You are reading the article Personalization Myths: Are They Holding You Back? updated in November 2023 on the website Moimoishop.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 Personalization Myths: Are They Holding You Back?Are the myths about deploying web personalisation tools holding you back?
The benefits are indisputable. Research by Forrester found personalization to be the top priority for 55% of retailers. Similarly, it was found to be this year’s top digital priority for B2C marketers. An effective personalization strategy is now widely recognised as essential to reaching the top global levels of conversion.
And it’s not just the marketers who agree. Another interesting survey revealed that 83% of consumers themselves want to receive a personalized cross-channel experience.
Yet, despite all of this, a third of marketers don’t personalize their websites at all. And of those that do, only a very small percentage are going beyond the very basic techniques.
There’s a reason for this; most people simply don’t know where to start.
Access resource – Web Personalisation guide
the options for delivering more persuasive, relevant dynamic web content at relevant points in the onsite customer journey for B2B and B2C sites. As well as the techniques and tools available for personalisation based on audience characteristics and behaviour.
Access theDemystifying Personalization
This is the first of a series of articles in which I will address the thorny issue of why an effective personalization strategy eludes many marketers, and, more importantly, how it can start to work for you.
Below, I will go through some common misconceptions which cause marketers to remain in the personalization slow lane. For each of these I’ll provide accompanying suggestions on how to get started – before you get left behind.
Myth 1: “Personalization is too time-consuming / expensive”
This is one of the most common myths surrounding personalization. Sure, if you were to build your own visitor profiling tools and content delivery system then you’d unlikely have much time or money for anything else. But today, there is an abundance of real-time personalization tools on the market to help. These apps are simple to use and require minimal effort, yet they will see your conversions rise and your customers return more frequently.
Additionally, many areas of personalization can be automated. Product recommendations engines, for example, are based purely on algorithms, and don’t require any maintenance at all, yet can have a huge impact on your sales.
Other features, such as cart abandonment emails can also be automated. 70% of carts are abandoned on retail sites, representing a huge loss in potential revenue. An automated abandoned cart recovery email system however can entice those customers to return to the site to complete their order.
All this automation means your conversions can rise without draining your most precious resource – time.
Myth 2: ”Personalization sounds complicated – I don’t know where to begin”
Again, a good personalization app will do the hard work for you. As soon as you plug one in, a plethora of invaluable data will be collected on your customers. This would likely include data such as their geographical location, gender and browsing device. E-commerce sites will normally also capture buying history, the content of their cart and product interests, etc.
With all this information at your fingertips, you can then easily segment your visitors based on relevant attributes – for example ‘when a visitor is male and expresses an interest in clothing: show men’s clothing products’. A few simple segments is all that’s needed to get results to begin with, based on characteristics that are meaningful to your business.
Next, you have to decide how to target these visitors. Perhaps an individualised offer? A recent survey of marketers found personalized offers to be the single most effective means of increasing conversions. I will explore other methods in my next article in this series.
Myth 3: “I don’t have enough content for personalization”
Another myth. Think of the content you already have on your site. Does every customer need to see all of it? Of course not – some parts will be relevant to some customers, others not. In many cases personalization is simply about targeting your existing content more effectively, not creating new material from scratch.
John Lewis show personalized content frequently. The homepage banner I saw on my first visit showed products for women. But after returning from the men’s clothing section… hey presto!
Customers are less patient than ever – according to HubSpot, three quarters of online consumers stated they felt frustrated when encountering content irrelevant to their interests online. Customers more than ever are likely to bounce when they struggle to find what they want.
Myth 4: “Personalization is only for the big players like Amazon”
This is a damaging misconception that ultimately results in smaller companies getting left behind.
I have continuously said that personalization should be available to all businesses – not just the behemoths like Amazon. But now, with personalisation services like our own Bunting to help you get started, there is no reason why any marketer, regardless of budget, can’t enjoy the benefits of personalization.
To conclude, for the digital marketer, personalization is becoming increasingly more significant. And, with automated tools becoming more available, it is easier than ever to get started with it. For many marketers, personalization is still a nut to be cracked, and this is not helped by common misconceptions. However, by taking steps to implement a suitable tool and start experimenting, the benefits of personalization can be enjoyed without forceful demands on time and budget.
