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Getting Started With Machine Learning




Getting Started With Machine Learning

Alvin Wan



The goal of machine learning is to find patterns in data and use those patterns to make predictions. It can also give us a framework to discuss machine learning problems and solutions — as you’ll see in this article.

First, we will start with definitions and applications for machine learning. Then, we will discuss abstractions in machine learning and use that to frame our discussion: data, models, optimization models, and optimization algorithms. Later on in the article, we will discuss fundamental topics that underlie all machine learning methods and conclude with practical guidance for getting started with using machine learning. By the end, you should have an understanding of how to advance your practice and study of machine learning.

Let’s begin.

So, What Exactly Is Machine Learning?

Machine learning is generically a set of techniques to find patterns in data. Applications range from self-driving cars to personal AI assistants, from translating between French and Taiwanese to translating between voice and text. There are a few common applications of machine learning that already or could potentially permeate your day-to-day.

  1. Detecting anomalies
    Recognize spikes in website traffic or highlight abnormal bank activity.
  2. Recommend similar content
    Find products you may be looking for or even Smashing Magazine articles that are relevant.
  3. Predict the future
    Plan the path of neighboring vehicles or identify and extrapolate market trends for stocks.

The above are few of many applications of machine learning, but most applications tie back to learning the underlying distribution of data. A distribution specifies events and probability of each event. For example:

  • With 50% probability, you buy an item $5 or less.
  • With 25% probability, you buy an item $5-$10.
  • With 24% probability, you buy an item $10-100.
  • With 1% probability, you buy an item > $100.

Using this distribution, we can accomplish all of our tasks above:

  1. Detecting anomalies
    With a $100 purchase, we can confidently call this an anomaly.
  2. Recommend similar content
    A purchase of $3 means we should recommend more items $5 or less.
  3. Predict the future
    Without any prior information, we can predict that the next purchase will be $5 or less.

With a distribution of data, we can accomplish a myriad of tasks. In sum, one goal in machine learning is to learn this distribution.

Even more generically, our goal is to learn a specific function with particular inputs and outputs. We call this function our model. Our input is denoted x. Say our model, which accepts input x, is

f(x) = ax

Here, a is a parameter of our model. Each parameter corresponds to a different instance of our model. In other words, the model where a=2 is different from the model where a=3. In machine learning, our goal is to learn this parameter, changing it until we do “well.” How do we determine which values of a do “well”?

We need to define a way to evaluate our model, for each parameter a. To start, the output of f(x) is our prediction. We will refer to y as our label, meaning the true and desired output. With our predictions and our labels, we can define a loss function. One such loss function is simply the difference between our prediction and our label, |f(x) - y|. Using this loss function, we can then evaluate different parameters for our model. Picking the best parameter for our model is known as training. If we have a few possible parameters, we can simply try each parameter and pick the one with the smallest loss!

However, most problems are not as simple. What happens if there are an infinite number of different parameters? Let’s say all decimal values between 0 and 1? Between 0 and infinity? This brings us to our next topic: abstractions in machine learning. We will discuss different facets of machine learning, to compartmentalize your knowledge into data, models, objectives, and methods of solving objectives. Beyond learning the right parameter, there are plenty of other challenges: how do we break down a problem as complex as controlling a robot? How do we control a self-driving car? What does it mean to train a model that identifies faces? The section below will help you organize answers to these questions.

Abstractions

There are countless topics in machine learning — at various levels of specificity. To better understand where each piece fits in the larger picture, consider the following abstractions for machine learning. These abstractions compartmentalize our discussion of machine learning topics, and knowing them will make it easier for you to frame topics. The following classifications are taken from Professor Jonathan Shewchuck at UC Berkeley:

  1. Application and Data
    Consider the possible inputs and the desired output for the problem.
    Questions: What is your goal? How is your data structured? Are there labels? Is it reasonable for us to extract output from the provided inputs?

    Example: The goal is to classify pictures of handwritten digits. The input is an image of a handwritten number. The output is a number.

  2. Model
    Determine the class of functions under consideration.
    Questions: Are linear functions sufficient? Quadratic functions? Polynomials? What types of patterns are we interested in? Are neural networks appropriate? Logistic regression?

    Example: Linear regression

  3. Optimization Problem
    Formulate a concrete objective in mathematics.
    Questions: How do we define loss? How do we define success? Should we apply additionally penalties to bias our algorithm? Are there imbalances in the data our objective needs to consider?

    Example: Find `x` that minimizes |Ax-b|^2

  4. Optimization Algorithm
    Determine how you will solve the optimization problem.
    Questions: Can we compute a solution by hand? Do we need an iterative algorithm? Can we convert this problem to an equivalent but easier-to-solve objective, and solve that one?

    Example: Take derivative of the function. Set it to zero. Solve for our optimal parameter.

Abstraction 1: Data

In practice, collecting, managing, and packaging data is 90% of the battle. The data contains samples in which each sample is a specific realization of our input. For example, our input may generically be images of dogs. The first sample is specifically a picture of Maxie, my Bernese Mountain dog-chow chow mix at home. The second sample is specifically a picture of Charlie, a young corgi.

While training your model, it is important to handle your data properly. This means separating our data accordingly and not peeking prematurely at any set of data. In general, our data is split into three portions:

  1. Training set
    This is the dataset you train your model on. The model may see this set hundreds of times.
  2. Validation set
    This is the dataset you evaluate your model on, to assess accuracy and tune your model or method accordingly.
  3. Test set
    This is the dataset you evaluate on to assess accuracy, once at the very end. Running on the test set prematurely could mean your model overfits to the test set as well, so run only once. We will discuss the notion of “overfitting” in more detail below.

