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A Practical Guide To Multi-touch Attribution

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A Practical Guide To Multi-touch Attribution

The customer journey involves multiple interactions between the customer and the merchant or service provider.

We call each interaction in the customer journey a touch point.

According to Salesforce.com, it takes, on average, six to eight touches to generate a lead in the B2B space.

The number of touchpoints is even higher for a customer purchase.

Multi-touch attribution is the mechanism to evaluate each touch point’s contribution toward conversion and gives the appropriate credits to every touch point involved in the customer journey.

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Conducting a multi-touch attribution analysis can help marketers understand the customer journey and identify opportunities to further optimize the conversion paths.

In this article, you will learn the basics of multi-touch attribution, and the steps of conducting multi-touch attribution analysis with easily accessible tools.

What To Consider Before Conducting Multi-Touch Attribution Analysis

Define The Business Objective

What do you want to achieve from the multi-touch attribution analysis?

Do you want to evaluate the return on investment (ROI) of a particular marketing channel, understand your customer’s journey, or identify critical pages on your website for A/B testing?

Different business objectives may require different attribution analysis approaches.

Defining what you want to achieve from the beginning helps you get the results faster.

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Define Conversion

Conversion is the desired action you want your customers to take.

For ecommerce sites, it’s usually making a purchase, defined by the order completion event.

For other industries, it may be an account sign-up or a subscription.

Different types of conversion likely have different conversion paths.

If you want to perform multi-touch attribution on multiple desired actions, I would recommend separating them into different analyses to avoid confusion.

Define Touch Point

Touch point could be any interaction between your brand and your customers.

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If this is your first time running a multi-touch attribution analysis, I would recommend defining it as a visit to your website from a particular marketing channel. Channel-based attribution is easy to conduct, and it could give you an overview of the customer journey.

If you want to understand how your customers interact with your website, I would recommend defining touchpoints based on pageviews on your website.

If you want to include interactions outside of the website, such as mobile app installation, email open, or social engagement, you can incorporate those events in your touch point definition, as long as you have the data.

Regardless of your touch point definition, the attribution mechanism is the same. The more granular the touch points are defined, the more detailed the attribution analysis is.

In this guide, we’ll focus on channel-based and pageview-based attribution.

You’ll learn about how to use Google Analytics and another open-source tool to conduct those attribution analyses.

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An Introduction To Multi-Touch Attribution Models

The ways of crediting touch points for their contributions to conversion are called attribution models.

The simplest attribution model is to give all the credit to either the first touch point, for bringing in the customer initially, or the last touch point, for driving the conversion.

These two models are called the first-touch attribution model and the last-touch attribution model, respectively.

Obviously, neither the first-touch nor the last-touch attribution model is “fair” to the rest of the touch points.

Then, how about allocating credit evenly across all touch points involved in converting a customer? That sounds reasonable – and this is exactly how the linear attribution model works.

However, allocating credit evenly across all touch points assumes the touch points are equally important, which doesn’t seem “fair”, either.

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Some argue the touch points near the end of the conversion paths are more important, while others are in favor of the opposite. As a result, we have the position-based attribution model that allows marketers to give different weights to touchpoints based on their locations in the conversion paths.

All the models mentioned above are under the category of heuristic, or rule-based, attribution models.

In addition to heuristic models, we have another model category called data-driven attribution, which is now the default model used in Google Analytics.

What Is Data-Driven Attribution?

How is data-driven attribution different from the heuristic attribution models?

Here are some highlights of the differences:

  • In a heuristic model, the rule of attribution is predetermined. Regardless of first-touch, last-touch, linear, or position-based model, the attribution rules are set in advance and then applied to the data. In a data-driven attribution model, the attribution rule is created based on historical data, and therefore, it is unique for each scenario.
  • A heuristic model looks at only the paths that lead to a conversion and ignores the non-converting paths. A data-driven model uses data from both converting and non-converting paths.
  • A heuristic model attributes conversions to a channel based on how many touches a touch point has with respect to the attribution rules. In a data-driven model, the attribution is made based on the effect of the touches of each touch point.

