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Bulk Loading Performance Tests With PageSpeed Insights API & Python

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Bulk Loading Performance Tests With PageSpeed Insights API & Python

Google offers PageSpeed Insights API to help SEO pros and developers by mixing real-world data with simulation data,  providing load performance timing data related to web pages.

The difference between the Google PageSpeed Insights (PSI) and Lighthouse is that PSI involves both real-world and lab data, while Lighthouse performs a page loading simulation by modifying the connection and user-agent of the device.

Another point of difference is that PSI doesn’t supply any information related to web accessibility, SEO, or progressive web apps (PWAs), while Lighthouse provides all of the above.

Thus, when we use PageSpeed Insights API for the bulk URL loading performance test, we won’t have any data for accessibility.

However, PSI provides more information related to the page speed performance, such as “DOM Size,” “Deepest DOM Child Element,” “Total Task Count,” and “DOM Content Loaded” timing.

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One more advantage of the PageSpeed Insights API is that it gives the “observed metrics” and “actual metrics” different names.

In this guide, you will learn:

  • How to create a production-level Python Script.
  • How to use APIs with Python.
  • How to construct data frames from API responses.
  • How to analyze the API responses.
  • How to parse URLs and process URL requests’ responses.
  • How to store the API responses with proper structure.

An example output of the Page Speed Insights API call with Python is below.

Screenshot from author, June 2022

Libraries For Using PageSpeed Insights API With Python

The necessary libraries to use PSI API with Python are below.

  • Advertools retrieves testing URLs from the sitemap of a website.
  • Pandas is to construct the data frame and flatten the JSON output of the API.
  • Requests are to make a request to the specific API endpoint.
  • JSON is to take the API response and put it into the specifically related dictionary point.
  • Datetime is to modify the specific output file’s name with the date of the moment.
  • URLlib is to parse the test subject website URL.

How To Use PSI API With Python?

To use the PSI API with Python, follow the steps below.

  • Get a PageSpeed Insights API key.
  • Import the necessary libraries.
  • Parse the URL for the test subject website.
  • Take the Date of Moment for file name.
  • Take URLs into a list from a sitemap.
  • Choose the metrics that you want from PSI API.
  • Create a For Loop for taking the API Response for all URLs.
  • Construct the data frame with chosen PSI API metrics.
  • Output the results in the form of XLSX.

1. Get PageSpeed Insights API Key

Use the PageSpeed Insights API Documentation to get the API Key.

Click the “Get a Key” button below.

psi api key Image from developers.google.com, June 2022

Choose a project that you have created in Google Developer Console.

google developer console api projectImage from developers.google.com, June 2022

Enable the PageSpeed Insights API on that specific project.

page speed insights api enableImage from developers.google.com, June 2022

You will need to use the specific API Key in your API Requests.

2. Import The Necessary Libraries

Use the lines below to import the fundamental libraries.

    import advertools as adv
    import pandas as pd
    import requests
    import json
    from datetime import datetime
    from urllib.parse import urlparse

3. Parse The URL For The Test Subject Website

To parse the URL of the subject website, use the code structure below.

  domain = urlparse(sitemap_url)
  domain = domain.netloc.split(".")[1]

The “domain” variable is the parsed version of the sitemap URL.

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The “netloc” represents the specific URL’s domain section. When we split it with the “.” it takes the “middle section” which represents the domain name.

Here, “0” is for “www,” “1” for “domain name,” and “2” is for “domain extension,” if we split it with “.”

4. Take The Date Of Moment For File Name

To take the date of the specific function call moment, use the “datetime.now” method.

Datetime.now provides the specific time of the specific moment. Use the “strftime” with the “%Y”, “”%m”, and “%d” values. “%Y” is for the year. The “%m” and “%d” are numeric values for the specific month and the day.

 date = datetime.now().strftime("%Y_%m_%d")

5. Take URLs Into A List From A Sitemap

To take the URLs into a list form from a sitemap file, use the code block below.

   sitemap = adv.sitemap_to_df(sitemap_url)
   sitemap_urls = sitemap["loc"].to_list()

If you read the Python Sitemap Health Audit, you can learn further information about the sitemaps.

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6. Choose The Metrics That You Want From PSI API

To choose the PSI API response JSON properties, you should see the JSON file itself.

It is highly relevant to the reading, parsing, and flattening of JSON objects.

It is even related to Semantic SEO, thanks to the concept of “directed graph,” and “JSON-LD” structured data.

