Creating a Quantitative Approach for Marketing Agencies: Bridging Finance and Digital Keyword Optimization
In the world of finance and economics, my educational journey began with a double major BBA in Economics & Finance. This foundation was further solidified with an M.Sc in Quantitative Financial Risk. Throughout my academic endeavors, I was consistently introduced to the intricate dance of thresholds and ranking methodologies. These concepts are pivotal in optimizing various financial and risk models. For instance, when constructing a mean-variance portfolio, determining the right cut-off points is crucial, as discussed by Markowitz (1952). Similarly, the importance of developing accurate thresholds in a peak over thresholds model is emphasized by Coles et al. (2001). But how does this knowledge transition into the realm of a marketing agency, especially in the context of digital marketing companies in Dubai?
Digital Marketing & Its Importance
Digital marketing, at its core, is about visibility and reach. One of the most challenging tasks a marketing agency faces is sifting through a plethora of keywords to determine which ones deserve their focus. The ultimate goal? Achieving higher visibility on a Search Engine Results Page (SERP). The primary dilemma here is striking a balance between keyword volume and keyword difficulty, a challenge many advertising agencies in Dubai grapple with.
The Challenge in Digital Marketing
Before delving into the solution, it's essential to understand the problem. In digital marketing, keyword volume represents the number of searches a particular keyword receives. In contrast, keyword difficulty indicates how challenging it would be to rank for that keyword, considering the competition. The higher the volume, the more potential traffic; the higher the difficulty, the harder it is to rank on the first page of search results.This is a common challenge faced by digital marketing companies in Dubai.
Bridging Financial Knowledge with Digital Marketing
Drawing parallels from financial models, just as we determine cut-off points in portfolio construction, we can establish thresholds in keyword optimization. The idea is to create a quantitative model that helps digital marketing companies in Dubai prioritize keywords based on a balance between volume and difficulty.
The Proposed Method: 1. Define Your Objectives:
In order to obtain the most accurate and thorough response, it is crucial to first establish your goals and priorities. Are you seeking immediate successes or are you prepared to dedicate time and resources towards ranking for highly competitive keywords that may generate greater long-term traffic? Once these objectives are clarified, we will delve into the methodology by examining a dataset pertaining to the source keyword "digital marketing" in the UAE. This dataset comprises of 216 keywords downloaded from Semrush, each accompanied by its respective Search Volume and Difficulty ranking.
2. Normalize the Data:
To compare and set thresholds, you need to normalize both the volume and keyword difficulty on a scale of 0 to 1. This will allow you to weigh them equally. Normalizing data is a fundamental step in data preprocessing, ensuring that different metrics can be compared on a common scale (Han, Pei, & Kamber, 2011).
2.1. Min-Max Normalization:
This is the most common method for normalization. The formula is:
Normalized Value=[Value−Min Value/Max Value−Min Value]
Where:
Value is the current value you want to normalize.
Min Value is the smallest value in the dataset.
Max Value is the largest value in the dataset.
2.2. Review and Adjust:
After normalization, review the values. If any keyword has a very low normalized volume or a very high normalized difficulty that seems out of place, you might want to revisit the data or consider other normalization techniques.
2.3. Use Normalized Data for Analysis:
Now that you have normalized values for volume and difficulty, you can use them for further analysis, like the weighted scoring method which will be explored in the next section.
Remember, normalization doesn't change the inherent distribution of your data; it just scales it to make it more comparable. Always keep the original data handy in case you need to refer back to it or use it for other analyses.
3. Weighted Scoring:
Assign weights to both volume and difficulty based on their importance to your objectives. For instance, if volume is more crucial for you, you might give it a weight of 0.6 and difficulty a weight of 0.4.
3.1. Determine Criteria:
For keyword selection, the two main criteria are:
Volume: Represents the number of searches for a keyword.
Keyword Difficulty: Represents how challenging it will be to rank for a keyword.
3.2. Assign Weights:
Decide the importance of each criterion. The sum of the weights should be equal to 1 (or 100% if you're thinking in terms of percentages).
For example:
Volume Weight: 0.6 (or 60%)
Keyword Difficulty Weight: 0.4 (or 40%)
The weights can be adjusted based on your specific objectives. If you prioritize volume over difficulty, you might assign a higher weight to volume and vice versa.
3.3. Calculate Weighted Score for Each Keyword:
Using the normalized values for volume and difficulty, calculate a weighted score for each keyword:
Score=(Normalized Volume×Volume Weight)−(Normalized Difficulty×Difficulty Weight)
The subtraction ensures that a higher difficulty (which is undesirable) reduces the overall score.
3.4. Rank Keywords:
Once you've calculated the weighted scores for all keywords, rank them in descending order. Keywords with higher scores are more valuable to target based on the criteria and weights you've set.
3.5. Set a Threshold:
Decide on a minimum score that a keyword must achieve to be considered for targeting. This will help you filter out less valuable keywords. For instance, if you're looking for a balance between volume and difficulty, you might set a threshold score of 0.5.
3.6. Review and Adjust:
As with any method, it's essential to periodically review your weights and scores. As your SEO strategy evolves, you might find that certain criteria become more or less important, requiring adjustments to the weights.
4. Develop Priority & Quartiles
To create a priority range for your weighted scores, we'll categorize them into four priority levels based on their values. Given that higher scores are better, we'll use the following approach:
1st Priority: Highest positive scores.
2nd Priority: Lower positive scores and scores close to zero.
3rd Priority: Negative scores that are closer to zero.
4th Priority: Lowest negative scores.
Let's break down the scores:
1. Sort the Scores: From highest to lowest.
2. Determine Quartiles: Divide the sorted list into four equal parts to determine the quartiles.
3. Assign Priority Levels: The top quartile gets the 1st priority. The second quartile gets the 2nd priority. The third quartile gets the 3rd priority. The bottom quartile gets the 4th priority.
This categorization provides a systematic way to prioritize keywords based on their weighted scores. Keywords in the 1st priority are the most valuable to target, while those in the 4th priority are the least valuable. However, always consider other factors like relevance and user intent when finalizing your keyword targeting strategy.
5. Regularly Review and Adjust:
SEO is dynamic. Regularly review your keywords, their performance, and adjust your threshold and weights as needed.
To provide a clearer and more concise overview of the methodology we've discussed, the following flowchart encapsulates the entire process step-by-step. This visual representation will aid in understanding the sequence and interrelation of each phase, ensuring a more holistic grasp of the approach.
Conclusion:
Utilizing this comprehensive methodology allows digital marketing companies in Dubai and advertising agencies in Dubai to integrate quantitative analysis techniques, inspired by financial practices, to refine their keyword strategies. This data-centric approach not only enhances SEO results but also paves the way for informed decision-making. To delve deeper into such transformative strategies and gain further insights, I invite you to explore our marketing agency's website www.trustlinedigital.com and contact us today to help your business reach new heights.
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References:
1. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
2. Coles, S., Bawa, J., Trenner, L., & Dorazio, P. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics.
3. Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