Abstraction 2: Models

Machine learning methods are split into the following two:

Supervised Learning

In supervised learning, our algorithm has access to labeled data. Still, we explore the following two classes of problems:

  • Classification
    Determine which of k classes C_1, C_2, ... C_k to which each sample belongs, e.g. “Which breed of dog is this?” The dog could be one of "corgi", "bernese mountain dog", "chow chow"...
  • Regression
    Determine a real-valued output (which are often probabilities), e.g. “What is the probability this patient has neuroblastoma (eye cancer)?”
Unsupervised Learning

In unsupervised learning, our algorithm does not have access to labels, and we explore the following classes of problems:

  • Clustering
    Cluster samples into k clusters. We do not have a label for the resulting clusters. “Which DNA sequences are most similar?”
  • Dimensionality reduction
    Reduce the number of “unique” (linearly independent) features we consider. “What are common features of faces?”

Abstraction 3: Optimization Objective

Before discussing optimization objectives and algorithms, we’ll need an example to discuss. Least squares are the canonical example. We will restrict our attention to a specific form of least squares: Let us return to our grade-school problem of fitting a line to some points.

Let’s recall the equation of a line:

y = m * x + b

Assume we have such a line. This is the true underlying model.


 true model


True model. The line that generates our data. (Large preview)

Now, sample points from this line.


true data


True data. Data that is sampled from the true model. (Large preview)

For each point, jiggle it a little bit. In other words, add noise, which is random perturbations. This noise is due to real-world processes.


noise


Noise. Real-world perturbations that affect our data. This may be due to imprecision in measurements, lossy compression, and so on. (Large preview)

This gives us our observed data. We will call these points (x_1, y_1), (x_2, y_2), (x_3, y_3).... This is the training data we are given to train a model on. We do not have access to the underlying line that generated this data (the original green line).


observations


Observations. Our true data with noise and ultimately what we will use to train a model. (Large preview)

Say we have an estimate for the parameters of a line. In this case, the parameters are m and b. This gives us a predicted line, drawn in blue below.


proposed model


Proposed model. The result of training a model on our observations. (Large preview)

We wish to evaluate our blue line, to see how accurate it is. To start, we use m and b to estimate y. We compute a set of ŷ values.

ŷ_i = m * x_i + b

The error for a single predicted ŷ_i and true y_i is simply

(ŷ_i−y_i)^2

Our total error is then the sum of squared differences, across all samples. This yields our loss.

∑(ŷ_i−y_i)^2

Presented visually, this is the vertical distance between our observed points and our predicted line.


observed error


Observed error. The distance between our observed data and our proposed model. (Large preview)

Plugging in ŷ_i from above, we then have the total error in terms of m and b.

∑(m * x_i + b − y_i)^2

Finally, we want to minimize this quantity. This yields our objective function, abstraction 3 from our list of abstractions above.

min_m, b ∑(m * x_i + b−y_i)^2

The above states in mathematics that the goal is to minimize the loss by changing values of m and b. The purpose of this section was to motivate fitting a line of best of fit, a special case of least squares. Additionally, we showed examined the least squares objective. Next, we need to solve this objective.

Abstraction 4: Optimization Algorithm

How do we minimize this? We take the derivative with respect to m`, set to 0 and solve. After solving, we obtain the analytical solution. Solving for an analytical solution was our optimization algorithm, the fourth and final abstraction in our list of abstractions.

Note: The important portion of this section is to inform you that least squares have a closed form solution, meaning that the optimal solution for our problem can be computed, explicitly. To understand why this is significant, we need to examine a problem without a closed-form solution. For example, we could never solve x=logx for a standard base-10 logarithm. Try graphing these two lines, and we see that they never intersect. In which case, we have no closed-form solution. On the other hand, ordinary least squares have a closed-form — which is good news. For any problem reduced to least squares, we can then compute the optimal solution, given our data and assumptions.

Fundamental Topics

Before studying more methods, it is necessary to understand the undercurrents of machine learning. These will govern the initial study of machine learning:

Bias-Variance Tradeoffs

One of machine learning’s most dreaded evils is overfitting in which a model is too closely tailored to the training data. In the limit, the most overfit model will memorize the data. This might mean that if one does well on exam A, one repeats every detail for exam B — down to the duration of an inter-exam restroom trip and whether or not one used the urinal.

A related but less common evil is underfitting, where the model is not sufficiently expressive to capture important information in the data. This could mean that one looks only at homework scores to predict exam scores, ignoring the effects of reading notes, completing practice exams, and more. Our goal is to build a model that generalizes to new examples while making the appropriate distinctions.

Given these two evils, there are a variety of approaches to fighting both. One is modifying your optimization objective to include a term that penalizes model complexity. Another is tuning hyperparameters that govern either your objective or your algorithm, which may correspond to notions such as “training speed” or “momentum.” The bias-variance tradeoff gives us a precise way of defining and handling both overfitting and underfitting.

Maximum Likelihood Estimation (MLE) + Maximum A Posteriori (MAP)

Say we have ice cream flavors A, B, and C. We observe different recipes. Our goal is to predict which flavor each recipe produces.

One way to predict flavors based on recipes is to first estimate the following probability:

P(flavor|recipe)

Given this probability and a new recipe, how can we predict the flavor? Given a recipe, simply consider the probability of each of the flavors A, B, C.

P(flavor=A|recipe) = 0.4
P(flavor=B|recipe) = 0.5
P(flavor=C|recipe) = 0.1

Then, pick the flavor that has the highest probability. Above, flavor B has the highest probability, given our recipe. Thus, we predict flavor B. Restating the above rule in mathematics, we have:

argmax_flavor P(flavor|recipe)  # argmax means take the flavor that corresponds to the max value

However, the only information at our disposal is the reverse: the probability of some recipe given the flavor.

P(recipe|flavor)

For Maximum Likelihood Estimates, we make assumptions and find that the two values are proportional.

