Archive for April 9th, 2021

1st Party Data Vs. 3rd Party Data: Why It Matters For Your Brand’s Strategy

1st Party Data Vs. 3rd Party Data: Why It Matters For Your Brand’s Strategy

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When Google announced it would be phasing out the use of third party cookies by 2022, it dawned on advertisers that much of their current data collection methods were as good as dead. While some organisations are still stressing, others are reviewing their response to the change and shifting their strategy.

You’re going to want to be in the second camp. With the resolution of third party cookies comes the focus on first party data. We built this guide to help you understand the differences between the two, and why this knowledge should form a key part of your marketing strategy this year.

What is first party data?

First party data is collected by your company. This data is taken directly from your audience, which includes site visitors, customers and social media followers. This type of data is most often used for retargeting campaigns; because it’s based on your audience, it provides the best indication of what future behaviour might look like.

This data is mostly collected from:

  • Actions/behaviour that take place on your website or app
  • Your CRM
  • Your social media communities
  • Subscription-based emails
  • Surveys and customer feedback

What is third party data?

Third party data is collected by an external business that doesn’t have a direct link to your own customers. This data is then sold to businesses like yours to help them with targeted marketing strategies, but this means it’s also available to your competitors.

Third party data is most often collected through surveys, interviews and feedback forms. Best practices dictate that third party data should be used as a complement – not in place of – your own data collection methods.

Customer data platforms at the heart of your strategy

Instead of dwelling on the absence of third party data, the companies getting ahead are those enhancing their marketing strategies using their own data. Taking control of your own data starts with building an effective customer data platform (CDP).

There are 3 stand-out reasons to make data collection a key part of your strategy:

  1. Effective targeted advertising
  2. Automation
  3. Customer privacy

Targeted advertising

A CDP is a type of marketing technology that’s gaining momentum at the moment, for obvious reasons. It combines all of your customer data into unified customer profiles, for use in marketing campaigns. When you make first party data collection a driving point in your brand strategy, that means you can continue rolling out effective relevant advertising.

Because this data is collected straight from the source (your audience), you know it’s accurate and tailored to your business. You can use this for retargeting, nurturing, and during the selling stage.


Customer data platforms collect first party transactional, behavioural and demographic data for a 360-degree profile that can be used by automation tools. Incorporating automation technology into your strategy boosts efficiency across processes and enhances personalisation throughout the buyer journey.

As put by the Founder of CPD Institute, David Raab:

“The most challenging barrier to marketing automation success is data integration between the various marketing systems of an organisation.”

From segmenting email lists through to sending out personalised promotions, first party data merged with automation enables you to engage your audience with the stuff that really matters to them.


A very key concern of today’s consumers is privacy. For a long time, brands and third parties have collected customer data without a great deal of transparency. With Netflix shows such as The Social Dilemma educating the general public on what’s happening behind the screen, users expect data protection – and many won’t interact with brands that don’t explicitly provide this. 87% of consumers are concerned about how their data is collected. So, having a transparent privacy policy in place has become more than just doing the right thing – it’s a competitive advantage.

Dive into a data-backed strategy

A survey carried out by Campaign found that 96% of advertisers feel ready for a world without third party cookies. That’s great news all around, because collecting data directly from consumers is more relevant, cheaper and competitively advantageous.

We use automation and leading marketing tech to build powerful customer data platforms, so your campaigns are backed by real insights and targeted to the people that care. Get in touch to find out more about how we put data at the heart of your strategy.


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Core Web Vitals: What You Need to Know in 2021

Core Web Vitals: What You Need to Know in 2021

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For a while now, better user experiences have been an important ranking factor in the eyes of Google. Studies and research by Google found that users prefer sites with a great page experience. No real surprise, there.

However, instead of sitting on this information, Google is placing a greater focus on Core Web Vitals (CWV) this year. This is all about making “the web more delightful for users across all web browsers and surfaces”, which should improve engagement and contribute to business’ SEO success. Let’s take a look at what Core Web Vitals are and why businesses need to be prepared for May 2021.

What are Core Web Vitals?

Core Web Vitals are a set of metrics that cover a website’s speed, responsiveness and visual stability. In other words, they’re important factors that contribute to user experience. 

Google itself described the concept in the following way:

“Core Web Vitals are a set of real-world, user-centered metrics that quantify key aspects of the user experience. They measure dimensions of web usability such as load time, interactivity, and the stability of content as it loads (so you don’t accidentally tap that button when it shifts under your finger – how annoying!)”

CWV can shift as time goes on and what users class as a “good page experience” changes. For now, the 3 Core Web Vitals as defined by Google are:

  1. LCP – Largest Contentful Paint: The time it takes for the largest content element to appear on the screen. An ideal LCP is 2.5 seconds or less.
  2. FID – First Input Delay: The time it takes for a web page to become interactive, which ideally should be less than 100ms.
  3. CLS – Cumulative Layout Shift: How much visual content unexpectedly shifts around the page. A good measurement is less than 0.1.

What’s all the fuss about May 2021?

In May 2021, we can expect a core algorithm update that will use Core Web Vitals as a major ranking factor. As the next step in Google’s focus on internet speeds and website load times, it’s important that businesses pay attention.

The roll-out follows increased interest in UX, with 70% more users engaging in Lighthouse and PageSpeed Insights.

