Author: Franz Malten Buemann

We’ve all had it happen to us: we try to choose an option on a website, and right before we click, the page jumps away and we end up clicking something we didn’t mean to. Doh!
Like a game of “down low, too slow”, this website behavior makes us feel slow and frustrated. Fortunately, website developers are now incentivized to improve their site experience with the release of Google’s Core Web Vitals; a set of metrics that help site owners measure and improve the user experience of their web pages.

Cumulative Layout Shift is one of these key metrics that measures the “jumpiness” of a website and how it unexpectedly moves as elements load. Let’s take a closer look at how this metric works, and how you can make sure your own website is following best practices so you rank higher on search engines and provide your users with a better experience.
What is Cumulative Layout Shift?
Cumulative Layout Shift (or CLS) is a measure of how much a webpage unexpectedly shifts during its life. For example, if a website visitor loaded a page and, while they were reading it, a banner loads and the page jumps down, that would constitute a large CLS score.
Along with Largest Contentful Paint (the amount of time it takes to load the largest piece of content) and First Input Delay (how long it takes for a page to be interactive or “clickable”), CLS is part of Google’s Core Web Vitals. Google’s web crawlers measure CLS on each page they index.
What causes Cumulative Layout Shift?
Page shifts happen when content loads at different speeds and causes the layout to change and alters what the viewer is looking at. Advertisements loading slowly, videos of unknown size suddenly appearing, or DOM elements being dynamically added are all potential causes of CLS.
The example below shows what happens when an ad banner is loaded after the rest of the webpage loads. The content is pushed down, and the user experience is negatively impacted.

Source
It can be difficult to know if your users are experiencing CLS, because not every device or environment operates in the same way. If you’re loading your website in a development environment you may have elements cached or they may be loading locally. Personalized web content based on cookies will behave differently for every visitor, especially depending on their location. Plus, mobile users can have a very different experience – a small shift on a web browser may be monumental to someone viewing the site on a small screen. Really the only way to understand your users’ experience is to measure CLS, which we’ll go over below.
Why is CLS important?
Understanding CLS is critical for two reasons: your visitors’ experience and your search engine ranking.
Your visitors have high expectations when it comes to your site’s performance. In 2020, 93% of people reported leaving a website because it didn’t load properly.
Jumpy websites that load in pieces or with unexpected behavior will cause your visitors to find another website to browse. And if they do stick around, a high CLS score is likely to cause usability problems like choosing the wrong option, checking out too early, or missing parts of your website altogether.
This problem is only exacerbated by the large number of internet users who are browsing on their smartphones. When viewing your site on a small screen, any jumps and layout shifts on the website are certain to have a big impact on mobile user experience.
Optimizing your site and reducing your cumulative layout shift is essential to providing customers with a good experience.
Secondly, Google ranks sites based on their page performance. A better user experience results in a higher search ranking. If your page doesn’t meet the standards that Google lays out in their Core Web Vitals guidelines, your site will be penalized.
Google doesn’t want to direct people to sites that don’t perform well. Aligning with CLS best practices can help your website move up the rankings. And since 68% of online experiences start with a search, making sure your site shows up on the search results page is important to generating inbound traffic.
How do you measure Cumulative Layout Shift?
The good news is that you don’t have to measure CLS yourself because Google makes it really easy to analyze your page performance with their PageSpeed Insights tool, or in the Chrome browser using Lighthouse Tools.
To analyze performance in PageSpeed Insights:

Enter a website URL into Google’s PageSpeed Insights tool.
Click ‘Analyze.’
Check your performance. You can review both mobile and desktop performance, which you can switch between using the top left corner navigation.

The page analyzed below shows a good cumulative layout shift score of 0.001.
To analyze performance using Lighthouse tools:

Open up the website you want to analyze in Chrome.
Navigate to Developer Tools by clicking the three dots in the top right corner of the browser window, choosing “More Tools” and then “Developer Tools.”
When the console opens, choose “Lighthouse” from the options along the top.
Click “Generate Report.”

