Effective Keyword Research Techniques to Improve Keyword Quality

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.

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, and keeping your bids within your budget.

In this article, we will discuss a powerful technique called “Topic Modeling.” 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.

Introduction to Topic Modeling

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 “clusters” (similar terms).

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.

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.

For the atopic classification approach, you first manually tag a subset of your text data to teach the program the kind of associations you’re making. This allows it to make smarter predictions and identify patterns faster.

Example

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.

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.

But in this blog, we will discuss the topic modeling.

 

How Does Topic Modeling Work?

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.

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.

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.

Remember that this approach can save time but won’t always be as accurate as its counterpart: topic classification. Let’s take a look at the key differences between the two.

Topic Modeling vs. Topic Classification

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.

In topic modeling, you will receive back clusters of documents or words that the program has identified as having a connection.

Topic classification, on the other hand, can go further by providing you the data in neatly packaged categories like Price, Color, etc.

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.

Topic Modeling Methods

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.

Let’s take a look at your options.

Latent Dirichlet Allocation (LDA)

LDA is a form of unsupervised modeling. It views documents as a “bag of words.” 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.

To achieve this association, LDA does the following:

• Assumes there are selected K number of topics in the documents
• Distributes these topics (K) across the documents (M) in question.
• Makes inferences based on similar words (W) in the documents and their associated topics.
• Repeats this process using previously inferred topics and words until all words have been processed.

Above is what is known as a plate diagram of an LDA model where:

• α is the per-document topic distributions
• β is the per-topic word distribution
• θ is the topic distribution for document M
• φ is the word distribution for topic K
• Z is the topic for the N-th word in document M, and
• W is the specific word

In the plate model diagram above, you can see that W is grayed out. This is because it is the only observable variable in the system, while the others are latent (i.e. hidden).

Latent Semantic Analysis (LSA)

LSA is another very powerful topic modeling method. This method looks at words within their context. It then compares the context of different words to each other to determine if they are semantically related.

LSA makes a computation of the frequency of words as they occur in the documents selected. It also considers how often words occur, with other words, to make assumptions about their relative connection.

Let’s have a basic example to understand:

Document1 = “the water in this bottle is low”

Document2 = “the container is short on liquid”

Now above both strings of text are equal.  You will see how much they are similar to vector representation.

The size of the document-term matrix is:

Number of documents * vocabulary size

Vocabulary size is the number of unique words present in all the documents altogether. Here the vocabulary size is 11 and the number of documents is 2.

Co-occurrence matrix

Now comes the idea of the co-occurrence matrix.  This matrix will have rows and columns on each word which will represent how each word appears in the same context.

From the above matrix, you can check “the” and “is” most common but are not much useful in the meaning of the context.

Moreover, LSA brings out the concept on the surface instead of the topic.

Concepts are the list of words that are in the document in the best possible way. Further single value decomposition (SVD) approach is used to reduce the computation complexity to get fruitful results.

From here, you can assign different topics or document qualities through a range of algebra formulas.

Knowing the exact math and programming protocols is understandably out of reach for the typical marketer. But now that you see how powerful this model can be, consider hiring a data specialist to create a model for your specific market.

Contextual Topic Modeling

Using LDA is useful for finding topics by frequency. However, sometimes, your text is incoherent or jumbled to such an extent that you need another approach to glean the context.

Why not combine LDA with effective keyword research techniques that provide this contextual data under the umbrella of BERT (Bidirectional Encoder Representations from Transformers)? It allows you to maintain the semantic information and create Contextual Topic Identification.

How the chart above was created?

First, LDA assigned a probabilistic topic vector.  Secondly, BERT provided embedded sentence vectors that add another layer of contextualization. These two types of vectors are concatenated using a weight hyperparameter to balance the importance of information from each method.

Since the vectors were joined across various spatial dimensions, an encoder was used to condense the graphic information. By applying clustering on these newly formed vectors, it becomes easier to discern data contextually.

The important thing to understand is that the data above has shape, form, and clarity. Without the right modeling, it would merely be a jumble of random points that wouldn’t provide you with any hints on how to categorize it. By condensing the data, you can make faster analyses about keywords.

Content Marketing Through Topic Modeling

Using effective keyword research techniques can help you not only with PPC campaigns but with your organic traffic from content marketing as well. Content marketing is an essential component of a well-rounded online presence. You can not only generate traffic but build authority and value within your niche.

Topic modeling helps with your content marketing by making your content more focused. When you create more in-depth, specific content, it becomes more attractive and shareable.

LDA helps you by narrowing down keywords and establishing parameters when writing. This gives your content improved chances of ranking for the search queries you choose, even if you don’t use them in the content body. It also gives your content direction and enables you to write with intent, whether to inform, persuade, or make a sale.

This is an important concept because the modern marketing funnel needs to contain multiple steps. You have your prospects at the top of the funnel, who may only be looking for initial information about a product or service. Then, on the other end, you have people who are ready to buy right now.

