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Many people come into the digital marketing industry with the soft skills they need to do a great job. These skills can make it easier for them to learn the hard skills necessary to perform their job duties.
It’s been said that soft skills can’t really be taught, but I strongly disagree with that.
It takes both hard and soft skills to do your best work in digital marketing. And the good news is, you can work on building both types.
Depending upon your experiences with the world, you may give up easily because you’ve never gotten what you wanted. You may not want to ask questions because you’ve had questions shut down before. With the proper environment, those skills can definitely sharpen.
Here are 20 essential skills to help you succeed in digital marketing.
Soft SkillsSoft skills are skills related to how you work. These are skills many people possess without really thinking about them.
Here are 10 soft skills that are the most important ones from my experience.
1. CuriosityI love when an employee wants to learn more. There are so many aspects of digital marketing and so many little niche areas. Craving more knowledge about how it all fits together truly makes you better in any role.
2. TenacityIf you give up easily, digital marketing is probably not the field for you.
You may work to rank a site and an update crushes you, or you may pitch ideas that get rejected. You may be called in to help figure out why a site isn’t doing well.
Every day there’s something new, and that’s what keeps it all interesting.
3. Willingness to Listen and LearnI have been wrong so many times it’s crazy. My employees (and clients) know to argue their points with me if they think I’m wrong, and I’ve learned to really trust what they say.
I’ve had clients give me instructions that I don’t think will work out but I try them and have been surprised quite often. Thinking you know everything means you don’t have the opportunity to get better.
4. AdaptabilityWith my team, assignments can vary from month to month depending upon our client roster. They might be working on a finance client for one month then they’ll need to switch up to a travel client.
They may need to pitch in and help someone else out on a client they’ve never worked on.
When I first started out, I got thrown into technical SEO, content writing, and PPC all at the same time. There’s always a chance that you’ll need to do something else or extra so you might as well be prepared for it.
5. Ability to MultitaskThere are always a ton of things going on at once in digital marketing. You want to read the latest articles, see the latest relevant tweets, do your job, figure out how to do something in a different way that saves time, do reports, etc.
If you can’t multitask well, you will quickly fall behind.
6. EmpathyBeing able to see things from someone else’s point of view is essential to marketing of any kind. It’s important to understand why someone thinks a certain way.
Empathy is so important that I wrote an entire article about it.
7. Taking Your Own Ego out of the PictureSometimes we are so caught up in what we think needs to be done, we can’t take a step back and listen to someone else because all we are thinking is that we know what’s best.
We all need to realize that we don’t always get it right, and even if we are right, sometimes it just doesn’t matter.
You can’t take it personally when you think you should bid on certain keywords and the client wants you to bid on different keywords.
I can’t take it personally when I submit an article to this very site and my editor asks me to make a change.
8. Strong Work EthicObviously, you really need this in most careers but with something like link building, you are never going to do well without wanting to work hard as it’s very frustrating and tedious at times.
When marketing fails, it can be extremely difficult to start over. You will encounter lots of roadblocks in some form or another so it’s critical to keep trying and not give up.
9. Honesty and TransparencyOne of my pet peeves is when someone can’t admit to a mistake.
You’ll always be found out.
We had a couple of employees who would leave work on the clock and think they wouldn’t be caught, for example. We had someone clock in from another state and pretend that I just hadn’t seen them in the office.
People say completely absurd things. With so many people available to replace you, not being honest is unacceptable.
10. Being Able to Say “I Don’t Know”I don’t know why this is so difficult but it seems to be. I worked for someone who told me to never admit to not knowing something, and I think that is ridiculous.
You don’t learn unless you admit that you don’t know something. If I don’t know something, I want to dig in and figure it out.
I don’t find it embarrassing to not know everything. I’ve never thought less of anyone who admitted to not knowing something.
With so much information thrown at us constantly, it’s impossible to keep up.
Hard SkillsHard skills are teachable skills. Here are the 10 hard skills that I think are the most achievable and the ones that can help you forge a broader knowledge of the industry.
11. How to Search WellPeople constantly ask questions they could easily query in Google. It can waste a lot of time.
You need to be able to dig for information and get better with your search queries so you aren’t wading through tons of irrelevant information.
We’ve had employees who started to work on a new client and would email to ask me to explain what a certain product was used for, for example.