The page below shows a CLS of 0.109, or “needs improvement.”
Lighthouse provides a detailed audit of what contributed to that score. To review the audit, scroll down and choose “Show audits relevant to CLS.”

More About Impact Fraction and Distance Fraction
Two terms you might see when researching CLS are “impact fraction” and “distance fraction.” These are the two variables that Google uses to calculate CLS.
layout shift score = impact fraction x distance fraction
Impact fraction relates to the size of the unstable element in comparison to the viewport. Distance fraction is the amount the unstable element moves as a ratio of the viewport.
So a high CLS would consist of a large element moving a long distance. A small CLS would be the result of a small element moving only a small distance.
CLS is the largest “burst” or group of layout shift scores that occur during a session window. Essentially, if a bunch of shifts happen within a five-second window, this would be considered disruptive and result in a large CLS score.
What is a good CLS score?
A good cumulative layout score is anything less than 0.1. The reports from PageInsights or Lighthouse tools will automatically flag any poor scores, as well as provide advice on how to optimize the page for better performance.
Source
How To Improve Cumulative Layout Shift
There are a few best practices that website owners can follow to improve their CLS score:
1. Use a CMS (content management system).
Especially one that integrates with Google Lighthouse or other diagnostic tools. This will make sure that you’re designing with best practices in mind, and flag any issues before you launch your site.
2. Specify size attributes for images and videos.
Rather than letting them set their own height and width, dictate size attributes for your media. By setting these attributes, you’re telling the browser how much space to set aside, even if the image isn’t loaded yet.
3. Understand how ads can influence your layout.
Google Publisher Tag offers extensive guidance about how to reserve space for ads.
Load new content below the viewport. Loading content above what the user is viewing will often cause a page to shift.
4. Use transitions and animation to provide context around page changes.
For example, a “Read more” link that scrolls the user down the page would not impact CLS because it’s an expected layout shift.
A Note On Expected Vs Unexpected Layout Shift
CLS only takes into account unexpected changes. If the layout changes because of a user-initiated action, there is no impact on CLS. This is a helpful tool to use when you don’t need to load everything all at once. Instead, offer users the opportunity to choose which elements they want to view through “read more” links or “expand topic” accordions within your page.
Offer A Better User Experience With CLS Optimization
Paying attention to CLS not only provides a better user experience, but it also boosts your search result rankings. It’s a win-win.
To meet Google’s standards for CLS, start by using a diagnostic tool to measure your website’s current performance. Take into account the basic guidelines outlined above, and keep layout shifts top of mind when designing your website, especially around transitions and content additions. With these few simple considerations, you’ll see better results across the board.

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Learn how to maximize your audience growth in a FREE webinar on December 7th. In this webinar you’ll learn:
Top email and influencer marketing trends for 2022 How to push your email strategy and create the ideal email narrative How to best leverage Influencer Marketing through your customers

It’s also the perfect opportunity to ask all your questions live! Register today: https://app.livestorm.co/sendinblue/email-and-influencer-marketing-for-growth?type=detailed
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Looking for new ways to grow your marketing agency? Here are the 10 best agency partner programs to earn money for recommending tools you’re already using.

 

 

As the company matures, its MarTech stack grows more or less complete and it leaves less room for new tools and improvements, not to mention the costs, that grow rapidly. Yet, the CDP market growth estimations show some whooping values, and this fact clearly indicates that even mature companies see the potential in enriching the stack with Customer Platform. This happens, because CDP not only integrates seamlessly with the existing MarTech stack without major changes or costs, but unlocks the potential of this stack in the first place, making money invested in the MarTech in the past pay off more. In this article we will explain, in detail, how. 

 

CDPs and CIPs rapid rise and the question why?

 

It is estimated that the customer data platform market will grow from USD 3.5 billion in 2021 to USD 15.3 billion by 2026, at CAGR 34.6%.