How To Evaluate The Effect Of A Touch Point

A common algorithm used by data-driven attribution is called Markov Chain. At the heart of the Markov Chain algorithm is a concept called the Removal Effect.

The Removal Effect, as the name suggests, is the impact on conversion rate when a touch point is removed from the pathing data.

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This article will not go into the mathematical details of the Markov Chain algorithm.

Below is an example illustrating how the algorithm attributes conversion to each touch point.

The Removal Effect

Assuming we have a scenario where there are 100 conversions from 1,000 visitors coming to a website via 3 channels, Channel A, B, & C. In this case, the conversion rate is 10%.

Intuitively, if a certain channel is removed from the conversion paths, those paths involving that particular channel will be “cut off” and end with fewer conversions overall.

If the conversion rate is lowered to 5%, 2%, and 1% when Channels A, B, & C are removed from the data, respectively, we can calculate the Removal Effect as the percentage decrease of the conversion rate when a particular channel is removed using the formula:

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Image from author, November 2022

Then, the last step is attributing conversions to each channel based on the share of the Removal Effect of each channel. Here is the attribution result:

Channel Removal Effect Share of Removal Effect Attributed Conversions
A 1 – (5% / 10%) = 0.5 0.5 / (0.5 + 0.8 + 0.9) = 0.23 100 * 0.23 = 23
B 1 – (2% / 10%) = 0.8 0.8 / (0.5 + 0.8 + 0.9) = 0.36 100 * 0.36 = 36
C 1 – (1% / 10%) = 0.9 0.9 / (0.5 + 0.8 + 0.9) = 0.41 100 * 0.41 = 41

In a nutshell, data-driven attribution does not rely on the number or position of the touch points but on the impact of those touch points on conversion as the basis of attribution.

Multi-Touch Attribution With Google Analytics

Enough of theories, let’s look at how we can use the ubiquitous Google Analytics to conduct multi-touch attribution analysis.

As Google will stop supporting Universal Analytics (UA) from July 2023, this tutorial will be based on Google Analytics 4 (GA4) and we’ll use Google’s Merchandise Store demo account as an example.

In GA4, the attribution reports are under Advertising Snapshot as shown below on the left navigation menu.

After landing on the Advertising Snapshot page, the first step is selecting an appropriate conversion event.

GA4, by default, includes all conversion events for its attribution reports.

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To avoid confusion, I highly recommend you pick only one conversion event (“purchase” in the below example) for the analysis.

advertising snapshot GA4Screenshot from GA4, November 2022

 

Understand The Conversion Paths In GA4

Under the Attribution section on the left navigation bar, you can open the Conversion Paths report.

Scroll down to the conversion path table, which shows all the paths leading to conversion.

At the top of this table, you can find the average number of days and number of touch points that lead to conversions.

GA4 touchpoints to conversionScreenshot from GA4, November 2022 

 

In this example, you can see that Google customers take, on average, almost 9 days and 6 visits before making a purchase on its Merchandise Store.

Find Each Channel’s Contribution In GA4

Next, click the All Channels report under the Performance section on the left navigation bar.

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In this report, you can find the attributed conversions for each channel of your selected conversion event – “purchase”, in this case.

All channels report GA4Screenshot from GA4, November 2022

 

Now, you know Organic Search, together with Direct and Email, drove most of the purchases on Google’s Merchandise Store.

Examine Results From Different Attribution Models In GA4

By default, GA4 uses the data-driven attribution model to determine how many credits each channel receives. However, you can examine how different attribution models assign credits for each channel.

Click Model Comparison under the Attribution section on the left navigation bar.

For example, comparing the data-driven attribution model with the first touch attribution model (aka “first click model” in the below figure), you can see more conversions are attributed to Organic Search under the first click model (735) than the data-driven model (646.80).

