The importance of data has been recognised for quite some time, but with so much qualitative and quantitative data to collect, analyse, hypothesise, deploy and report on; in many forms and across multiple channels, navigating the world of data can be a huge task for any CMO.
In this blog, we’ll take a deep dive into the different types of data; how it can be deployed within a content marketing campaign and answer key questions to help marketers maximise the value they can get from it.
Question one: What types of data should you consider?
There are two main types of data: quantitative and qualitative.
Quantitative data deals with numbers and results that can be measured objectively. Results of a quantitative research survey can be displayed numerically, such as in percentages, charts and graphs. This type of data can be gathered from a big sample to get a large representation.
Qualitative data can’t be measured as easily, but that doesn’t mean it isn’t valuable - it is! Qualitative data deals with characteristics and descriptors, and is a very valuable way of gathering insights and opinions, but is usually conducted with smaller sample sizes.
Research projects are increasingly recognised as being of significant value in providing content that engages with and adds value to a CMO’s prospective audience. Within the development of such content marketing campaigns, it can be important to utilise both types of data; this is the best way to get an in depth, accurate answer to the question(s) you want to explore. For example, a survey seeking to understand how CMOs plan to use their marketing budgets in the next quarter could seek to gain quantitative data around the proportion of the budget allocated to various activities, as well as qualitative data around the CMO’s opinion on the current state of the industry.
There is no right or wrong type of data to use, but what’s important is understanding which type of data - or a combination of the two - will help you to answer your initial question, reflect on your hypothesis (more on this to come!) and get the best outcome.
Question two: How do I construct a strong, research-led content marketing campaign?
All research projects must start with a question, or hypothesis, that the project should seek to answer. What do you want the results of the research to show? What are the key media headlines that you want to come out of the survey?
As with a good scientific experiment, by working backwards from the main hypothesis you want to explore, you will be able to decide which type of data you should use, which questions you need to ask within the survey itself, how many questions need to be asked, who the audience of your research project should be and the timescales.
Adopting a scientific approach to the data within the research / content generation campaign means that the outcome should always be front of mind: creating a purpose for each stage of the project. It is with this mindset that valuable results will be gathered that will really make a difference to both the organisation itself, and the target audience.
Question three: What are the different stages of a scientific approach to data?
Hypothesis: As highlighted, all research projects should start by constructing a hypothesis; asking a question and (ideally) predicting what the outcome of the research will be.
Background research: Next, background research should be conducted. This will help to determine if there is similar research that exists, or any other organisation seeking to answer the same question. This, in turn, will either tell you that you should try to answer another question, take a different stance on the issue, or if a collaboration with another organisation could be possible on the same topic.
Developing the question set: Once this has been achieved, it’s time to develop your questions. These will form the basis of the research and will provide the data to support - or refute - the original hypothesis, depending on whether you want to generate a positive or negative headline message.
Data collection: Now it’s time to let the research spread its wings! If it’s a larger, quantitative research survey, then it’s time to press the ‘send’ button and wait for the results. If it’s a smaller, qualitative research survey, then it’s time to arrange 1-1 interviews or questionnaires and start collecting responses.
Data analysis: When the results are in, it’s onto the analysis. This is where you can draw conclusions from the data and compare it against your initial hypothesis.
Question four: How can you learn from data results?
All research projects will be a learning curve, and it’s important to use the insights gained to inform future campaigns and projects. What other areas could be explored in future research projects? Did the results really answer the questions and match the hypothesis?
Take time to analyse and reflect on areas such as how certain questions were phrased, if the project really reached the intended audience or if more or fewer questions could have been asked. Could a different type of data have been used and if so, how would that have impacted the results?
It is these learnings that will impact the success of future campaigns and projects.
Question five: How can you effectively use your data in marketing and sales campaigns?
The final part of a research project or campaign is effectively using the results to drive the marketing campaign and sales objectives.
Which channels can you use to make sure the results reach your target audience, and how can you measure the effectiveness of these methods? What forms of content should you use to distill the message from your research project in different ways?
Thought leadership content, for example, is a form of content that allows you to really unpack an issue and speak directly to your target audience, highlighting the insights from the data you’ve gathered. This content can also then be shortened into a series of soundbites, or used as the basis of an email campaign or social media updates. The possibilities are endless - but using the results of your research in as many forms of possible will ensure you get the message across.
So there we have it - we’ve taken a deep dive into data and answered five common data questions. Are you ready to get started? Get in touch, we’d love to hear from you!
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