I’d then spend my own time searching Google and figuring it out, then emailing back. I’d much rather do my own research than ask someone else to do it for me.
13. Conducting Research and Gathering DataYou will most likely need to pull data from various sources at some point. You may have to do a technical audit on a website.
There are so many tools and sources for information that it’s critical you can figure out where to look and how to get what you need.
If you’re creating content, you’ll also need to be able to find and verify information.
14. Using Google AnalyticsYou can get so much information from Google Analytics that it would be a real missed opportunity not to try and master it.
If Google is giving you information about your site, you absolutely need to use it.
From looking at traffic to tracking conversions, Google Analytics is a must-have tool, and it’s free.
15. Using at Least One Major SEO ToolOutside of Google Analytics, it’s good to know how to use at least one tool that can give you a different dataset. I use a few because each has its strong points.
It’s amazing to see how much information you can get from these tools and their reports.
16. Analyzing the Effectiveness of Your EffortsSome people measure progress by increased traffic. Some like conversions.
Whatever your KPIs are, you need to know how to track them reliably.
17. CommunicationWhether you communicate better through writing or speaking, good communication skills are absolutely critical.
My employees are remote workers and none of my clients are anywhere near me, so I spend a lot of time emailing back and forth with everyone.
I think good communication skills come naturally to some people. But if they don’t to you, it’s definitely something you can work towards improving.
18. Figuring Out What’s Going On and What’s Gone WrongIf traffic suddenly drops or your bounce rate drastically increases, it’s important that you know how to start tracking down potential causes.
Not everything is cause for alarm, of course. There may be logical explanations for what you’re seeing.
You simply need to know where to look and how to grab enough information to get an idea of what’s happening and then start to fix it.
19. Using a CrawlerThere are several great crawling tools out there and you should familiarize yourself with at least one of them.
Even if you aren’t getting too technical with your work, just being able to get information about redirects or duplicate content can be incredibly helpful.
20. Coding or Understanding CodeI came into SEO from a programming background so I’m a bit biased, but I do think that SEO professionals should at least know basic HTML.
Coding also teaches you how to think very logically and improves your problem-solving skills. Even if you never have a chance to code, you will be better equipped to think through problems.
Do You Really Need to Possess All of These Skills to Be Great at Your Job?Absolutely not. There are countless SEO pros who don’t know how to code, for example, and they can do their jobs well.
There are people who don’t possess a lot of the soft skills and they’re fine.
With today’s level of remote work situations there is more flexibility than ever to be yourself, work where you like, sometimes work whenever you feel like it, and simply get the job done.
But when it’s time to grow your career and enhance your professional value, you’ll definitely want to work on a few of the key digital marketing skills above.
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60% Of B2B Marketers Lack Funding For Digital Transformation
Aspiration for better digital experiences is never the problem but buy-in and execution can be.
Aspiration for better digital experiences is never the problem but buy-in and execution can be. Faced with rising digital expectations, B2B marketers and their IT colleagues know something must be done but often have too many internal roadblocks to get there.
Episerver recently surveyed 700 global B2B decision-makers and has provided insights into what professionals want to do today, tomorrow and in the future to fix the customer-centricity gap between what their customers want and what they actually deliver.
Digital experience todayA large percentage of marketers and technology professionals say their website delivers an exceptional customer experience and follows industry best practices. According to Episerver’s research, 86% of professionals surveyed said they deliver industry-leading websites and 84% said they deliver exceptional customer experiences online.
This confidence reverses, however, when marketers and technology professionals were asked about their internal weaknesses. Specifically, marketing professionals were more likely than their IT peers to admit their websites delivered poor digital experiences for their customers, partners, and salesforce.
Further to this, marketing professionals call out internal culture as a weakness, more so than their IT peers.
6% of marketing professionals agree or strongly agree that there is cultural resistance to the adoption of digital technologies.
40% of technology professionals disagreed or strongly disagreed that there is cultural resistance to the adoption of digital technologies.
Marketing professionals say they lack funding to pull of digital transformation whereas technology professionals disagreed.
60% of marketing professionals agree or strongly agree that they lack funding from senior executives to execute digital transformation projects.
48% of technology professionals disagreed or strongly disagreed they lack funding from senior executives to execute digital transformation projects.
