It is changing how you advertise and manage SEO. Marketers, product managers, and SMBS all have new tools. The next wave in MarTech is on the rise and could put some of us out of business.
It is important to keep an eye on cutting-edge machine learning for SEO technologies and marketing and AI. These technologies can help make market assessments more accurate, campaigns more effective, and customers more satisfied. But don’t get too involved in the algorithm’s workings. Keep in mind their purpose.
“Is the user getting the result they desire based on the way they communicated their search query?”
Maximizing ROI is possible only if you understand how machine learning algorithms work. These are the top nine machine learning algorithms that influence keyword ranking, ad design, and content construction as well as campaign direction.
9 Ways to Use Machine Learning Algorithms for SEO and Marketing
1. Support Vector Machines (SVM)
Segmentation is made easier by classification. SVMs are predictive algorithms that classify customer data according to features. This allows for segmentation. You can choose from age, gender, purchase history, and channel used.
SVM is based on taking a set of features, plotting them in “n” space (‘n’ being their number), and trying to find a clear separation in the data. This allows for classifications.
Mailchimp, for example, is a popular CMR tool that predicts user behavior using its proprietary algorithm. This allows them to forecast which segments will have high Customer Lifetime Values and Costs per Acquisition (CPA)
2. Information Retrieval
Keywords, keywords, and keywords… Sometimes the simplest solutions can be the most effective. It can be hard to understand the many ML algorithms used to evaluate the market.
Information Retrieval algorithms, such as the one used by Google’s “Relevance score” metric, use keywords to determine user queries’ accuracy. These algorithms are simple, effective, and straight to the point. This is why SEO software like SE Ranking uses Elasticsearch in order to provide marketers with a list of keywords created using input from users. The RL algorithm is based on a four-step process.
Find the answer to your user question
- Separate the keywords
- Make a list of all relevant documents.
- Use a Relevance Score to rank each document
- Step 4: The Relevance Score algorithm adds the sum of certain criteria.
- Keyword Frequency (numbers of times a keyword appears in a document).
- Inverse Document Frequency (if a keyword appears too frequently, it actually degrades the ranking).
- Coordination refers to how many keywords from the original query appear within the document.
- The algorithm attaches a score to each document that was retrieved during the preliminary pull.
3. K-Nearest Neighbors Algorithm
K-Nearest Neighbors algorithm (K-NN), is the most basic. K-NN, also known as the “lazy learner algorithm”, classifies new data according to how similar it is to existing data points. Here’s how it works.
Imagine that you have an image of a fruit that looks like a pear or an Apple. You want to find out which category it belongs to. KNN models will compare the features of your new fruit image with the datasets of pear images and datasets of apple images. Based on the similarities, the model will then sort the image into the appropriate category.
This is how the KNN algorithm works in a nutshell. This algorithm is best used when data must be classified according to predefined categories and defining characteristics.
KNN algorithms are useful for recommendation systems, such as the one found on online video streaming platforms. These recommendations are based on similar users’ viewing habits.
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4. Learning to Rank
The Learning to the Rank algorithm is used to solve keyword relevancy problems. Users expect search results from their searches to appear on a page and then be ranked according to relevancy. LTRs are used by companies like Wayfair, Slack, and others in their search queries.
There are three ways to separate the LTR: Pointwise, Pairwise, and Listwise.
Pointwise compares the relevancy score of one document to the keywords. Pairwise evaluates each document against the keywords and adds another document to the calculation to get a better score.
It’s like getting an A on a test but then realizing that your score isn’t as impressive because the kid next to you answered one more question correctly than you. Listwise employs a more complex algorithm that is based on probabilities of ranking based upon search results relevance.
5. Decision Trees
Decision trees are used for predictive modeling A marketing analogy is that as users move through a sales funnel they are likely to use a few criteria.
- Behavior-based triggers — The user clicked on or opened a field or link;
- Trait-based Values — Demographics, location, affiliation, and other information about the user.
- Numerical Thresholds – If a user has already spent X dollars, they are more likely to spend X+ in the future.
- Decision trees are simple and easy to use, making them very useful for:
- Regressions and classifications — plotting binary values and floating values within the same model (ex. Gender vs. annual income);
- Multiple parameters can be handled simultaneously — each node in a tree may represent one parameter, without overloading the model;
- Visual and interpretive diagnostics — It’s easy for you to see patterns and relationships among values.
- A word of caution — The more decision trees you create, the less interpretive they become. Soon you lose sight of the forest.
6. K-means Clustering Algorithms
K-means clustering algorithms form part of unsupervised learn partitioning methods. This is Layman’s definition. It’s a method of machine learning that can be used for breaking down unlabelled data into meaningful groups.
For example, suppose you owned a supermarket and wanted to break down your customers into smaller segments. You could also use K-means Clustering to identify customer groups. This will allow you to tailor your marketing campaigns and promotions to each customer segment.
This would allow you to make more of your marketing budget. K-means Clustering is unique because it allows you to predefine the number of categories or “clusters”, which you would like the algorithm to produce from your data.
7. Convolutional Neural Networks
Convolutional Neural Networks (or CNN) are used to assist computers in seeing images as humans see them.
A human can identify an apple if it is shown to them, but not a machine. Computers can only see another set of numbers and then identify the object based on the pattern of numbers.
CNN works by teaching a computer how to recognize the patterns in objects by giving it millions of images. Each new image improves the computer’s ability to spot an object.
It’s easy for anyone to pull out their smartphone and snap a photo wherever they are. This makes it easy to see how powerful CNN can make any application that requires you to pick out objects from images. Companies like Google use CNN to recognize facial expressions,
You can match a face with a name by looking at the distinctive features of each face in an illustration. CNN is also being tested in document and handwriting analyses. CNN can quickly scan and compare writings with big data results.
8. Naive Bayes
The Naive Bayes algorithm (NB) is based on Bayes’ famous theorem, which determines the probability that two outcomes will occur — the probability A given B. This algorithm is so “Naive” because it assumes that predictor variables are independent.
This can be used by marketers to determine the likelihood of a lead magnet, campaign, or advertisement being a success. If you have the right features such as height, age, and purchase history or big data about your customer base, this can be retooled.
Great Learning offers a great introduction to math if you are interested in getting into it. The NB algorithm answers two questions.
- “Is this person the right type to do X?”
- “Is this the content that will achieve X outcome?”
NB excels in handling large quantities of text-based behavioral data such as customer chatter online.
An NB algorithm feeds customer dialogue to predict product and service reviews and measure social media & influencer market sentiment for trend predictions.
9. Principal Component Analysis
Segmentation can be achieved through classification. Principal Component Analysis can be used to determine strong or weak relationships between two components. It involves plotting them on graphs and looking for a trend line.
What happens when the target market has more than 30 features? This is where machine learning and PCA combine to make multivariate data analysis possible.
Instead of two groups that are correlated, you begin to see clusters that relate to each other. The distance between clusters indicates strong or weak relationships.
Marketers understand that the component axes of a product are not single features, but are determined by the PCA algorithm.
All this leads to the answer to the question: These features can be used to improve segmentation targeting by identifying which are highly correlated.
Marketers, agencies, and SMBs will continue to ask for better tools that can assess consumer sentiments and behavior.
Machine learning and neural network tools will continue to analyze consumer markets and provide new insights. These insights will be used by marketers, agencies, as well as SMBs, to seek out better tools for assessing consumer sentiment and behavior.
This feedback loop is essential if you want to succeed in the future, especially given the rise of online shopping activity that is influenced by geopolitical factors.