 

 

During the same forecast period, the global analytics market size is estimated to grow from USD 9.6 billion in 2021 to USD 25.3 billion by 2026, at CAGR 21.3%. 

 

source

 

TheNext Gen CDPs or AI-empowered, Customer Intelligence Platforms, which are basically the same, fall simultaneously into these two categories. On top of this,  84% of marketers plan to include AI capabilities in-house. AI plays an essential role in a modern CDP.

 

Companies around the world adopt CDPs into their MarTech stack, and they do it rather rapidly, like it would be a must-have for a company to even keep up with the competition. 

 

But why? 

 

As we will show in the next chapter, the current MarTech stack, modeled on an average, mature eCommerce company, is already rather impressive. Various expensive systems already are supposed to work in synergy, to provide the companies with outstanding customer insights and assure unparalleled customer experience throughout all the touchpoints.

 

The truth is, that they don’t. Or actually didn’t, until Next Gen CDPs / CIPs entered the stage. Each of the tools in the model stack works well, does what it was designed to do. It is their orchestration that proves to be lacking. 

 

In this article we will uncover the reason behind the rapid CDPs market growth, by explaining, how the adoption of a CDP not only adds it’s individual value, but, finally, unlocks all the repressed potential of all the tools already in disposition, enabling true synergy and becoming functional foundation of the modern MarTech stack. 

 

First let’s take a look at a model stack, often in use today.

 

Traditional MarTech stack model

 

There are plenty of tools in a typical MarTech stack. In this chapter we will briefly introduce some of them and explain how they are supposed to work together to collect the customer data and then activate it in various channels/touchpoints.

 

Data collection

 

Enterprise Tag Management

 

Tag management systems control the deployment of all other tags and mobile vendor deployments via web interface, without any software coding. Tag management systems make it easy to add, edit or remove any tag with point and click simplicity.

manages data collection
manages third-party tags
manges digital data distribution

 

Digital Analytics

 

Digital analytics tools gather and analyze digital data from various sources like websites, mobile applications, among others. It provides a vision on how users or customers are behaving. Through digital analytics, companies obtain an insight into the areas where they need improvement. 

behavior and segmentation
analytics
historical segmentation

 

Data Management Platform (DMP)

 

A direct predecessor to CDP platforms, that was “almost it”, emerged in the early 2000’s. DMP gathers and organizes second and third-party data and shares it with other marketing technology systems to gain deeper insights into customers. It can also segment anonymous ID’s.

customer recognition
audience segmentation, activation and orchestration
look alike modeling and third-party data

 

CRM

 

As of 2021, we can easily call Customer Relation Management the legacy system. It came into existence even before DMPs, in the early 1990’s. It is a technology for managing a company’s relationships and interactions with all of its customers and potential customers. It started with sales, then customer service and marketing came along. Finally commerce joined. Primarily however, CRM works with operational data of known customers. 

customer data, scoring and attributes
opt-in preferences
products/orders

 

Data Activation

 

CMS and eCommerce tools

DAM and Content Management
Template creation and publication
Content authoring

 

On-site and app personalization tools

A/B and MV testing
Targeting and personalization
Recommendation

 

Media tools

DSP/Ad Server
Retargeting platforms
SEM tools
Paid social platforms

 

Cross-channel Campaign Mgtm.

Direct Marketing automation management
Contact and Offers management

 

How they all are supposed to work together

 

This classic package of tools is fundamental to lay a baseline for digital data collection and activation. 

 

In the collection part, data from the Tag Management tool is sent for analysis and segmentation to the Digital Analytic tool. Such prepared data is then sent to DMP, that should provide customer recognition, segmentation and orchestration, using third-party data. DMP also exchanges the data with the legacy CRM, enriching it and taking what is needed for recognition and segmentation. From this central, DMP part, data is then activated.