Marketing professionals say they deliver poor digital experiences, technology professionals disagreed.
54% of marketing professionals agree or strongly agree they deliver a poor digital experience for their customers, partners and sales team.
56% of technology professionals disagreed or strongly disagreed they deliver a poor digital experience for their customers, partners and sales team.
Digital experience tomorrowWhen asked about external threats to their business, marketing and technology professionals were concerned about many areas. From digitally native startups to increasing costs, almost 50% of respondents agreed or strongly agreed the following threats would be faced in the next three years:
Digitally native startups.
Increasing cost to acquire new customers.
Channel conflict will prevent them from selling online.
Increasing digital expectations of our customers or partners.
Being outspent by our competitors on digital technologies.
Both marketing and technology professionals saw increasing digital expectations of their customers and partners as threats (48%, 47% strongly agree respectively). This indicates changing customer expectations for digital tools is the largest concern for both marketing and IT professionals. Competitors may innovate and costs may increase, but the ever-increasing and ever-evolving digital expectations of customers and partners seem to be the most threatening to these groups.
Marketing professionals were particularly sensitive to being outspent by competitors (71% agree/strongly agree) and facing digitally native startups disrupting their industry (69% agree/strongly agree). Technology professionals were more neutral or disagreed with these threats but still acknowledged them as real.
Digital experience of 2023How do they think they will respond to these threats? Improving online customer experience, equipping their salesforce with digital tools, and selling direct to end customers rank are the biggest opportunities over the next three years.
When asked “What are the most significant opportunities for your business in the next three years?” marketing professionals and technology professionals agree:
Improving online customer experience (17%)
providing digital selling tools to their salesforce (13%)
and selling direct to customers online (13%) are top priorities
However, within their peer group, both marketing and technology professionals have slightly different secondary and tertiary goals.
Some 57% of technology professionals said improving online customer experience was a top-three opportunity; this is a generic statement that may have served as a ‘catch-all’ for IT leaders.
Marketing professional took are more nuanced approach with some marketing professionals reporting that reducing vendor complexity (28%), selling to new segments online profitably (27%) and delivering personalized experiences (26%) were top-three priorities. These opportunities drew more responses from marketers than technologists.
Technologists did stand out in three other areas compared to marketers. Some 19% of technologists said migrating from on-premise to cloud-based technologies was a top-three opportunity (compared to 9% of marketers). Technologists were also slightly more optimistic about expanding into new geographic markets (30% compared to 23%) and 26% of technologists said that centralizing and analyzing customer data was a top-three opportunity (compared to 18% of marketers).
Marketing and technology professionals were consistent when asked about investment plans, focusing on analytics, email marketing and ecommerce to realize these opportunities.
59% of technology professionals said web analytics was a top-three likely investment compared to 49% of marketing professionals.
Email marketing systems took a larger share of the top three likely investments for marketing professionals (47%) compared to only 29% of technology professionals.
Both marketing and technology professionals agreed that ecommerce platforms were a top-three priority.
Looking to 2023, will AI have replaced human workers? Most think so; marketers believe AI will replace human workers in marketing functions more than their IT colleagues.
While over 50% of all respondents said they agreed or strongly agreed that AI will be used to replace human workers, marketing professionals were more inclined to agree that AI would replace human workers; 68% either agreed or strongly agreed that AI would replace human workers in marketing roles. Compare that to 59% of technology professionals who agree/strongly agree that AI will replace human workers.
Perhaps marketing professionals are already seeing the use of AI technology replace routine tasks in their jobs more than technology professionals. Or perhaps technology professionals are more skeptical than marketing about the ability for AI to replace a human workforce. Either way, there is broad consensus that AI will be used to replace human workforce in the next three years.
While an organization’s digital experience of today could look completely different tomorrow with the right investments and decisions, understanding how each department views digital transformation is one of the first stops in a rewarding journey.
Download Episerver’s, B2B Digital Experiences Report 2023: How Companies are Meeting Rising Expectations.