P(recipe|flavor) ~ P(flavor|recipe)

Since we’re only interested in the class with maximum probability P(flavor|recipe), we can simply find the class with maximum probability, for a proportional value P(recipe|flavor).

argmax_flavor P(recipe|flavor)

MLE offers the above objective as one way to predict, using the probability of data given the labels.

However, allow me to convince you that it’s reasonable to assume we have (x|y). We can estimate this from observed, real-world data. For example, say we wish to estimate the number of marbles each student in your class carries, based on the number of rubber ducks the student carries.

Each student’s number of rubber ducks is the data x, and the number of marbles she or he has is y. We will use this sample data below.

| x | y |
|---|---|
| 1 | 2 |
| 1 | 1 |
| 1 | 2 |
| 2 | 1 |
| 2 | 2 |
| 1 | 2 |

For every y, we can compute the number of x, given us P(x|y). For the first one, P(x=1|y=1), consider all of the rows where y=1. There are 2, and only one of them has x=1. Therefore, P(x=1|y=1) = 12. We can repeat this for all values of x and y.

P(x=1|y=1) = 1/2
P(x=2|y=1) = 1/2
P(x=1|y=2) = 3/4
P(x=2|y=2) = 1/4

Featurizations, Regularization

Least squares draw lines of best fit for us. Note that least squares can fit the model anytime the model is linear in its inputs x and outputs y.

Say m=1. We have the following equation:

y = x + b

However, what if we had data that doesn’t generally follow a line? Specifically, consider a set of data sampled along a circle. Recall that the equation for a circle is:

x^2 + y^2 = r^2

Can least squares fit this well? As it stands, no. The model is not linear in its inputs x and outputs y. Instead, the model above is quadratic in x and y. However, it turns out that we can use still use least squares, just with a modification. To accomplish this, we featurize our samples.

Consider the following: what if the input to our model was x_ = x^2 and y_ = y^2? Then, our model is trying to learn the following model.

x_ + y_ = r^2

Is this linear in the model’s input x_ and output y_? Yes. Note the subtlety. The current model is still quadratic in x,y but it is linear in x_,y_. This means that least squares can fit the data if we square x^2 and y^2 before training least squares.

More generally, we can take any non-linear featurization to apply least squares to labels that are non-linear in the features. This is a fairly powerful tool, known as featurization.

However, featurizations lead to more complex models. Regularization allows us to penalize model complexity, ensuring that we do not overfit the training data.

Conclusion

In this article, you’ve touched on major topics in the fundamentals of machine learning. Using the abstractions above, you now have a framework to discuss machine learning problems and solutions. Using the fundamental topics above, you now also have quintessential concepts to learn more about, giving you the necessary tools to evaluate risk and other concerns in a machine learning application.

Further Reading

We will continue to explore these topics in depth, both the undercurrents of machine learning and specific methods. In the interim, here are resources to further your study and exploration of machine learning:

Smashing Editorial
(ra, il)


Continue reading here: 

Getting Started With Machine Learning

5 Really Bad Website Popup Examples

If you want to craft a delightful marketing experience and you’re using popups, you need to make sure you hold them to the same high standards as the content they are covering up. You can learn a lot by looking at bad website popup examples.

Once you understand what not to do, you’ll default to starting your own popup designs from a better baseline.

What does a bad popup design actually look like?

Well, it depends on your judging criteria, and for the examples below, I was considering these seven things, among others:

  1. Clarity: Is it easy to figure out the offer really quickly?
  2. Relevance: Is it related to the content of the current page?
  3. Manipulation: Does it use psychological trickery in the copy?
  4. Design: Is it butt ugly?
  5. Control: Is it clear what all options will do?
  6. Escape: Can you get rid of it easily?
  7. Value: Is the reward worth more than the perceived (or actual) effort?

The following popup examples, each make a number of critical errors in their design decisions. Take a look, and share your own worst popup design examples in the comments!


#1 – Mashable Shmashable

What’s so bad about it?

If you peer into the background behind the popup, you’ll see a news story headline that begins with “Nightmare Alert”. I think that’s a pretty accurate description of what’s happening here.

  • Design: Bad. The first thing I saw looks like a big mistake. The Green line with the button hanging off the bottom looks like the designer fell asleep with their head on the mouse.
  • Clarity: Bad. And what on earth does the headline mean? click.click.click. Upon deeper exploration, it’s the name of the newsletter, but that’s not apparent at all on first load.
  • Clarity: worse. Then we get the classic “Clear vs. Clever” headline treatment. Why are you talking about the pronunciation of the word “Gif”? Tell me what this is, and why I should care to give you my email.
  • Design: Bad. Also, that background is gnarly.

#2 – KAM Motorsports Revolution!

What’s so bad about it?

It’s motorsports. It’s not a revolution. Unless they’re talking about wheels going round in circles.

  • Clarity: Bad. The headline doesn’t say what it is, or what I’ll get by subscribing. I have to read the fine print to figure that out.
  • Copy: Bad. Just reading the phrase “abuse your email” is a big turn off. Just like the word spam, I wasn’t thinking that you were going to abuse me, but now it’s on my mind.
  • Relevance: Bad. Newsletter subscription popups are great, they have a strong sense of utility and can give people exactly what they want. But I don’t like them as entry popups. They’re much better when they use an exit trigger, or a scroll trigger. Using a “Scroll Up” trigger is smart because it means they’ve read some of your content, and they are scrolling back up vs. leaving directly, which is another micro-signal that they are interested.

#3 – Utterly Confused


(Source unknown – I found it on confirmshaming.tumblr.com)

What’s so bad about it?

I have no earthly clue what’s going on here.

  • Clarity: Bad. I had to re-read it five times before I figured out what was going on.
  • Control: Bad. After reading it, I didn’t know whether I would be agreeing with what they’re going to give me, or with the statement. It’s like an affirmation or something. But I have no way of knowing what will happen if I click either button. My best guess after spending this much time writing about it is that it’s a poll. But a really meaningless one if it is. Click here to find out how many people agreed with “doing better”…
  • It ends with “Do Better”. I agree. They need to do a lot better.