In this article, we won’t focus on examining the specific PSI API Response’s JSON hierarchies.

You can see the metrics that I have chosen to gather from PSI API. It is richer than the basic default output of PSI API, which only gives the Core Web Vitals Metrics, or Speed Index-Interaction to Next Paint, Time to First Byte, and First Contentful Paint.

Of course, it also gives “suggestions” by saying “Avoid Chaining Critical Requests,” but there is no need to put a sentence into a data frame.

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In the future, these suggestions, or even every individual chain event, their KB and MS values can be taken into a single column with the name “psi_suggestions.”

For a start, you can check the metrics that I have chosen, and an important amount of them will be first for you.

PSI API Metrics, the first section is below.

    fid = []
    lcp = []
    cls_ = []
    url = []
    fcp = []
    performance_score = []
    total_tasks = []
    total_tasks_time = []
    long_tasks = []
    dom_size = []
    maximum_dom_depth = []
    maximum_child_element = []
    observed_fcp  = []
    observed_fid = []
    observed_lcp = []
    observed_cls = []
    observed_fp = []
    observed_fmp = []
    observed_dom_content_loaded = []
    observed_speed_index = []
    observed_total_blocking_time = []
    observed_first_visual_change = []
    observed_last_visual_change = []
    observed_tti = []
    observed_max_potential_fid = []

This section includes all the observed and simulated fundamental page speed metrics, along with some non-fundamental ones, like “DOM Content Loaded,” or “First Meaningful Paint.”

The second section of PSI Metrics focuses on possible byte and time savings from the unused code amount.

    render_blocking_resources_ms_save = []
    unused_javascript_ms_save = []
    unused_javascript_byte_save = []
    unused_css_rules_ms_save = []
    unused_css_rules_bytes_save = []

A third section of the PSI metrics focuses on server response time, responsive image usage benefits, or not, using harms.

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    possible_server_response_time_saving = []
    possible_responsive_image_ms_save = []

Note: Overall Performance Score comes from “performance_score.”

7. Create A For Loop For Taking The API Response For All URLs

The for loop is to take all of the URLs from the sitemap file and use the PSI API for all of them one by one. The for loop for PSI API automation has several sections.

The first section of the PSI API for loop starts with duplicate URL prevention.

In the sitemaps, you can see a URL that appears multiple times. This section prevents it.

for i in sitemap_urls[:9]:
         # Prevent the duplicate "/" trailing slash URL requests to override the information.
         if i.endswith("/"):
               r = requests.get(f"https://www.googleapis.com/pagespeedonline/v5/runPagespeed?url={i}&strategy=mobile&locale=en&key={api_key}")
         else:
               r = requests.get(f"https://www.googleapis.com/pagespeedonline/v5/runPagespeed?url={i}/&strategy=mobile&locale=en&key={api_key}")

Remember to check the “api_key” at the end of the endpoint for PageSpeed Insights API.

Check the status code. In the sitemaps, there might be non-200 status code URLs; these should be cleaned.

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         if r.status_code == 200:
               #print(r.json())
               data_ = json.loads(r.text)
               url.append(i)

The next section appends the specific metrics to the specific dictionary that we have created before “_data.”

               fcp.append(data_["loadingExperience"]["metrics"]["FIRST_CONTENTFUL_PAINT_MS"]["percentile"])
               fid.append(data_["loadingExperience"]["metrics"]["FIRST_INPUT_DELAY_MS"]["percentile"])
               lcp.append(data_["loadingExperience"]["metrics"]["LARGEST_CONTENTFUL_PAINT_MS"]["percentile"])
               cls_.append(data_["loadingExperience"]["metrics"]["CUMULATIVE_LAYOUT_SHIFT_SCORE"]["percentile"])
               performance_score.append(data_["lighthouseResult"]["categories"]["performance"]["score"] * 100)

Next section focuses on “total task” count, and DOM Size.

               total_tasks.append(data_["lighthouseResult"]["audits"]["diagnostics"]["details"]["items"][0]["numTasks"])
               total_tasks_time.append(data_["lighthouseResult"]["audits"]["diagnostics"]["details"]["items"][0]["totalTaskTime"])
               long_tasks.append(data_["lighthouseResult"]["audits"]["diagnostics"]["details"]["items"][0]["numTasksOver50ms"])
               dom_size.append(data_["lighthouseResult"]["audits"]["dom-size"]["details"]["items"][0]["value"])

The next section takes the “DOM Depth” and “Deepest DOM Element.”