Seek The Required Skills And Essential Online Courses For A Professional Cloud Consultant
Average Salary (per annum): US$1,19,804 Roles and Responsibilities: A Cloud Consultant has specialisation in Cloud systems to suggest and offer appropriate designs and high-level architecture to their clients. The candidate needs to research the daily routines through multiple questions and implement a Cloud system for customisation according to the client’s needs. Cloud consultant is required to facilitate the efficient use of Cloud computing in designing migration policies and usage of Cloud environment. Qualifications:
A Bachelor’s degree in Computer Science, Information Science, Computer Engineering or IT
Strong customer service skills with communication skills
Good recommending skills including analytical abilities
Problem-solving skills with an in-depth knowledge of one operating system such as Linux, Windows, Unix and many more
Experience with network administration like DNS, VPN, BGP and programming languages like chúng tôi Java, Python and so on
Practical experience in compute infrastructure, networking, DevOps and Hadoop
Top Online Courses:Salesforce Certified Sales Cloud Consultant Courses from Udemy: The course includes 11 hours of on-demand videos with 13 articles covering core concepts of the Salesforce Sales Cloud Certification examination. The candidate can create multiple Salesforce Sales processes and configure Salesforce Lightning pages. PG Certification in Cloud Computing and DevOps from IIT Roorkee-WileyNXT: It is the most comprehensive PG certification programme to develop strong skills to manage and maintain Cloud infrastructure and deploy Cloud applications. It is a seven-month programme for professionals interested in Cloud computing, aspiring Cloud consultants and established Cloud consultants. The curriculum covers best-in-class content with videos, case studies and projects through 17 modules. Introduction to Cloud Computing-IBM from edX: A candidate can master the core concepts of Cloud computing and become a professional Cloud consultant with the help of this course. This course covers the fundamental knowledge required for a better understanding of Cloud computing such as essential characteristics, history, trends, Cloud service models, deployment models as well as key components of a Cloud architecture. It is suitable for everyone who wants to be in the Cloud ecosystem as a Cloud consultant.
Top Institutes offering the programme:
B. Tech in IT and Computer Science: SRM Institute of Science and Technology
PG Diploma in Management—Emerging Technology with AWS Educate Cloud curriculum: ASM Institute of Management and Computer Studies
B. Tech in IT and Computer Science: Noida Institute of Engineering and Technology
Top Recruiters for this Job:Amazon: Being one of the most popular companies in Cloud computing, Amazon is driven to build technologies, products and services that can transform the lives of society. The employees research and develop new technologies from AWS to Alexa to be the most well-known customer-centric company in the world. Microsoft Azure: Microsoft Azure is focused on making an organisation efficient with an open and flexible cloud computing platform. It provides on-premise, hybrid and multi-cloud to create secure and future-ready cloud solutions for its clients. There are multiple products from Azure— Synapse Analytics, Stack HCI, Functions, SQL Database and many more. XenonStack: XenonStack is focused on building Cloud platforms for data intelligence, business agility and resilience. It offers an end-to-end solution to develop and deploy applications on the Cloud and build AI applications to enhance business outcomes. It is known as the Cloud-native and platform engineering company solving complex problems with technology.
20 Questions To Test Your Skills On Dimensionality Reduction (Pca)
This article was published as a part of the Data Science Blogathon
IntroductionPrincipal Component Analysis is one of the famous Dimensionality Reduction techniques which helps when we work with datasets having very large dimensions.
Therefore it becomes necessary for every aspiring Data Scientist and Machine Learning Engineer to have a good knowledge of Dimensionality Reduction.
In this article, we will discuss the most important questions on Dimensionality Reduction which is helpful to get you a clear understanding of the techniques, and also for Data Science Interviews, which cover its very fundamental level to complex concepts.
Let’s get started, 1. What is Dimensionality Reduction?In Machine Learning, dimension refers to the number of features in a particular dataset.
In simple words, Dimensionality Reduction refers to reducing dimensions or features so that we can get a more interpretable model, and improves the performance of the model.
2. Explain the significance of Dimensionality Reduction.There are basically three reasons for Dimensionality reduction:
Visualization
Interpretability
Time and Space Complexity
Let’s understand this with an example:
Imagine we have worked on an MNIST dataset that contains 28 × 28 images and when we convert images to features we get 784 features.
If we try to think of each feature as one dimension, then how can we think of 784 dimensions in our mind?
We are not able to visualize the scattering of points of 784 dimensions.
That is the first reason why Dimensionality Reduction is Important!
Let’s say you are a data scientist and you have to explain your model to clients who do not understand Machine Learning, how will you make them understand the working of 784 features or dimensions.
