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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: 


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OpenAI Expected to Integrate Real-Time Data In ChatGPT

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OpenAI ChatGPT announcement

Sam Altman, CEO of OpenAI, dispelled rumors that a new search engine would be announced on Monday, May 13. Recent deals have raised the expectation that OpenAI will announce the integration of real-time content from English, Spanish, and French publications into ChatGPT, complete with links to the original sources.

OpenAI Search Is Not Happening

Many competing search engines have tried and failed to challenge Google as the leading search engine. A new wave of hybrid generative AI search engines is currently trying to knock Google from the top spot with arguably very little success.

Sam Altman is on record saying that creating a search engine to compete against Google is not a viable approach. He suggested that technological disruption was the way to replace Google by changing the search paradigm altogether. The speculation that Altman is going to announce a me-too search engine on Monday never made sense given his recent history of dismissing the concept as a non-starter.

So perhaps it’s not a surprise that he recently ended the speculation by explicitly saying that he will not be announcing a search engine on Monday.

He tweeted:

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“not gpt-5, not a search engine, but we’ve been hard at work on some new stuff we think people will love! feels like magic to me.”

“New Stuff” May Be Iterative Improvement

It’s quite likely that what’s going to be announced is iterative which means it improves ChatGPT but not replaces it. This fits into how Altman recently expressed his approach with ChatGPT.

He remarked:

“And it does kind of suck to ship a product that you’re embarrassed about, but it’s much better than the alternative. And in this case in particular, where I think we really owe it to society to deploy iteratively.

There could totally be things in the future that would change where we think iterative deployment isn’t such a good strategy, but it does feel like the current best approach that we have and I think we’ve gained a lot from from doing this and… hopefully the larger world has gained something too.”

Improving ChatGPT iteratively is Sam Altman’s preference and recent clues point to what those changes may be.

Recent Deals Contain Clues

OpenAI has been making deals with news media and User Generated Content publishers since December 2023. Mainstream media has reported these deals as being about licensing content for training large language models. But they overlooked a a key detail that we reported on last month which is that these deals give OpenAI access to real-time information that they stated will be used to give attribution to that real-time data in the form of links.

That means that ChatGPT users will gain the ability to access real-time news and to use that information creatively within ChatGPT.

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Dotdash Meredith Deal

Dotdash Meredith (DDM) is the publisher of big brand publications such as Better Homes & Gardens, FOOD & WINE, InStyle, Investopedia, and People magazine. The deal that was announced goes way beyond using the content as training data. The deal is explicitly about surfacing the Dotdash Meredith content itself in ChatGPT.

The announcement stated:

“As part of the agreement, OpenAI will display content and links attributed to DDM in relevant ChatGPT responses. …This deal is a testament to the great work OpenAI is doing on both fronts to partner with creators and publishers and ensure a healthy Internet for the future.

Over 200 million Americans each month trust our content to help them make decisions, solve problems, find inspiration, and live fuller lives. This partnership delivers the best, most relevant content right to the heart of ChatGPT.”

A statement from OpenAI gives credibility to the speculation that OpenAI intends to directly show licensed third-party content as part of ChatGPT answers.

OpenAI explained:

“We’re thrilled to partner with Dotdash Meredith to bring its trusted brands to ChatGPT and to explore new approaches in advancing the publishing and marketing industries.”

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Something that DDM also gets out of this deal is that OpenAI will enhance DDM’s in-house ad targeting in order show more tightly focused contextual advertising.

Le Monde And Prisa Media Deals

In March 2024 OpenAI announced a deal with two global media companies, Le Monde and Prisa Media. Le Monde is a French news publication and Prisa Media is a Spanish language multimedia company. The interesting aspects of these two deals is that it gives OpenAI access to real-time data in French and Spanish.