 

The data is transferred to various activation systems: CMS and eCommerce, On-site and app personalization tools are used to manage the content of the websites and apps as well as for testing, targeting, and personalization of this content. Media tools optimize the content for paid external media, and Cross-channel Campaign Mgtm. has the use in Direct Marketing, like email, sms or chat channels.

 

In reality, such a stack does not work smoothly enough to feed the activation tools with real-time, unified data. 

 

Traditional MarTech stack painpoints

 

Dispersed data

 

Systems like DMP and CRM keep the data in their own silos. They are connected, they are able to mutually enrich or correct their datasets, but none of them puts them all together to create a unified, single source of truth about the customer.

 

From this problem another emerges. Traditional MarTech stack suffers from connection issues between different tools and technologies. The difference in implementation of these tools results in loss of data consistency across the stack. 

 

Security and privacy issues

 

When there is inconsistency and connection issues, the often invaluable data gets inevitably lost. Traditional MarTech stack puts the companies at a major risk of either have their customer data stolen by the outside agent, or misused by i.eg. marketing team.

 

It is next to impossible to control and protect from the hackers all the data flowing back and forth between different systems and technologies, when each has its technological weak spots and it is hard to determine if a data loss, leak or change was an effect of connection problems, human error or outside intrusion. It is no less difficult to manage the customer marketing consents in this situation. 

 

Identity resolution

 

Another problem, linked to data consistency and time to activation, is insufficient ability of the traditional MarTech stack to solve identity resolution. Without a single source of truth about the consumer for the company, marketers are forced to use third-party data in their efforts to unify the customer profile. Third-party data are, as for 2021, becoming a thing of the past. Not to mention, how much time and effort has to be put into, in the end, futile trying to achieve a unified customer profile.

 

Time to activation and time to market

 

Marketing teams and, actually all teams in a company, struggle to deliver the effect of their efforts to the market on time. They are forced to manage the data using too many systems and tools that capture different datasets.    

 

Unfulfilled potential of the MarTech tools

 

All these problems combined diminish and repress the potential that each of the tools in the stack has. Fed with inconsistent data, transferred across differently implemented systems, the tools work slow and fail to produce really usable results. And delivering them in real-time is out of the question.

 

To put it simply, traditional MarTech Stack fails in the task of providing consistent, connected and actionable customer experience in all touchpoints offline and online.

 

CDP / CIP definition

 

The Customer Data Platforms emerged to deal with this problem exactly. Before we explain how the CDPs work, let’s define them. They were designed not to displace all the expensive MarTech stack, but to finally unlock it’s potential and make the data gathered in it usable.

 

The most clear and understandable definition of a Customer Data Platform is probably this on, provided by Gartner: “A customer data platform is a marketing system that unifies a company’s customer data from marketing and other channels to enable customer modeling, and optimize the timing and targeting of messages and offers.”. 

 

Another, also interesting, has been coined by the CDP Institute. According to them, the Customer Data Platform is a “packaged software that creates a persistent, unified customer database that is accessible to other systems.”.  

 

“Packaged software” means that CDP is ready-to-use, off-the-shelf software, provided usually by the vendor. 

 

The part about “persistent, unified customer database” means that CDP collects the data from many different sources, basically, all the company’s touchpoints, where acquiring first-party consumer data is possible, like sales, loyalty, customer service, social media, etc. Data from different sources is stored in CDP, then merged and unified into a single customer profile.

 

Finally, “accessible to other systems” means that customer data is shared with any other system that needs it, like those used by sales, marketing, commerce. 

 

Customer Intelligence Platform (CIP) is the next step in CDPs evolution. CIPs include zero-party data along with first-party data to provide insights. And they leverage AI and machine learning to understand, resolve and evaluate both structured and unstructured data available to the company.  

 

CDP / CIP is just one system, collecting and analyzing data from all other tools/systems/touchpoints simultaneously, and building a unified customer profile, providing a single source of truth for the whole company in real-time.

