Your company might not have data scientists on the payroll at the moment. And it might be doing totally fine: content is being created and published, driving reads, likes, comments, and shares. But let me as you a few questions.
Do you really know what characteristics of your content drive engagement?
Are you sure your content will minimize the customer churn rate this quarter?
Are you even engaging the right people?
That’s a lot to think about, I know. But there’s a great chance that you won’t be able to answer “yes” to all of these questions simply because you’re not sure.
Because there are no metrics and analytics to back up your answers.
Every online business out there is using content marketing. They publish blog articles, social media posts, industry reports, and awesome infographics. According to recent surveys, 73 percent of digital marketers even track website analytics to monitor their performance.
Source: Content Marketing Institute
Seems like data analysis is already used by many to improve content marketing, right?
Not exactly.
The reason is that website analytics they’re using are very basic and often limited to a few indicators in Google Analytics.
Besides, if anyone is doing SEO writing and other content marketing strategies, it doesn’t mean they’re actually hitting their goals.
When asked about their results, only 29 percent of marketers are “extremely” or “very successful.”
The vast majority say their content marketing strategies are “moderately” or “minimally, or “not at all” successful.
A lack of data insights is one of the reasons why their content fails to engage customers.
Truly data-driven digital marketing strategies - the ones based on sophisticated data analysis and insights - are still uncommon.
However, using data science as a strategy for creating more engaging content is slowly but steadily gaining momentum. Here are some of the most important benefits it brings to the table.
The value of data in terms of digital marketing - content in particular - is truly immense.
There are two major areas in which data science can help:
Here’s how what it means in more detail.
“Time-series forecasting” might sound like something complicated. It’s really not. You’ve seen many examples of it already. In fact, much of the data you have in Google Analytics and other tools is shown in the “time-series” format.
Put simply, it’s a series of data points indexed in time order, typically with equal time periods.
Here’s a visualization of a simple “time-series” analysis done with Python.
For content marketing, it allows you to segment data like website sessions, conversions, or other KPIs by month, week, and even day, so you could assess your strategies. Data scientists can take time-series analysis one step further by using it to make helpful predictions.
“SEO is one area in which your business can benefit,” says Estelle Liotard, a senior writer at TrustMyPaper. “By applying predictive time-series forecasting to analyze search queries during specific seasons to reveal the weaknesses of your keyword research.”
Analyzing the search queries and keywords over time will also help to predict where your content might underperform compared to competitors.Your marketing team can work together with data scientists to improve your content.
The same applies to social media and other types of content. Basically, anything that has historical data can be predicted with data science and “time-series.”
So, doing this type of data assessment means you can find data patterns and make predictions from the insights.
Like many other marketing approaches, content marketing relies on testing. Some types of content, digital ads, or lead magnets could work well for one business but fail another, so testing is incredibly important for the best results. Data science has a bunch of testing methods that could help with refining your content.
Serial testing is one of these methods. It involves running a set of tests one after another instead of concurrently, which reduces the risk of misunderstanding and overestimation of risks. Using these data science methods provides more detailed results.
For example, you can assess how effective different words in emotional B2B campaigns are in generating leads and driving registrations. Also, using an unsupervised machine learning algorithm, data scientists can determine the characteristics of the most popular social media content.
This image, for example, shows how Cortex finding out that the most popular Instagram content had one feature in common - the most prominent color.
There are many more stats and other data to work on, so one can greatly advance their understanding of the effective content with unsupervised learning algorithms.
Knowing the most effective content types and characteristics can help your marketing because you can focus on what works. Using writing tools like Grammarly, ClassyEssay, BestEssayEducation, Hemingway Editor, and SupremeDissertations can speed up the content production process.
Since data science is all about deep testing, it can also help your business go beyond generic content practices based on what one thinks rather than knows.
Read more: How to Market in a Crisis: Finding Patterns in Travel Influencers’ Content.
Reputation management is a major part of successful content marketing. Businesses are using different emotion-based content strategies - brand storytelling to improve reputation is one of them - to achieve different branding-related goals.
If you understand the opinions and feedback given by the target audience, you can assess the performance of different content types.
Sentiment analysis, or the process of identifying that feedback, can be run manually (and still is by many businesses).
But going through every message, comment, or reaction your content receives takes too much time, so you need to do it quicker.
This is where machine learning algorithms like Naive-Bayes, Support Vector Machines, and Decision Trees come in.
Data scientists can use them to conduct sentiment analysis quickly and effectively. For example, with the Naive-Bayes method, they categorize predefined words into sentiment groups - negative, neutral, and positive - and assign values to them. Here are some examples of common words used in sentiment analysis.
|
Negative words (value “-1”) |
Terrible, boring, annoying, waste, poor, dumb, mistake, mess, hated, sad. |
|
Neutral words (value “0”) |
Okay, ok-ish, fair. |
|
Positive words “(value “+1”) |
Great, thanks, awesome, high-quality, love, wow, excellent, fantastic, satisfied. |
The algorithm assesses tons of social media content in minutes and finds those words. Having a data scientist work on the sentiment analysis can help with creating a more detailed report on the impact of the social media content you’re producing. Your marketing team could use the insights to adjust their strategies and work on increasing your brand recognition on social media.
Customer segmentation is a must-do digital marketing method to make content more relevant and engaging for potential customers.A business can segment their audience using a lot of indicators:
Machine learning algorithms can help businesses to speed up the process and define more precise categories of customers. A data scientist using such an algorithm can predict the behavior of a customer group such as:
Digital marketers can use this data to make more relevant content for customer segments.
The full range of benefits of data science methods for digital marketing is well beyond the scope of this article. These three benefits, although very powerful to improve content marketing strategies, just barely gave a glimpse into the applications of data science.
As more marketers understand how they can use it to gain a competitive advantage, using data science methods will become a major predictor of success.
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