Web designers employ user testing to evaluate a website’s functionality and overall UX (user experience). Various methods are used to gather feedback, but they all boil down to observing how users interact with the UI (user interface).
In May 2018, the General Data Protection Regulation (GDPR) revolutionized the landscape of digital privacy, directly impacting how businesses across the globe approach email marketing. With its stringent rules on consent, data access, and the rights of individuals, GDPR has reshaped the principles of customer engagement in the digital era.
This article dives into the strategies for navigating the challenges and opportunities of email marketing in a post-GDPR world. Let’s dive in!
Here are some key GDPR requirements affecting email marketing:
GDPR empowers individuals with the right to access their personal data held by companies. For those in email marketing, this means being prepared to provide individuals with a copy of their data upon request. This level of transparency aims to enhance consumer trust by giving them insight and control over how their information is used.
The regulation also introduces the concept of data portability, allowing individuals to move, copy, or transfer personal data easily from one IT environment to another. For marketers, this emphasizes the need to keep data in formats that are accessible and easily transferrable, facilitating a smoother experience for users wishing to take their data elsewhere.
Also known as the right to erasure, this gives individuals the power to have their personal data deleted under certain circumstances. This challenges marketers to implement efficient ways to manage and delete data as requested, which may require adjustments in data management and retention policies.
GDPR has established stringent rules to protect personal data in the realm of email marketing (as well as the European digital ad market overall). Your understanding of these regulations is crucial for both compliance and the effectiveness of your marketing campaigns. Here are key email marketing strategies to ensure they are GDPR-compliant and still impactful:
A fundamental aspect of GDPR is its emphasis on clear, explicit consent for data collection and processing. Email marketers must now ensure that consent is freely given, specific, informed, and unambiguous. This means using clear language when asking for permission to send emails and being transparent about how you plan to use subscribers’ data.
Practical Tip: Instead of simply aiming to increase your email list size, focus on quality. Use double opt-in methods where users first sign up and then confirm their subscription through an email link. This not only ensures compliance but also builds a list of engaged subscribers.
Segmentation involves dividing your email list into smaller groups based on set criteria, such as demographics, purchase history, or behavior. GDPR has made it more important than ever to use data wisely and responsibly for segmentation.
Practical Tip: Use the information that subscribers have willingly provided and their engagement with past emails to tailor your content. This personalized approach not only respects the privacy of your subscribers but also significantly enhances the relevance of your emails, improving open rates and engagement.
Personalization and GDPR compliance can go hand in hand. The key is to use the data you have permission to use, to craft messages that resonate with your audience.
Practical Tip: Leveraging data such as purchase history and subscriber preferences can help you create content that feels tailor-made. Ensure that you’re transparent about how you use data for personalization and give subscribers easy options to control or opt-out of data usage for personalization.
Regularly cleaning your email list is not only a best practice for engagement but also a requirement under GDPR to ensure you’re not holding onto data without a legitimate reason. This involves removing inactive subscribers and those who have not engaged with your emails over a specified period.
Practical Tip: Conduct periodic audits of your email list to identify and remove subscribers who have not engaged with your emails for an extended period. Offer an easy re-engagement process for those who may wish to remain on the list but haven’t shown recent activity.
GDPR encourages the principle of data minimization, meaning only collecting data that is directly relevant and necessary for your email campaigns. This approach not only complies with GDPR but also reduces the risk of data breaches.
Practical Tip: Regularly review the data you collect at sign-up and through other interactions. Eliminate any fields that don’t directly contribute to the customer experience or the effectiveness of your campaigns.
Referral campaigns can be a powerful tool in your email marketing arsenal, especially in a post-GDPR landscape where acquiring new subscribers through compliant methods is crucial. By incentivizing your current subscribers to refer others, you can encourage organic list growth while ensuring new subscribers are genuinely interested in your brand.
Practical Tip: Design a referral program that rewards both the referrer and the referred for signing up and engaging with your content. Rewards can range from exclusive content, discounts, or access to premium features. Ensure that the referral process is as straightforward as possible, with clear instructions on how to refer new subscribers and how rewards can be claimed.
A common misconception is that GDPR spells the end for email marketing. On the contrary, GDPR presents an opportunity to refine email marketing strategies, making them more targeted, personalized, and with a base of subscribers who are genuinely interested in your brand.
Another misunderstanding is that GDPR compliance is too complex and costly for small businesses. While compliance does require an initial investment in time and resources, the regulation applies equally to all organizations processing the personal data of EU citizens, regardless of size.
The benefits of compliance, such as enhanced consumer trust and improved data management practices, can outweigh the costs.
In navigating the post-GDPR landscape, your email marketing strategies must prioritize compliance to foster trust with your audience. Aligning your practices with GDPR not only adheres to legal requirements but also demonstrates respect for user privacy—crucial for your brand’s integrity.