 

Four CDP steps in making data consistent and usable

 

The CDP actually provides consistent, connected and actionable customer experience in all touchpoints offline and online. Unlike in the case of traditional MarTech stack, without CDP, where data is integrated in every tool separately, Customer Data Platform simply does it all, utilizing the whole stack and touchpoints, physical and digital. 

 

Collecting data from omnichannel sources

 

The  first task for CDP is to collect data from all channels, physical, like stores or call centres, and digital. The data originating from these omnichannel is sometimes anonymous, sometimes nominative. For example when they are linked to an email account or loyalty number.

This data, gathered in real time, is then linked to a wide range of attributes: promotional campaign, content consumed, purchase history, and stored. The goal is to gather all the precious first-party data to help develop customer intelligence.

personal and demographic data
onsite bahavioural data
engagement data
transactiona data
mobile data

 

Matching data to the same individual

 

Traditional marketing primarily targets devices. Today’s approach builds the contact around the person, the individual customer for better relevance.The next step CDP does is matching collected data for a people-based approach.

Cross-channel and cross-device matching is a painful task. First-party and zero-party data can prove useful in helping link information originating from different channels and devices to one single person. CDP also makes use of the existing stack, as the CRM, to extract old and nominative information that it can employ to successfully build a unified customer profile.  It can also take data from CRM and send it back enriched from other sources.

preparation
integration
enrichment

 

Segmentation and activation

 

Personal relationships with the consumer is a fundamental goal for modern marketers. To provide the means to do this, CDPs have  the ability to precisely segment profiles. This is its third step.

 

The CDPs task is to increase the number of possible segments based on several criteria: demography, geography, behaviour, etc. The CDP will then use these audience segments as a foundation to activate the solutions available, i. e.g. Dynamic Creative Optimization or personalised advertisements, marketing automation sequences or website personalisation.

The CDP can also activate data in physical touchpoints such as stores and call centres.

defining rules
building the audience
real time
email
push messaging
sms
social
web

 

Analyzing and optimizing

 

In the third step  activations are applied to segments and the subsequent results are analysed and serve as feedback to further refine these segments and activations. For example CDP gets the best out of A/B testing solutions. CDP can be connected to DMP to provide segments obtained from advanced statistics module as predicted ones.

 

The CDP breaks down metric silos and minimises interpretation bias for campaign results. In the case of the traditional MarTech stack, the attribution models are still rather basic.  The ways in which the performance of i.e.g. campaigns are too isolated to provide the company with sufficient, unbiased results. CDP’s central coordinator role allows for distinct performance metrics from one scenario to another.

modeling
analysing
automation

 

CDP fit into marketing stack

 

Since CDP effectively utilizes all existing MarTech stack employed by the company, one can ask, what this platform really is and how it fits into the existing stack? Is it just another solitary, monolith solution? A system built from several services? Or maybe an architecture, putting together and squeezing the most of already existing tools? 

 

Since the answer to all above questions may easily sound “yes” it is clear that CDP means for the company more than just its definition. 

 

The most practical role or a function for CDP in a MarTech stack is to be its foundation, making all the data gathered in there a single truth, actionable for all the company’s divisions. It does not change the precious stack itself, instead acts as a support for each and every individual tool and adds a value to it. 

 

Challenges for CDPs in the future

 

As for 2021, the biggest challenge for CDP’s, both platform providers and their clients, is an accurate assessment of how much data is actually enough. 

 

Everybody knows that more data provides the analyses with more context and provides the company with more meaningful results. But the major obstacles for unobscured customer data landscape can be summed up in these questions:

How can deeper insight into customers’ patterns be gained?
How to store all this data?
How to do it safely?
How much of it is actually useful in terms of enhancing customer experience?

 

Currently, we work to address all these issues, in order to provide our clients with even more useful datasets that will be actionable across all the company’s touchpoints on the customer journey.

 

To see what immense possibilities in providing consistent, connected and actionable data a modern CDP can offer you today,  request a SALESmanago demo.