In order to properly market to your audience and get buy-in from relevant stakeholders, you need to have data that supports your decision to move forward with a new campaign, implement a new automation system, etc. With the abundance of statistical measurements that can be used to make a decision, it’s best to separate the data into two categories — quantitative and qualitative — in order to avoid confusion, better segment data for analysis and draw appropriate conclusions.
Qualitative vs Quantitative Data
Quantitative data are numerical measures that help explain the current business situation, find trends and seasonality patterns within historical data and attempt to draw conclusions about what will happen in the future. These numerical measures are often presented as either a static number, a percentage or a ratio. In marketing, common forms of quantitative data include the number of page views, the conversion rate between lifecycle stages or the bounce rate from web pages. These measures have the advantage of both delivering insights and being easily manipulatable in databases. As a result, quantitative data is generally the more popular form of data within modern marketing departments.
By contrast, qualitative data looks to identify engagement among users and find out why they took the actions they did. Interviews and small group sessions are common ways of acquiring qualitative data but new marketing technologies are providing marketers with a faster way to grab this data through measurements such as heatmaps, scroll depth and page journeys. Qualitative data is usually more scarce because it’s harder to obtain and typically requires deeper analysis than simply reading a number.
Why do we need both?
Quantitative data gives us a factual idea of the present condition. That is, in all configurations and campaigns we have set up, quantitative data tells us exactly what we have going on. This type of data is useful when we want to know hard numbers for historical or current data, or when we want to troubleshoot and monitor systems. For instance, monitoring quantitative data such as the number of page views for a page on a weekly basis will give the marketer insight on when a page may be failing to load or has a lower number of views on average than others.
However, the strength of the insights derived from quantitative data is completely dependent on the marketer’s ability to interpret this raw information. Quantitative data paints a picture of how we as a company are performing, rather than looking at how users interpret the information we send them or engage with us. While these data points are useful, they are meaningless unless we can provide some insight into what they mean and how they affect our marketing efforts.
By contrast, qualitative marketing data tells us how the user is engaging with our brand and provides insights regarding their perceptions. This type of data is crucial in understanding whether our marketing campaigns and the way we present information to prospects and customers are working as intended. For example, we may know that we have 1,000 views of our home page but this won’t help us much if we don’t know how visitors are actually interacting with the content there.
It could very well be the case that many of those “views” come from a single visitor refreshing the page multiple times in the hopes of loading it faster. Here, we see a mixture of needs from both qualitative and quantitative angles — we need quantitative data to tell us how many page views we are obtaining and qualitative data to tell us what users are interacting with on the page. By segmenting the data into these two categories, we get a clearer picture of what is going on and what we should do next (quantitative) but also why the numbers are the way they are (qualitative).
Marketing Use Case Scenario — Attributing Leads
In order to attribute leads to a specific marketing campaign, we have to use both qualitative and quantitative data. To illustrate, field marketers create campaigns, obtain leads and then push those leads over to sales in order to obtain a conversion. Quantitative data allows us to analyze the number of leads obtained per campaign, which sources they are coming from (i.e. organic search, referrals, etc.), and whether or not they have ultimately, become a “closed-won” opportunity. These numerical records depict a static image of how the marketing campaigns performed.
Once we have these numbers, the next step is to address the qualitative aspect of the process by looking at user engagement. It’s simple to say that organic search traffic accounted for 50% of lead generation within a given period but that data is of limited value if we don’t know what actually lead a prospect to become a lead. In other words, while the quantitative number tells us that organic search is performing well, we can use qualitative data to assess why that is the case. For example, if we find that the majority of users coming in through organic search traffic are viewing pages related to a specific blog and then bounce off the page, we can assume that the blog material is used for educational purposes. From there, we can recommend creating more marketing collateral on the blog’s original topic to inspire more traffic.
By understanding the actions a user went through such as pages read, white papers downloaded or nurture campaigns experienced, we gain more insight into how to replicate successful results for the future and identify exactly what is peaking user interest. In other words, it’s only through combining qualitative and quantitative data sets that we gain a complete understanding of marketing success.