On the other hand, Email has more attributed conversions under the data-driven attribution model (727.82) than the first click model (552).

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Attribution models for channel grouping GA4Screenshot from GA4, November 2022

 

The data tells us that Organic Search plays an important role in bringing potential customers to the store, but it needs help from other channels to convert visitors (i.e., for customers to make actual purchases).

On the other hand, Email, by nature, interacts with visitors who have visited the site before and helps to convert returning visitors who initially came to the site from other channels.

Which Attribution Model Is The Best?

A common question, when it comes to attribution model comparison, is which attribution model is the best. I’d argue this is the wrong question for marketers to ask.

The truth is that no one model is absolutely better than the others as each model illustrates one aspect of the customer journey. Marketers should embrace multiple models as they see fit.

From Channel-Based To Pageview-Based Attribution

Google Analytics is easy to use, but it works well for channel-based attribution.

If you want to further understand how customers navigate through your website before converting, and what pages influence their decisions, you need to conduct attribution analysis on pageviews.

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While Google Analytics doesn’t support pageview-based attribution, there are other tools you can use.

We recently performed such a pageview-based attribution analysis on AdRoll’s website and I’d be happy to share with you the steps we went through and what we learned.

Gather Pageview Sequence Data

The first and most challenging step is gathering data on the sequence of pageviews for each visitor on your website.

Most web analytics systems record this data in some form. If your analytics system doesn’t provide a way to extract the data from the user interface, you may need to pull the data from the system’s database.

Similar to the steps we went through on GA4, the first step is defining the conversion. With pageview-based attribution analysis, you also need to identify the pages that are part of the conversion process.

As an example, for an ecommerce site with online purchase as the conversion event, the shopping cart page, the billing page, and the order confirmation page are part of the conversion process, as every conversion goes through those pages.

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You should exclude those pages from the pageview data since you don’t need an attribution analysis to tell you those pages are important for converting your customers.

The purpose of this analysis is to understand what pages your potential customers visited prior to the conversion event and how they influenced the customers’ decisions.

Prepare Your Data For Attribution Analysis

Once the data is ready, the next step is to summarize and manipulate your data into the following four-column format. Here is an example.

data manipulation: 4-column formatScreenshot from author, November 2022

 

The Path column shows all the pageview sequences. You can use any unique page identifier, but I’d recommend using the url or page path because it allows you to analyze the result by page types using the url structure.  “>” is a separator used in between pages.

The Total_Conversions column shows the total number of conversions a particular pageview path led to.

The Total_Conversion_Value column shows the total monetary value of the conversions from a particular pageview path. This column is optional and is mostly applicable to ecommerce sites.

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The Total_Null column shows the total number of times a particular pageview path failed to convert.

Build Your Page-Level Attribution Models

To build the attribution models, we leverage the open-source library called ChannelAttribution.

While this library was originally created for use in R and Python programming languages, the authors now provide a free Web app for it, so we can use this library without writing any code.

Upon signing into the Web app, you can upload your data and start building the models.

For first-time users, I’d recommend clicking the Load Demo Data button for a trial run. Be sure to examine the parameter configuration with the demo data.

Load Demo Data buttonScreenshot from author, November 2022

When you’re ready, click the Run button to create the models.

Once the models are created, you’ll be directed to the Output tab, which displays the attribution results from four different attribution models – first-touch, last-touch, linear, and data-drive (Markov Chain).

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Remember to download the result data for further analysis.

For your reference, while this tool is called ChannelAttribution, it’s not limited to channel-specific data.

Since the attribution modeling mechanism is agnostic to the type of data given to it, it’d attribute conversions to channels if channel-specific data is provided, and to web pages if pageview data is provided.

Analyze Your Attribution Data

Organize Pages Into Page Groups

Depending on the number of pages on your website, it may make more sense to first analyze your attribution data by page groups rather than individual pages.

