No business can do without qualitative analysis
Updated: Mar 28
Businesses, be it big or small, constantly follow their bottom-line by measuring numbers. How many units did I sell? How much revenue did I make? How many likes and shares did I receive over social media? How many customers converted from my google and Facebook ads? But as every business' fortunes rely on such key takeaways, its owners and employees also learn to complement such data and draw conclusions from qualitative data.
When comparing quantitative vs. qualitative data aspects, quantities and numbers make up the quantitative data, while descriptions and words analysis defines the qualitative data side. And in the case of every business, the customers' opinions and actions, as described by them over many different online outlets, combine to tell a much deeper story with important lessons to take and implement.
Quantitative and qualitative data analysis is used to evaluate the true position of every brand at any given time in its life-cycle and must be understood together in order to learn what has happened and prepare its next moves. Qualitative and quantitative research, when done constantly, may show the direction for a brand to advance. As data collection is an ongoing process, a business can find out that while its numbers look great, some problems may loom on the horizon as a result of what customers are saying. So the numbers alone do not suffice. Paying attention to what customers are discussing shows a business what's really going on from the people who have bought its products and services.
So for every business, it is always key to gather qualitative data. A business can find lots of important data information regarding its customers, such as learning about their certain behaviors, finding out what they complain about and what in the product or service made them happy, and whether they are likely to come back or churn. Content analysis of their comments can help a business discover patterns in their purchasing and product usage behavior while also giving in-depth insights towards their future plans regarding a brand.
The picture gets more complicated when millions of data points must be analyzed.
Companies with a huge customer base are finding it even harder to complement their numerical value aspects with much-needed qualitative research. Market trends, a brand's possible future directions, handling of customer complaints, and overall product perceptions rely heavily upon, and sometimes only, on qualitative data. Business managers must find a way to balance quantitative and qualitative data aspects of their products in order to stay on the pulse with their customers. Analyzing qualitative data with the right research objectives can serve as a great help for the sake of every business's continued success.
In order to fully grasp the difference between quantitative data and the qualitative approach, here are examples of each.
Quantitative customer data analysis services
Quantitative data detailing quantitative analysis can be found in two of the leading advertising online services, Google and Facebook. Data analysis here refers to counting specific actions by customers but without explaining what stands behind these actions. It is a straightforward quantitative data analysis of numerical values for specific actions. Checking each system's offerings in more detail helps to understand their analysis methods.
Google Analytics is a service that tracks and reports website traffic. It shows the owner of the website quantitative data as well as statistical analysis of data such as how many people visited the site, how many views each of the website's pages had, what was the session duration for each visit (that is, how much time a visitor stayed on the website) and many other numerical data features. Such descriptive statistics detail actual website usage numbers and can tell a lot about the site's overall popularity as well as break it down to the specific page visits and success.
Special quantitative data attention goes towards visits to a website's store, including counting clicks and visits every step of the way from the sale information and all the way to actual product purchases. Such data collection can show the website's owner how many people have churned at the last minute, that is decided not to buy, for whatever reason, at the purchase page. Google off-course can't explain why they decided to drop here, or on any other part of the funnel, and assuming there is no technical error, the salesperson will not be able to know what happened. Google does supply other e-commerce related metrics, that is everything the system is able to measure from a quantitative data perspective.
Google also operates a quantitative data system to measure the performance of Google Ads, showing advertisers how many people clicked or converted, in accordance with specific parameters set in advance. Advertisers can decide to pay for brand awareness, lead generation, or retarget existing customer ads and then measure the results via the statistical analysis provided by Google. It keeps improving its quantitative analysis methods and helps its customers learn what happened but now why it happened.
Google's main advertising online rival is Facebook which earns large parts of its revenues from operating Facebook ads. Also called Facebook for business, it is a system where advertisers can market and sell their products and services with targeted audiences, who receive the ads to their feeds. Running and tracking Facebook ads is done through Facebook Manager, which allows advertisers to count how many people clicked on the ad, but also, after setting the specific goals of the ad, learn and measure what were the results during the ad run-time.