               maximum_dom_depth.append(data_["lighthouseResult"]["audits"]["dom-size"]["details"]["items"][1]["value"])
               maximum_child_element.append(data_["lighthouseResult"]["audits"]["dom-size"]["details"]["items"][2]["value"])

The next section takes the specific observed test results during our Page Speed Insights API.

               observed_dom_content_loaded.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedDomContentLoaded"])
               observed_fid.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedDomContentLoaded"])
               observed_lcp.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["largestContentfulPaint"])
               observed_fcp.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["firstContentfulPaint"])
               observed_cls.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["totalCumulativeLayoutShift"])
               observed_speed_index.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedSpeedIndex"])
               observed_total_blocking_time.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["totalBlockingTime"])
               observed_fp.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedFirstPaint"])
               observed_fmp.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["firstMeaningfulPaint"])
               observed_first_visual_change.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedFirstVisualChange"])
               observed_last_visual_change.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["observedLastVisualChange"])
               observed_tti.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["interactive"])
               observed_max_potential_fid.append(data_["lighthouseResult"]["audits"]["metrics"]["details"]["items"][0]["maxPotentialFID"])

The next section takes the Unused Code amount and the wasted bytes, in milliseconds along with the render-blocking resources.

               render_blocking_resources_ms_save.append(data_["lighthouseResult"]["audits"]["render-blocking-resources"]["details"]["overallSavingsMs"])
               unused_javascript_ms_save.append(data_["lighthouseResult"]["audits"]["unused-javascript"]["details"]["overallSavingsMs"])
               unused_javascript_byte_save.append(data_["lighthouseResult"]["audits"]["unused-javascript"]["details"]["overallSavingsBytes"])
               unused_css_rules_ms_save.append(data_["lighthouseResult"]["audits"]["unused-css-rules"]["details"]["overallSavingsMs"])
               unused_css_rules_bytes_save.append(data_["lighthouseResult"]["audits"]["unused-css-rules"]["details"]["overallSavingsBytes"])

The next section is to provide responsive image benefits and server response timing.

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               possible_server_response_time_saving.append(data_["lighthouseResult"]["audits"]["server-response-time"]["details"]["overallSavingsMs"])      
               possible_responsive_image_ms_save.append(data_["lighthouseResult"]["audits"]["uses-responsive-images"]["details"]["overallSavingsMs"])

The next section is to make the function continue to work in case there is an error.

         else:
           continue

Example Usage Of Page Speed Insights API With Python For Bulk Testing

To use the specific code blocks, put them into a Python function.

Run the script, and you will get 29 page speed-related metrics in the columns below.

pagespeed insights apiScreenshot from author, June 2022

Conclusion

PageSpeed Insights API provides different types of page loading performance metrics.

It demonstrates how Google engineers perceive the concept of page loading performance, and possibly use these metrics as a ranking, UX, and quality-understanding point of view.

Using Python for bulk page speed tests gives you a snapshot of the entire website to help analyze the possible user experience, crawl efficiency, conversion rate, and ranking improvements.

More resources:

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WordPress Releases A Performance Plugin For “Near-Instant Load Times”

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WordPress speculative loading plugin

WordPress released an official plugin that adds support for a cutting edge technology called speculative loading that can help boost site performance and improve the user experience for site visitors.

Speculative Loading

Rendering means constructing the entire webpage so that it instantly displays (rendering). When your browser downloads the HTML, images, and other resources and puts it together into a webpage, that’s rendering. Prerendering is putting that webpage together (rendering it) in the background.

What this plugin does is to enable the browser to prerender the entire webpage that a user might navigate to next. The plugin does that by anticipating which webpage the user might navigate to based on where they are hovering.

Chrome lists a preference for only prerendering when there is an at least 80% probability of a user navigating to another webpage. The official Chrome support page for prerendering explains:

“Pages should only be prerendered when there is a high probability the page will be loaded by the user. This is why the Chrome address bar prerendering options only happen when there is such a high probability (greater than 80% of the time).

There is also a caveat in that same developer page that prerendering may not happen based on user settings, memory usage and other scenarios (more details below about how analytics handles prerendering).

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The Speculative Loading API solves a problem that previous solutions could not because in the past they were simply prefetching resources like JavaScript and CSS but not actually prerendering the entire webpage.

The official WordPress announcement explains it like this:

Introducing the Speculation Rules API
The Speculation Rules API is a new web API that solves the above problems. It allows defining rules to dynamically prefetch and/or prerender URLs of certain structure based on user interaction, in JSON syntax—or in other words, speculatively preload those URLs before the navigation. This API can be used, for example, to prerender any links on a page whenever the user hovers over them.”