#4 – Purple Nurple

What’s so bad about it?

  • Manipulation: Bad. Our first “Confirm Shaming” example. Otherwise known as “Good Cop / Bad Cop”. Forcing people to click a button that says “Detest” on it is so incongruent with the concept of a mattress company that I think they’re just being cheap. There’s no need to speak to people that way.
  • I found a second popup example by Purple (below), and have to give them credit. The copy on this one is significantly more persuasive. Get this. If you look at the section I circled (in purple), it says that if you subscribe, they’ll keep you up to date with SHIPPING TIMES!!! Seriously? If you’re going to email me and say “Hey Oli, great news! We can ship you a mattress in 2 weeks!”, I’ll go to Leesa, or Endy, or one of a million other Casper copycats.


#5 – Hello BC

What’s so bad about it?

Context: This is an entry popup, and I have never been to this site before.

  • Relevance: Bad. The site is Hellobc.com, the title says “Supernatural British Columbia”, and the content on the page is about skydiving. So what list is this for? And nobody wants to be on a “list”, stop saying “list”. It’s like saying email blast. Blast your list. If you read the first sentence it gets even more confusing, as you’ll be receiving updates from Destination BC. That’s 4 different concepts at play here.
  • Design: Bad. It’s legitimately butt ugly. I mean, come on. This is for Beautiful Supernatural British Columbia ffs. It’s stunning here. Show some scenery to entice me in.
  • Value: Bad. Seeing that form when I arrive on the page is like a giant eff you. Why do they think it’s okay to ask for that much info, with that much text, before I’ve even seen any content?
  • Control: Bad. And there’s not any error handling. However, the submit button remains inactive until you magically click the right amount of options to trigger it’s hungry hungry hippo mouth to open.

Train. Wreck.


Well, that’s all for today, folks. You might be wondering why there were so few popup examples in this post. Honestly, when the team was rallying to find me a bunch of examples, we all struggled to find many truly awful ones. We also struggled to find many really awesome ones.

This is where YOU come in!

Send me your terrible and awesome popup examples!

If you have any wonderfully brutal, or brutally wonderful examples of website popup design, I’d really appreciate a URL in the comments. If you could share the trigger details too that would be rad (e.g. exit, entrance, scroll, delay etc.).

Tomorrow’s Post is about Awesome Popup Examples! YAY.

So get your butt back here same time tomorrow, where I’ll be sharing my brand new Popup Delight Equation that you can use to grade your own popup designs.

Cheers,
Oli

p.s. Don’t forget to subscribe to the weekly updates.

Source article – 

5 Really Bad Website Popup Examples

Technology isn’t the Problem, We Are: 5 Horrific Website Popup Examples

It’s Day 5 of Product Marketing Month. Today I get to bash some really bad popup examples. Yuss! — Unbounce co-founder Oli Gardner

But before I bring the heat, I want to talk a bit about what it’s like, as a product marketer, to be marketing something that’s difficult to market.

You see, there’s a common problem that many marketers face, and it’s also one of the most asked questions I hear when I’m on the road, as a speaker:

“How do I great marketing for a boring product or service?”

That’s a tough challenge for sure, although the good news is that if you can inject some originality you’ll be a clear winner, as all of your competitors are also boring. However, I think I can one-up that problem:

“How do I do great marketing for something that’s universally hated, like popups?”

We knew we had a big challenge ahead of us when we decided to release the popups product because of the long legacy of manipulative abuse it carries with it.

In fact, as the discussion about product direction began in the office, there were some visceral (negative) reactions from some folks on the engineering team. They feared that we were switching over to the dark side.

It makes sense to me that this sentiment would come from developers. In my experience, really good software developers have one thing in common. They want to make a difference in the world. Developers are makers by design, and part of building something is wanting it to have a positive impact on those who use it.

To quell those types of fears requires a few things;

  • Education about the positive use cases for the technology,
  • Evidence in the form of good popup examples, showcasing how to use them in a delightful and responsible manner,
  • Features such as advanced triggers & targeting to empower marketers to deliver greater relevance to visitors,
  • And most important of all – it requires us to take a stance. We can’t change the past unless we lead by example.

It’s been my goal since we started down this path, to make it clear that we are drawing a line in the sand between the negative past, and a positive future.

Which is why we initially launched with the name “Overlays” instead of popups.

Overlays vs. Popups – The End of an Era

It made a lot of sense at the time, from a branding perspective. Through podcast interviews and public speaking gigs, I was trying to change the narrative around popups. Whenever I was talking about a bad experience, I would call it a popup. When it was a positive (and additive) experience, I’d call it an overlay. It was a really good way to create a clear separation.

I even started to notice more and more people calling them overlays. Progress.

Unfortunately, it would still require a lot of continued education to make a dent in the global perception of the terminology, that with the search volume for “overlays” being tiny compared to popups, factored heavily into our decision to pivot back to calling a popup a popup.

Positioning is part of a product marketer’s job – our VP of Product Marketing, Ryan Engley recently completed our most recent positioning document for the new products. Just as the umbrella term “Convertables” we had been using to include popups and sticky bars had created confusion, “Overlays” was again making the job harder than it should have been. You can tell, just from reading this paragraph alone that it’s a complex problem, and we’re moving in the right direction by re-simplifying.

The biggest challenge developing our positioning was the number of important strategic questions that we needed to answer first. The market problems we solve, for who, how our product fits today with our vision for the future, who we see ourselves competing with, whether we position ourselves as a comprehensive platform that solves a unique problem, or whether we go to market with individual products and tools etc. It’s a beast of an undertaking.

My biggest lightbulb moment was working with April Dunford who pushed me to get away from competing tool-to-tool with other products. She said in order to win that way, you’d have to be market leading in every tool, and that won’t happen. So what’s the unique value that only you offer and why is it important?

