As a marketer, you are likely focused on keeping your content marketing machine running. Recruiting writers, researching relevant and trending topics, reviewing content, and publishing it are all examples of activities that fall on a content marketer’s shoulders.
However, most content marketers are so focused on content creation they often forget about one key element that can make all the difference in performance — and that is big data.
Honestly, that’s what I love most about digital marketing. Data tells us what’s working, what isn’t, and where we should invest time and money.
And, yes, data can be overwhelming. Data marts, data warehouses, data lakes — there are a number of ways in which data is becoming more pervasive in marketing. There are also a number of ways to store and manage that data, which can add another layer of complexity.
Qualitative vs. quantitative data: What is the difference?
Before we dive into the different types of data management tools, let’s first review the different categories of data and why they matter for marketers. The two primary categories are qualitative data and quantitative data.
Quantitative data are measures of values or counts and are expressed as numbers. Data about numeric variables (i.e. how many, how much, or how often).
For example, if you recently published and shared a blog post, you will likely see a spike in website traffic or social media engagement. By reviewing quantitative data on a regular (most likely monthly) basis, you will be able to see how much traffic, mentions, or other social engagement that blog generated. From there, you can determine whether or not it’s worth writing a future blog on a similar or related topic.
Qualitative data is a measure of data that helps marketers answer questions of why or how to fix a problem.
For example, measuring or monitoring data as a result of usability testing, field research or even A/B testing are all examples of how marketers might leverage qualitative data. Analyzing qualitative data allows marketers to understand patterns related to user or buyer behavior as well as why they perform certain actions (or don’t).
Implicit vs. explicit data
In addition to qualitative and quantitative data, there are also implicit and explicit data. What is the difference between the two? And how are they different from qualitative and quantitative data? Let’s break it down…
Implicit data is information that is gathered at mass and at scale from various available data streams but might not necessarily be provided intentionally.
Furthermore, implicit data can also be gathered through the analysis of explicit data. Explicit data is information that is provided intentionally, such as through surveys and form fill-outs.
What to do with all that marketing data
Of course, understanding the different categories of data can help you to also better understand the types of data you are working with and why they are important. This can help when building better data capturing and management processes for teams.
The good news is there are many tools available that can help marketers gather both qualitative and quantitative data. For example, Google Analytics and Quantcast are two popular tools today. Many CRMs, CMS’s, and lead management systems are also designed to build, publish, and capture data all in one platform.
HubSpot is a prime example of this. HubSpot offers a full marketing suite, complete with CMS, CRM, lead management system, and even website and blog analytics and data. Marketers can simply log in and see all activity in one comprehensive dashboard. HubSpot also allows you to see a high-level, big-picture view of data or a detailed, granular view.
However, understanding qualitative versus quantitative data is only one step in the process; it’s what you do with it that matters most.
Data storage and management
Now that you understand the different types of research and categories of data, what do you now do with all that data that you collect? The next important step is storing and managing data.
Marketers deal with large amounts of data flooding in from various sources, such as social media, form fill-outs, website visits, and more. As a result, many marketing teams end up working with their IT departments to find the best tools and platforms to help manage and store that data.
For example, a data warehouse provides storage for data that is structured for a database, such as a CRM that houses customer information. Data warehouses are best for teams that deal with data that is already organized before it filters into a data warehouse.
One example of a top data warehouse tool is Oracle. Oracle’s Data Management Platform allows users to personalize marketing programs as well as the customer experience. It also allows users to build customer profiles from third-party sources, including social media, advertising channels, and even mobile sources.
Unlike data warehouses, data lakes are designed to accept and store virtually any type of data. Data lakes can process data from any source, including video and audio streams, facial recognition, social media, and more. Data lakes typically also leverage artificial intelligence (AI) in some form to help characterize, format, process, and manage that data.
In regards to marketing, data lakes allow marketers to review and analyze a deeper level of data sources than what they would find in a data warehouse.
One example of a data lake tool is IBM. IBM helps marketers to meet their biggest data challenges while also driving real-time analytics into their hands. IBM offers a wide variety of solutions to help marketers build their own data lakes and then manage, access, explore, and discover big data and insights.
Depending on the industry in which you work or the client base in which you serve, you may need to handle, store, and manage data that must be compliant.
For example, marketers that work in health care that might capture patent data must follow specific data warehouse methods or use tools that have encryption or other advanced security features.
Why data is important for your content marketing strategy
All in all, the process of understanding, capturing, and storing and managing data can be overwhelming, especially if you aren’t a data analyst or a “numbers” person. However, data is an important aspect of any content marketing strategy.
To help break it down, when looking at data, you can ask yourself the following questions:
- What questions do we need to ask ourselves when looking at and interpreting data?
- What are we trying to understand?
- What are we trying to solve?
- Why is a certain event happening?
The goal is to turn questions into confidence. By understanding and capturing data, marketers are in a better position to understand what content, marketing methods, or marketing channels are working and what aren’t. After all, big data is actionable data.
Then, marketers can then ditch the marketing methods or content that aren’t working and revamp their marketing strategies to focus on the high-performing content and channels to yield better results and maximize marketing ROI.
At the end of the day, the data is all that matters.