A page group can contain as few as just one page to as many pages as you want, as long as it makes sense to you.

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Taking AdRoll’s website as an example, we have a Homepage group that contains just the homepage and a Blog group that contains all of our blog posts.

For ecommerce sites, you may consider grouping your pages by product categories as well.

Starting with page groups instead of individual pages allows marketers to have an overview of the attribution results across different parts of the website. You can always drill down from the page group to individual pages when needed.

Identify The Entries And Exits Of The Conversion Paths

After all the data preparation and model building, let’s get to the fun part – the analysis.

I’d suggest first identifying the pages that your potential customers enter your website and the pages that direct them to convert by examining the patterns of the first-touch and last-touch attribution models.

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Pages with particularly high first-touch and last-touch attribution values are the starting points and endpoints, respectively, of the conversion paths. These are what I call gateway pages.

Make sure these pages are optimized for conversion.

Keep in mind that this type of gateway page may not have very high traffic volume.

For example, as a SaaS platform, AdRoll’s pricing page doesn’t have high traffic volume compared to some other pages on the website but it’s the page many visitors visited before converting.

Find Other Pages With Strong Influence On Customers’ Decisions

After the gateway pages,  the next step is to find out what other pages have a high influence on your customers’ decisions.

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For this analysis, we look for non-gateway pages with high attribution value under the Markov Chain models.

Taking the group of product feature pages on AdRoll.com as an example, the pattern of their attribution value across the four models (shown below) shows they have the highest attribution value under the Markov Chain model, followed by the linear model.

This is an indication that they are visited in the middle of the conversion paths and played an important role in influencing customers’ decisions.

4 attribution models bar chartImage from author, November 2022

 

These types of pages are also prime candidates for conversion rate optimization (CRO).

Making them easier to be discovered by your website visitors and their content more convincing would help lift your conversion rate.

To Recap

Multi-touch attribution allows a company to understand the contribution of various marketing channels and identify opportunities to further optimize the conversion paths.

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Start simply with Google Analytics for channel-based attribution. Then, dig deeper into a customer’s pathway to conversion with pageview-based attribution.

Don’t worry about picking the best attribution model.

Leverage multiple attribution models, as each attribution model shows different aspects of the customer journey.

More resources: 


Featured Image: Black Salmon/Shutterstock



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Reddit Post Ranks On Google In 5 Minutes

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Google apparently ranks Reddit posts within minutes

Google’s Danny Sullivan disputed the assertions made in a Reddit discussion that Google is showing a preference for Reddit in the search results. But a Redditor’s example proves that it’s possible for a Reddit post to rank in the top ten of the search results within minutes and to actually improve rankings to position #2 a week later.

Discussion About Google Showing Preference To Reddit

A Redditor (gronetwork) complained that Google is sending so many visitors to Reddit that the server is struggling with the load and shared an example that proved that it can only take minutes for a Reddit post to rank in the top ten.

That post was part of a 79 post Reddit thread where many in the r/SEO subreddit were complaining about Google allegedly giving too much preference to Reddit over legit sites.

The person who did the test (gronetwork) wrote:

“…The website is already cracking (server down, double posts, comments not showing) because there are too many visitors.

…It only takes few minutes (you can test it) for a post on Reddit to appear in the top ten results of Google with keywords related to the post’s title… (while I have to wait months for an article on my site to be referenced). Do the math, the whole world is going to spam here. The loop is completed.”

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Reddit Post Ranked Within Minutes

Another Redditor asked if they had tested if it takes “a few minutes” to rank in the top ten and gronetwork answered that they had tested it with a post titled, Google SGE Review.

gronetwork posted:

“Yes, I have created for example a post named “Google SGE Review” previously. After less than 5 minutes it was ranked 8th for Google SGE Review (no quotes). Just after Washingtonpost.com, 6 authoritative SEO websites and Google.com’s overview page for SGE (Search Generative Experience). It is ranked third for SGE Review.”