Prisa Media is a global Spanish language media company based in Madrid, Spain that is comprised of magazines, newspapers, podcasts, radio stations, and television networks. It’s reach extends from Spain to America. American media companies include publications in the United States, Argentina, Bolivia, Chile, Colombia, Costa Rica, Ecuador, Mexico, and Panama. That is a massive amount of real-time information in addition to a massive audience of millions.

OpenAI explicitly announced that the purpose of this deal was to bring this content directly to ChatGPT users.

The announcement explained:

“We are continually making improvements to ChatGPT and are supporting the essential role of the news industry in delivering real-time, authoritative information to users. …Our partnerships will enable ChatGPT users to engage with Le Monde and Prisa Media’s high-quality content on recent events in ChatGPT, and their content will also contribute to the training of our models.”

That deal is not just about training data. It’s about bringing current events data to ChatGPT users.

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The announcement elaborated in more detail:

“…our goal is to enable ChatGPT users around the world to connect with the news in new ways that are interactive and insightful.”

As noted in our April 30th article that revealed that OpenAI will show links in ChatGPT, OpenAI intends to show third party content with links to that content.

OpenAI commented on the purpose of the Le Monde and Prisa Media partnership:

“Over the coming months, ChatGPT users will be able to interact with relevant news content from these publishers through select summaries with attribution and enhanced links to the original articles, giving users the ability to access additional information or related articles from their news sites.”

There are additional deals with other groups like The Financial Times which also stress that this deal will result in a new ChatGPT feature that will allow users to interact with real-time news and current events .

OpenAI’s Monday May 13 Announcement

There are many clues that the announcement on Monday will be that ChatGPT users will gain the ability to interact with content about current events.  This fits into the terms of recent deals with news media organizations. There may be other features announced as well but this part is something that there are many clues pointing to.

Watch Altman’s interview at Stanford University

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Google’s Strategies For Dealing With Content Decay

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Disappointed stressed businessman staring at the stacked paperwork on his desk

In the latest episode of the Search Off The Record podcast, Google Search Relations team members John Mueller and Lizzi Sassman did a deep dive into dealing with “content decay” on websites.

Outdated content is a natural issue all sites face over time, and Google has outlined strategies beyond just deleting old pages.

While removing stale content is sometimes necessary, Google recommends taking an intentional, format-specific approach to tackling content decay.

Archiving vs. Transitional Guides

Google advises against immediately removing content that becomes obsolete, like materials referencing discontinued products or services.

Removing content too soon could confuse readers and lead to a poor experience, Sassman explains:

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“So, if I’m trying to find out like what happened, I almost need that first thing to know. Like, “What happened to you?” And, otherwise, it feels almost like an error. Like, “Did I click a wrong link or they redirect to the wrong thing?””

Sassman says you can avoid confusion by providing transitional “explainer” pages during deprecation periods.

A temporary transition guide informs readers of the outdated content while steering them toward updated resources.

Sassman continues:

“That could be like an intermediary step where maybe you don’t do that forever, but you do it during the transition period where, for like six months, you have them go funnel them to the explanation, and then after that, all right, call it a day. Like enough people know about it. Enough time has passed. We can just redirect right to the thing and people aren’t as confused anymore.”

When To Update Vs. When To Write New Content

For reference guides and content that provide authoritative overviews, Google suggests updating information to maintain accuracy and relevance.

However, for archival purposes, major updates may warrant creating a new piece instead of editing the original.

Sassman explains:

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“I still want to retain the original piece of content as it was, in case we need to look back or refer to it, and to change it or rehabilitate it into a new thing would almost be worth republishing as a new blog post if we had that much additional things to say about it.”

Remove Potentially Harmful Content

Google recommends removing pages in cases where the outdated information is potentially harmful.

Sassman says she arrived at this conclusion when deciding what to do with a guide involving obsolete structured data:

“I think something that we deleted recently was the “How to Structure Data” documentation page, which I thought we should just get rid of it… it almost felt like that’s going to be more confusing to leave it up for a period of time.