In the next article in this series I will delve deeper into how marketers can take practical steps to implement personalization and reap the rewards it brings.
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blog / Entrepreneurship Mastering Entrepreneurial Skills: What Makes Them Important for Success?
Why are entrepreneurial skills so vital in today’s competitive business environment? For starters, these abilities enable professionals to identify and pursue new opportunities, take calculated risks, and innovate for success in an ever-changing market. You don’t have to be an entrepreneur or a business owner to develop entrepreneurial skills – they can help you drive success in your organization and stay ahead of the curve, even as a corporate executive. In this blog, let’s look at the most important entrepreneurial skills, their importance in business, and how you can cultivate them to achieve your goals as a business leader.Top 10 Entrepreneurial Skills
To recognize opportunities, innovate, establish, and grow new businesses, individuals must possess a distinct set of abilities known as entrepreneurial skills. These abilities are crucial for launching and operating a successful business. Here’s a list of some of the most critical entrepreneurial skills you’ll need to succeed:
: Ability to come up with new and innovative ideas and solutions
Willingness to take calculated risks and seize opportunities
Finding solutions to complex problems and overcoming obstacles
Adjusting to changing market conditions and pivoting when necessary
Inspiring and motivating others to achieve common goals
Establishing relationships with stakeholders and conveying ideas clearly and persuasively
Managing cash flow, budgeting, and financial planning
Marketing and sales:
Promoting products and services and building a customer base
Establishing goals and objectives and developing a plan to achieve them
Prioritizing tasks and effectively managing time to maximize productivityWhy Entrepreneurial Skills are Critical for Business Success in Today’s World
Elon Musk and Jeff Bezos are two of the most successful entrepreneurs in business history. Both have transformed their respective industries, and their success can be attributed to their exceptional entrepreneurial abilities.
Elon Musk, the founder of SpaceX, Tesla, and several other businesses, is known for his ingenuity, risk-taking, and ability to solve complex problems. He has a history of taking on seemingly impossible tasks and making them a reality, such as launching a car into space or developing reusable rockets. Musk’s ability to think outside the box and take calculated risks has allowed him to push the limits of what is possible in the technology and space industries.
Amazon founder Jeff Bezos is well-known for his leadership, effective communication, and strategic planning abilities. He founded Amazon as an online bookstore and grew it to become the world’s largest online retailer. Bezos is well-known for his customer-centric approach and ability to build a strong brand and customer base. He also possesses exceptional strategic planning abilities, as evidenced by Amazon’s successful expansion into new markets and industries.
These entrepreneurs’ success demonstrates the growing importance of entrepreneurial skills across industries. They enable individuals to identify opportunities, take risks, innovate, and create and grow new ventures, which can lead to personal and professional growth. Therefore, to succeed as an entrepreneur or business leader in today’s rapidly changing business world, it is critical to recognize, develop, and hone these skills.Tips for Developing and Improving Entrepreneurial Skills
The good news, you can develop and hone your entrepreneurial skills. Here are some tips to help you improve:Read Books and Articles
Reading entrepreneurship books and articles can provide valuable insights and ideas for developing and improving entrepreneurial skills. ‘Crushing It!’ by Gary Vaynerchuk and ‘That Will Never Work’ by Marc Randolph are two books that come highly recommended.Attend Workshops and Seminars
Attending entrepreneurship workshops and seminars can provide learning opportunities and networking. Look for local events or online seminars relevant to your interests and needs.Take Online Courses
Online courses can be a convenient and cost-effective way to improve entrepreneurial skills. Creativity, innovation, and leadership courses are available online on multiple platforms, such as the entrepreneurship courses on the Emeritus platform.Practice Problem-Solving
To overcome obstacles and challenges, entrepreneurs must be skilled at problem-solving. To improve your problem-solving abilities, practice identifying problems and developing solutions.Seek Feedback
Seeking feedback from peers, mentors, and customers can help entrepreneurs improve their skills. Be open to constructive criticism and use it for your improvement.
ALSO READ: How to Pursue a Career in Entrepreneurship: A Comprehensive GuideFrequently Asked Questions What are the Most Common Issues That Entrepreneurs Face?