— Ryan Engley, VP Product Marketing at Unbounce

You can read more about our initial product adoption woes, and how our naming conventions hurt us, in the first post in the series – Product Marketing Month: Why I’m Writing 30 Blog Posts in 30 Days.

Let’s get back to the subject of popups. I think it’s important to look back at the history of this device to better understand how they came about, and why they have always caused such a stir.

Browser Interaction Models & the History of the Popup

The talk I was doing much of last year was called Data-Driven Design. As part of the talk, I get into interaction design trends. I’ve included the “Trendline” slide below.

You can see that the first occurrence of a popup was back in 1998. Also, note that I included Overlays in late 2016 when we first started that discussion.

Like many bad trends, popups began as web developers started trying to hack browser behavior to create different interruptive interaction modes. I know I made a lot of them back in the day, but I was always doing it to try to create a cool experience. For example, I was building a company Intranet and wanted to open up content in a new window, resize it, and stick it to the side of the screen as a sidebar navigation for the main window. That was all good stuff.

Tabbed browsers have done a lot to help clean up the mess of multiple windows, and if you couple that with popup blockers, there’s a clear evolution in how this type of behavior is being dealt with.

Then came the pop-under, often connected to Malware virus schemes where malicious scripts could be running in the background and you wouldn’t even know.

And then the always fun “Are you sure you want to do that?” Inception-like looping exit dialogs.

Developers/hackers took the simple Javascript modal “Ok” “Cancel” and abused it to the point where there was no real way out of the page. If you tried to leave the page one modal would lead to another, and another, and you couldn’t actually close the browser window/tab unless you could do it within the split second between one dialog closing and the next opening. It was awful.

So we have a legacy of abuse that’s killed the perception of popups.

What if Popups Had Been Built Into Browsers?

Imagine for a moment that a popup was simply one of many available interaction models available in the browsing experience. They could have had a specification from the W3C, with a set of acceptable criteria for display modes. It would be an entirely different experience. Sure, there would still be abuse, but it’s an interesting thought.

This is why it’s important that we (Unbounce and other like-minded marketers and Martech software providers) take a stance, and build the right functionality into this type of tool so that it can be used responsibly.

Furthermore, we need to keep the dialog going, to educate the current and future generations of marketers that to be original, be delightful, be a business that represents themselves as professionals, means taking responsibility for our actions and doing everything we can to take the high road in our marketing.

Alright, before I get to the really bad website popup examples, I’ll leave you with this thought:

Technology is NOT the problem, We Are.

It’s the disrespectful and irresponsible marketers who use manipulative pop-psychology tactics for the sake of a few more leads, who are the problem. We need to stop blaming popups for bad experiences, and instead, call out the malicious marketers who are ruining it for those trying to do good work.

It’s a tough challenge to reverse years of negative perception, but that’s okay. It’s okay because we know the value the product brings to our customers, how much extra success they’re having, and because we’ve built a solution that can be configured in precise ways that make it simple to use in a responsible manner (if you’re a good person).


Follow our Product Marketing Month journey >> click here to launch a popup with a subscribe form (it uses our on-click trigger feature).


5 Really Bad Website Popup Examples

What does a bad popup actually look like? Well, it depends on your judging criteria, and for the examples below, I was considering these seven things, among others:

  1. Clarity: Is it easy to figure out the offer really quickly?
  2. Relevance: Is it related to the content of the current page?
  3. Manipulation: Does it use psychological trickery in the copy?
  4. Design: Is it butt ugly?
  5. Control: Is it clear what all options will do?
  6. Escape: Can you get rid of it easily?
  7. Value: Is the reward worth more than the perceived (or actual) effort?

#1 – Mashable Shmashable

What’s so bad about it?

If you peer into the background behind the popup, you’ll see a news story headline that begins with “Nightmare Alert”. I think that’s a pretty accurate description of what’s happening here.

  • Design: Bad. The first thing I saw looks like a big mistake. The Green line with the button hanging off the bottom looks like the designer fell asleep with their head on the mouse.
  • Clarity: Bad. And what on earth does the headline mean? click.click.click. Upon deeper exploration, it’s the name of the newsletter, but that’s not apparent at all on first load.
  • Clarity: worse. Then we get the classic “Clear vs. Clever” headline treatment. Why are you talking about the pronunciation of the word “Gif”? Tell me what this is, and why I should care to give you my email.
  • Design: Bad. Also, that background is gnarly.

#2 – KAM Motorsports Revolution!

What’s so bad about it?

It’s motorsports. It’s not a revolution. Unless they’re talking about wheels going round in circles.

  • Clarity: Bad. The headline doesn’t say what it is, or what I’ll get by subscribing. I have to read the fine print to figure that out.
  • Copy: Bad. Just reading the phrase “abuse your email” is a big turn off. Just like the word spam, I wasn’t thinking that you were going to abuse me, but now it’s on my mind.
  • Relevance: Bad. Newsletter subscription popups are great, they have a strong sense of utility and can give people exactly what they want. But I don’t like them as entry popups. They’re much better when they use an exit trigger, or a scroll trigger. Using a “Scroll Up” trigger is smart because it means they’ve read some of your content, and they are scrolling back up vs. leaving directly, which is another micro-signal that they are interested.

#3 – Utterly Confused


(Source unknown – I found it on confirmshaming.tumblr.com)

What’s so bad about it?

I have no earthly clue what’s going on here.

  • Clarity: Bad. I had to re-read it five times before I figured out what was going on.
  • Control: Bad. After reading it, I didn’t know whether I would be agreeing with what they’re going to give me, or with the statement. It’s like an affirmation or something. But I have no way of knowing what will happen if I click either button. My best guess after spending this much time writing about it is that it’s a poll. But a really meaningless one if it is. Click here to find out how many people agreed with “doing better”…
  • It ends with “Do Better”. I agree. They need to do a lot better.