The official WordPress page about this new functionality describes it:

“The Speculation Rules API is a new web API… It allows defining rules to dynamically prefetch and/or prerender URLs of certain structure based on user interaction, in JSON syntax—or in other words, speculatively preload those URLs before the navigation.

This API can be used, for example, to prerender any links on a page whenever the user hovers over them. Also, with the Speculation Rules API, “prerender” actually means to prerender the entire page, including running JavaScript. This can lead to near-instant load times once the user clicks on the link as the page would have most likely already been loaded in its entirety. However that is only one of the possible configurations.”

The new WordPress plugin adds support for the Speculation Rules API. The Mozilla developer pages, a great resource for HTML technical understanding describes it like this:

“The Speculation Rules API is designed to improve performance for future navigations. It targets document URLs rather than specific resource files, and so makes sense for multi-page applications (MPAs) rather than single-page applications (SPAs).

The Speculation Rules API provides an alternative to the widely-available <link rel=”prefetch”> feature and is designed to supersede the Chrome-only deprecated <link rel=”prerender”> feature. It provides many improvements over these technologies, along with a more expressive, configurable syntax for specifying which documents should be prefetched or prerendered.”

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See also: Are Websites Getting Faster? New Data Reveals Mixed Results

Performance Lab Plugin

The new plugin was developed by the official WordPress performance team which occasionally rolls out new plugins for users to test ahead of possible inclusion into the actual WordPress core. So it’s a good opportunity to be first to try out new performance technologies.

The new WordPress plugin is by default set to prerender “WordPress frontend URLs” which are pages, posts, and archive pages. How it works can be fine-tuned under the settings:

Settings > Reading > Speculative Loading

Browser Compatibility

The Speculative API is supported by Chrome 108 however the specific rules used by the new plugin require Chrome 121 or higher. Chrome 121 was released in early 2024.

Browsers that do not support will simply ignore the plugin and will have no effect on the user experience.

Check out the new Speculative Loading WordPress plugin developed by the official core WordPress performance team.

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How Analytics Handles Prerendering

A WordPress developer commented with a question asking how Analytics would handle prerendering and someone else answered that it’s up to the Analytics provider to detect a prerender and not count it as a page load or site visit.

Fortunately both Google Analytics and Google Publisher Tags (GPT) both are able to handle prerenders. The Chrome developers support page has a note about how analytics handles prerendering:

“Google Analytics handles prerender by delaying until activation by default as of September 2023, and Google Publisher Tag (GPT) made a similar change to delay triggering advertisements until activation as of November 2023.”

Possible Conflict With Ad Blocker Extensions

There are a couple things to be aware of about this plugin, aside from the fact that it’s an experimental feature that requires Chrome 121 or higher.

A comment by a WordPress plugin developer that this feature may not work with browsers that are using the uBlock Origin ad blocking browser extension.

Download the plugin:
Speculative Loading Plugin by the WordPress Performance Team

Read the announcement at WordPress
Speculative Loading in WordPress

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See also: WordPress, Wix & Squarespace Show Best CWV Rate Of Improvement

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10 Paid Search & PPC Planning Best Practices

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10 Paid Search & PPC Planning Best Practices

Whether you are new to paid media or reevaluating your efforts, it’s critical to review your performance and best practices for your overall PPC marketing program, accounts, and campaigns.

Revisiting your paid media plan is an opportunity to ensure your strategy aligns with your current goals.

Reviewing best practices for pay-per-click is also a great way to keep up with trends and improve performance with newly released ad technologies.

As you review, you’ll find new strategies and features to incorporate into your paid search program, too.

Here are 10 PPC best practices to help you adjust and plan for the months ahead.

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1. Goals

When planning, it is best practice to define goals for the overall marketing program, ad platforms, and at the campaign level.

Defining primary and secondary goals guides the entire PPC program. For example, your primary conversion may be to generate leads from your ads.

You’ll also want to look at secondary goals, such as brand awareness that is higher in the sales funnel and can drive interest to ultimately get the sales lead-in.

2. Budget Review & Optimization

Some advertisers get stuck in a rut and forget to review and reevaluate the distribution of their paid media budgets.