And actually it would be negative if people are still adding markup, thinking they’re going to get something. So what we ended up doing was just delete the page and redirect to the changelog entry so that, if people clicked “How To Structure Data” still, if there was a link somewhere, they could still find out what happened to that feature.”

Internal Auditing Processes

To keep your content current, Google advises implementing a system for auditing aging content and flagging it for review.

Sassman says she sets automated alerts for pages that haven’t been checked in set periods:

“Oh, so we have a little robot to come and remind us, “Hey, you should come investigate this documentation page. It’s been x amount of time. Please come and look at it again to make sure that all of your links are still up to date, that it’s still fresh.””

Context Is Key

Google’s tips for dealing with content decay center around understanding the context of outdated materials.

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You want to prevent visitors from stumbling across obsolete pages without clarity.

Additional Google-recommended tactics include:

  • Prominent banners or notices clarifying a page’s dated nature
  • Listing original publish dates
  • Providing inline annotations explaining how older references or screenshots may be obsolete

How This Can Help You

Following Google’s recommendations for tackling content decay can benefit you in several ways:

  • Improved user experience: By providing clear explanations, transition guides, and redirects, you can ensure that visitors don’t encounter confusing or broken pages.
  • Maintained trust and credibility: Removing potentially harmful or inaccurate content and keeping your information up-to-date demonstrates your commitment to providing reliable and trustworthy resources.
  • Better SEO: Regularly auditing and updating your pages can benefit your website’s search rankings and visibility.
  • Archival purposes: By creating new content instead of editing older pieces, you can maintain a historical record of your website’s evolution.
  • Streamlined content management: Implementing internal auditing processes makes it easier to identify and address outdated or problematic pages.

By proactively tackling content decay, you can keep your website a valuable resource, improve SEO, and maintain an organized content library.

Listen to the full episode of Google’s podcast below:


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25 Snapchat Statistics & Facts For 2024

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25 Snapchat Statistics & Facts For 2024

Snapchat, known for its ephemeral content, innovative augmented reality (AR) features, and fiercely loyal user base, is a vital player in the social media landscape.

While it sometimes flies under the radar – as other platforms like TikTok, YouTube, and Instagram tend to dominate the cultural conversation – Snapchat is an incredibly powerful marketing tool that holds a unique place in the hearts and minds of its users.

In this article, we’ll explore what you need to know about Snapchat, with insights that shed light on what audiences think of the app and where its strengths lie.

From user growth trends to advertising effectiveness, let’s look at the state of Snapchat right now.

What Is Snapchat?

Snapchat is a social media app that allows users to share photos and videos with friends and followers online.

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Unlike other social platforms like Facebook, Instagram, and TikTok – where much of the content is stored permanently – Snapchat prioritizes ephemeral content only.

Once viewed, Snapchat content disappears, which adds a layer of spontaneity and privacy to digital interactions.

Snapchat leverages the power of augmented reality to entertain its audience by creating interactive and immersive experiences through features like AR lenses.

Users can also explore a variety of stickers, drawing tools, and emojis to add a personal touch to everything they post.

What started as a small collection of tools in 2011 has now expanded to a massive library of innovative features, such as a personalized 3D Snap Map, gesture recognition, audio recommendations for lenses, generative AI capabilities, and much more.

Creating an account on Snapchat is easy. Simply download the app on Google Play or the App Store. Install it on your device, and you’re ready!

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Screenshot from Google Play, December 2023

25 Surprising Facts You Didn’t Know About Snapchat

Let’s dive in!

1. Snapchat Has 406 Million Daily Active Users

That number, released by the company in October 2023, represents an increase of 43 million year-over-year – a 12% increase.

Here’s a chart from Statista showing Snapchat’s user growth from 2014 to 2023:

Snapchat’s user growth from 2014 to 2023Screenshot from Statista.com, December 2023

And with 750 million monthly active users (MAUs), Snapchat is the fifth-biggest social media network in the world.