Refine your approach, viewing GDPR as an advantageous catalyst. This shift can lead to higher-quality subscriber lists and more engaged customer relationships. Remember, this regulatory environment affords a chance to enhance your marketing communication’s relevance and value, leading to increased trust and potential customer loyalty.
By committing to these principles, you’ll set a strong foundation for sustainable success in your email marketing efforts.
Featured Image by John Schnobrich on Unsplash
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Link building is important for bringing in organic visitors to your website. And HARO link building can make it possible for you to raise the likelihood of prospective clients finding you by enabling you to be mentioned on reputable websites.
HARO link building, however, is not just spending some hours during the day sending generic pitches to journalists. It is a lot more than that. HARO link building is about people’s real value, becoming a trusted voice, and creating long-lasting connections in your vertical.
This is why HARO link building is extremely important. You get to enjoy all these perks and many more.
Want to know more about the awesome benefits of using HARO for link building for your business?
Keep reading to find out.
Also known as Help A Reporter Out, HARO was developed to assist journalists in obtaining timely and pertinent opinions from experts for their publications. However, SEO specialists realized how important HARO’s link-building resources can be. As a result, they also found out how simple it was to use the platform to obtain a backlink from a well-performing online newspaper like Forbes or the Wall Street Journal (WSJ).
In simpler words, HARO is a newsletter published and delivered straight to your email three times a day, Monday through Friday. CEOs, CMOs, and CTOs signed up for it all at once when it became famous. These high-profile people might reply to a question related to their niche or area of expertise get published or featured in a blog online.
You can take your link building strategy to the next level with HARO.
By tapping into HARO’s network, you gain access to a pool of journalists actively seeking expert opinions and insights, paving the way for organic and authoritative backlinks.
Here are a few more benefits of HARO link building:
HARO link building can increase your brand’s recognition and awareness. People get more familiar with your brand when they see links to it from other websites (websites they like to visit frequently). Therefore, one benefit of this could be better name recognition for you and your brand.
In the end, it will assist you in becoming more respected in your field. People are therefore more likely to share your content. An upward spiral in traffic and search engine rankings may follow from this.
You can build meaningful and long-lasting connections with journalists by interacting with them on HARO. This is more than just a one time thing; it’s a collaborative effort that will hopefully lead to future media relations opportunities for you.
Finding credible websites to provide you with high-authority backlinks is one of the main benefits of using HARO. Answering pertinent questions that journalists submit on HARO increases the likelihood that your input will be included in their publications or articles with a backlink pointing to your website. In addition to increasing visitors, these backlinks will raise the authority and search engine rankings of your website.
Obtaining high quality and credible backlinks to your website is your only option if you want to rank highly on Google’s first page. HARO actually makes it possible for you to acquire these backlinks at a reasonable price so you can climb the Google search results page.
In other words, Help A Reporter Out is the place you need to be at if you are a blogger or a website owner if you want to boost your site’s backlink profile.
Using HARO for achieving your link building to achieve your marketing goals presents a cost-effective marketing opportunity for you, as compared to the run-of-the-mill advertising strategies that frequently need large financial inputs in order to work. You can get significant results without going over budget if you put effort into creating persuasive pitches and quickly responding to pitches relevant to your niche on HARO.
Your website’s Click-through rates (CTRs) will also increase when you use the credible backlinks acquired through HARO. Plus, since those backlinks have a higher level of trustworthiness, articles with HARO backlinks encourage readers to click on the internal links.
Higher CTRs are a sign of well-written content and well placed links, which can further convince search engines that a website is a useful resource. This can therefore have a favorable effect on your website’s visibility and SEO.
Furthermore, when the website becomes more visible due to HARO backlinks, there is a greater chance that it will be shared and mentioned on other platforms, which will raise the CTR and your website’s online presence.
HARO makes it easy, quick, and free for journalists looking for expertise to connect with companies that are willing to offer it. As a result, you get increased visibility and an authoritative presence in your target market.
Getting to know insider info about current events and industry trends is another advantage of using HARO for link building. By keeping an eye on HARO queries on a regular basis, you can spot new subjects or areas where your knowledge can be put to use, helping you establish yourself as an authority in your industry and giving journalists insightful information.
A lot of people believe that link building is a completely technical activity best left to the professionals. However, one of the most crucial elements of any SEO strategy is link building, as any seasoned digital marketer or SEO expert will tell you. It’s so crucial, in fact, that ignoring it will negatively impact the exposure and rating of your website.
The importance of link building can be attributed to several factors. The fact that links are a primary factor in how Google and other search engines assess the authority of a website is among the most significant. A website’s likelihood of ranking highly in search results increases with the number of high-quality links it possesses.
