Quantitative market research is a fundamental arm of data collection that aids brands in their business goals.
It focuses on collecting data from across your industry – from your customers to your competitors – and making sense of all the numbers.
Without quantitative data, it’s almost impossible to gauge customer behaviors and market trends or strategize for the future.
Market research doesn’t have to be difficult. Around 85% of brands worldwide use some form of online survey to collect quantitative data.
So long as you ask the right questions and have the right tools, conducting quantitative market research is really straightforward.
But what type of quantitative research questions should you be asking? And what tools can help you unearth hidden data? This guide reveals all of this and explains how CisionOne helps marketers collect market research data in one place.
In this guide:
What is Quantitative Market Research?
Key Quantitative Research Methods and Techniques
Examples of Quantitative Market Research in Action
Time to Leverage Quantitative Insights for Smarter Decisions
What is Quantitative Market Research?
Quantitative market research involves the collection and analysis of numerical data to understand market trends, consumer behavior, and other business-related variables.
Marketers use tools and software like CisionOne to pull data into one place and make sense of the numbers.
Do it right, and you’ll get measurable insights about your business rather than subjective opinions, which is known as qualitative data.
Some key characteristics of quantitative research include:
Objective, structured data: Questions are standardized, and responses are numerical (e.g., ratings on a 1–10 scale, percentages of respondents, etc.). This yields results that are less open to interpretation and bias.
Large sample sizes: By surveying or observing a large number of people or data points, quantitative market research ensures results have statistical significance and can be generalized to a broader population.
Statistical analysis: Analytics tools process data to reveal trends, correlations, and differences with a high degree of confidence.
Answers “what” and “how much”: Quantitative methods excel at answering questions like "What features do customers want?" and "How many people prefer option A over B?".
Using primary and secondary research
You can use both primary and secondary research when seeking to obtain quantifiable data. Primary research collects new data directly from a source, such as running a survey or experiment. Secondary research analyzes existing data from reports, databases, or studies.
Using both in tandem is the best way to get a full picture of your market.
For example, a marketing team might conduct a primary survey to quantify customer satisfaction and examine secondary data like industry sales figures or census demographics to inform their strategy.
The emphasis in both cases is on numerical insight that can guide decision-making.
>> Learn more about types of market research
Key Quantitative Research Methods and Techniques
Before we look at specific examples of quantitative market research, let's explore the most popular methods and techniques brands use to gather this vital data.
Not every method is workable for every brand, and it may be the case that you select only one or two to begin your research before weaving in further methods at a later date.
We’ve split these research methods into primary and secondary types:
Primary quantitative research methods
Surveys and Questionnaires: Surveys are the most widely used quantitative method in market research. They involve asking a series of standardized questions to a sample of respondents. Quantitative questions use closed-ended formats – multiple-choice, rating scales (e.g., 1 to 5 stars), yes/no, etc. – so that responses can be tallied and quantified. You can also ask qualitative questions and receive useful opinionated feedback.
Polls: A poll is essentially a single-question survey (or just a few questions), often used to get a quick pulse of public opinion. Brands drop them on social media all the time to engage with audiences and extract data from them. Polls are useful for their simplicity and high response rates, though they capture less detail than full surveys.
Experiments and A/B Testing: A/B testing is experimental research where you tweak one variable to observe its effect compared to another. You might present two versions of a webpage (Version A with a red “Buy” button and Version B with a blue button) to two randomized groups of visitors and measure which version yields higher conversion rates. By controlling conditions and using statistical tests, experiments can establish cause-and-effect relationships.
Secondary quantitative research methods
Analytics of Behavioral Data: Not all quantitative research comes from asking questions – a lot comes from observing actual behavior. Web analytics, for example, track users’ actions on a site (pages visited, time spent, items clicked). Product analytics measures how often customers use a software feature. Social listening observes audiences on social media to understand them better. These observational data techniques are quantitative goldmines for understanding what customers actually do.
Existing Market Data and Statistics: Market researchers also rely on databases and reports that aggregate quantitative information about industries and consumers. Examples include government census data, industry association reports, or syndicated research (like Nielsen ratings or Gartner forecasts). Analyzing these datasets – quantifying a trend in consumer spending or benchmarking competitor market shares – is a form of quantitative research. It’s especially useful for market trend analysis, which aims to understand big-picture movements through numbers.
Each of these methods can be deployed depending on the research question at hand.
Researchers often use multiple methods in combination – for example, running a survey and analyzing web analytics – to triangulate insights.