Financial constraints, competition, regulatory obstacles, and staffing issues are all factors one needs to consider. To overcome this, you should stay focused on your goals, be flexible, and seek help from mentors or other entrepreneurs who have faced similar obstacles.Is Formal Education in Entrepreneurship Required for Success?
No, formal education helps but is not required for success. In fact. many successful entrepreneurs have learned through trial and error, and a wealth of information is available online and in books to assist individuals in developing entrepreneurial skills.
To conclude, developing entrepreneurial skills is critical for success. Individuals can overcome challenges and achieve personal and professional growth by studying successful entrepreneurs, practicing and applying key skills, and seeking out resources for improvement. A culture of innovation and creativity can thus be created in your organization by emphasizing the importance of entrepreneurial skills. To learn more about entrepreneurship and hone those abilities, explore these online entrepreneurship courses by Emeritus in tie-up with the best universities around the world.
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From Sylvester in Looney Tunes to Mr. Mistoffelees in the 1980s musical, some of the most famous (albeit fictional) cats share a distinctively sharp appearance thanks to their black and white tuxedo-style coats. Cats with skin and fur marked by white patches in this way are known as bicolor or piebald. Piebaldism is also common in a range of domestic and farm animals including dogs, cows and pigs, deer, horses, and appears more rarely in humans. It is caused by a mutation in a gene called KIT.
Our team of researchers from the universities of Bath, Edinburgh and Oxford have been working to unlock the mystery of how these animals get their distinctive patterns. We have discovered that the way these striking pigment patterns form is far more random than originally thought. Our findings have implications for the study of a wide range of serious embryonic disorders in humans, including diseases affecting hearing, vision, digestion, and the heart.
Piebaldism usually manifests as white areas of fur, hair or skin due to the absence of pigment-producing cells in those regions. These areas usually arise on the front of an animal, commonly on the belly and the forehead. Piebald patterns are among the most striking animal coat patterns in nature.
Although the effects of piebaldism are relatively mild, it is one of a range of more serious defects called neurocristopathies. These result from defects in the development of tissues and can manifest as heart problems, deafness, digestive problems and even cancer. The diseases are all linked by their reliance on a family of embryonic cells called neural crest cells. By understanding piebaldism better, we can improve our understanding of these related and more serious diseases.
Animals acquire piebald pigmentation patterns on their skin when they are still developing embryos. Piebaldism arises when the precursors of pigment-producing cells spread incorrectly through the embryo. In normal development, pigment cells start near the back of the embryo and spread through its developing skin to the belly. As the cells spread they also multiply, creating more cells, some of which are left behind to ensure all the skin is pigmented.
With piebaldism, however, the darkly coloured pigment cells don’t make it as far as the belly in time to pigment the hair and skin. This results in distinctive white patches of fur and skin, usually around the belly of the animal, the furthest point from where they started. It has long been thought that pigment cells migrate directly from the back to the front and that the lack of pigmentation at the front is due to pigment cells not moving fast enough.
However, our findings, published in Nature Communications, paint a different picture. We found that, if anything, cells in piebald animals migrate faster than in normal animals, but that they don’t divide as often. This means that there simply aren’t enough cells to pigment all the areas of the developing embryo.
Cells starting near the back of the embryo migrate around to the front. Richard Mort
Chimaeric animals develop from a fusion of two early-stage embryos. If the original embryos would have been differently coloured (for example, black and white), the chimaeric animal often has striped or patchy coat patterns, a mix of the two colours. Previously, the predominant theory was that each stripe was created by a small number of initiator cells that spread from back to front.
Our study used a combination of biological experimentation and complex mathematical modelling to demonstrate that pigment cells migrate randomly. Rather than moving in a specific direction like the sprinters in a 100-metre race, the cells move with little or no persistence, like drunks staggering out of the local bar at closing time. The striped patterns seen in some chimaeric mice may simply be the result of several groups of cells of the same colour coming together by chance.
Using our mathematical model, we can explore and evaluate a huge range of possible alternative biological hypotheses for pattern formation. This gives us a deeper understanding that would be impossible with experiments alone. It also means we could reduce the number of animals used in experiments in this important research area.