Facebook measures impressions, for example, which refers to the moment an ad enters the screen of a desktop browser or a mobile app. It also measures views, as in video views on a post or an ad, with the video view rate calculated as the percentage of users who viewed a post and actually watched the video for at least three seconds. Facebook counts likes and shares and most importantly clicks on ads, which can tell an advertiser just how appealing was the ad to consumers. That is called CTR, Click-through rate, which is the ratio of how many people have clicked on the ad to the number of times the ad was viewed (clicks over impressions).
Again, as is the situation with Google Analytics and quantitative data, Facebook is not able to present advertisers with the "why", so sellers don't know what motivated or distracted customers from purchasing a product or a service. Its data collection methods resort to the "what happened" only and its data analytics are dedicated to numerical data collection methods only.
Qualitative customer data analysis services
Qualitative research methods look for certain characteristics in the content analyzed, which can provide insights about the "why" in any data. Such qualitative methods come as a great help for content providers, be it in an organic or a paid situation since the people behind the content are trying to learn as much as possible about what people think about products and how they perceive them. On its own, qualitative data may not suffice, but as an addition to the quantitative data, it can serve as a complementary source of great information which can help content providers with great insights.
Two examples in the qualitative data area are UXCam and Countly. Both services operate within the field of analyzing how people use apps.
UXCam is a cloud-based user experience service that helps businesses optimize app functionality by recording and analyzing every user micro-interaction. This operation monitors the activities of users and analyses their behavior. By doing so, and by the app creators and managers being able to understand how users functioned on their app, improvements can be implemented. Called also "App experience analytics", UXCam mainly helps mobile production teams receive quick, contextual, and high-fidelity insights. Using such qualitative data enables app producers to make informed product decisions. Analyzing data from features such as audio recordings and data visualization gives product people valuable insights as to what works and doesn't work on the app they designed.
Countly is yet another leading analytics platform that tracks data of customer journeys in web, desktop, and mobile applications. Such data analysis enables product teams to improve their applications' performances. When comparing UXCam vs. Countly, at least on what reviewers are saying on G2, UXCam was generally favored. They have determined that UXCam was easier to use, set up, and administer.
In overview, both services take a deep dive into app functionality as performed by users, and give product teams a thorough qualitative data analysis of such apps. Numbers countless here, as both services analyze data that focuses on qualitative research and the primary data refers to describing functionality aspects only.
The benefits of each data analysis system
Qualitative and quantitative data enables businesses to receive both data sides of the coin. Numbers give a company the bottom line and the answer to “what happened or didn't happen”, while qualitative data collection methods enable us to learn information that can't be measured or counted. As quantitative data refers to the bottom-line numbers and figures, qualitative data answer the "why" and sometimes the "how" behind the numbers.
When comparing qualitative vs. quantitative data, it is also worth mentioning that qualitative data covers open-ended studies, allowing customers to show their true thoughts and feelings about products and services. Unlike quantitative data, the "why" side describes trends, feelings, thoughts, and even complaints in a manner that enables teams to evaluate and act upon.
Affogata offers the best of both worlds
Data collection and analysis have advanced greatly over the last few years. So when customer feedback is collected, it doesn't have to be qualitative vs. quantitative but more a combination of both.
Affogata tracks and monitors real-time millions of customer data feedback points, which results in a wide qualitative and quantitative data analysis for its customers.
The quantitative aspects show the brand sentiment score, a number which details positive vs. negative mentions of a brand over a specific period of time. But this is just a temperature check of what customers are saying about the company. What follows is a detailed qualitative data analysis of their conversations, which enables a company to learn what they truly think and feel about its products, services, and overall position.
Instead of employing focus groups, or organizing research questions, the simplest way is to gather unguided opinions of countless customers from all over the open web. Such tracking. monitoring and the analysis that follows them, present companies with specific information about their market position. The data also enables them to decide on their next strategic or product moves, learn how marketing campaigns have performed, and even receive daily reports showing results of both quantitative and qualitative research.
One side complements the other for actionable insights, planning, decision-making and for never staying out of touch with the market, and most importantly with the customers.