#4 – Purple Nurple

What’s so bad about it?

  • Manipulation: Bad. Our first “Confirm Shaming” example. Otherwise known as “Good Cop / Bad Cop”. Forcing people to click a button that says “Detest” on it is so incongruent with the concept of a mattress company that I think they’re just being cheap. There’s no need to speak to people that way.
  • I found a second popup example by Purple (below), and have to give them credit. The copy on this one is significantly more persuasive. Get this. If you look at the section I circled (in purple), it says that if you subscribe, they’ll keep you up to date with SHIPPING TIMES!!! Seriously? If you’re going to email me and say “Hey Oli, great news! We can ship you a mattress in 2 weeks!”, I’ll go to Leesa, or Endy, or one of a million other Casper copycats.


#5 – Hello BC

What’s so bad about it?

Context: This is an entry popup, and I have never been to this site before.

  • Relevance: Bad. The site is Hellobc.com, the title says “Supernatural British Columbia”, and the content on the page is about skydiving. So what list is this for? And nobody wants to be on a “list”, stop saying “list”. It’s like saying email blast. Blast your list. If you read the first sentence it gets even more confusing, as you’ll be receiving updates from Destination BC. That’s 4 different concepts at play here.
  • Design: Bad. It’s legitimately butt ugly. I mean, come on. This is for Beautiful Supernatural British Columbia ffs. It’s stunning here. Show some scenery to entice me in.
  • Value: Bad. Seeing that form when I arrive on the page is like a giant eff you. Why do they think it’s okay to ask for that much info, with that much text.
  • Control: Bad. And there’s not any error handling. However, the submit button remains inactive until you magically click the right amount of options to trigger it’s hungry hungry hippo mouth to open.

Trainwreck.


Well, that’s all for today, folks. You might be wondering why there were so few popup examples in this post, keep reading and I’ll explain why.

Coming Up Tomorrow – Good Popups, YAY!!!

One of the most interesting things I’ve noticed of late is that there is a shift in quality happening in the popup world. When the team rallied to find the bad popup examples above, we found at least 10x as many good ones as bad. That’s something to feel pretty good about. Perhaps the positive energy we’re helping to spread is having an impact.

So get your butt back here tomorrow to see 20+ delightful website popup examples. More importantly, I’ll also be sharing “The Delight Equation”, my latest formula for measuring quantifying how good your popups really are.

See you then!

Cheers
Oli

p.s. Don’t forget to subscribe to the weekly updates.

Excerpt from: 

Technology isn’t the Problem, We Are: 5 Horrific Website Popup Examples

Technology isn’t the Problem, We Are. An Essay on Popups + 5 Horrific Popup Examples

Before I bring the heat, I want to talk a bit about what it’s like, as a marketer, to be marketing something that’s difficult to market.

You see, there’s a common problem that many marketers face, and it’s also one of the most asked questions I hear when I’m on the road, as a speaker:

“How do I great marketing for a boring product or service?”

That’s a tough challenge for sure, although the good news is that if you can inject some originality you’ll be a clear winner, as all of your competitors are also boring. However, I think I can one-up that problem:

“How do I do great marketing for something that’s universally hated, like popups?”

We knew we had a big challenge ahead of us when we decided to release the popups product because of the long legacy of manipulative abuse it carries with it.

In fact, as the discussion about product direction began in the office, there were some visceral (negative) reactions from some folks on the engineering team. They feared that we were switching over to the dark side.

It makes sense to me that this sentiment would come from developers. In my experience, really good software developers have one thing in common. They want to make a difference in the world. Developers are makers by design, and part of building something is wanting it to have a positive impact on those who use it.

To quell those types of fears requires a few things;

  • Education about the positive use cases for the technology,
  • Evidence in the form of good popup examples, showcasing how to use them in a delightful and responsible manner,
  • Features such as advanced triggers & targeting to empower marketers to deliver greater relevance to visitors,
  • And most important of all – it requires us to take a stance. We can’t change the past unless we lead by example.

It’s been my goal since we started down this path, to make it clear that we are drawing a line in the sand between the negative past, and a positive future.

Which is why we initially launched with the name “Overlays” instead of popups.

Overlays vs. Popups – The End of an Era

It made a lot of sense at the time, from a branding perspective. Through podcast interviews and public speaking gigs, I was trying to change the narrative around popups. Whenever I was talking about a bad experience, I would call it a popup. When it was a positive (and additive) experience, I’d call it an overlay. It was a really good way to create a clear separation.

I even started to notice more and more people calling them overlays. Progress.

Unfortunately, it would still require a lot of continued education to make a dent in the global perception of the terminology, that with the search volume for “overlays” being tiny compared to popups, factored heavily into our decision to pivot back to calling a popup a popup.

Positioning is part of a product marketer’s job – our VP of Product Marketing, Ryan Engley recently completed our most recent positioning document for the new products. Just as the umbrella term “Convertables” we had been using to include popups and sticky bars had created confusion, “Overlays” was again making the job harder than it should have been. You can tell, just from reading this paragraph alone that it’s a complex problem, and we’re moving in the right direction by re-simplifying.

The biggest challenge developing our positioning was the number of important strategic questions that we needed to answer first. The market problems we solve, for who, how our product fits today with our vision for the future, who we see ourselves competing with, whether we position ourselves as a comprehensive platform that solves a unique problem, or whether we go to market with individual products and tools etc. It’s a beast of an undertaking.

My biggest lightbulb moment was working with April Dunford who pushed me to get away from competing tool-to-tool with other products. She said in order to win that way, you’d have to be market leading in every tool, and that won’t happen. So what’s the unique value that only you offer and why is it important?