Excitingly, there is now the potential to use the same mathematical model to investigate other cell types during early development. This creates a new opportunity to learn more about medical conditions linked to early cell positioning, including those that give rise to certain types of cancers of the nervous system and other debilitating diseases such as Waardenburg syndrome, Hirschsprung disease and Ondine’s curse, a respiratory disorder that is fatal if left untreated.
This article was originally published on The Conversation. Read the original article.
Emails are a popular way to keep in touch with people, be it friends, family, or co-workers, but often companies that you deal business with will send you the occasional promotional email if you signed up for subscriptions.
In this piece, we’ll go over some of the things you can look for to tell if the emails you’re getting are legitimate, or if they’re a con artist trying to scam you of your personal information.What is phishing, and why is it a thing?
Low-life people do this for all kinds of malicious intents and purposes; one of the most common is identity theft. You can, however, protect yourself from these kinds of emails; the best way to go about that is to know how to spot a phishing email, and know what you should do when you come about one.Signs that an email you received is a phishing email
In a detailed support document, Apple explains some of the most common characteristics of a phishing email. We’ll go over them with you below and try to explain them to the best of our abilities.
1. The email headers have incorrect information in them
For example, if you receive an email from “Walmart” about your most recent purchase, and the final received line says something like “Recevied from chúng tôi (123.456.789.120)” then it’s probably a fraud because “machax” has nothing to do with Walmart, and the IP address probably doesn’t match that of Walmart’s web servers either.
2. Links in the email take you somewhere other than where it should
Another sure sign that you’ve been phished is when an email you’re sent has links in it that claim to take you to a certain website, but take you to another one instead. You can hover your mouse over a link in an email, and OS X will automatically display the URL that the link will want to take you to.
3. Websites you visit from the email are fake
You have to be careful in many of the instances where you see a link in an email, because some hackers will throw together a really good mock-up of a legitimate website that can be very convincing when you load it up. Many of the websites are designed the same and have the same logos all over them, but there’s one pretty good way you can tell if the site is legitimate or not.
The latest versions of many web browsers, such as Safari, Firefox, and Chrome will do a check to ensure the website is legitimate. If it passes the Extended Validation (EV) check, which is a check to ensure the website is legitimate, then the company name in the URL bar will be shown in green color instead of black.
If you visit a website you usually see a green company name in the URL bar, but if you see it black instead, you might consider backing out before you enter any valuable or personally identifiable information, such as credit card numbers, because the website might just be waiting for you to submit that information to a hacker for malicious use.
4. The email refers to you generically, instead of by name
Since most high-end companies that you subscribe for emails with will have your name on record, legitimate emails will typically call you by your name. A phishing email often refers to the recipient with a generic name that could fit the picture in many cases, no matter what your name is.
For example, if the beginning of an email from Walmart says, “Hi Anthony,” then you’d have less to be wary off than an email from Walmart that begins with, “Dear valued customer” because it shows that the email came from a source that knows who you are rather than a source that doesn’t and is just trying to refer to you as something universal so it fits the thousands of other people who are receiving the same phishing email.
5. The email came to an email that you didn’t give the company
If you subscribe to a company with one email (email A) and you end up receiving an email from that company in another one of your email inboxes (email B), then you have a strong reason to be wary of the email.
Since you didn’t give the company email B, how could they have possibly known they were sending the email to you? Better yet, how did they get that email in the first place? Since you subscribed with email A, you should have received the email in the inbox of email A.
If you can’t explain why an email arrived in the wrong email inbox, you should steer clear of it. Emails should only arrive in the inbox of the correct email address when you subscribe for emails. Any emails that end up in the inboxes of your other accounts are probably fake and may be trying to lure you into a trap.Protecting yourself from emails you think are phishing
If you receive emails like any of the above, then you probably have a strong case to believe the emails are trying to lure you into providing personal information. If you receive a phishing email, here are some safe practices for you to keep in mind.
1. Compare information of the new email with past emails
If you have received legitimate emails from a company before, such as Walmart, you should compare the contact details of that email to another email claiming to be from that same company.
For example, if I received an email from Walmart before, and the new email claims to be from Walmart too, I can compare the email addresses to see if the sender is coming from a Walmart address or not.
Although email address spoofing is possible, this is a good first place to check, because some morons will send emails from their personal email or a completely unrelated email instead of taking the time to properly spoof their email addresses.