When you and your team have put together enough data from your market, keywords, and searches, you can begin to construct hyper-specific content for each stage of the funnel. Therefore all the hard work in implementing these effective keyword research techniques will pay off in a big way.

How to Apply Topic Modeling to Content Development

Google already uses LDA to determine which words are likely to appear in the same document as a user’s search query. And emulating Google has led to the discovery of many effective keyword research techniques. So begin by narrowing down your selection of keywords.

First and foremost, you must determine which keywords are most relevant to your industry.

Creating a list of terms that should never be associated with your brand is a good start. These could be terms that are viewed as unethical in your industry, or phrases that might confuse users into thinking you offer a different product or service.

This will leave you with a selection of keywords that represent your brand accurately. It is easier to establish how you don’t want to be represented first so that you’re left with an image you only need to refine.

Next, work on the content itself. Keep your discussion within the parameters of the topic your chosen keyword belongs to. Mentioning the keyword is still necessary for an exact match search, but you don’t have to stuff your entire article with the keyword. Choose ideas that highlight your chosen topic to increase your relevancy.

Don’t worry about how that keyword will appear – write as naturally as possible and stay on topic. Google’s algorithms today are much smarter, and they will detect the intent of your content using the context clues you provide.

It is important to note that you should not rely on LDA alone. Topic modeling is beneficial when selecting keywords and plotting content outline, but at the end of the day, well-written, well-researched content placed in the right context is still what counts.

How Topic Modeling Improves Keyword Quality and Relevancy

Through topic modeling, you’ll learn a set of methodologies to help you acquire, refine, and organize your PPC keywords, all in the service of better targeted and more effective pay-per-click campaigns.

At the end of this, you can have a pretty impressive keyword list. This algorithm groups the targeted keywords that are closely related to each other.

Topic modeling can help refine your keywords in the following categories:

• Brand Terms – People who are familiar with your brand already are the easiest of all to convert. In terms of volume, this category of keywords might not produce the most impressions or page views, but they usually will have the best conversion rate. You should have an idea either you have to bid on branded keyword or not.
• Product Terms – This is what your product is or does and what problems it solves. Depending on how diverse your array of products or services is, this list could be expansive. People searching for these terms are a little further back in the decision cycle, and your cost on these terms will usually be higher than on brand terms. But you can get only so much volume from brand terms, so eventually you’ll want to start winning with product terms, too.
• Competitor Terms – There was a time earlier in the life of paid search marketing that competitor terms were second only to brand terms for conversion rate and cost-efficiency. However, in recent years, Google has tightened up their quality score requirements; and now it is nearly impossible to find a reasonable cost per click (CPC) on competitor terms. If you have money to spend and are trying to make inroads against a strong competitor, these could be good terms for you.
• Audience terms – This category covers all kinds of other terms that people in your target audience might be searching for. Usually, the impression volume on these words is vast, so that it can be a tempting category. Furthermore, since this category is more aligned with traditional display targeting, which is based on the interests and pastimes of the audience.

 

 

Shifting from Keyword Targeting to Topic Model Targeting

Numerous effective keyword research techniques will make your marketing more robust. LDA, topic classification, and other machine learning methods can incorporate much-needed science and automation into your keyword strategy.

To get the most out of topic modeling, apply it to a specific niche at first. Allow the method to build semantic associations between keywords and content continually. This helps ensure that you aren’t just relying on keyword density, but also relevancy for your SEO needs. With greater semantic knowledge, you can better grasp your searchers’ intent and create content accordingly.

While there aren’t many mainstream tools that help SEO marketers achieve this just yet, Google provides some help. Using Google’s related searches and suggestions, you can piggyback off the modeling they used to determine that certain content is relevant to a search. Even if you initially scrape this data from Google, it will give you a starting point upon which you can build your campaigns.

 

A Word on Semantic Digital Advertising

In brief, semantic digital advertising is an upgraded approach to online advertising. It is an approach that looks at specific user intent and considers Google’s new algorithms to deliver relevant content and drive traffic that converts.

Topic modeling enhances the user experience when applied correctly to digital advertising. To help visualize the difference, consider the table of effective keyword research techniques applied to digital advertising below:

Wrap Up

Every great PPC campaign starts with defining the best keywords to target. However, this is easier said than done. Today, you are competing with potentially hundreds of brands or more in the same market.

If your strategy is simply to bid on the highest volume keywords, you will face two primary challenges. First, your ad budget will run dry much faster, leaving you unable to drive traffic. Secondly, you might not see ads that are targeted enough to connect with your audiences’ specific needs.

The art and science of effective keyword research techniques always pay off in the long run. If you use topic modeling and the associated strategies above, you can save time while discovering relevant keywords for your niche. In doing so, you give your brand a better chance for more online growth and higher profitability.