In simple language, how we interpret the model to the clients.
That is the second reason why Dimensionality Reduction is Important!
Let’s say you are working for an internet-based company where the output of something must be in milliseconds or less than that, so “Time complexity” and “Space Complexity” matter a lot. More features need more Time which these types of companies can’t afford.
That is the third reason why Dimensionality Reduction is Important!
3. What is PCA? What does a PCA do?Principal Component analysis. It is a dimensionality reduction technique that summarizes a large set of correlated variables (basically high dimensional data) into a smaller number of representative variables, called the Principal Components, that explains most of the variability of the original set i.e, not losing that much of the information.
PCA stands for. It is a dimensionality reduction technique that summarizes a large set of correlated variables (basically high dimensional data) into a smaller number of representative variables, called the Principal Components, that explains most of the variability of the original set i.e, not losing that much of the information.
PCA is a deterministic algorithm in which we have not any parameters to initialize and it doesn’t have a problem of local minima, like most of the machine learning algorithms has.
Image Source: Google Images
4. List down the steps of a PCA algorithm.The major steps which are to be followed while using the PCA algorithm are as follows:
Step-1: Get the dataset.
Step-2: Compute the mean vector (µ).
Step-3: Subtract the means from the given data.
Step-4: Compute the covariance matrix.
Step-5: Determine the eigenvectors and eigenvalues of the covariance matrix.
Step-6: Choosing Principal Components and forming a feature vector.
Step-7: Deriving the new data set by taking the projection on the weight vector.
5. Is it important to standardize the data before applying PCA?Usually, the aim of standardization is to assign equal weights to all the variables. PCA finds new axes based on the covariance matrix of original variables. As the covariance matrix is sensitive to the standardization of variables therefore if we use features of different scales, we often get misleading directions.
Moreover, if all the variables are on the same scale, then there is no need to standardize the variables.
6. Is rotation necessary in PCA? If yes, Why? Discuss the consequences if we do not rotate the components?Yes, the idea behind rotation i.e, orthogonal Components is so that we are able to capture the maximum variance of the training set.
If we don’t rotate the components, the effect of PCA will diminish and we’ll have to select more Principal Components to explain the maximum variance of the training dataset.
7. What are the assumptions taken into consideration while applying PCA?The assumptions needed for PCA are as follows:
1. PCA is based on Pearson correlation coefficients. As a result, there needs to be a linear relationship between the variables for applying the PCA algorithm.
2. For getting reliable results by using the PCA algorithm, we require a large enough sample size i.e, we should have sampling adequacy.
3. Your data should be suitable for data reduction i.e., we need to have adequate correlations between the variables to be reduced to a smaller number of components.
4. No significant noisy data or outliers are present in the dataset.
8. What will happen when eigenvalues are roughly equal while applying PCA?While applying the PCA algorithm, If we get all eigenvectors the same, then the algorithm won’t be able to select the Principal Components because in such cases, all the Principal Components are equal.
9. What are the properties of Principal Components in PCA?The properties of principal components in PCA are as follows:
1. These Principal Components are linear combinations of original variables that result in an axis or a set of axes that explain/s most of the variability in the dataset.
2. All Principal Components are orthogonal to each other.
3. The first Principal Component accounts for most of the possible variability of the original data i.e, maximum possible variance.
4. The number of Principal Components for n-dimensional data should be at utmost equal to n(=dimension). For Example, There can be only two Principal Components for a two-dimensional data set.
10. What does a Principal Component in a PCA signify? How can we represent them mathematically?The Principal Component represents a line or an axis along which the data varies the most and it also is the line that is closest to all of the n observations in the dataset.
In mathematical terms, we can say that the first Principal Component is the eigenvector of the covariance matrix corresponding to the maximum eigenvalue.
Accordingly,
Sum of squared distances = Eigenvalue for PC-1
Sqrt of Eigenvalue = Singular value for PC-1
11. What does the coefficient of Principal Component signify?If we project all the points on the Principal Component, they tell us that the independent variable 2 is N times as important as of independent variable 1.
12. Can PCA be used for regression-based problem statements? If Yes, then explain the scenario where we can use it.Yes, we can use Principal Components for regression problem statements.
, we can use Principal Components for regression problem statements.