Although there are numerous link-building techniques, one of the greatest ways to obtain organic editorial links is through HARO link-building resources. So, if you want to enjoy all the benefits listed above, you need to get started on HARO link building ASAP!
Featured Image by The Climate Reality Project on Unsplash
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Have you ever wondered if your money will be managed by AI and not by a bank? What if your bank doesn’t exist in a real place and just on some massive supercomputer situated thousands of kilometers away? This might happen someday, so let’s see how it happens!!
In this article, we will examine the meaning of AI and cloud computing and how they currently influence and will transform the future of finance.
Investigating probable challenges and exploring detailed case studies, such as JP Morgan, Goldman Sachs, and Citigroup. Illustrating how AI and cloud computing, growing at a CAGR of 16.40 % (2024-2029) and 28.46% (2024-2030), will create innovation and the possibility of a dazzling global financial future.
Before seeing the future, let’s look at AI and cloud computing and how they relate to finance. AI stands for Artificial Intelligence; in a nutshell, it means “teaching computers to think and learn on their own.” Instead of following just a set of fixed instructions, AI helps computers analyze data, understand patterns, and make decisions based on that information. AI has the potential to reach a market volume of US$826.70bn by 2030, indicating its extensive outreach in finance is inevitable.
Cloud Computing, on the other hand, means the on-demand delivery of computing services, such as servers, unlimited storage, databases, etc. It offers services at unrivaled speed, with minimal charges, and with time flexibility. With a market potential of 1.44 trillion USD by 2029, cloud computing will eventually take over the finance world.
In Finance, AI and cloud computing are codependent on each other, as cloud computing provides the infrastructure for AI to function. Furthermore, AI escalates cloud computing services by providing advanced analytics and decision-making support.
Discussing the influence of AI and cloud services on finance and how they will affect the future. With insight into concepts like predictive analytics, fraud detection, and algorithmic trading we will understand how AI and cloud computing will contribute to these technologies.
Personalization in finance means delivering financial services and products to meet individual customers’ distinctive needs and choices. The data becomes immense, requiring heavy storage capacity with cost efficiency, and effective service tailoring can only be done by AI models.
The role of chatbots in AI helps automate the interrogation and response process of many finance apps and websites, eventually reducing time and saving organizations money. Hence, operational costs are cut down. AI establishes its technologies for answering queries, guiding customers through financial processes, and offering suggestive recommendations based on the user’s history and patterns.
DeFi (Decentralized Finance) exhibits a great example of personalization in finance. It helps eliminate intermediaries and utilize decentralized networks, ensuring distributed low costs. Moreover, cloud computing assists in storing and processing enormous amounts of customer data. Personalized financial services include digital financial advising, investment and expenditures planning, savings framework, and many more, thus striving for better customer satisfaction.
Integrating AI and cloud computing has revolutionized traditional financial processes by self-operating repetitive tasks. Automation has been the backbone of AI, and with the help of cloud services, it aims to achieve greater heights in finance.
The global artificial intelligence market was valued at $136.55 billion in 2022. AI algorithms, such as Robotic Process Automation (RPA) and linear and logistic regression, can be trained to provide outstanding results, hence reducing the need for manual human intervention and automating data entry, transaction reconciliation, financial reporting, and compliance documentation.
Cloud computing provides the necessary infrastructure for deploying and scaling AI-powered automation solutions, enabling financial institutions to streamline operations and reduce functional costs. Cloud computing provides and maintains the prerequisite infrastructure for deploying and scaling AI-powered robotized solutions, enabling financial institutions to carry out their tasks effectively and efficiently.
Frauds in finance are frequent and prevalent, like the infamous WorldCom scam, the Ponzi Scheme, and many others. Security has been a prolonged issue since the start of finance around 3000 BC. With progressive technology, many revolutionary steps have been taken, but with advanced technology, the risk of breaching has also increased.
The fraud detection systems, which AI powers analyze patterns, irregularities, and distrustful behaviors in the financial data of users to investigate potential fraud and cases. “AI makes fraud detection faster, more reliable, and more efficient where traditional fraud-detection models fail.” The contribution of AI in cybersecurity has been rapidly increasing with applications like threat detection, vulnerability assessment, and risk management.
Cloud services protect from unauthorized access, cyber-attacks, and the storage of confidential and sensitive financial data. Furthermore, the latest improvements in the cloud allow AI fraud detection systems to function more efficiently and valuably.
In finance, prediction is everything. Predicting what stock will go up and down, how much losses or gains you can get from a trade, or which company will crash. These are some examples of prediction. Organizations have recently integrated AI into cloud services to make these predictions. Hence, finding future trends for their customers and clients from historical and real-time data. We will see how these two technologies help in predictive analytics and decision-making.
AI analyzes customer data to predict future behaviors. Various financial institutions use AI-driven predictive databases. Many applications are made by these databases, like portfolio regulation, credit risk assessment, loan underwriting, and customer filtration acc. to demographics and behavior.