If you receive an email claiming to be from Walmart, and it’s an @Gmail account, then you obviously know something’s up. Also pay attention to the language in the email, such as the way the email refers to you.
2. Don’t provide personal information
Unless you’ve confirmed with the company that the email was legitimate, you should never provide any personal information to an email that you believe is a phishing email.
This includes personal information like:
Your credit card details
Your social security number
Your maiden name
And so forth…
Typically, legitimate emails will never ask for your personal information. They’ll just link you to a site where you have to log in with that site’s username and password, and will give you information from there, but if an email asks you for your login information for a service, which is unlikely from business-dealing companies, you should not provide it in the chance that the email could be trying to steal an account of yours.
3. Don’t download or open attachments
If you suspect an email is a phishing email, don’t download or open attachments that may be connected to the email. Some of the attachments may contain malware, which may try to spy on your key presses or steal passwords and other valuable information as you enter them.
Most automated emails from legitimate companies rarely ever include attachments, and will be fully-coded in HTML instead so you don’t have to open any attachments.How to report a suspected phishing email to Apple
Of course, reporting an email is no guarantee that the messages will stop, and reporting legitimate emails won’t help the cause, so you should only ever report obvious phishing emails and ones that you truly believe are doing nothing but trying to steal your personal information or cause harm to your computer.Conclusion
Don’t be a victim of a phishing email. Although many phishing emails are completely obvious to some, they may not be so obvious to your mother, or grandmother, or someone else who isn’t as technology-literate as you are. Spread the word and keep the emailing system safe for everyone!
According to a survey by Anaconda, data scientists spend nearly half of their time on data preparation.
Figure 1. How long does it take to put a trained model into production?
Figure 1: The time it takes for trained models to put in production, Source: Algorithmia
Organizations have started to adopt MLOps practices to overcome the challenges of model development and deployment processes and to streamline the machine learning lifecycle. One of the key components of MLOps are feature stores, which are used by companies like UberWhat is a feature?
A feature is an independent variable that affects the prediction of an ML model. For example, when predicting the delivery time of shipping, a feature can be the stock status of the product, the current workload of the shipper, the weather conditions, etc. Since raw data is rarely suitable to be used as an input to ML models, these features that are relevant to the problem at hand must be created through feature engineering.
A feature can be offline or online:
Offline features are variables that do not vary too often. For instance, in case of order shipping, the preparation time of the order is relatively constant if the ordered item is in stock. These types of features can be recalled via the data warehouse.
Online features are variables that change often and need to be updated in real-time. For instance, the weather condition is an online feature when predicting the delivery time.
In order for an ML model to function efficiently, both kinds of features should be considered.What is a feature store?
Cleaning and extracting data through feature engineering, and the creation of data pipelines are complex operational processes. As the number and complexity of ML models increase, the time and computational power required to accomplish these processes also increase.
A feature store enters the picture at this point. It is a platform that
Stores the created features to be accessed and reused in future problems,
Enables the management of all features from a centralized place and makes them accessible for different teams,
Provides the metadata of a feature.
Feature stores enable businesses and data science teams to:
Creating features is a time-consuming process that requires domain expertise about the problem. Feature stores help data science teams to store the features that are created for a problem and reuse them for another problem. For example, the processed data about the average shipping time of a company or the standard delivery time of a restaurant can be utilized by various models. Reusability of features helps ML teams to accelerate ML model development process.Improve collaboration
Different teams can use the same feature for different purposes. Storing features in a centralized platform makes them accessible to different teams developing different ML models. This improves collaboration between ML teams such as data scientists, data engineers, or MLOps engineers.Make model training and model inference consistent
Feature stores can help eliminate the “online/offline skew” problem. This problem refers to the difference in model performance during model training and during inference due to the use of different features in these settings. By improving accessibility and reusability of features, feature stores eliminate this problem and improve the consistency of model performance between training and inference.When to use a feature store and how to get started?
Feature stores can benefit all ML projects but if you have different teams that develop and manage multiple ML models, a feature store is a must-have tool. Scaling ML and AI projects across your organization requires a systematic approach such as MLOps and feature stores are often a part of MLOps platforms.
