The way to advertise and manage your SEO is changing. The tools of the trade for marketers, product managers and SMBS are ever-evolving. This next wave of MarTech has been ramping up and might put some of us out of business.
We should keep an eye on the cutting-edge machine learning in marketing and SEO and neural network (AI) technologies being used to make our market assessments more accurate, campaigns more successful and our customers ultimately more satisfied.
However, don’t get too lost in how the algorithms work. Just remember their purpose:
“Is the end-user getting the result they want based on how they’ve communicated their search query?”
Understanding how machine learning algorithms work is critical to maximizing ROI. This is going to be a 2 part blog series. So here is Part-1 of the Top 9 Machine learning algorithms that work to influence keyword ranking, ad design, content construction and campaign direction:
Top 9 Machine learning algorithms
1. Support Vector Machines (SVM)
Classification is the process that facilitates segmentation. Simply put, SVMs are predictive algorithms used to classify customer data by feature, leading to segmentation. Features include anything from age and gender to purchase history and channels used.
SVM works by taking a set of features, plotting them in ‘n’ space, ‘n’ being the number of features, and trying to find a clear line of separation in the data. This creates classifications.
For example, Mailchimp is a popular customer relationship management (CMR) tool that uses its own proprietary algorithm to predict user behavior. This allows them to forecast which segments are likely to have high Customer Lifetime Values (LTV) and Costs Per Acquisition (CPA).
2. Information Retrieval
Keywords, keywords, keywords…Sometimes the simplest solutions are the most powerful ones. A lot of ML algorithms designed to assess the market can be difficult to comprehend.
Information Retrieval algorithms, like the one that powers Google’s “Relevance Score” metric, use keywords to determine the accuracy of user queries. These types of algorithms are elegant, powerful and to the point.
Which is part of the reason why SEO software such as SE Ranking uses a version of it called Elasticsearch to provide marketers with a list of keywords built using input from the user. The RL algorithm’s basic process follows a 4-step process:
- Get the user query
- Break up the keywords
- Pull a preliminary list of relevant documents
- Apply a Relevance Score and rank each document
In step 4, The Relevance Score algorithm takes the sum of specific criteria:
- Keyword Frequency (number of times the keyword appears in the document)
- Inverse Document Frequency (if the keyword appears too often, it actually demotes the ranking)
- Coordination (how many keywords from the original query appear in the document)
The algorithm then attaches a score that gets used to rank all of the documents retrieved in the preliminary pull.
3. K-Nearest Neighbors Algorithm
The K-Nearest Neighbors (K-NN) algorithm is one of the most basic of its kind. Also known as a “lazy learner algorithm,” K-NN classifies new data based on how similar it is to existing data points. Here’s how it works:
Say you have an image of some kind of fruit that resembles either a pear or an apple, and you want to know which of the two categories it belongs to.
A KNN model will compare the features of the new fruit image to the datasets for pear images and the datasets of apple images, and based on which category the new fruit’s features are most similar to, the model will sort the image into the respective category.
In a nutshell, that’s how the KNN algorithm works. It’s best used in instances where data need to be classified based on preset categories and defining characteristics.
For example, KNN algorithms come in handy for recommendation systems such as the one you might find on an online video streaming platform, where suggestions are made based on what similar users are watching.
If you want to learn further how to implement a K-NN algorithm in Python, sign up for a training program to get you started with Python.
4. Learning to Rank (LTR)
The Learning to Rank class of algorithms is used to solve keyword search relevancy problems. Users expect their search results to populate a page and be ranked in order of relevancy. Companies like Wayfair and Slack use LTRs as part of their search query solutions.
The LTR can be separated into three methods: Pointwise, Pairwise and Listwise.
Pointwise assesses the relevance score of one document against the keywords. Pairwise compares each document against the keywords and includes another document into the calculation for a more accurate score.
It’s like getting an ‘A’ on a test, but then you notice that the kid sitting next to you got one more correct question than you, and suddenly your ‘A’ isn’t so impressive. Listwise uses a more complicated algorithm based on probabilities to rank based on search result relevance.
Knowing how machine learning algorithms work and learning practical skills via our PGP in Data Science program will provide you with marketing insights and make you better at communicating ad, content, and campaign strategies to your staff, clients, and customers. This will ultimately lead you to better ROI.
So this was Part-1 for you. Please stay tuned for the next part which will talk about the rest of the top 9 ML algorithms that you need to check out. If you want to read more such blogs, visit us at www.blog.insaid.co