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It’s no secret: SEO is not a flavor of the month. Some people, however, still doubt its effectiveness.

The cost of SEO has grown exponentially. The cost of content production along with increasing expectations from the audience and fierce competition require larger budgets each year.

Then there’s the ROI of SEO, which can be hard to establish. That’s especially true today, when organic traffic has merged with content marketing and many other disciplines, making it much harder to distinguish its impact on the organization.

And of course, if done incorrectly, SEO can also cause some serious damage to a business or even destroy it completely.

The result of such perception is a slow shift towards PPC (even also among many SEOs). Its ROI is (supposedly) easier to establish and the results are instant. Not to mention that the risk to business is none while job prospects keep growing.

But of course as SEOs, you and I know better. We know how important our role is in the company’s marketing chúng tôi challenge lies in convincing the non-believers to see it that way.

Here are some ideas how you can prove the value SEO brings to your organisation.

Set the Context

First of all, it’s important for companies to understand what’s already happening with SEO across all industries. Data on the effectiveness of organic search performance across industries can help you explain what’s involved and show how SEO can help achieving overall company objectives. Companies like Gartner, eMarketer, and others can provide you with data and statistics you need for this.

Define Point of Reference

The second step is to illustrate where your company is today and where it was (if possible) before engaging with an SEO, regardless of whether it was you or your predecessor. Setting up those benchmarks will help you to illustrate the impact SEO has had on the company and make the case for why it’s an important part of the marketing mix.

Show Opportunities

Lastly, you need to show what else your company can achieve with SEO. This will help you present the road ahead but also set goals to report on. Unlike a common perception, SEO plays a number of roles in the company, including:

Generates new business opportunities

Wins attention of the target audience in different stages of the buying cycle

Raises awareness of your brand

Helps to build relationships with prospects and customers

Expands on your reach

Builds up your brand and authority

5 Metrics that Prove Your SEO is Working

The above points are just the groundwork you need to set before you can start to regularly present the value you add to the company. Below is a list of elements you should include in your overall reporting practice.

1. Define Goals

You probably know exactly what you are trying to achieve. Others, however, might be oblivious or have completely different expectations. First and foremost then you should define the goals you are working towards. After all, there is so much you can achieve with SEO, so you need to pin down exactly which elements you want to be responsible for.

2. Track CPL (Cost per Lead)

3. Add & Measure Monetary Value On Every Customer Touch Point

By its nature, SEO affects many channels. Customers find your site and call in to your office or grab the phone and dial your number. They send emails or inquire through other channels — and that doesn’t even include work of mouth references. Place a value on each of these touch points. Naturally, such research can never be 100% accurate. But even approximate values can highlight the benefit of your channel to the business.

4. Measure Assisted Conversions

Even with online conversions, what ended up as a paid channel sale might have started through an organic listing. Therefore, you need to measure the impact of SEO on assisted conversions. Luckily that’s easy to do in Analytics.

5. Run Correlation Tests

There are many theories you test in your work, from the effect of a simple meta data change may have on page rankings to more complex ones. But proving that all this work is making a difference can be more problematic. Simple correlation tests can help you visualize and show beyond a doubt that your theories work.

Conclusion

Organizations become skeptical about SEO. To some it seems to offer no ROI and tangible results. Some start to consider PPC as a much viable option and thus diminish the influence SEO have on the organization. To change that you need to prove the value your work brings to the organization through proper reporting and data.

Have you had success proving the value of SEO to your clients? What have you discovered works well?

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Anne / Creative Commons License

Ken Teegardin / Creative Commons License

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Unlock The Value Of Google Trends Data With Web Scraping

Google Trends is an excellent data source for e-commerce businesses and website owners. It enables businesses to

Identify trends in their industry.

Monitor their competitors.

Optimize SEO and content marketing strategy.

Identify their niche market.

Understand customers’ behaviors and expectations.

Aside from the benefits of Google Trends data, extracting data manually is time-consuming. Web scraping enables e-commerce businesses to automatically collect Google Trends data in a structured format.