— Ryan Engley, VP Product Marketing at Unbounce

You can read more about our initial product adoption woes, and how our naming conventions hurt us, in the first post in the series – Product Marketing Month: Why I’m Writing 30 Blog Posts in 30 Days.

Let’s get back to the subject of popups. I think it’s important to look back at the history of this device to better understand how they came about, and why they have always caused such a stir.

Browser Interaction Models & the History of the Popup

The talk I was doing much of last year was called Data-Driven Design. As part of the talk, I get into interaction design trends. I’ve included the “Trendline” slide below.

You can see that the first occurrence of a popup was back in 1998. Also, note that I included Overlays in late 2016 when we first started that discussion.

Like many bad trends, popups began as web developers started trying to hack browser behavior to create different interruptive interaction modes. I know I made a lot of them back in the day, but I was always doing it to try to create a cool experience. For example, I was building a company Intranet and wanted to open up content in a new window, resize it, and stick it to the side of the screen as a sidebar navigation for the main window. That was all good stuff.

Tabbed browsers have done a lot to help clean up the mess of multiple windows, and if you couple that with popup blockers, there’s a clear evolution in how this type of behavior is being dealt with.

Then came the pop-under, often connected to Malware virus schemes where malicious scripts could be running in the background and you wouldn’t even know.

And then the always fun “Are you sure you want to do that?” Inception-like looping exit dialogs.

Developers/hackers took the simple Javascript modal “Ok” “Cancel” and abused it to the point where there was no real way out of the page. If you tried to leave the page one modal would lead to another, and another, and you couldn’t actually close the browser window/tab unless you could do it within the split second between one dialog closing and the next opening. It was awful.

So we have a legacy of abuse that’s killed the perception of popups.

What if Popups Had Been Built Into Browsers?

Imagine for a moment that a popup was simply one of many available interaction models available in the browsing experience. They could have had a specification from the W3C, with a set of acceptable criteria for display modes. It would be an entirely different experience. Sure, there would still be abuse, but it’s an interesting thought.

This is why it’s important that we (Unbounce and other like-minded marketers and Martech software providers) take a stance, and build the right functionality into this type of tool so that it can be used responsibly.

Furthermore, we need to keep the dialog going, to educate the current and future generations of marketers that to be original, be delightful, be a business that represents themselves as professionals, means taking responsibility for our actions and doing everything we can to take the high road in our marketing.

Alright, before I get to the really bad website popup examples, I’ll leave you with this thought:

Technology is NOT the problem, We Are.

It’s the disrespectful and irresponsible marketers who use manipulative pop-psychology tactics for the sake of a few more leads, who are the problem. We need to stop blaming popups for bad experiences, and instead, call out the malicious marketers who are ruining it for those trying to do good work.

It’s a tough challenge to reverse years of negative perception, but that’s okay. It’s okay because we know the value the product brings to our customers, how much extra success they’re having, and because we’ve built a solution that can be configured in precise ways that make it simple to use in a responsible manner (if you’re a good person).


Follow our Product Marketing Month journey >> click here to launch a popup with a subscribe form (it uses our on-click trigger feature).


5 Really Bad Website Popup Examples

What does a bad popup actually look like? Well, it depends on your judging criteria, and for the examples below, I was considering these seven things, among others:

  1. Clarity: Is it easy to figure out the offer really quickly?
  2. Relevance: Is it related to the content of the current page?
  3. Manipulation: Does it use psychological trickery in the copy?
  4. Design: Is it butt ugly?
  5. Control: Is it clear what all options will do?
  6. Escape: Can you get rid of it easily?
  7. Value: Is the reward worth more than the perceived (or actual) effort?

#1 – Mashable Shmashable

What’s so bad about it?

If you peer into the background behind the popup, you’ll see a news story headline that begins with “Nightmare Alert”. I think that’s a pretty accurate description of what’s happening here.

  • Design: Bad. The first thing I saw looks like a big mistake. The Green line with the button hanging off the bottom looks like the designer fell asleep with their head on the mouse.
  • Clarity: Bad. And what on earth does the headline mean? click.click.click. Upon deeper exploration, it’s the name of the newsletter, but that’s not apparent at all on first load.
  • Clarity: worse. Then we get the classic “Clear vs. Clever” headline treatment. Why are you talking about the pronunciation of the word “Gif”? Tell me what this is, and why I should care to give you my email.
  • Design: Bad. Also, that background is gnarly.

#2 – KAM Motorsports Revolution!

What’s so bad about it?

It’s motorsports. It’s not a revolution. Unless they’re talking about wheels going round in circles.

  • Clarity: Bad. The headline doesn’t say what it is, or what I’ll get by subscribing. I have to read the fine print to figure that out.
  • Copy: Bad. Just reading the phrase “abuse your email” is a big turn off. Just like the word spam, I wasn’t thinking that you were going to abuse me, but now it’s on my mind.
  • Relevance: Bad. Newsletter subscription popups are great, they have a strong sense of utility and can give people exactly what they want. But I don’t like them as entry popups. They’re much better when they use an exit trigger, or a scroll trigger. Using a “Scroll Up” trigger is smart because it means they’ve read some of your content, and they are scrolling back up vs. leaving directly, which is another micro-signal that they are interested.

#3 – Utterly Confused


(Source unknown – I found it on confirmshaming.tumblr.com)

What’s so bad about it?

I have no earthly clue what’s going on here.

  • Clarity: Bad. I had to re-read it five times before I figured out what was going on.
  • Control: Bad. After reading it, I didn’t know whether I would be agreeing with what they’re going to give me, or with the statement. It’s like an affirmation or something. But I have no way of knowing what will happen if I click either button. My best guess after spending this much time writing about it is that it’s a poll. But a really meaningless one if it is. Click here to find out how many people agreed with “doing better”…
  • It ends with “Do Better”. I agree. They need to do a lot better.

#4 – Purple Nurple

What’s so bad about it?