Cloud servers store big chunks of data, and their quick access to information helps them make decisions faster. The decision-making process is fastened by real-time data analysis, on-demand scalability, and accessibility. With heavy investments, cloud computing will exemplify these factors in the coming years, hence creating more data-driven decisions.
Whether AI’s super effective systems or the cloud’s unlimited storage, the users want ease of access and comfort with the app or program. Services play a pivotal role in shaping the future of finance. The collaboration of cloud offerings and AI automation helps improve banking services and customer experience.
Customer experience in finance includes all interactions between the company and the customer. AI majorly ameliorates customer interactions with its smooth and fast learning intelligence. AI deploys chatbots, virtual assistants, and recommendation engines. The automation and data extraction done by AI models help shape the future of financial services.
Cloud computing’s scalability function facilitates deploying AI-generated solutions for organizations and users. The astonishing speed of cloud computing accelerates and ensures secure and consistent cloud services to AI algorithms for better customer engagement. Cloud can help financial institutions foster loyalty and drive business success.
The Global Algorithmic Trading market size is projected to grow from $2.19 billion in 2023 to $3.56 billion by 2030. With such a potential, the possibilities of uncertainties and threats also become imminent. Thus, unifying these technologies provides a seamless experience for algorithmic trading and managing associated risks.
AI algorithms and models analyze market data and trading opportunities and gauge market sentiments for algorithmic trading. Machine learning techniques learn from data and adapt to transforming conditions with high speed and frequency. AI helps enhance risk management through real-time analytics, predictive modeling, and scenario forecasting skills. Various risks, such as market, credit, and operational risks, are mitigated, identified, and assessed in a timely manner.
Cloud computing provides global infrastructure, compliance, and security. It also streamlines complex trading algorithms, making trading and risk management more effective and scalable. Many risks in finance, like data security, disaster recovery, global accessibility, etc., can be neutralized by cloud computing, which also provides a storage facility, a prerequisite for risk management processes.
Numerous companies use AI finance for various applications, including fraud detection and risk management; one of these companies is JPMorgan Chase. AI and machine learning help JP assist employees, speed up responses, and help clients. OmniAI is their in-house innovation; it extracts insights from big piles of data and creates data-driven value for clients and customers. CEO Jamie Dimon said that AI is going to make the employees’ lives more qualitative by cutting down the work week by three and a half days for some.
Goldman Sachs says that “the generative AI could raise global GDP by 7%”. GS is using AI with a different approach; they are utilizing it to generate and test codes, making their developer’s work more tranquil and effortless; they use cloud infrastructure for quantitative trading, investment management, and enhancing operational efficiency.
Meanwhile, Citigroup wields AI to predict analytics from big chunks of data, and the cloud lets them do algorithmic trading (a program that follows a set of instructions for placing a trade all by itself). They are going to modernize the company’s systems using AI, and it’s going to cost them millions of dollars, according to Stuart Riley (Citi’s CCIO).
Other financial giants like Ant Group and HSBC use AI and On-Demand Computing to provide anti-money laundering and wealth management services.
AI and cloud computing have a bright future but the bright light can be harmful sometimes. In this section, we will look at probable challenges and tactical solutions that can arise with the onset of AI and cloud services.
Data gets breached. With ever-evolving technology, new ways of hacking and breaching have also come into existence. The breaching of data becomes a significant security concern. Storing Personally Identifiable Information (PII) and confidential data in the cloud becomes jeopardized by unauthorized access, data breaches, and cyber-attacks. With better security and accountability, we can eradicate these concerns.
AI usage raises ethical concerns, including biases because of biased input or data. AI will replace jobs with computers, servers, and algorithms, which can create socio-economic disparities. There should be higher management for taking accountability for AI algorithms, as algorithmic mistakes can be havoc and wide-ranging.
While AI and cloud computing services offer potential cost savings and operational efficiencies, managing infrastructure, licensing fees, and talent acquisition costs can be challenging. Intensive financial deployment and investment in managing the infrastructure of cloud servers have been a requisite as the finance industry is booming daily. Financial institutions that use AI must assess the return on investment for a clear and concise track of expenditures and revenues.
Connectivity is a necessity for the effective and absolute use of cloud computing. Without a proper internet connection, the services (Infrastructure-as-a-Service, Platforms-as-a-Service, and Software-as-a-Service) will be compromised and result in massive outages of cloud functions. Ensuring consistent internet connectivity throughout the system is essential for the smooth running of AI algorithms in finance.
The approach and eradication of these challenges require an excellent technical team, risk management professionals, and extraordinary top-level leadership. With progressive technology and improved security measures, financial institutions can utilize AI and cloud computing to their fullest, ensuring low costs and high reliability with clients’ and customer’s trust.