In this guide, we will explain how web scraping bots extract data from Google Trends step by step. In addition, we will provide you with the top 5 web scraping use cases in Google Trends, along with tips and business outcomes.

What is Google Trends data?

Google Trends is a free analytics tool displaying trending searches and keyword popularity. It provides anonymized and categorized datasets to make data easier to understand and use. It allows users to access real-time (data from the last 7 days) and non-real time (data from 2004 to the last 72 hours).

Is it legal to scrape Google Trends?

It is legal to scrape publicly available web data. However, data protection regulations such as GDPR and CCPA make it illegal to scrape personally identifiable information (PII). Scraping publicly available Google data is not illegal unless your scraping activities do not harm the website or you use scraped data for any harmful purpose.

How to scrape Google Trends data?

Identify your search term or topic.

Select your geographical location (see Figure 1).

Figure 1: Country-Region dropdown menu in Google Trends

Search for the term or topic you identified (see Figure 2).

Figure 2: Two search functions of Google Trends for the same search term

Google Trends will provide a chart displaying interest in your specific term over the past 12 months. The highest point on the chart represents the peak popularity for the web scraping search (see Figure 3).

Figure 3: Search interest for web scraping over the given time.

Enter the exact search term into your Google Trends scraper.

Select the time range for the keyword (i.e., past 12 months).

Choose your geographical location.

Run the scraper to get the dataset from Google Trends

Download the scraped data in the format of your choice.

How To Use Chatgpt For Data Analysis

Using Z-score method

User prompt: Hey, can you help me find the outliers in this dataset using the Z-score method? Here’s the data: [15, 25, 32, 50, 75, 100, 120, 130]

AI response: Based on the Z-score method, the outliers in your dataset are: [120, 130]

Using the IQR method

User prompt: Can you help me identify the outliers in this data using the IQR method? The data is: [10, 12, 15, 20, 25, 30, 50, 75]

AI response: Using the IQR method, the outliers in your data are: [50, 75]

The interquartile range (IQR) method is another way to find outliers in your data. You can ask the AI to calculate the IQR (Q3 – Q1) and identify data points outside the range [Q1 – 1.5 * IQR, Q3 + 1.5 * IQR] as outliers.

Detecting anomalies with moving averages

User prompt: I have a time series dataset with daily temperatures: [65, 67, 68, 64, 40, 66, 70]. Can you find any anomalies using a simple moving average with a window of 3 days?

AI response: The simple moving average for your data is [66.67, 66.33, 57.33, 56.67, 58.67]. The anomaly in your dataset is the temperature of 40.

When working with time series data, you can use moving averages to detect anomalies. You can ask the AI to calculate the moving average with a specified window size and identify data points significantly deviating from the average as potential anomalies.

Using clustering algorithms for anomaly detection

User prompt: I have 2D data points: [(2, 3), (3, 4), (5, 6), (8, 9), (12, 13), (15, 16)]. Can you help me find anomalies using the DBSCAN clustering algorithm with a maximum distance of 2 and a minimum of 2 points to form a cluster?

AI response: Based on DBSCAN with the given parameters, the anomalies in your data are: [(12, 13), (15, 16)]

You can use clustering algorithms like DBSCAN or K-means for anomaly detection. You can ask the AI to apply the chosen clustering algorithm with specific parameters and identify data points that do not belong to any cluster or have a low probability of belonging to a cluster as anomalies.

Detecting seasonality in time series data

User prompt: I have a time series dataset with monthly sales: [100, 120, 150, 200, 190, 180, 150, 130, 110, 100, 95, 90]. Can you help me detect seasonality in this data?

AI response: Based on your data, it appears there is a yearly seasonality, with sales peaking around the 4th month and decreasing towards the end of the year.

When working with time series data, you can ask the AI to help you detect seasonality or trends in the data. This can be useful for identifying periodic patterns or fluctuations, which can help you better understand the underlying behavior of your dataset and make more informed decisions in your analysis or forecasting.

