At first glance, big data seems like a huge concept to grasp. However, it may surprise you to know that you’re already using big data in your business. Analytics tools and software actually use big data to gather statistics you compile and metrics you analyze. And if you want to maximize your branding’s reach to your audiences, you can in fact tailor big data algorithms to accommodate your organic link building strategy. How can we make this work, though?
The Problem With Reactive Planning
In order for our organic link building strategy to accommodate big data algorithms, we’ve got to understand how the two concepts actually correlate:
- Organic link building refers to the link building strategy that prioritizes using user-oriented and context-first methods to acquire backlinks. Organic link building pushes users to make unique content that answers user concerns while providing value to both the brand and the industry in question.
- Big data algorithms refer to processes different software use to sort huge amounts of datasets into useful data. They can do things such as basic sorting, sorting according to particular metrics, or even analyzing how certain characteristics of data may relate to one another.
Analytics tools utilize big data algorithms to help brands acquire relevant business metrics (impressions and engagements, conversions and returns, among others) that help you make your marketing strategies. However, relying solely on this data only encourages reactive planning – where we make choices for our brands only after certain things happen.
This might make strategizing too late, especially when we consider that it only takes 5 to 7 brand impressions before users recall your brand. If you don’t make an impact by then, your users may not recognize you at all.
Organic Link Building: Improve Your Game With AI, Big Data
What we want is to use proactive planning, where we use data we have to make well-informed strategies that take into account long-term performance. Using the right big data algorithms can help you create an organic link building strategy that adjusts and reacts to trends as they happen.
With the above in mind, here are some of the most efficient ways we can use big data algorithms to improve our organic link building strategies:
1. Analyze audiences and consumers more efficiently with targeting.
Thanks to existing data analysis tools, we can use big data algorithms to better understand consumer patterns and behaviors. This has actually become much easier to do thanks to web analytics tools for websites and built-in analysis tools for social media platforms. We can use advanced big data algorithms to take metrics from these tools to further tailor our organic link building approach to specific audiences. Here are some applications:
- Regression and classification trees can help analyze consumer trends by using an algorithm that “classifies” elements according to their progression in a series of “questions” that then categorizes these elements based on their answers. Some classification trees may be simpler than others, while others can be very complex and comprise many “trees” that interconnect to form a final categorization. When you have many elements to consider when choosing the right audience for your organic link building campaign, a classification tree can take into account all your variables to help give you a more informed analysis.
- Of all big data algorithms, classification trees may be the easiest to apply. That’s because these tend to be extremely straightforward, and allows us to identify action items based on simple interactions. Thanks to regression trees, we can easily modify our campaigns to tell us whether we need to keep something in the backlog, release content in certain platforms, or when we should use particular keywords and concepts in our pieces.
- Thanks to social platforms becoming more information-rich, we can utilize cookies and other metrics from our platforms to gather data to understand customers beyond general time-based, geographic, and demographic options. Thanks to information in customer satisfaction surveys, A/B tests, and other forms of data analysis methods, we can in turn use this information to provide more personalized content and drive more organic traffic to our websites.
2. Enhance your ability to plan and forecast for your strategies.
Marketers can use big data to plan for their organic link building strategies through data-empowered forecasts. This means using algorithms to check how strategies can affect returns and predict the potential outcomes of your campaigns. Big data can be a great asset to your forecasts as they can take into account various factors and how they interplay with other elements affecting your brand. Here are some applications:
- Linear regression algorithms can be used to give marketers a more general insight on how two variables can affect each other, making this a widely-used algorithm for big data analysis. In essence, this can help us understand how a dependent variable can be changed based on an independent variable. Once their relationship has been analyzed, we can predict how a dependent variable will react regardless of where we place an independent variable. This makes it useful for forecasting organic link building results based on independent variables such as time, demographics, and even web traffic.
- We can use linear regression algorithms to assess existing metrics we have and determine actionable insights. For instance, regression algorithms can be used to check how social shares, pageviews, and engagements by platforms impact the overall Domain Authority of your website. Identifying weaknesses and strengths in these values allow us to tweak our strategies much more efficiently.
- Forecasting in real-time allows us to understand how particular link building strategies can be affected by elements such as traffic, bounce, impressions, and engagements. Big data can overcome dynamic elements such as web traffic and ad spend by clustering similar data points and tailoring the metrics we see according to our needs.
3. Use multi-touch attribution to find your best sources of traffic, revenue.
We need to be able to identify sources of traffic and conversions to understand where we can best position our content and the rest of our offerings. Unfortunately, multiple elements can affect these metrics, which can make analyzing them with ordinary methods difficult for software. Big data makes this possible by using algorithms to check, assess, and evaluate different factors that interplay within the customer purchase funnel. Here are some applications to take note of:
- K-means clustering allows users to form different groups of related attributes which they can use to properly classify different instances. Aspects of your link building strategy that relies on data exploration can benefit from K-means clusters as you can organize variables into groups useful for your assessment. In turn, this algorithm can be used to predict things such as prospect value or even the value of a guest post based on different elements.
- You can use K-means clustering to check the value of different touchpoints from the perspective of your guest post and link building approach. You can use these algorithms to accurately determine the best (and worst) sources of your organic traffic, which can help you modify your campaigns to produce the best results. Touchpoints will become more useful as you can finally determine which ones work best for a particular piece of content, especially when content appeal is gauged through customer segmentation.
- Market complexity can make multi-touch difficult to process by regular metrics tools. Thanks to big data algorithms, multiple variables such as customer opinions and context, partners and stakeholders, and even business insights can help us understand which touchpoints (customer service, CTAs, etc.) directly affect web traffic and our organic link building campaigns.
4. Real time optimization for your campaigns.
Marketers need to understand data quickly in order to make informed decisions on their campaigns, especially when they need to make adjustments to their strategies. Unfortunately, we can’t always handle data with multiple sources and variables, and we’re bound to miss some points. Big data removes this risk thanks to algorithms that can help link building services optimize our campaigns in real-time and adjust to constantly-changing variables. Here are some applications:
- Logistic regression can provide immediate input on relationships of different variables, making this a great algorithm for real-time campaign optimization. This algorithm focuses on categorization, where variable assessment leads to “yes/no” responses based on different elements. In the case of organic link building, we can use logistic regression to analyze prospects, the potential of a piece to provide value to a niche or a keyword, or what we can do to improve certain elements of a campaign to suit our link building goals.
- For instance, you can use logistic regression to predict whether your existing content can be enough to increase customer retention. This is a great way of cross-checking whether your organic links actually produce results, or if certain aspects of your campaigns aren’t hitting their targets properly.
- Big data can add a lot of variables to conventional analysis tools, allowing us to gain more advanced metrics related to elements like responsive design, integrated experience, mobile, social media, and search optimization. Algorithms can help provide the much-needed boost needed for analytics tools to provide real-time analysis of these advanced metrics.
Big Data for Organic Link Building: Go Natural With the Right Data
With the above tips in mind, it’s important to remember that the right application of big data algorithms can give your brand an edge with your organic link building strategies. Always remember that understanding what your metrics mean and how you can tweak them to give you more insights can give your link building strategies more opportunities to reach a wider audience and even give you better traffic and backlinks.