To best utilize budgets, consider the following:

  • Reconcile your planned vs. spend for each account or campaign on a regular basis. Depending on the budget size, monthly, quarterly, or semiannually will work as long as you can hit budget numbers.
  • Determine if there are any campaigns that should be eliminated at this time to free up the budget for other campaigns.
  • Is there additional traffic available to capture and grow results for successful campaigns? The ad platforms often include a tool that will provide an estimated daily budget with clicks and costs. This is just an estimate to show more click potential if you are interested.
  • If other paid media channels perform mediocrely, does it make sense to shift those budgets to another?
  • For the overall paid search and paid social budget, can your company invest more in the positive campaign results?

3. Consider New Ad Platforms

If you can shift or increase your budgets, why not test out a new ad platform? Knowing your audience and where they spend time online will help inform your decision when choosing ad platforms.

Go beyond your comfort zone in Google, Microsoft, and Meta Ads.

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Here are a few other advertising platforms to consider testing:

  • LinkedIn: Most appropriate for professional and business targeting. LinkedIn audiences can also be reached through Microsoft Ads.
  • TikTok: Younger Gen Z audience (16 to 24), video.
  • Pinterest: Products, services, and consumer goods with a female-focused target.
  • Snapchat: Younger demographic (13 to 35), video ads, app installs, filters, lenses.

Need more detailed information and even more ideas? Read more about the 5 Best Google Ads Alternatives.

4. Top Topics in Google Ads & Microsoft Ads

Recently, trends in search and social ad platforms have presented opportunities to connect with prospects more precisely, creatively, and effectively.

Don’t overlook newer targeting and campaign types you may not have tried yet.

  • Video: Incorporating video into your PPC accounts takes some planning for the goals, ad creative, targeting, and ad types. There is a lot of opportunity here as you can simply include video in responsive display ads or get in-depth in YouTube targeting.
  • Performance Max: This automated campaign type serves across all of Google’s ad inventory. Microsoft Ads recently released PMAX so you can plan for consistency in campaign types across platforms. Do you want to allocate budget to PMax campaigns? Learn more about how PMax compares to search.
  • Automation: While AI can’t replace human strategy and creativity, it can help manage your campaigns more easily. During planning, identify which elements you want to automate, such as automatically created assets and/or how to successfully guide the AI in the Performance Max campaigns.

While exploring new features, check out some hidden PPC features you probably don’t know about.

5. Revisit Keywords

The role of keywords has evolved over the past several years with match types being less precise and loosening up to consider searcher intent.

For example, [exact match] keywords previously would literally match with the exact keyword search query. Now, ads can be triggered by search queries with the same meaning or intent.

A great planning exercise is to lay out keyword groups and evaluate if they are still accurately representing your brand and product/service.

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Review search term queries triggering ads to discover trends and behavior you may not have considered. It’s possible this has impacted performance and conversions over time.

Critical to your strategy:

  • Review the current keyword rules and determine if this may impact your account in terms of close variants or shifts in traffic volume.
  • Brush up on how keywords work in each platform because the differences really matter!
  • Review search term reports more frequently for irrelevant keywords that may pop up from match type changes. Incorporate these into match type changes or negative keywords lists as appropriate.

6. Revisit Your Audiences

Review the audiences you selected in the past, especially given so many campaign types that are intent-driven.

Automated features that expand your audience could be helpful, but keep an eye out for performance metrics and behavior on-site post-click.

Remember, an audience is simply a list of users who are grouped together by interests or behavior online.

Therefore, there are unlimited ways to mix and match those audiences and target per the sales funnel.

Here are a few opportunities to explore and test:

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  • LinkedIn user targeting: Besides LinkedIn, this can be found exclusively in Microsoft Ads.
  • Detailed Demographics: Marital status, parental status, home ownership, education, household income.
  • In-market and custom intent: Searches and online behavior signaling buying cues.
  • Remarketing: Advertisers website visitors, interactions with ads, and video/ YouTube.

Note: This varies per the campaign type and seems to be updated frequently, so make this a regular check-point in your campaign management for all platforms.

7. Organize Data Sources

You will likely be running campaigns on different platforms with combinations of search, display, video, etc.

Looking back at your goals, what is the important data, and which platforms will you use to review and report? Can you get the majority of data in one analytics platform to compare and share?

Millions of companies use Google Analytics, which is a good option for centralized viewing of advertising performance, website behavior, and conversions.

8. Reevaluate How You Report

Have you been using the same performance report for years?

It’s time to reevaluate your essential PPC key metrics and replace or add that data to your reports.