Using statistical tests for anomaly detection

User prompt: I have daily website visitor counts: [1200, 1225, 1300, 1150, 1250, 1280, 10000]. Can you help me find any anomalies using the Grubbs’ test with a significance level of 0.05?

AI response: Based on the Grubbs’ test at a 0.05 significance level, the anomaly in your dataset is the visitor count of 10000.

Statistical tests like Grubbs’ test can be used to detect anomalies in your data. You can ask the AI to perform the test with a specified significance level and identify data points that are considered statistically significant outliers.

By leveraging the AI’s capabilities, you can identify outliers and anomalies in your data using various methods, depending on the nature of your dataset and the specific requirements of your analysis. Remember to always consider the context of your data and your analysis goals when interpreting the results and deciding how to handle the detected outliers or anomalies.

Big Data For The Midmarket: Value In Six Areas

Also see: Choosing a Big Data Solution: Seven Steps

Big returns are already being achieved by mid-market companies that have deployed Big Data projects. The Big Data package – heavy-duty storage, processors and analytics tools combing through huge volumes of varied data types in real time or near real time – has moved beyond early adopter phase. It’s quickly becoming a mainstream revenue and profit generator, according to a new survey and report project I participated in.

An important insight for IT professionals is the widespread use of Big Data among organizations. The conventional wisdom says that marketing is the only department that has taken the Big Data deep dive, but our survey and others are finding important applications in customer service, operations, supply chain management, financial analysis and human resources. As you can see in the following table, marketing systems are expected to be the sixth most important source of internal data for Big Data programs over the next few years:

Darin Bartik, Dell’s executive director of information management products, explains that while marketing and social media have become synonymous with Big Data in large companies, mid-market companies find Big Data opportunities in other areas since they may not have large social media activities underway. “Manufacturing firms have a tremendous opportunity to analyze sensor data and other manufacturing operations,” he says. “Supply chain data, such as partner behavior, is one example. There are many different aspects beyond sales and marketing.”

However, IT pros may want to think twice before approaching the CFO or the COO with a Big Data project for the finance or operations departments. Successful Big Data projects are typically based on a collaboration between the business units and the IT department, where the line of business takes the lead. Bartik and others warn that, like other big and important IT initiatives, a Big Data project needs executive sponsorship first and foremost.

“Senior executives in an organization know their big operational challenges, such as global competition or a tough sales environment, and should tackle those problems first,” Bartik says. Since solving those problems will be most likely to attract top executive interest, they are the most likely to be funded and supported. And once those Big Data solutions are successful they will naturally lead to addressing other corporate pain points.

While we all know that executive sponsorship is always a pre-requisite for a large successful IT project, other ingredients are quite important for Big Data success, too. As you can see from this list of Big Data challenges confronted by the early adopters, there is no one major sink hole, but lots of potholes. Here’s the top 10 list:

Survey respondents also noted a wide variety of explanations for Big Data project failures. Note that many of them are either not on the top 10 challenge list, or have widely different rankings:

The paucity of data scientists and others who are skilled in statistical analysis, business and IT issues is well known, but is especially acute for the mid market. “These are tough people to find,” notes Bartik. “I often recommend that companies create a data science team that is not located in one line of business or isolated in IT. Take your best and brightest from the technical, analytical and business areas and create a separate team. They need to exercise their Big Data muscles first.”

Closely related to the paucity of Big Data skills is the surplus of gut feel. Many Big Data projects fail because there isn’t a data-driven decision culture. Too many senior executives will insist that their prior experience trumps analytics based insights. While these cultural issues are way outside of the IT professionals’ control, they can capsize even the most well-funded and well-executed Big Data project.

Photo courtesy of Shutterstock.

3 Data Skill Sets You Need To Succeed In Data Seo

Data SEO is a scientific approach to search optimization that relies on the analysis and activation of data to make decisions.

But that’s not all it entails.

If you want your organization to succeed in data SEO, there are three distinct specializations you need to develop in addition to SEO knowledge and experience.