Examples of Quantitative Market Research in Action
Now let’s look at some viable examples of quantitative market research being conducted.
From customer surveys to pricing experiments to trend analysis, these examples demonstrate the versatility of quantitative methods for solving common business questions.
1. Customer Satisfaction Surveys and NPS Scores
Scenario: A SaaS software company has noticed an uptick in customer churn (cancellations) and wants to understand why users are leaving. The marketing team decides to conduct a customer satisfaction survey to gather feedback from recent customers.
Approach: They design an online survey including rating-scale questions about various aspects of the product (ease of use, feature set, customer support experience, etc.).
They include a classic Net Promoter Score (NPS) question: “On a scale from 0 to 10, how likely are you to recommend our software to a friend or colleague?”
The company emails this survey to all customers who have been using the product for at least six months. Within two weeks, they collect 1,200 responses – a robust sample.
Quantitative Findings: The data reveals some clear patterns. Overall customer satisfaction averages only 6.8 out of 10, and the NPS comes in at a relatively low +10 (meaning promoters barely outnumber detractors).
When the team breaks down the scores, they discover customer support responsiveness received the lowest satisfaction ratings, with 40% of respondents giving support a neutral or poor score.
They also see a trend in users who had contacted support in the last month giving an NPS of 15 points lower (on average) than those who never contacted support. Statistically, there’s a significant correlation between support satisfaction and the likelihood of recommendation.
Outcome: These quantitative results point to a concrete action item – improve customer support.
This example shows how quantitative survey data can directly drive improvements. By quantifying customer sentiment, the company pinpointed a weakness that was impacting revenue (through churn) and took data-informed action.
2. Pricing Strategy Tests and Price Sensitivity
Scenario: A popular fashion brand is considering raising its premium product line prices. However, they fear that a price hike could drive away customers and hurt sales volume. The brand’s research team conducts a quantitative pricing study to make an informed decision.
Approach 1 – Conjoint Survey: First, they deploy a conjoint analysis survey to a sample of 1,000 target customers. In this survey, respondents are shown different hypothetical product scenarios with varying price points (and possibly varying features or service levels) and are asked which option they would purchase.
By statistically analyzing respondents’ choices, the conjoint analysis quantifies how sensitive demand is to price changes – essentially revealing the price elasticity.
Approach 2 – A/B Test: To complement the survey, the brand also runs an A/B pricing test on its website for a month. New visitors to the site are randomly split into two groups: one group sees the original price, the other sees a 10% higher price for the same product.
All other conditions (marketing, product display, etc.) remain identical. By the end of the month, they compared conversion rates and revenue per visitor between the two groups.
Quantitative Findings: The survey-based analysis produces a demand curve indicating expected sales volume at various price points. It shows, for instance, that raising the price by 10% would likely result in only a 5% decrease in the quantity demanded.
The A/B test data aligns closely with this model (a percentage point drop in conversion, as observed). Both pieces of quantitative evidence suggest the brand has pricing power – they can increase prices moderately with minimal impact on overall revenue.
Decision & Outcome: Armed with the data, the company’s executives decide to implement a 10% price increase on the premium line. They’re confident that, based on the research, this will boost margins without significantly hurting sales.
This example underscores how quantitative research takes the gamble out of pricing decisions. Pricing is one of the trickiest decisions for any business – a price too high, and you lose customers; a price too low, and you leave money on the table.
By using surveys and controlled experiments, companies can empirically determine the optimal price range.
3. Market Trend Analysis and Forecasting
Scenario: A marketing team at a beverage company wants to stay ahead of emerging market trends in order to guide product development.
In particular, they suspect that consumer tastes are shifting toward healthier, low-sugar drinks, and they want quantitative evidence of how big this trend is and how fast it’s growing.
Approach: The team embarks on a market trend analysis using several quantitative data sources. They obtain secondary industry sales data over the last five years, categorizing beverages based on sugar quantities.
They also commission a large-scale consumer survey of 2,000 people and ask questions about beverage consumption habits.
On top of that, they use social media analytics to gauge trend momentum, analyzing the volume of social media mentions of “low-sugar drinks” and related keywords over time.
Quantitative Findings: The industry sales data shows that, over the past five years, sparkling water sales are increasing while regular soda sales are in decline. Low-sugar or diet beverages have steadily gained market share year-over-year.
