Article | September 13, 2021
In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting.
The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’.
The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience.
Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years.
Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online.
We have highlighted the steps you need for successful data-driven marketing.
5 Steps to Take for Successful Data-Driven Content Marketing
For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform.
For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales.
If you're not putting out relevant content in relevant places, you don't exist.
Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality.
Customize Campaigns for Target Audience
Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them.
Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where.
Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts.
Regular Content Optimization
One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day.
Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment.
Keep running SEO audits on your pages regularly to keep your content in the best shape possible.
Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness.
Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track.
There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives.
In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns.
Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges.
Frequently Asked Questions
What is the future of content marketing?
Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business.
What are the top content marketing trends for 2022?
1. Layout Objectives
2. Customize Campaigns for Target Audience
3. Regular Content Optimization
4. Content Repurposing
5. Track Analytics
How is content-based marketing a proven strategy?
Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness.
"name": "What is the future of content marketing?",
"text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business."
"name": "What are the top content marketing trends for 2022?",
A. 1. Layout Objectives
2. Customize Campaigns for Target Audience
3. Regular Content Optimization
4. Content Repurposing
5. Track Analytics"
"name": "How is content-based marketing a proven strategy?",
"text": "Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness."
Article | March 21, 2020
Splunk extracts insights from big data. It is growing rapidly, it has a large total addressable market, and it has tremendous momentum from its exposure to industry megatrends (i.e. the cloud, big data, the "internet of things," and security). Further, its strategy of continuous innovation is being validated as the company wins very large deals. Investors should not be distracted by a temporary slowdown in revenue growth, as the company has wisely transitioned to a subscription model. This article reviews the business, its strategy, valuation the sell-off is overdone and risks. We conclude with our thoughts on investing.
Article | December 21, 2020
Machine Learning (ML) has taken strides over the past few years, establishing its place in data analytics. In particular, ML has become a cornerstone in data science, alongside data wrangling, and data visualization, among other facets of the field. Yet, we observe many organizations still hesitant when allocating a budget for it in their data pipelines. The data engineer role seems to attract lots of attention, but few companies leverage the machine learning expert/engineer. Could it be that ML can add value to other enterprises too? Let's find out by clarifying certain concepts.
What Machine Learning is
So that we are all on the same page, let's look at a down-to-earth definition of ML that you can include in a company meeting, a report, or even within an email to a colleague who isn't in this field. Investopedia defines ML as "the concept that a computer program can learn and adapt to new data without human intervention." In other words, if your machine (be it a computer, a smartphone, or even a smart device) can learn on its own, using some specialized software, then it's under the ML umbrella. It's important to note that ML is also a stand-alone field of research, predating most AI systems, even if the two are linked, as we'll see later on.
How Machine Learning is different from Statistics
It's also important to note that ML is different from Statistics, even if some people like to view the former as an extension of the latter. However, there is a fundamental difference that most people aren't aware of yet. Namely, ML is data-driven while Statistics is, for the most part, model-driven. This statement means that most Stats-based inferences are made by assuming a particular distribution in the data, or the interactions of different variables, and making predictions based on our mathematical models of these distributions. ML may employ distributions in some niche cases, but for the most part, it looks at data as-is, without making any assumptions about it.
Machine Learning’s role in data science work
Let’s now get to the crux of the matter and explore how ML can be a significant value-add to a data science pipeline. First of all, ML can potentially offer better predictions than most Stats models in terms of accuracy, F1 score, etc. Also, ML can work alongside existing models to form model ensembles that can tackle the problems more effectively. Additionally, if transparency is important to the project stakeholders, there are ML-based options for offering some insight as to what variables are important in the data at hand, for making predictions based on it. Moreover, ML is more parametrized, meaning that you can tweak an ML model more, adapting it to the data you have and ensuring more robustness (i.e., reliability). Finally, you can learn ML without needing a Math degree or any other formal training. The latter, however, may prove useful, if you wish to delve deeper into the topic and develop your own models. This innovation potential is a significant aspect of ML since it's not as easy to develop new models in Stats (unless you are an experienced Statistics researcher) or even in AI. Besides, there are a bunch of various "heuristics" that are part of the ML group of algorithms, facilitating your data science work, regardless of what predictive model you end up using.
Machine Learning and AI
Many people conflate ML with AI these days. This confusion is partly because many ML models involve artificial neural networks (ANNs) which are the most modern manifestation of AI. Also, many AI systems are employed in ML tasks, so they are referred to as ML systems since AI can be a bit generic as a term. However, not all ML algorithms are AI-related, nor are all AI algorithms under the ML umbrella. This distinction is of import because certain limitations of AI systems (e.g., the need for lots and lots of data) don't apply to most ML models, while AI systems tend to be more time-consuming and resource-heavy than the average ML one. There are several ML algorithms you can use without breaking the bank and derive value from your data through them. Then, if you find that you need something better, in terms of accuracy, you can explore AI-based ones. Keep in mind, however, that some ML models (e.g., Decision Trees, Random Forests, etc.) offer some transparency, while the vast majority of AI ones are black boxes.
Learning more about the topic
Naturally, it's hard to do this topic justice in a single article. It is so vast that someone can write a book on it! That's what I've done earlier this year, through the Technics Publications publishing house. You can learn more about this topic via this book, which is titled Julia for Machine Learning(Julia is a modern programming language used in data science, among other fields, and it's popular among various technical professionals). Feel free to check it out and explore how you can use ML in your work. Cheers!
Article | May 19, 2021
There are some fundamental differences between Business Analytics and Data Analytics, though both hold their own importance. For example, to discover patterns and observations that are ultimately used to make informed organizational decisions, Data Analytics includes analyzing datasets. On the other hand, to make realistic, data-driven business decisions, Business Analytics focuses on evaluating different kinds of information and making improvements based on those decisions. In this blog, we discuss in more detail their individual benefits and areas of expertise. Data Analytics vs. Business Analytics attracts a lot of interest from budding analysts; we will take multiple factors into account and help explain the difference between data analyst and business analyst.