  • Manipulation: Bad. Our first “Confirm Shaming” example. Otherwise known as “Good Cop / Bad Cop”. Forcing people to click a button that says “Detest” on it is so incongruent with the concept of a mattress company that I think they’re just being cheap. There’s no need to speak to people that way.
  • I found a second popup example by Purple (below), and have to give them credit. The copy on this one is significantly more persuasive. Get this. If you look at the section I circled (in purple), it says that if you subscribe, they’ll keep you up to date with SHIPPING TIMES!!! Seriously? If you’re going to email me and say “Hey Oli, great news! We can ship you a mattress in 2 weeks!”, I’ll go to Leesa, or Endy, or one of a million other Casper copycats.


#5 – Hello BC

What’s so bad about it?

Context: This is an entry popup, and I have never been to this site before.

  • Relevance: Bad. The site is Hellobc.com, the title says “Supernatural British Columbia”, and the content on the page is about skydiving. So what list is this for? And nobody wants to be on a “list”, stop saying “list”. It’s like saying email blast. Blast your list. If you read the first sentence it gets even more confusing, as you’ll be receiving updates from Destination BC. That’s 4 different concepts at play here.
  • Design: Bad. It’s legitimately butt ugly. I mean, come on. This is for Beautiful Supernatural British Columbia ffs. It’s stunning here. Show some scenery to entice me in.
  • Value: Bad. Seeing that form when I arrive on the page is like a giant eff you. Why do they think it’s okay to ask for that much info, with that much text.
  • Control: Bad. And there’s not any error handling. However, the submit button remains inactive until you magically click the right amount of options to trigger it’s hungry hungry hippo mouth to open.

Trainwreck.


Well, that’s all for today, folks. You might be wondering why there were so few popup examples in this post, keep reading and I’ll explain why.

Coming Up Tomorrow – Good Popups, YAY!!!

One of the most interesting things I’ve noticed of late is that there is a shift in quality happening in the popup world. When the team rallied to find the bad popup examples above, we found at least 10x as many good ones as bad. That’s something to feel pretty good about. Perhaps the positive energy we’re helping to spread is having an impact.

So get your butt back here tomorrow to see 20+ delightful website popup examples. More importantly, I’ll also be sharing “The Delight Equation”, my latest formula for measuring quantifying how good your popups really are.

See you then!

Cheers
Oli

p.s. Don’t forget to subscribe to the weekly updates.

Continue reading:

Technology isn’t the Problem, We Are. An Essay on Popups + 5 Horrific Popup Examples

How To Offer More Personal Customer Support Through Effective Automation

Robots are great for cleaning the floor and are perfect for exploring the moon. They’re just not that great at customer support. The last thing your customers want is another “We received your message” email or “Thank you for holding” recording. Robots only succeed in making customers feel like another number, a dubious accomplishment for your team. They’re the opposite of the personal touch that effective support is supposed to be all about.

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How To Offer More Personal Customer Support Through Effective Automation

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Sailing With Sails.js: An MVC-style Framework For Node.js


I had been doing server-side programming with Symfony 2 and PHP for at least three years before I started to see some productivity problems with it. Don’t get me wrong, I like Symfony quite a lot: It’s a mature, elegant and professional framework. But I’ve realized that too much of my precious time is spent not on the business logic of the application itself, but on supporting the architecture of the framework.

Sailing With Sails.js

I don’t think I’ll surprise anyone by saying that we live in a fast-paced world. The whole startup movement is a constant reminder to us that, in order to achieve success, we need to be able to test our ideas as quickly as possible. The faster we can iterate on our ideas, the faster we can reach customers with our solutions, and the better our chances of getting a product-market fit before our competitors do or before we exceed our limited budget. And in order to do so, we need instruments suitable to this type of work.

The post Sailing With Sails.js: An MVC-style Framework For Node.js appeared first on Smashing Magazine.

Continued here: 

Sailing With Sails.js: An MVC-style Framework For Node.js

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Help Your Content Go Anywhere With A Mobile Content Strategy

You’ve put a lot of thought, time and effort into creating great content, and you want users to have a great experience with your content. While you might have created the best content in the world, you don’t get to choose how users access it. That’s why it’s important to make sure your content works beautifully on every platform and device, desktop, mobile or something else entirely.
Before you panic, I’m not advocating that you create individual content strategies for each device or network that your content is published to.

Continued: 

Help Your Content Go Anywhere With A Mobile Content Strategy

How To Simplify Mobile App Data With Google Analytics And Tag Manager

Information about customers has never been available on the scale it is today. Businesses are learning new ways to leverage data to improve themselves on a daily basis. They’re realizing that data collection and data analysis have a measurable return on investment, and decision-makers are asking to see them.
As a developer, business owner or marketer, you need to know how to gather data and how to do it efficiently and in a scalable way.

Continue reading here:  

How To Simplify Mobile App Data With Google Analytics And Tag Manager

Journey Through The JavaScript MVC Jungle

When writing a Web application from scratch, it’s easy to feel like we can get by simply by relying on a DOM manipulation library (like jQuery) and a handful of utility plugins. The problem with this is that it doesn’t take long to get lost in a nested pile of jQuery callbacks and DOM elements without any real structure in place for our applications.
In short, we’re stuck with spaghetti code.

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Journey Through The JavaScript MVC Jungle

35 Beautiful Commercial And Free Joomla Templates

Joomla is one of the most powerful, robust and feature-rich content management systems out there. Besides being open-source, Joomla is capable of some really stunning designs through the use of modules and built-in display customizations. Below, we present a list of 20 35 of the coolest, slickest, most eye-catching Joomla template designs.
Also consider our previous article:
How WordPress Took The CMS Crown From Drupal And Joomla Joomla And WordPress: A Matter Of Mental Models How To Modify A Default Joomla 1.

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35 Beautiful Commercial And Free Joomla Templates