{"id":10267,"date":"2023-12-08T19:35:36","date_gmt":"2023-12-08T14:35:36","guid":{"rendered":"https:\/\/ppcexpo.com\/blog\/?p=10267"},"modified":"2022-02-24T11:06:04","modified_gmt":"2022-02-24T11:06:04","slug":"effective-keyword-research-techniques","status":"publish","type":"post","link":"https:\/\/ppcexpo.com\/blog\/effective-keyword-research-techniques","title":{"rendered":"Effective Keyword Research Techniques to Improve Keyword Quality"},"content":{"rendered":"

Keyword research is arguably the most critical part of the equation if you want to run a successful PPC campaign. You can have a beautifully designed landing page and hire the most persuasive marketers in the world, but without the right keywords, your audience won’t find you. Therefore, you need quality keywords that are most likely to deliver clicks and conversions to your brand.<\/p>\n

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But there is more to it than only looking at the potential upside: keywords with higher competition also bring higher costs. There is an art to selecting relevant keywords, improving your Quality Score<\/a>, and keeping your bids within your budget.<\/p>\n

In this article, we will discuss a powerful technique called \u201cTopic Modeling.\u201d It will help you identify the best keywords for your market while meeting your conversion and traffic needs. Before we jump in if you are looking for a keyword research tool, PPCexpo Keyword Planner comes with astonishing results which really reduce your effort in keyword finding for your PPC or SEO content.<\/p>\n

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Introduction to Topic Modeling<\/h2>\n

Topic modeling is one of the most effective keyword research techniques. It is a type of machine learning technique that automates the analysis of text data and determines patterns in the text, such as \u201cclusters\u201d (similar terms).<\/p>\n

One significant advantage of topic modeling is that it uses unsupervised machine learning, meaning that you don’t need to prompt the process with training or predefined tags. This makes it quick and easy to implement.<\/p>\n

However, what you gain in automation, you may lose in accuracy. That is where topic classification comes in. You can provide training data so that your analysis is “supervised”, in other words, it has some human input to start with.<\/p>\n

For the atopic classification approach, you first manually tag a subset of your text data to teach the program the kind of associations you\u2019re making. This allows it to make smarter predictions and identify patterns faster.<\/p>\n

Example<\/h3>\n

Imagine a software company that wants to evaluate feedback from users. First, you create a list of tags (topics) that are relevant to your goals. In this example, it could be User Experience, Features, Data Analysis, etc. You use preexisting data samples and manually apply the tags to teach the program what you’re looking for.<\/p>\n

As you can see, this approach is more hands-on than the more vanilla topic modeling approach. However, the extra legwork can deliver more accurate results that lead to discovering keywords backed with more data instead of hunches. That’s what makes it one of the most effective keyword research techniques.<\/p>\n

But in this blog, we will discuss the topic modeling.<\/p>\n

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How Does Topic Modeling Work?<\/h2>\n

The technology behind topic modeling is surprisingly simple yet powerful. It counts words in the text data you provide. Then, it groups similar words and patterns to infer relevant topics.<\/p>\n

Instead of spending hours of your time or assigning this task to your team, you can let the software do it. This means you can analyze your business content, product content, and do the same across the competition to identify topics of interest.<\/p>\n

Again, with topic modeling, you don’t need to train it with any data. It will simply look at the text and make distinctions automatically. You can deduce what a large amount of text is about from a set of clusters instead of reading the whole thing.<\/p>\n

Remember that this approach can save time but won\u2019t always be as accurate as its counterpart: topic classification. Let\u2019s take a look at the key differences between the two.<\/p>\n

Topic Modeling vs. Topic Classification<\/h2>\n

Software only does what you tell it to do. Even if there is an unfathomable array of possible actions and calculations, they all stem from the initial information and commands that humans provided.<\/p>\n

In topic modeling, you will receive back clusters of documents or words that the program has identified as having a connection.<\/p>\n

Topic classification, on the other hand, can go further by providing you the data in neatly packaged categories like Price, Color, etc.<\/p>\n

Whether or not you decide to use topic modeling or topic classification depends on your priorities. If you want to save time up front, then topic modeling is the way to go. If you would rather invest more time and resources to train your data, then topic classification lends itself to greater organization and clarity in the long run.<\/p>\n

Topic Modeling Methods<\/h2>\n

The type of topic modeling you use depends on your marketing goals. But these various effective keyword research techniques can help you with customer service, keywords, and more.<\/p>\n

Let\u2019s take a look at your options.<\/p>\n

Latent Dirichlet Allocation (LDA)<\/h3>\n

LDA is a form of unsupervised modeling. It views documents as a \u201cbag of words.\u201d In other words, the order of the text does not matter. It works by making assumptions about the topics in a document and their associated words.<\/p>\n

To achieve this association, LDA does the following:<\/p>\n