There are two great resources to kick off this exercise:

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Your objectives in reevaluating the reporting are:

  • Are we still using this data? Is it still relevant?
  • Is the data we are viewing actionable?
  • What new metrics should we consider adding we haven’t thought about?
  • How often do we need to see this data?
  • Do the stakeholders receiving the report understand what they are looking at (aka data visualization)?

Adding new data should be purposeful, actionable, and helpful in making decisions for the marketing plan. It’s also helpful to decide what type of data is good to see as “deep dives” as needed.

9. Consider Using Scripts

The current ad platforms have plenty of AI recommendations and automated rules, and there is no shortage of third-party tools that can help with optimizations.

Scripts is another method for advertisers with large accounts or some scripting skills to automate report generation and repetitive tasks in their Google Ads accounts.

Navigating the world of scripts can seem overwhelming, but a good place to start is a post here on Search Engine Journal that provides use cases and resources to get started with scripts.

Luckily, you don’t need a Ph.D. in computer science — there are plenty of resources online with free or templated scripts.

10. Seek Collaboration

Another effective planning tactic is to seek out friendly resources and second opinions.

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Much of the skill and science of PPC management is unique to the individual or agency, so there is no shortage of ideas to share between you.

You can visit the Paid Search Association, a resource for paid ad managers worldwide, to make new connections and find industry events.

Preparing For Paid Media Success

Strategies should be based on clear and measurable business goals. Then, you can evaluate the current status of your campaigns based on those new targets.

Your paid media strategy should also be built with an eye for both past performance and future opportunities. Look backward and reevaluate your existing assumptions and systems while investigating new platforms, topics, audiences, and technologies.

Also, stay current with trends and keep learning. Check out ebooks, social media experts, and industry publications for resources and motivational tips.

More resources: 

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Google Limits News Links In California Over Proposed ‘Link Tax’ Law

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A brown cardboard price tag with a twine string and a black dollar sign symbol, influenced by the Link Tax Law, set against a dark gray background.

Google announced that it plans to reduce access to California news websites for a portion of users in the state.

The decision comes as Google prepares for the potential passage of the California Journalism Preservation Act (CJPA), a bill requiring online platforms like Google to pay news publishers for linking to their content.

What Is The California Journalism Preservation Act?

The CJPA, introduced in the California State Legislature, aims to support local journalism by creating what Google refers to as a “link tax.”

If passed, the Act would force companies like Google to pay media outlets when sending readers to news articles.

However, Google believes this approach needs to be revised and could harm rather than help the news industry.

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Jaffer Zaidi, Google’s VP of Global News Partnerships, stated in a blog post:

“It would favor media conglomerates and hedge funds—who’ve been lobbying for this bill—and could use funds from CJPA to continue to buy up local California newspapers, strip them of journalists, and create more ghost papers that operate with a skeleton crew to produce only low-cost, and often low-quality, content.”

Google’s Response

To assess the potential impact of the CJPA on its services, Google is running a test with a percentage of California users.

During this test, Google will remove links to California news websites that the proposed legislation could cover.

Zaidi states:

“To prepare for possible CJPA implications, we are beginning a short-term test for a small percentage of California users. The testing process involves removing links to California news websites, potentially covered by CJPA, to measure the impact of the legislation on our product experience.”

Google Claims Only 2% of Search Queries Are News-Related

Zaidi highlighted peoples’ changing news consumption habits and its effect on Google search queries (emphasis mine):

“It’s well known that people are getting news from sources like short-form videos, topical newsletters, social media, and curated podcasts, and many are avoiding the news entirely. In line with those trends, just 2% of queries on Google Search are news-related.”

Despite the low percentage of news queries, Google wants to continue helping news publishers gain visibility on its platforms.

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However, the “CJPA as currently constructed would end these investments,” Zaidi says.

A Call For A Different Approach

In its current form, Google maintains that the CJPA undermines news in California and could leave all parties worse off.

The company urges lawmakers to consider alternative approaches supporting the news industry without harming smaller local outlets.

Google argues that, over the past two decades, it’s done plenty to help news publishers innovate:

“We’ve rolled out Google News Showcase, which operates in 26 countries, including the U.S., and has more than 2,500 participating publications. Through the Google News Initiative we’ve partnered with more than 7,000 news publishers around the world, including 200 news organizations and 6,000 journalists in California alone.”

Zaidi suggested that a healthy news industry in California requires support from the state government and a broad base of private companies.

As the legislative process continues, Google is willing to cooperate with California publishers and lawmakers to explore alternative paths that would allow it to continue linking to news.

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Featured Image:Ismael Juan/Shutterstock

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