The consumer survey results align with this: 60% of respondents say they are more health-conscious about beverages now than a year ago, and among those, nearly half report switching from sugary drinks to low or no-sugar options.
Additionally, the social media and search analysis shows a sharp uptick in buzz around health-focused beverage terms. These quantitative indicators confirm that the trend is not only real but accelerating.
Decision & Outcome: Armed with these trend insights, the beverage company’s decision-makers move quickly to adjust their product roadmap. They green-light development of a new zero-sugar version of a popular drink and increase marketing for their existing line of flavored sparkling waters.
This example highlights the use of quantitative market research for trend analysis and forecasting. By looking at hard data – sales figures, percentage changes, survey statistics – the company quantifies a trend that many had a hunch about.
It’s one thing to sense “people seem to be moving toward healthier options,” but it’s far more convincing to see “category X is growing at 10% annually while category Y is shrinking 3% annually.”
4. Advertising Effectiveness and Media Analytics
Scenario: A mid-sized ecommerce company runs a multi-channel advertising campaign (social media ads, search engine ads, and a bit of TV advertising) and wants to understand the impact and return on investment (ROI) of these efforts.
They need to decide which channel to double down on and if any aren’t worth the spend. This calls for a quantitative analysis of marketing performance data.
Approach: The marketing team sets up a dashboard of key metrics for each advertising channel with CisionOne. They track metrics such as impressions, click-through rate (CTR), conversion rate (how many viewers actually purchased), cost per click, and ultimately cost per acquisition (CPA) for each channel.
Over the campaign period, they collect thousands of data points. They also use a customer survey after the campaign to get another piece of data: asking a random sample of recent customers, “How did you hear about us?” and giving options corresponding to the channels (social media, web search, TV, referral, etc.). This provides a self-reported data point on attribution.
Quantitative Findings: Analyzing the numbers, the team finds that the social media ads had a click-through rate of 2% and a conversion rate of 5% (of those who clicked, 5% bought something). With the spend and conversions known, they calculate CPA for social at $10 per acquisition. For search ads, the CTR was higher at 5%, but the conversion rate was slightly lower at 3%; given the cost of search keywords, the CPA comes out to $15 per acquisition.
The TV ads are trickier to evaluate, but by correlating the timing of the ads with spikes in direct and organic traffic, plus the survey responses, they estimate roughly 300 purchases can be attributed to the TV campaign. When divided by the TV ad cost, the CPA is about $40.
The survey results showed 25% of respondents recalled social media as how they discovered the brand, 15% search, and only 5% cited TV – most others came organically or via word-of-mouth.
Decision & Outcome: With these clear numbers, the company reallocates its marketing budget for the next quarter – increasing the social media ad budget, maintaining a steady spend on search ads (but negotiating on keywords or optimizing targeting to try to lower that CPA), and significantly reducing spend on traditional TV ads.
They also implement better tracking (like using media monitoring to spot spikes in brand references) to more precisely track brand conversations
This scenario illustrates how quantitative analysis in marketing guides tactical decisions. Digital marketing provides a deluge of real-time metrics – impressions, clicks, conversions, and costs – all quantifiable performance indicators. The key is interpreting them to understand effectiveness.
In these situations, tools and platforms become invaluable. Companies leverage advanced marketing analytics or media monitoring platforms like CisionOne to consolidate and analyze such data.
By monitoring quantitative metrics like share of voice, sentiment scores, and engagement rates, communications professionals can measure the effectiveness of their PR and marketing efforts and adjust strategy accordingly.
Time to Leverage Quantitative Insights for Smarter Decisions
Marketers need knowledge to act with confidence, and quantitative market research provides proof that your actions will pay off.
We’ve seen how metrics and data points – from survey percentages to experimental results – can illuminate the path forward, whether it’s refining a marketing campaign, delighting customers, or seizing a new market opportunity.
Numbers take the ambiguity out of choices and lend confidence to your strategies.
As you plan your next marketing strategy or business move, ask yourself: What do the numbers say? Embrace a data-driven mindset and seek out those facts and figures. Be willing to act on them and share insights with stakeholders in your business.
Ultimately, being data-driven is not just about having data but about creating actionable insights and driving them through to execution.
Platforms like CisionOne are designed to help connect the dots, from data collection to insight to action. Marketing and PR teams can seamlessly gather data, analyze it with the help of AI and expert services, and turn those findings into informed strategies.
It’s a holistic approach that ensures no insight slips through the cracks. Want to know more? Request a demo today and see how Cision can help.