In conclusion, we stand on the verge of a new era of finance powered by AI and cloud computing that is possessing astounding speed. By optimizing the potential of these ubiquitous and transformative technologies, we can lead the way in creating a better and more economically driven world.
Financial institutions’ involvement and collaboration will be enhanced as they will be the trailblazers for emulative organizations. Continuous learning and innovation will give early adapters a competitive advantage.
These technologies will show us the new face of finance through cautious growth, responsible accountability, and rectifying probable challenges.
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This year, we’ve seen a wave of groundbreaking apps and tools. AI is reshaping the industry, enhancing productivity, and helping us work smarter, not harder.
Mouse data is a subcategory of interaction data, a broad family of data about users generated as the immediate result of human interaction with computers. Its siblings from the same data family include logs of key presses or page visits. Businesses commonly rely on interaction data, including the mouse, to gather insights about their target audience. Unlike data that you could obtain more explicitly, let’s say via a survey, the advantage of interaction data is that it describes the actual behavior of actual people.
Collecting interaction data is completely unobtrusive since it can be obtained even as users go about their daily lives as usual, meaning it is a quantitative data source that scales very well. Once you start collecting it continuously as part of regular operation, you do not even need to do anything, and you’ll still have fresh, up-to-date data about users at your fingertips — potentially from your entire user base, without them even needing to know about it. Having data on specific users means that you can cater to their needs more accurately.
Of course, mouse data has its limitations. It simply cannot be obtained from people using touchscreens or those who rely on assistive tech. But if anything, that should not discourage us from using mouse data. It just illustrates that we should look for alternative methods that cater to the different ways that people interact with software. Among these, the mouse just happens to be very common.
When using the mouse, the mouse pointer is the de facto conduit for the user’s intent in a visual user interface. The mouse pointer is basically an extension of your arm that lets you interact with things in a virtual space that you cannot directly touch. Because of this, mouse interactions tend to be data-intensive. Even the simple mouse action of moving the pointer to an area and clicking it can yield a significant amount of data.
Mouse data is granular, even when compared with other sources of interaction data, such as the history of visited pages. However, with machine learning, it is possible to investigate jumbles of complicated data and uncover a variety of complex behavioral patterns. It can reveal more about the user holding the mouse without needing to provide any more information explicitly than normal.
For starters, let us venture into what kind of information can be obtained by processing mouse interaction data.
What Are Mouse Dynamics?
Mouse dynamics refer to the features that can be extracted from raw mouse data to describe the user’s operation of a mouse. Mouse data by itself corresponds with the simple mechanics of mouse controls. It consists of mouse events: the X and Y coordinates of the cursor on the screen, mouse button presses, and scrolling, each dated with a timestamp. Despite the innate simplicity of the mouse events themselves, the mouse dynamics using them as building blocks can capture user’s behavior from a diverse and emergently complex variety of perspectives.
If you are concerned about user privacy, as well you should be, mouse dynamics are also your friend. For the calculation of mouse dynamics to work, raw mouse data does not need to inherently contain any details about the actual meaning of the interaction. Without the context of what the user saw as they moved their pointer around and clicked, the data is quite safe and harmless.
Some examples of mouse dynamics include measuring the velocity and the acceleration at which the mouse cursor is moving or describing how direct or jittery the mouse trajectories are. Another example is whether the user presses and lets go of the primary mouse button quickly or whether there is a longer pause before they release their press. Four categories of over twenty base measures can be identified: temporal, spatial, spatial-temporal, and performance. Features do not need to be just metrics either, with other approaches using a time series of mouse events.
Temporal mouse dynamics:
Spatial mouse dynamics:
Spatial-temporal mouse dynamics:
Performance mouse dynamics:
Note: For detailed coverage of varied mouse dynamics and their extraction, see the paper “Is mouse dynamics information credible for user behavior research? An empirical investigation.”
The spatial angular measures cited above are a good example of how the calculation of specific mouse dynamics can work. The direction angle of the movements between points A and B is the angle between the vector AB and the horizontal X axis. Then, the curvature angle in a sequence of points ABC is the angle between vectors AB and BC. Curvature distance can be defined as the ratio of the distance between points A and C and the perpendicular distance between point B and line AC. (Definitions sourced from the paper “An efficient user verification system via mouse movements.”)
Even individual features (e.g., mouse velocity by itself) can be delved into deeper. For example, on pages with a lot of scrolling, horizontal mouse velocity along the X-axis may be more indicative of something capturing the user’s attention than velocity calculated from direct point-to-point (Euclidean) distance in the screen’s 2D space. The maximum velocity may be a good indicator of anomalies, such as user frustration, while the mean or median may tell you more about the user as a person.