Alternatively, you can build your feature store with in-house resources if you have specific needs. However, this can also be a time-consuming process and often preferred by large companies.Further reading
If you have other questions about feature stores or MLOps, feel free to reach out:
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Debunking the top 10 data science myths that you should ignore in the year 2023
In the world of Big Data, there are numerous job profiles available, such as Data Engineers, Data Analysts, Data Scientists, Business Analysts, and so on. Beginners need clarification on these profiles, as Data Scientist is the most popular and sought-after. They require assistance in determining whether Data Science is a good fit and identifying the best resources. There are several misconceptions about data science myths. As a data scientist, there are several data science myths to ignore for a successful career.
Transitioning into data science is difficult, not because you need to learn math, statistics, or programming. You must do so, but you must also combat the myths you hear from others and carve your path through them! In this article let us see the top 10 data science myths that you should ignore in 2023.
Myth 1 – Data Scientists Need to Be Pro-Coders
Your job as a Data Scientist would be to work extensively with data. Pro-coding entails working on the competitive programming end and having a strong understanding of data structures and algorithms. Excellent problem-solving abilities are required. Languages like Python and R in Data Science provide strong support for multiple libraries that can be used to solve complex data-related problems.
Myth 2 – Ph.D. or Master’s Degree is Necessary
This statement is only partly correct. It will be determined by the job role. A Master’s or Ph.D. is required if you want to work in research or as an applied scientist. However, if you want to solve complex data mysteries using Deep Learning/Machine Learning, you will need to use Data Science elements such as libraries and data analysis approaches. If you do not have a technical background, you can still enter the Data Science domain if you have the necessary skill sets.
Myth 3- All Data Roles are the Same
People believe that Data Analysts, Data Engineers, and Data Scientists all perform the same function. Their responsibilities, however, are completely different. The confusion arises because all of these roles fall under the Big Data umbrella. A Data Engineer’s role is to work on core parts of engineering and build scalable pipelines of data so that raw data from multiple sources can be pulled, transformed, and dumped into downstream systems.
Myth 4 – Data Science Is Only for Graduates of Technology
This is one of the most crucial myths. Many people in the Data Science domain come from non-tech backgrounds. Few people are transitioning from computer science to data science. Companies hire for data science and related positions, and many of those hired come from non-tech backgrounds with strong problem-solving abilities, aptitude, and understanding of business use cases.
Myth 5 – Data Science Requires a Background in Mathematics
As a Data Scientist, being good at math is essential, as data analysis requires mathematical concepts such as data aggregation, statistics, probability, and so on. However, these are not required to become a Data Scientist. We have some great programming languages in Data Science, such as Python and R, that provide support for libraries that we can use for mathematical computations. So, unless you need to innovate or create an algorithm, you don’t need to be a math expert.
Myth 6- Data Science Is All About Predictive Modelling
Data scientists spend 80% of their time cleaning and transforming data, and 20% of their time modeling data. There are numerous steps involved in developing a big data solution. The first step is data transformation. The raw data contains some error-prone values as well as garbage records. We need meaningful transformed data to build an accurate machine-learning model.
Myth 7- Learning Just a Tool Is Enough to Become a Data Scientist
The Data Science profile requires a diverse set of technical and non-technical skills. You must rely on something other than programming or any particular tool that you believe is used in Data Science. We need to interact with stakeholders and work directly with the business to get all of the requirements and understand the data domain as we work on complex data problems.
Myth 8- Companies Aren’t Hiring Freshers
This statement made sense a few years ago. However, today’s freshmen are self-aware and self-motivated. They are interested in learning more about data science and data engineering and are making efforts to do so. Freshers actively participate in competitions, hackathons, open-source contributions, and building projects, which aid in their acquisition of the necessary skill set for the Data Science profile, allowing companies to hire freshers.
Myth 9 – Data Science competitions will make you an expert
Data Science competitions are ideal for learning the necessary skills, gaining an understanding of the Data Science environment, and developing developer skills. However, competition will not help you become a Data Scientist. It will improve the value of your resume. However, to become an expert, you must work on real-world use cases or production-level applications. It is preferable to obtain internships.
Myth 10 – Transitioning cannot be possible in the Data Science domain
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