral data and qualitative motivational insights are needed to inform each question. Add any missing pieces.

Budget & Timelines

Weigh the costs, speed and agility of primary research versus big data analysis for each question. Pick faster, cheaper options that provide adequate inputs.

Capabilities

Consider your organization’s skills, infrastructure and culture. Seek outside help to fill any experience gaps required for advanced analytics or market research.

The Customer Insights Toolkit: Assemble Yours

Customer understanding is a must-have competitive advantage. But there is no one-size-fits-all approach. Savvy organizations adapt their analytics and research mix to their specific situation and needs.

With focused market research and expansive big data complementing each other, the possibilities are endless. So be creative in crafting your own customer insights toolkit. Just be sure to keep the end goal in mind: not just more data, but deeper human understanding. Align your tools to this north star, and you’re sure to keep customers happy and stay ahead.

Related Posts

• 15 Market Research Tools for Startups to Validate Their Ideas
• How to Conduct Small Business Market Research Surveys?
• Market Research vs Marketing Research

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