From Data To Tangible Value
The introduction of mouse dynamics above, of course, is an oversimplification for illustrative purposes. Just by looking at the physical and geometrical measurements of users’ mouse trajectories, you cannot yet tell much about the user. That is the job of the machine learning algorithm. Even features that may seem intuitively useful to you as a human (see examples cited at the end of the previous section) can prove to be of low or zero value for a machine-learning algorithm.
Meanwhile, a deceptively generic or simplistic feature may turn out unexpectedly quite useful. This is why it is important to couple broad feature generation with a good feature selection method, narrowing the dimensionality of the model down to the mouse dynamics that help you achieve good accuracy without overfitting. Some feature selection techniques are embedded directly into machine learning methods (e.g., LASSO, decision trees) while others can be used as a preliminary filter (e.g., ranking features by significance assessed via a statistical test).
As we can see, there is a sequential process to transforming mouse data into mouse dynamics, into a well-tuned machine learning model to field its predictions, and into an applicable solution that generates value for you and your organization. This can be visualized as the pipeline below.
Machine Learning Applications Of Mouse Dynamics
To set the stage, we must realize that companies aren’t really known for letting go of their competitive advantage by divulging the ins and outs of what they do with the data available to them. This is especially true when it comes to tech giants with access to potentially some of the most interesting datasets on the planet (including mouse interaction data), such as Google, Amazon, Apple, Meta, or Microsoft. Still, recording mouse data is known to be a common practice.
With a bit of grit, you can find some striking examples of the use of mouse dynamics, not to mention a surprising versatility in techniques. For instance, have you ever visited an e-commerce site just to see it recommend something specific to you, such as a gendered line of cosmetics — all the while, you never submitted any information about your sex or gender anywhere explicitly?
Mouse data transcends its obvious applications, as is replaying the user’s session and highlighting which visual elements people interact with. A surprising amount of internal and external factors that shape our behavior are reflected in data as subtle indicators and can thus be predicted.
Let’s take a look at some further applications. Starting some simple categorization of users.
For businesses, knowing users well allows them to provide accurate recommendations and personalization in all sorts of ways, opening the gates for higher customer satisfaction, retention, and average order value. By itself, the prediction of user characteristics, such as gender, isn’t anything new. The reason for basing it on mouse dynamics, however, is that mouse data is generated virtually by the truckload. With that, you will have enough data to start making accurate predictions very early.
If you waited for higher-level interactions, such as which products the user visited or what they typed into the search bar, by the time you’d have enough data, the user may have already placed an order or, even worse, left unsatisfied.
The selection of the machine learning algorithm matters for a problem. In one published scientific paper, six various models have been compared for the prediction of biological gender using mouse dynamics. The dataset for the development and evaluation of the models provides mouse dynamics from participants moving the cursor in a broad range of trajectory lengths and directions. Among the evaluated models — Logistic regression, Support vector machine, Random forest, XGBoost, CatBoost, and LightGBM — CatBoost achieved the best F1 score.
Putting people into boxes is far from everything that can be done with mouse dynamics, though. Let’s take a look at a potentially more exciting use case — trying to predict the future.
Another e-commerce application predicts whether the user has the intent to make a purchase or even whether they are likely to become a repeat customer. Utilizing such predictions, businesses can adapt personalized sales and marketing tactics to be more effective and efficient, for example, by catering more to likely purchasers to increase their value — or the opposite, which is investigating unlikely purchasers to find ways to turn them into likely ones.
Interestingly, a paper dedicated to the prediction of repeat customership reports that when a gradient boosting model is validated on data obtained from a completely different online store than where it was trained and tuned, it still achieves respectable performance in the prediction of repeat purchases with a combination of mouse dynamics and other interaction and non-interaction features.
It is plausible that though machine-learning applications tend to be highly domain-specific, some models could be used as a starting seed, carried over between domains, especially while still waiting for user data to materialize.
Applications of mouse dynamics are a lot more far-reaching than just the domain of e-commerce. To give you some ideas, here are a couple of other variables that have been predicted with mouse dynamics:
The Mouse-Shaped Caveat
When you think about mouse dynamics in-depth, some questions will invariably start to emerge. The user isn’t the only variable that could determine what mouse data looks like. What about the mouse itself?
Many brands and models are available for purchase to people worldwide. Their technical specifications deviate in attributes such as resolution (measured in DPI or, more accurately, CPI), weight, polling rate, and tracking speed. Some mouse devices have multiple profile settings that can be swapped between at will. For instance, the common CPI of an office mouse is around 800-1,600, while a gaming mouse can go to extremes, from 100 to 42,000. To complicate things further, the operating system has its own mouse settings, such as sensitivity and acceleration. Even the surface beneath the mouse can differ in its friction and optical properties.
Can we be sure that mouse data is reliable, given that basically everyone potentially works under different mouse conditions?