2. Users 18-24 Years Old Account For The Biggest Chunk Of Snapchat’s Audience

According to Snapchat’s own advertising data, the platform has 243.5 million users aged 18 to 24 – representing 38.6% of its total ad audience.

The second largest group of users are between the ages of 25 and 34, followed by 13-17-year-olds – proving that Snapchat is reaching young people around the world.

On the flip side, the platform isn’t huge with older users; people aged 50 and over account for only 3.8% of Snapchat’s total ad audience.

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As a marketer, you can take a hint on what your campaign should focus on if you use Snapchat. As Snapchat’s own report puts it:

“From its inception, Snapchat has inherently created a frictionless space where Gen Z creatives can experiment with their identities, yet not have to feel like they’re ‘on brand’ in communicating to their close friend groups.”

3. Snapchat Reaches 90% Of The 13 To 24-Year-Old Population

It also reaches 75% of people between the ages of 13 and 34 in over 25 countries, according to Snapchat’s estimates.

In the US, 59% of American teenagers (between the ages of 13 and 17) report using Snapchat. This number amounts to roughly six in 10 US teens.

4. Snapchat Users Open The App Nearly 40 Times A Day

According to the company, this means people interact with their social circles on Snapchat more than any other social network.

In the US, about half of teenagers (51%) report using Snapchat at least once a day – making it slightly more popular than Instagram, but not quite as popular as YouTube or TikTok.

5. Taco Bell Paid $75,000 For 24 Hours Of The Taco Filter/Ad

To boost sales, Taco Bell launched the taco filter on Snapchat. Here’s what it looked like.

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The filter is humorous, relevant, and unique. Users adored it, and it got 224 million views.

That’s great, considering Taco Bell paid $75,000 for the ad – which actually proved to be a great investment for the exposure the brand received.

6. More Than Half (50.6%) Of Snapchat Users Are Female

In contrast, 48.7% of the platform’s global users are male.

While there is not a huge discrepancy between the demographics here, it’s helpful information for any marketers looking to put together Snapchat campaigns.

7. Snapchat Is The No. 1 App People Use To Share What They Bought

Is your brand looking to reach young social media users around the world? Snapchat could be the perfect platform for you.

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People are 45% more likely to recommend brands to friends on Snapchat compared to other platforms.

They’re also 2X more likely to post about a gift after receiving it – making Snapchat a powerful tool for influencer marketing and brand partnerships.

8. Snapchat Pioneered Vertical Video Ads

Once a novelty in the social media industry, vertical video ads have become one of the most popular ways to advertise on social media and reach global audiences.

What are vertical video ads? It’s self-explanatory: They’re ads that can be viewed with your phone held vertically. The ad format is optimized for how we use our mobile devices and designed to create a non-disruptive experience for users.

You’ve definitely seen countless video ads by now, but did you know Snapchat pioneered them?

9. You Can Follow Rock Star Business Experts On Snapchat

Who knew Snapchat could be a powerful business tool? Here are the top three experts you should follow right now:

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10. More Than 250 Million Snapchatters Engage With AR Every Day, On Average

Snapchat was the first social media app to really prioritize the development of AR features, and it’s paid off.

Over 70% of users engage with AR on the first day that they download the app – and, to date, there have been more than 3 million lenses launched on Snapchat.

11. People Are 34% More Likely To Purchase Products They See Advertised On Snapchat

When compared to watching the same ad on other social media platform, Snapchat proves to be an effective way to reach and convert.

12. Snapchat Is The King Of Ephemeral Content Marketing

Ephemeral content marketing uses video, photos, and media that are only accessible for a limited time.

Here are three reasons it works:

  • It creates a sense of urgency.
  • It appeals to buyers who don’t want to feel “sold.”
  • It’s more personalized than traditional sales funnel marketing.

Guess who’s one of the kings of ephemeral content marketing? That’s right: Snapchat.