These are the skill sets of the data scientist, data analyst, and data engineer.

Whatever your budget, it is possible to improve your SEO with a data-backed approach. In fact, the concepts used by data scientists are becoming increasingly accessible.

Here are the skill sets you need to make data SEO a part of your repertoire.

1. The Data Engineer

Data engineers are the professionals who prepare the company’s foundational big data infrastructure.

They are often software engineers who design, build, integrate data from various resources and manage large amounts of data.

Their main goal is to optimize performance where it comes to the company’s access to its own data.

In large companies, data engineers work with a legal manager for GDPR or CCPA compliance, and often with a security manager.

They frequently use ETL (Extract, Transform and Load) to centralize data, creating large data warehouses that can be used for reporting or analysis.

The main skills and tools can be summarized in the following list:

Hadoop.

MapReduce.

Hive.

Pig.

Data streaming.

NoSQL.

SQL.

Programming.

Why Should You Centralize Your Data?

First of all, you don’t have infinite time available. Not only is it a waste of time to juggle between tools, but it is also a waste of information not to be able to combine data from different sources.

Often, you have to combine your data with business data (CRM), finance data, and many other types of data that always come with access and security concerns.

Therefore, it is wise to build your SEO data warehouse by ensuring that your SEO tools allow you to export the data properly.

However, there are many difficulties.

The first difficulty concerns the volume of information.

If you have more than 100,000 pages on your website and a lot of web traffic, weekly crawls and daily logs will quickly take up a lot of space.

This becomes even more complex if you add your CRM data and data on your competitors.

And if the system is not based on the right technologies you can have incomplete, missing, or false data.

There are many traps in addition to the volume of data.

These include currency concerns if you work internationally, where you will have to deal with the exchange rates issued each day by the authoritative financial institution in your country.

They might also include time differences. If you calculate a turnover per day in France and that a part of the turnover takes place in Canada, for example, you have to launch the calculation when it is midnight in Canada and not midnight in France.

These are just a couple of examples, but every business is full of traps.

Next, you have to keep a close eye on the veracity of the data because data can be corrupted quickly:

A JavaScript script for GA disappears and your traffic data becomes erroneous.

An API changes its return parameters and several fields no longer obtain a value.

A database is no longer updated because the hard disk is full.

No matter what the case, you must quickly detect this type of anomaly and correct it as soon as possible.

Otherwise, the dashboards produced by this data will be erroneous. It’s tedious and time-consuming to launch retroactive scripts to recalculate everything.

If you don’t have a data engineer on your team, you must at least have a manager who verifies the consistency of the data you retrieve from the different SEO tools.

SEO tools now allow you to easily pull the following data, which you need to monitor for variations up or down:

Analytics data: lost script, tracking error.

Crawl data: crawl too long, crawl canceled.

Server log data: missing periods.

Keyword tools data: adding new keywords.

Communication is key. With good incident management, the whole data chain becomes coherent for use by SEO experts, data analysts, and SEO consultants.

2. The Data Scientist

The data scientist will enrich the data with statistical models, machine learning, or analytical approaches.

Their main mission is to help the company transform the data made available by the data engineers into valuable and exploitable information.

Compared to data analysts (see below), data scientists must have strong programming skills to design new algorithms, as well as good business knowledge.

They must be able to explain, justify and communicate results to non-scientists.

Which Languages Should Be Used & Which Methodology?

The most popular technologies in 2023 for data science are, in the order of popularity:

If you can’t decide on a programming language, I can give you some tips.

First of all, use the most popular language in your company.

If the majority of the developers are using Python, there’s no need to push for R because trying to maintain code in R will double the maintenance cost. This way, you show your ability to adapt.

Then, let the technologies on which you want to deploy your applications guide your choice.

For example, if your team produces its dashboards with Shiny, then R will become your best friend.

After that, note that R and Python are relatively similar if you compare them to C or to Scala. If you’re building your CV, it is ideal to master both.