For the sake of argument, let’s say that as a part of a web app you’re developing, you implement biometric authentication with mouse dynamics as a security feature. You sell it by telling customers that this form of auth is capable of catching attackers who try to meddle in a tab that somebody in the customer’s organization left open on an unlocked computer. Recognizing the intruder, the app can sign the user out of the account and trigger a warning sent to the company. Kicking out the real authorized user and sounding the alarm just because somebody bought a new mouse would not be a good look. Recalibration to the new mouse would also produce friction. Some people like to change their mouse sensitivity or use different computers quite often, so frequent calibration could potentially present a critical flaw.
We found that up until now, there was barely anything written about whether or how mouse configuration affects mouse dynamics. By mouse configuration, we refer to all properties of the environment that could impact mouse behavior, including both hardware and software.
From the authors of papers and articles about mouse dynamics, there is barely a mention of mouse devices and settings involved in development and testing. This could be seen as concerning. Though hypothetically, there might not be an actual reason for concern, that is exactly the problem. There was just not even enough information to make a judgment on whether mouse configuration matters or not. This question is what drove the study conducted by UXtweak Research (as covered in the peer-reviewed paper in Computer Standards & Interfaces).
The quick answer? Mouse configuration does detrimentally affect mouse dynamics. How?
It is not automatically guaranteed that prediction based on mouse dynamics will work equally well for people on different devices. Even the same person making the exact same mouse movements does not necessarily produce the same mouse dynamics if you give them a different mouse or change their settings.
We cannot say for certain how big an impact mouse configuration can have in a specific instance. For the problem that you are trying to solve (specific domain, machine learning model, audience), the impact could be big, or it could be negligible. But to be sure, it should definitely receive attention. After all, even a deceptively small percentage of improvement in prediction performance can translate to thousands of satisfied users.
Tackling Mouse Device Variability
Knowledge is half the battle, and so it is also with the realization that mouse configuration is not something that can be just ignored when working with mouse dynamics. You can perform tests to evaluate the size of the effect that mouse configuration has on your model’s performance. If, in some configurations, the number of false positives and false negatives rises above levels that you are willing to tolerate, you can start looking for potential solutions by tweaking your prediction model.
Because of the potential variability in real-world conditions, differences between mouse configurations can be seen as a concern. Of course, if you can rely on controlled conditions (such as in apps only accessible via standardized kiosks or company-issued computers and mouse devices where all system mouse settings are locked), you can avoid the concern altogether. Given that the training dataset uses the same mouse configuration as the configuration used in production, that is. Otherwise, that may be something new for you to optimize.
Some predicted variables can be observed repeatedly from the same user (e.g., emotional state or intent to make a purchase). In the case of these variables, to mitigate the problem of different users utilizing different mouse configurations, it would be possible to build personalized models trained and tuned on the data from the individual user and the mouse configurations they normally use. You also could try to normalize mouse dynamics by adjusting them to the specific user’s “normal” mouse behavior. The challenge is how to accurately establish normality. Note that this still doesn’t address situations when the user changes their mouse or settings.
Where To Take It From Here
So, we arrive at the point where we discuss the next steps for anyone who can’t wait to apply mouse dynamics to machine learning purposes of their own. For web-based solutions, you can start by looking at MouseEvents in JavaScript, which is how you’ll obtain the elementary mouse data necessary.
Mouse events will serve as the base for calculating mouse dynamics and the features in your model. Pick any that you think could be relevant to the problem you are trying to solve (see our list above, but don’t be afraid to design your own features). Don’t forget that you can also combine mouse dynamics with domain and application-specific features.
Problem awareness is key to designing the right solutions. Is your prediction problem within-subject or between-subject? A classification or a regression? Should you use the same model for your whole audience, or could it be more effective to tailor separate models to the specifics of different user segments?
For example, the mouse behavior of freshly registered users may differ from that of regular users, so you may want to divide them up. From there, you can consider the suitable machine/deep learning algorithm. For binary classification, a Support vector machine, Logistic regression, or a Random Forest could do the job. To delve into more complex patterns, you may wish to reach for a Neural network.
Of course, the best way to uncover which machine/deep learning algorithm works best for your problem is to experiment. Most importantly, don’t give up if you don’t succeed at first. You may need to go back to the drawing board a few times to reconsider your feature engineering, expand your dataset, validate your data, or tune the hyperparameters.
Conclusion
With the ongoing trend of more and more online traffic coming from mobile devices, some futurist voices in tech might have you believe that “the computer mouse is dead”. Nevertheless, those voices have been greatly exaggerated. One look at statistics reveals that while mobile devices are excessively popular, the desktop computer and the computer mouse are not going anywhere anytime soon.
Classifying users as either mobile or desktop is a false dichotomy. Some people prefer the desktop computer for tasks that call for exact controls while interacting with complex information. Working, trading, shopping, or managing finances — all, coincidentally, are tasks with a good amount of importance in people’s lives.