PCA would perform well in cases when the first few Principal Components are sufficient to capture most of the variation in the independent variables as well as the relationship with the dependent variable.
The only problem with this approach is that the new reduced set of features would be modeled by ignoring the dependent variable Y when applying a PCA and while these features may do a good overall job of explaining the variation in X, the model will perform poorly if these variables don’t explain the variation in Y.
13. Can we use PCA for feature selection?Feature selection refers to choosing a subset of the features from the complete set of features.
No, PCA is not used as a feature selection technique because we know that any Principal Component axis is a linear combination of all the original set of feature variables which defines a new set of axes that explain most of the variations in the data.
Therefore while it performs well in many practical settings, it does not result in the development of a model that relies upon a small set of the original features.
14. Comment whether PCA can be used to reduce the dimensionality of the non-linear dataset.PCA does not take the nature of the data i.e, linear or non-linear into considerations during its algorithm run but PCA focuses on reducing the dimensionality of most datasets significantly. PCA can at least get rid of useless dimensions.
However, reducing dimensionality with PCA will lose too much information if there are no useless dimensions.
15. How can you evaluate the performance of a dimensionality reduction algorithm on your dataset?A dimensionality reduction algorithm is said to work well if it eliminates a significant number of dimensions from the dataset without losing too much information. Moreover, the use of dimensionality reduction in preprocessing before training the model allows measuring the performance of the second algorithm.
We can therefore infer if an algorithm performed well if the dimensionality reduction does not lose too much information after applying the algorithm.
Comprehension Type Question: (16 – 18) Consider a set of 2D points {(-3,-3), (-1,-1),(1,1),(3,3)}. We want to reduce the dimensionality of these points by 1 using PCA algorithms. Assume sqrt(2)=1.414.Now, Answer the Following Questions:
SOLUTION:
2 i.e, two-dimensional space, and our objective is to reduce the dimensionality of the data to 1 i.e, 1-dimensional data ⇒ K=1
Here the original data resides in Ri.e, two-dimensional space, and our objective is to reduce the dimensionality of the data to 1 i.e, 1-dimensional data ⇒
We try to solve these set of problem step by step so that you have a clear understanding of the steps involved in the PCA algorithm:
Step-1: Get the DatasetHere data matrix X is given by [ [ -3, -1, 1 ,3 ], [ -3, -1, 1, 3 ] ]
Step-2: Compute the mean vector (µ)Mean Vector: [ {-3+(-1)+1+3}/4, {-3+(-1)+1+3}/4 ] = [ 0, 0 ]
Step-3: Subtract the means from the given dataSince here the mean vector is 0, 0 so while subtracting all the points from the mean we get the same data points.
Step-4: Compute the covariance matrixTherefore, the covariance matrix becomes XXT since the mean is at the origin.
Therefore, XXT becomes [ [ -3, -1, 1 ,3 ], [ -3, -1, 1, 3 ] ] ( [ [ -3, -1, 1 ,3 ], [ -3, -1, 1, 3 ] ] )T
= [ [ 20, 20 ], [ 20, 20 ] ]
Step-5: Determine the eigenvectors and eigenvalues of the covariance matrixdet(C-λI)=0 gives the eigenvalues as 0 and 40.
Now, choose the maximum eigenvalue from the calculated and find the eigenvector corresponding to λ = 40 by using the equations CX = λX :
Accordingly, we get the eigenvector as (1/√ 2 ) [ 1, 1 ]
Therefore, the eigenvalues of matrix XXT are 0 and 40.
Step-6: Choosing Principal Components and forming a weight vectorHere, U = R2×1 and equal to the eigenvector of XXT corresponding to the largest eigenvalue.
Now, the eigenvalue decomposition of C=XXT
And W (weight matrix) is the transpose of the U matrix and given as a row vector.
Therefore, the weight matrix is given by [1 1]/1.414
Step-7: Deriving the new data set by taking the projection on the weight vectorNow, reduced dimensionality data is obtained as xi = UT Xi = WXi
x1 = WX1= (1/√ 2 ) [ 1, 1 ] [ -3, -3 ]T = – 3√ 2
x2 = WX2= (1/√ 2) [ 1, 1 ] [ -1, -1 ]T = – √ 2
x3 = WX3= (1/√ 2) [ 1, 1 ] [ 1, 1]T = – √ 2
x4 = WX4= (1/√ 2 ) [ 1, 1 ] [ 3, 3 ]T = – 3√ 2
Therefore, the reduced dimensionality will be equal to {-3*1.414, -1.414,1.414, 3*1.414}.