As far as methodology is concerned, the scientific method prevails and leaves no room for empiricism.

You want to clearly define the context and objectives, then explain the different methods identified and present reproducible results.

Finally, it’s entirely possible that you don’t have the time or the vocation to do data science yourself. In this case, I recommend using a service provider.

Regardless of the agency, the deliverables and criteria for success must be clearly defined with the chosen agency so that there are no unpleasant surprises when using the solution.

Additionally, you may also need to consider data science platforms. The options available to you will vary widely depending on your budget.

3. The Data Analyst

Data analysts are business-oriented data professionals who can query and process data, provide reports, summarize and visualize data.

They know how to leverage existing tools and methods to solve a problem and help people across the company understand specific queries through ad hoc reporting and graphics.

They base their work on the data warehouses of data engineers and the results of the algorithms of data scientists.

Their skills are diverse and can include statistics, data mining, and data visualization.

What Software Should Be Used?

Data Studio is well known in the field of SEO but in business, the market is dominated by Tableau Software, SAP, Microsoft, and IBM.

The recent acquisition of Looker by Google positions it to be among the leaders in the years to come, as well.

Be careful in choosing a data visualization solution.

Data analysts’ ability to quickly adapt to tools brings us back to a “Make or Buy” issue. If you have the budget, proprietary solutions will save you a lot of time.

How to Create Perfect Dashboards

There are many methods but here is the SMART goals framework is easy to remember and can apply here, as well:

Keep charts specific and simple, as too much information kills the information.

The y-axis and x-axis must illustrate measurable data.

A graph should focus on achievable metrics, as there is no point in monitoring metrics that will have no influence on your business. Weather is an excellent example: it has a crucial role on some sites and none on others.

Dashboards should always have relevant summaries in order to be read quickly and understood. If it takes more than three seconds to understand them, you can improve the end result. First, users may be satisfied with an overview, but then they may need a more granular view of the data by juggling filters.

The most important data is time, so be sure to track time-based data comparing each day, month, year, etc.

Of course, keep in mind that if data analysts master SQL, they can turn to open source solutions like Metabase or Superset.

Finally, analysts with programming skills will want to look at Shiny for R or Dash for Python.

Data SEO Projects

The world of data SEO has certainly become less obscure.

As for any project, you will either need to surround yourself with the right people to succeed in large-scale data projects or be well-trained in the professional skillsets we covered in this article: data engineering, data analysis, data science.

At this point, you have probably identified weaknesses or strengths within your company while reading this article.

Don’t hesitate to build out on your weak points by recruiting, outsourcing or training.

More Resources:

Image Credits

All screenshots taken by author, May 2023

How To Calculate Absolute Value In Python?

In this article, we will show you how to calculate the absolute value in python. Below are the methods to accomplish this task:

Using User-Defined Function (Brute Method)

Using abs() function

Using math.fabs() function

The magnitude of a number, whether positive or negative, is referred to as its absolute value. For example, the absolute value of -2, is 2, and 2 is simply 2.

Important Points

The absolute value is returned by the built-in abs() function.

The math.fabs() function also returns the absolute value, but as a floating-point value.

When we pass an integer or float value to abs(), we obtain the absolute integer or float value return.

However, if we pass a complex number to abs(), the function returns the magnitude of that number.

Using User-Defined Function (Brute Method) Algorithm (Steps)

Following are the Algorithm/steps to be followed to perform the desired task −

Create a function that returns the absolute value of the number passed to it as an argument.

Use the if conditional statement to check whether the number is less than 0.

Return the negative of the value if the condition is true i.e -(-) value becomes plus(+) which is the absolute value.

Return the number passed if it is a positive number directly.

The following program returns the absolute value of a number −

def

getAbsoluteValue

(

value

)

(

value

)

return

–

value

return

value

(

“Absolute value of 5 = “

getAbsoluteValue

(

)

(

“Absolute value of -5 = “

getAbsoluteValue

(

–

)

Output

On executing, the above program will generate the following output −

Absolute value of 5 = 5 Absolute value of -5 = 5 Using abs() function

The abs() function is used to find the absolute value of a number. It can be applied for both real and complex numbers.