To wrap things up, mouse data can be a powerful information source for improving digital products and services and getting yourself a headway against the competition. Advantageously, data for mouse dynamics does not need to involve anything sensitive or in breach of the user’s privacy. Even without identifying the person, machine learning with mouse dynamics can shine a light on the user, letting you serve them more proper personalization and recommendations, even when other data is sparse. Other uses include biometrics and analytics.
Do not underestimate the impact of differences in mouse devices and settings, and you may arrive at useful and innovative mouse-dynamics-driven solutions to help you stand out.
Even though the CSS :has() pseudo-class is relatively new, we already know a lot about it, thanks to many, many articles and tutorials demonstrating its powerful ability to conditionally select elements based on their contents. We’ve all seen the card component and header examples, but the conditional nature of :has() actually makes it adept at working with form controls, which are pretty conditional in nature as well.
Let’s look specifically at the element. With it, we can make a choice from a series of s. Combined with :has(), we are capable of manipulating styles based on the selected .
<select>
<option value="1" selected>Option 1</option>
<option value="2">Option 2</option>
<option value="3">Option 3</option>
<option value="4">Option 4</option>
<option value="5">Option 5</option>
</select>
This is your standard usage, producing a dropdown menu that contains options for user selection. And while it’s not mandatory, I’ve added the selected attribute to the first to set it as the initial selected option.
Applying styles based on a user’s selection is not a new thing. We’ve had the Checkbox Hack in our pockets for years, using the :checked CSS pseudo-class to style the element based on the selected option. In this next example, I’m changing the element’s color and the background-color properties based on the selected .
See the Pen demo 01 – Using the :has selector on a dropdown menu by Amit Sheen.
But that’s limited to styling the current element, right? If a particular is :checked, then we style its style. We can write a more complex selector and style child elements based on whether an is selected up the chain, but that’s a one-way road in that we are unable to style up parent elements even further up the chain.
That’s where :has() comes in because styling up the chain is exactly what it is designed to do; in fact, it’s often called the “parent selector” for this reason (although “family selector” may be a better descriptor).
For example, if we want to change the background-color of the element according to the value of the selected , we select the element if it has a specific [value] that is :checked.
See the Pen demo 02 – Using the :has selector on a dropdown menu by Amit Sheen.
Just how practical is this? One way I’m using it is to style mandatory elements without a valid selected . So, instead of applying styles if the element :has() a :checked state, I am applying styles if the required element does :not(:has(:checked)).
See the Pen demo 02.1 – Using the :has selector on a dropdown menu by Amit Sheen.
But why stop there? If we can use :has() to style the element as the parent of an , then we can also use it to style the parent of the , as well as its parent, in addition to its parent, and even its parent… all the way up the chain to the :root element. We could even bring :has() all the way up the chain and sniff out whether any child of the document :root :has() a particular that is :checked:
:root:has(select [value="foo"]:checked) {
// Styles applied if <option value="foo"> is <select>-ed
}
This is useful for setting a custom property value dynamically or applying a set of styles for the whole page. Let’s make a little style picker that illustrates the idea of setting styles on an entire page.
See the Pen demo 03 – Using the :has selector on a dropdown menu by Amit Sheen.
Or perhaps a theme picker:
See the Pen demo 04 – Using the :has selector on a dropdown menu by Amit Sheen.
How that last example works is that I added a class to each element and referenced that class inside the :has() selector in order to prevent unwanted selections in the event that there are multiple elements on the page.
And, of course, we don’t have to go all the way up to the :root element. If we’re working with a specific component, we can scope :has() to that component like in the following demo of a star rating component.
See the Pen demo 05 – Using the :has selector on a dropdown menu by Amit Sheen.
Watch a short video tutorial I made on using CSS to create 3D animated stars.
Conclusion
We’d be doing :has() a great disservice if we only saw it as a “parent selector” rather than the great conditional operator it is for applying styles all the way up the chain. Seen this way, it’s more of a modern upgrade to the Checkbox Hack in that it sends styles up like we were never able to do before.
There are endless examples of using :has() to create style variations of a component according to its contents. We’ve even seen it used to accomplish the once-complicated linked card pattern. But now you have an example for using it to create dropdown menus that conditionally apply styles (or don’t) to a page or component based the currently selected option — depending on how far up the chain we scope it.
I’ve used this technique a few different ways — e.g., as form validation, a style picker, and star ratings — but I’m sure there are plenty of other ways you can imagine how to use it in your own work. And if you are using :has() on a element for something different or interesting, let me know because I’d love to see it!
Design trends evolve at a blistering pace, especially in web design. On multi-month projects, you might work on a cutting-edge design after the kick-off meeting, only to launch a dated-looking site.