This completes our example!
19. What are the Advantages of Dimensionality Reduction?1. Less misleading data means model accuracy improves.
2. Fewer dimensions mean less computing. Less data means that algorithms train faster.
3. Less data means less storage space required.
4. Removes redundant features and noise.
5. Dimensionality Reduction helps us to visualize the data that is present in higher dimensions in 2D or 3D.
2. It can be computationally intensive.
3. Transformed features are often hard to interpret.
4. It makes the independent variables less interpretable.
End NotesThanks for reading!
Please feel free to contact me on Linkedin, Email.
About the author Chirag GoyalCurrently, I am pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, Deep Learning, and Artificial Intelligence.
The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.
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Native Advertising: An Introduction For Ppc Marketers
Consumer priorities are shifting rapidly as the world faces an unprecedented healthcare crisis.
Sensitivity to consumers’ needs is more critical than ever.
Brands must think carefully about how to engage with consumers in meaningful ways that not only increase conversions but help build brand trust.
Throughout the day, we consume content from blogs, news channels, television shows, social media channels, etc.
All of that content has the potential for native ad placements, which is why the native industry can be complex.
Advertisers know that in order to reach their target customers, they need to have a presence on channels where consumers spend their time.
This is where native comes into play.
Is Content Network Targeting the Same as Native?In short, no.
But native today isn’t the contextual targeting of yore.
Microsoft deprecated the content network in 2023.
However, Google Ads still allows content targeting in the Google Display network.
Unlike the content network, native ad placements are not based on the keyword or the keywords within the article on page, they are based on audience targeting.
It is worth noting that Google Ads still allows content targeting in the Google Display Network based on:
Topics: Pages about specific topics. Google Ads uses factors such as text, language, links and page structure to determine the topics of a page.
Placement: Specific websites, or subsets of a website.
Keywords: Just that, keywords.
Display expansion for search: A combination of automated bidding and smart targeting.
What Is Native Advertising?The publisher controls and is responsible for rendering the ad.
For example, a native ad might show up within an article you’re reading on your favorite online news source, or as a post on your Facebook feed.
In-feed placements appear directly in the article or blog post.
Recommendation widgets appear on a publisher’s website and presents recommended content or products that are related to the content you’re already consuming.
Promoted listings, also referred sometimes as sponsored content, are designed to fit seamlessly into the browsing experience.
Is Native Advertising Programmatic?It depends. (A marketer’s favorite answer.)
Is Google Display Native Advertising?Yes, Google Display & Video 360 has native creative formats that can be integrated into a display campaign.
The native creative can target:
App install (Google Play or Apple App Store).
Site creative (square or rectangular display format).
Video (similar to site creative, but uses video instead of an image.)
Is Microsoft Advertising Native Advertising?Currently, Microsoft Audience Ads are available in three formats:
Text Ads.
Product Ads.
They can be set up as part of an existing search campaign that is extended to native or as a separate audience campaign.
Even if you choose to opt into the Microsoft Audience network from within your search campaigns, the ad placement is based on audience targeting using the Microsoft Graph.
The Microsoft Advertising Graph captures billions of signals across our consumer products, such as browsing data, search history and behavior, and deep profile data from LinkedIn.
Microsoft’s audience network spans:
A wide range of brand-safe environments, including MSN, chúng tôi and Microsoft Edge.
Select premium partner properties such as CBS Sports, Everyday Health, Fox Business, the Atlantic, Apartment Therapy, and Reuters.
According to ComScore, the Microsoft Audience Network reaches 92% of the online audience throughout the U.S.
AI-Powered Placements Focused on Quality & Giving Complete Control to the AdvertiserThe audience network was created with two priorities in mind: quality and control.
Control encompasses its ability to provide brand-safe environments and data privacy.
Microsoft enforces strict publisher standards and reviews and publisher partners are closely managed and thoroughly vetted.
Global blocklists and the ability to exclude certain sites gives you even more control – and peace of mind.
The Success of Native Advertising Depends on TrustHow do organizations establish and maintain consumer trust?