In addition to the Core Web Vitals mentioned above, the following existing search signals will still be taken into account:

  • Mobile friendliness: Sites optimised for mobile devices
  • Safe browsing: Sites are free of malware and security issues
  • HTTPS: The protocol represents a secure connection
  • No intrusive interstitials: No disturbing pop-ups that cover site content

Why it matters for your business

Now you know what to expect from the May algorithm update, you can get prepared. Making sure users interact with your content and have a speedy, positive experience, can mean the difference between a conversion and a bounce.

When you don’t optimise your website’s load time, a few things happen. The buying decision of 70% of consumers will be impacted, and your conversion rates will drop by around 4.42% with each second that passes.

That means it’s time to ask yourself: Are my core vitals up to scratch? Unfortunately, there’s no blanket solution to making sure page speed and interactivity are optimal, since each CMS and online store platform works differently. 

You can, however, use PageSpeed Insights and other reporting tools to check that the following levers for each Core Web Vital are optimised:

  1. LCP – server response time, loading time for CSS, images and fonts, and rendering 
  2. FID – code from third parties, JavaScript run time, volume of server requests 
  3. CLS – size of images and videos, stable size of preloaded elements, space for potential advertising

Get a site analysis from Abstract Digital

If you’re not sure how to optimise your site for the May 2021 algorithm update, we can help. Our team of SEO technicians carry out in-depth site analyses to ensure CWVs meet the expectations needed to rank.

Just send us a message for a quote.

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Algorithms in Advertising: How Machine Learning & AI Help Marketers Sell

Algorithms in Advertising: How Machine Learning & AI Help Marketers Sell

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We’re not mind readers or magicians, but the algorithms can sometimes make it seem that way. Love them or hate them, algorithms simply come back to the basics of computer science. They work on a set of “if-this-then-that” rules, but have developed to offer marketers a whole host of ways to sell to their customers.

Using knowledge about specific customers, demographics, previous and predicted behaviour, and data about similar customers, companies have information at their fingertips that better equips them to sell, upsell, cross-sell, promote, and so on.

Algorithms are everywhere. They play a part in what we buy, what we watch, and even what we believe. Estimates say that 35% of what we buy on Amazon, and 75% of what we watch on Netflix can be put down to algorithms. For our purposes, we’re going to look at the core algorithms we as digital marketers use: Google and Facebook.

Machine learning in Google Ads

Google messes around with its algorithms all the time. It’s impossible for advertisers to predict what’s going to happen next. As of 2021, Google has more than 200 ranking factors in its organic search portfolio, and uses machine learning in services including Gmail, Google Search and Google Maps. But we’re going to look at how Google Ads uses machine learning and artificial intelligence in PPC advertising.

Google uses a Smart Bidding feature that automatically optimises your PPC campaign for you. Once you’ve completed the initial setup, this leverages Google’s user data metrics to optimise bids and maximise conversions. Machine learning is based on patterns, so Google uses stored information from years of PPC ads to automatically boost new campaigns.

This helps advertisers in a few ways:

  • Google is a mega name in the space, with access to massive amounts of data. It uses this to find your ideal audience and to understand when they’re most likely to convert.
  • You can save time and leave most of the optimising to the machines.
  • There’s little manual maintenance needed; Google Ads campaigns need tweaks every now and again, but machine learning takes care of actually showing your ads at the most optimal times.
  • Your audience gets a more personalised experience that’s essential to appeal to modern consumers.

Machine learning & Artificial Intelligence in Facebook

Artificial Intelligence relies on a lot of data – something Facebook isn’t short of. Facebook uses the technology in a way that focuses on emulating human intelligence, with Mark Zuckerberg saying he wants to “enable computers to understand language more like humans would”.

Following the Cambridge Analytica scandal, the company pledged to use AI to solve issues across 7 key categories:

  1. Hate speech
  2. Terrorism
  3. Nudity
  4. Graphic violence
  5. Spam
  6. Suicides
  7. Fake accounts

But of course, advertisers provide the biggest chunk of the company’s income (98% of its revenue comes from selling ads), so Facebook is also using machine learning and AI to help marketers connect with their customers.

The way users communicate and interact on Facebook is a huge source of its data. What they like, don’t like, their lifestyle, the devices they use, what they buy and watch, and who they speak to. It’s all collected and quantified by AI, to give structure to the messy information and generate valuable insights from it.

Some (slightly futuristic) ways AI benefits marketers on Facebook include:

  • Facebook uses a tool called Deeptext, which deciphers the meaning of textual content and then shows users relevant ads based on the conversations they’re having.
  • Machine learning models predict how likely a person is to take an advertiser’s desired action. It uses information like the person’s behaviour on and off Facebook, time of day, and the ad’s content.
  • Facebook revealed that it uses multiple layers of machine learning (ML) models to manage what people see in their News Feed. ML predicts what users are likely to be interested in or engage in, based on who/what they follow, and their previous engagements. When ranking ads in the News Feed, this information helps machines to work out which users are most likely to take action when they see particular ads.

When the machines & humans work together

Relying on ML and AI is only half of the story. We believe that effective advertising needs that personal touch. The specialists at Abstract Digital have an intimate understanding of core advertising algorithms, and can use this to boost your digital marketing and ensure it reaches the right users at the point of intent.

For more information about how we strengthen data-backed ads with creativity, get in touch.

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