It’s true, not only does that specific post (Google SGE Review) rank in the top 10, the post started out in position 8 and it actually improved ranking, currently listed beneath the number one result for the search query “SGE Review”.

Screenshot Of Reddit Post That Ranked Within Minutes

Anecdotes Versus Anecdotes

Okay, the above is just one anecdote. But it’s a heck of an anecdote because it proves that it’s possible for a Reddit post to rank within minutes and get stuck in the top of the search results over other possibly more authoritative websites.

hankschrader79 shared that Reddit posts outrank Toyota Tacoma forums for a phrase related to mods for that truck.

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Google’s Danny Sullivan responded to that post and the entire discussion to dispute that Reddit is not always prioritized over other forums.

Danny wrote:

“Reddit is not always prioritized over other forums. [super vhs to mac adapter] I did this week, it goes Apple Support Community, MacRumors Forum and further down, there’s Reddit. I also did [kumo cloud not working setup 5ghz] recently (it’s a nightmare) and it was the Netgear community, the SmartThings Community, GreenBuildingAdvisor before Reddit. Related to that was [disable 5g airport] which has Apple Support Community above Reddit. [how to open an 8 track tape] — really, it was the YouTube videos that helped me most, but it’s the Tapeheads community that comes before Reddit.

In your example for [toyota tacoma], I don’t even get Reddit in the top results. I get Toyota, Car & Driver, Wikipedia, Toyota again, three YouTube videos from different creators (not Toyota), Edmunds, a Top Stories unit. No Reddit, which doesn’t really support the notion of always wanting to drive traffic just to Reddit.

If I guess at the more specific query you might have done, maybe [overland mods for toyota tacoma], I get a YouTube video first, then Reddit, then Tacoma World at third — not near the bottom. So yes, Reddit is higher for that query — but it’s not first. It’s also not always first. And sometimes, it’s not even showing at all.”

hankschrader79 conceded that they were generalizing when they wrote that Google always prioritized Reddit. But they also insisted that that didn’t diminish what they said is a fact that Google’s “prioritization” forum content has benefitted Reddit more than actual forums.

Why Is The Reddit Post Ranked So High?

It’s possible that Google “tested” that Reddit post in position 8 within minutes and that user interaction signals indicated to Google’s algorithms that users prefer to see that Reddit post. If that’s the case then it’s not a matter of Google showing preference to Reddit post but rather it’s users that are showing the preference and the algorithm is responding to those preferences.

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Nevertheless, an argument can be made that user preferences for Reddit can be a manifestation of Familiarity Bias. Familiarity Bias is when people show a preference for things that are familiar to them. If a person is familiar with a brand because of all the advertising they were exposed to then they may show a bias for the brand products over unfamiliar brands.

Users who are familiar with Reddit may choose Reddit because they don’t know the other sites in the search results or because they have a bias that Google ranks spammy and optimized websites and feel safer reading Reddit.

Google may be picking up on those user interaction signals that indicate a preference and satisfaction with the Reddit results but those results may simply be biases and not an indication that Reddit is trustworthy and authoritative.

Is Reddit Benefiting From A Self-Reinforcing Feedback Loop?

It may very well be that Google’s decision to prioritize user generated content may have started a self-reinforcing pattern that draws users in to Reddit through the search results and because the answers seem plausible those users start to prefer Reddit results. When they’re exposed to more Reddit posts their familiarity bias kicks in and they start to show a preference for Reddit. So what could be happening is that the users and Google’s algorithm are creating a self-reinforcing feedback loop.

Is it possible that Google’s decision to show more user generated content has kicked off a cycle where more users are exposed to Reddit which then feeds back into Google’s algorithm which in turn increases Reddit visibility, regardless of lack of expertise and authoritativeness?

Featured Image by Shutterstock/Kues

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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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Featured Image: Vanatchanan/Shutterstock

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