Syntax abs(number)

When used to a real number, the abs() function returns the magnitude of that number. A real number is defined as being on the real number line, which is depicted in the graphic below. A real number’s magnitude is its distance along a straight line from the origin.

The sign of the number denotes which direction the number is along the line; positive values are along the positive axis, and negative values are along the negative axis. In the introduction’s quick example, -5 is a real number.

The following program returns the absolute values of integers using the abs() function −

num_1

num_2

–

num_3

num_4

–

875

(

“absolute value of 4 = “

abs

(

num_1

)

(

“absolute value of -6 = “

abs

(

num_2

)

(

“absolute value of 0 = “

abs

(

num_3

)

(

“absolute value of -875 = “

abs

(

num_4

)

Output

On executing, the above program will generate the following output −

absolute value of 4 = 4 absolute value of -6 = 6 absolute value of 0 = 0 absolute value of -875 = 875 Calculating absolute values for floating-point numbers using abs()

The following program returns the absolute values of floating-point numbers using the abs() function −

num_1

–

4.5

num_2

6.789

num_3

–

10.56

num_4

8.23

(

“absolute value of -4.5 = “

abs

(

num_1

)

(

“absolute value of 6.789 = “

abs

(

num_2

)

(

“absolute value of -10.56 = “

abs

(

num_3

)

(

“absolute value of 8.23 = “

abs

(

num_4

)

Output

On executing, the above program will generate the following output −

absolute value of -4.5 = 4.5 absolute value of 6.789 = 6.789 absolute value of -10.56 = 10.56 absolute value of 8.23 = 8.23 Calculating Absolute Value of Complex Numbers using abs()

The abs() method can also be used on complex numbers.

A complex number is made up of both real and imaginary numbers. An imaginary number is one that is expressed in terms of the square root of a negative number. They are typically stated in terms of value, which is the square root of -1.

Many mathematical gaps are filled by imaginary numbers. As a result, they are widely utilized in math-intensive fields, particularly in electrical engineering. An example of a complex number is seen in the figure below:

If we pass a complex number to abs(), the function returns the magnitude of that number.

For example:

$mathrm{{6+sqrt{-49}}}$

$mathrm{{=6+sqrt{-1.49}}}$

$mathrm{{=6+7sqrt{-1}=overset{Real}{6}+overset{Imaginary}{7i}}}$

The following program returns the absolute values of complex numbers using the abs() function −

num_1

num_2

–

num_3

–

(

“absolute value of 4+5j = “

abs

(

num_1

)

(

“absolute value of 2-3j = “

abs

(

num_2

)

(

“absolute value of -3-4j = “

abs

(

num_3

)

Output

On executing, the above program will generate the following output −

absolute value of 4+5j = 6.4031242374328485 absolute value of 2-3j = 3.605551275463989 absolute value of -3-4j = 5.0 Calculating the Absolute Value of Numbers using math.fabs() Function

Python has the math.fabs() function in addition to the standard abs() method. This function requires one argument as well. The absolute value of that argument is then returned as a floating-point value.

The following program returns the absolute values of complex numbers using abs() function −

import

math

num_1

num_2

–

6.5

num_3

–

num_4

–

(

“absolute value of 4 = “

math

fabs

(

num_1

)

(

“absolute value of -6.5 = “

math

fabs

(

num_2

)

(

“absolute value of -5 = “

math

fabs

(

num_3

)

(

“absolute value of -8.65 = “

math

fabs

(

num_4

)

Output

On executing, the above program will generate the following output −

absolute value of 4 = 4.0 absolute value of -6.5 = 6.5 absolute value of -5 = 5.0 absolute value of -8.65 = 8.0 Conclusion

We covered how to calculate the absolut value in Python using three distinct ways in this tutorial. With examples, we also learned how to determine the absolute value of a complex number and a floating point number.

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