By putting long-term strategies in place for actively engaging with consumers, listening and acting on customer feedback, adhering to data privacy and protection, and being transparent and authentic.
iProspect proposes that there are three key components to consumer trust: credibility, relevance, and reliability.
Is your brand competent and legitimate?
Do you listen to and act on customer feedback and provide relevant content, products, and services?
Do you deliver a consistent experience that meets customer expectations across every customer interaction?
Native Case Studies: Reaching Untapped AudiencesAds are credible, relevant and consistent – but not invasive or intrusive.
2X higher on MSN Infopane.
3X higher on publisher partner sites.
Volvo and marketing agency Mindshare decided to test the Microsoft Audience network as a strategy to support awareness and help maintain sales for their best-seller the XC90 luxury SUV.
They combined LinkedIn, gender, remarketing and In-Market audience data to find new audiences to target and to uncover previously untapped audiences.
The campaign drove significant traffic and exceeded their expectations with conversions:
65,000 incremental site visits.
CPA on-par with their non-brand search campaigns.
Running native campaigns in conjunction with search campaigns helped Buyerzone reach business-to-business audience.
1,700% increase in impressions.
20% profit increase on top of their traditional search campaigns.
75% decrease in CPC.
Alan Barish, senior online marketing analyst from BuyerZone said, “almost every single conversion we’ve gotten from the Microsoft Audience Network has converted into a lead, which is amazing.”
Maintaining Trust & Engagement in Uncertain TimesNever in modern history has it been so critical to create meaningful connections between your brand and consumers, and earn and maintain their trust.
With data privacy rising to the forefront of consumers’ consciousness and as recent global events add complexity to the consumer-brand dynamic, delivering targeted, trusted and relevant content is a critical strategy for continued business growth.
Start with a foundation of trust, success is inevitable.
Test using native as a way for your brand to make meaningful connections with consumers that build trust and drive conversions, setting the stage for ongoing credibility, relevance, and consistency, even in uncertain times.
More Resources:
Image Credits
All screenshots taken by author, March 2023
Html5: An Essential Weapon For Seos
SEOs rely on traditional HTML optimization as a standard tool in their fight to improve search rankings. Just as the bayonet has evolved since the 17th century, HTML is set to receive a major upgrade in the form of HTML5. The update contains a collection of new tags and APIs. Five stand out as major SE0 innovations.
“Well, Governor, we also have fewer horses and bayonets.” That was President Obama’s now-famous zinger to Mitt Romney during the third Presidential debate in 2012. But the President was mistaken — the military does NOT have fewer bayonets. In fact, every U.S. Marine still receives a bayonet. Apparently, it is considered an essential weapon. The same can be said for HTML and SEOs today.
1. Nofollow’s little brothers and sisters
In 2005 Google announced that they would support a new way for webmasters to tell search engines not to pass PageRank through a link by adding a small code snippet to the link called rel=nofollow. Below is an example of a link that does not pass any PageRank:
2. Alt text gets some much-needed support
One of the key roles of an SEO is to take rich content that search engines have trouble understanding — such as images and video — and convert it into a text based alternative. And until now, SEOs used “alt text” as the primary way to help a search engine understand what is going on in an image. However, with HTML5’s new “figure” and “figurecaption” tags, we now have a much better way to explain images to search engines and users.
3. Identifying the most important links on your page
4. No more Flash for videos
Web designers love using Flash, especially to embed video on a web page. But do search engines feel the same way? Um…not so much. That’s because they have a hard time accessing the content in Flash video. In fact, without the aid of special technologies like SWFobject and video sitemaps, search engines would be clueless about a video.
<track kind=”captions” src=”transcript.en.hoh.vtt” srclang=”en” label=”English for the
5. AJAX gets search engine friendly
While Flash is a favorite of designers and creative types, AJAX is a favorite of developers and programmers looking to make their sites faster and more interactive. The drawback, of course, is that search engines struggle to read content delivered with AJAX.
But HTML5 has a solution for that. It’s a new feature called the History API. It lets developers change the URL in the address bar of the browser without refreshing the page. This subtle change helps search engines tie AJAX content to a unique URL, which is crucial for their ranking algorithms.
Overall, the improvements in HTML5 include numerous features that will help you “fight the good fight,” and improve your search rankings. It should be considered an essential weapon for SEOs today.
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