Consider that if it weren’t for Snapchat, Instagram Stories would likely not exist right now.

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13. More Than 5 Million People Subscribe To Snapchat+

Snapchat+ is the platform’s paid subscription service that gives users access to exclusive and pre-release features on the platform.

Subscribers also receive a range of other perks, including options to customize their app experience and the ability to see how many times their content has been rewatched.

The fact that so many millions of users are willing to pay for special access and features to Snapchat should be a sign to brands and marketers everywhere that the platform has a strong pull with its audience.

Beyond that, the fact that Snapchat+ drew 5 million subscribers within just a year or so of launching is impressive on its own.

14. Snapchat Reaches Nearly Half Of US Smartphone Users

According to Statista, approximately 309 million American adults use smartphones today.

Snapchat’s ability to reach such a considerable portion of US smartphone users is notable.

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15. Snapchat Users Spend An Average Of 19 Minutes Per Day On The App

That’s 19 minutes brands can use to connect with people, grow brand awareness, and convey their message.

16. Snapchat’s Original Name Was Picaboo

In fact, Snapchat did run as Picaboo for about a year.

17. Snapchat Was Created After 34 Failures

Snapchat creators Evan Spiegel, Bobby Murphy, and Frank Reginald Brown worked on the Snapchat project while they were studying at Stanford University.

After 34 failures, they finally developed the app as we know it today.

18. Snapchat’s Creators Had A Major Falling-Out Before The App Was Released

Frank Reginald Brown was ousted from the Snapchat project by his friends.

Although no one knows the real story, Brown claims Spiegel and Murphy changed the server passwords and ceased communication with him a month before Snapchat was launched.

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19. Snapchat Downloads Doubled After The Launch Of The Toddler & Gender Swap Filters

Users downloaded Snapchat 41.5 million times in a month after the release of these filters!

20. Mark Zuckerberg Tried To Buy Snapchat

Snapchat’s owners refused to sell Snapchat to Zuckerberg (even though the offer went as high as $3 billion!).

21. Snapchat’s Mascot Is Called Ghostface Chillah

The mascot was inspired by Ghostface Killah of the Wu-Tang Clan – and when you consider that the app was once called “Picaboo,” the ghost logo makes more sense.

Apparently, Snapchat co-founder and CEO Evan Spiegel has said that he developed the mascot himself and chose a ghost based on the ephemeral nature of Snapchat content.

22. Facebook And Instagram Borrowed Ephemeral Content From Snapchat

As we mentioned above, we have Snapchat to thank for Facebook and Instagram Stories, which have since become integral to the social media experience.

Snapchat also pioneered the use of AR filters, which were adopted by Instagram and paved the way for the filters that dominate the world of TikTok today.

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23. 75% Of Gen Z And Millennials Say Snapchat Is The No. 1 Platform For Sharing Real-Life Experiences

Social media is all about authentic moments and human connection – and social media marketing is no different.

With such a large number of young people preferring Snapchat over other platforms for sharing their life experiences, marketers should follow suit.

Find ways to share behind-the-scenes moments with your team and company, and emphasize the humans behind the brand.

24. Snapchat Users Have Over $4.4 Trillion In Global Spending Power

That’s nothing to sneeze at.

25. In 2022, Snapchat Generated $4.6 Billion In Revenue

It is currently valued at over $20 billion.

Looking Ahead With Snapchat

Snapchat’s ephemeral content, intimacy, and spontaneity are strong points for everyday users, content creators, and businesses alike.

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Marketers should keep a keen eye on emerging trends within the platform, such as new AR advancements and evolving user demographics.

Those looking to reach younger audiences or show an authentic, human side of their brand should consider wading into the waters of Snapchat.

By harnessing the power of ephemeral content and engaging features, brands can effectively use Snapchat to grow their brand awareness, engage with audiences on a more personal level, and stay relevant in the fast-paced world of digital marketing.

More resources:


Featured Image: Trismegist san/Shutterstock



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