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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

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Best Practices For Mobile Form Design




Best Practices For Mobile Form Design

Nick Babich



(This article is kindly sponsored by Adobe.) Forms are the linchpin of all mobile interactions; it stands between the person and what they’re looking for. Every day, we use forms for essential online activities. Recall the last time you bought a ticket, booked a hotel room or made a purchase online — most probably those interactions contained a step with filling out a form.

Forms are just a means to an end. Users should be able to complete them quickly and without confusion. In this article, you’ll learn practical techniques that will help you design an effective form.

What Makes For An Effective Form

The primary goal with every form is completion. Two factors have a major impact on completion rate:

  • Perception of complexity
    The first thing users do when they see a new form is estimate how much time is required to complete it. Users do this by scanning the form. Perception plays a crucial role in the process of estimation. The more complex a form looks, the more likely users will abandon the process.
  • Interaction cost
    Interaction cost is the sum of efforts — both cognitive and physical — that the users put into interacting with an interface in order to reach their goal. Interaction cost has a direct connection with form usability. The more effort users have to make to complete a form, the less usable the form is. A high interaction cost could be the result of data that is difficult to input, an inability to understand the meaning of some questions, or confusion about error messages.

The Components Of Forms

A typical form has the following five components:

  • Input fields
    These include text fields, password fields, checkboxes, radio buttons, sliders and any other fields designed for user input.
  • Field labels
    These tell users what the corresponding input fields mean.
  • Structure
    This includes the order of fields, the form’s appearance on the page, and the logical connections between different fields.
  • Action buttons
    The form will have at least one call to action (the button that triggers data submission).
  • Feedback
    Feedback notifies the user about the result of an operation. Feedback can be positive (for example, indicating that the form was submitted successfully) or negative (saying something like, “The number you’ve provided is incorrect”).

This article covers many aspects related to structure, input fields, labels, action buttons and validation. Most points mentioned in this article have visual do and don’t examples; all such examples were created using Adobe XD.

Input Fields

When it comes to form design, the most important thing a designer can do is to minimize the need for typing. Reducing input effort is essential. Designers can achieve this goal by focusing on form field design.

Minimize The Total Number Of Fields

Every field you ask users to fill out requires some effort. The more effort is needed to fill out a form, the less likely users will complete the form. That’s why the foundational rule of form design is shorter is better — get rid of all inessential fields.

Baymard Institute analyzed checkout forms and found that a too long or too complicated checkout process is one of the top reasons for abandonment during checkout. The study found that the average checkout contains almost 15 form fields. Most online services could reduce the number of fields displayed by default by 20 to 60%.




Top reasons for abandonment during checkout. (Image: Baymard Institute) (Large preview)

Many designers are familiar with the “less is more” rule; still, they ask additional questions in an attempt to gather more data about their users. It might be tempting to collect more data about your users during the initial signup, but resist that temptation. Think about it this way: With every additional field you add to your form, you increase the chance of losing a prospective user. Is the information you gain from a field worth losing new users? Remember that, as long as you’ve collected a user’s contact information, you can always follow up with a request for more data.

Clearly Distinguish All Optional Fields

Before optimizing optional fields, ask yourself whether you really need to include them in your form. Think about what information you really need, not what you want. Ideally, the number of optional fields in your form should be zero.

If after a brainstorming session, you still want to include a few optional questions in your form, make it clear for users that those fields are optional:

  • Mark optional fields instead of mandatory ones.
    If you ask as little as possible, then the vast majority of fields in your form will be mandatory. Therefore, mark only those fields in the minority. For instance, if five out of six fields are mandatory, then it makes sense to mark only one field as optional.
  • Use the “Optional” label to denote optional fields.
    Avoid using the asterisk (*) to mean “optional.” Not all users will associate the asterisk with optional information, and some users will be confused by the meaning (an asterisk is often used to denote mandatory fields).

Clearly distinguish all optional fields.


Clearly distinguish all optional fields. (Large preview)

Size Fields Accordingly

When possible, use field length as an affordance. The length of an input field should be in proportion to the amount of information expected in the field. The size of the field will act as a visual constraint — the user will know how much text is expected to be entered just by looking at the field. Generally, fields such as ones for area codes and house numbers should be shorter than ones for street addresses.


The size of a field is used as a visual constraint.


The size of a field is used as a visual constraint. (Large preview)

Offer Field Focus

Auto-focus the first input field in your form. Auto-focusing a field gives the user an indication and a starting point, so that they are able to quickly start filling out the form. By doing that, you reduce the interaction cost — saving the user one unnecessary tap.

Make the active input field prominent and focused. The field focus itself should be crystal clear — users should be able to understand at a glance where the focus is. It could be an accented border color or a fade-in of the box.


Amazon puts strong visual focus on the input field.


Amazon puts strong visual focus on the input field. (Large preview)

Don’t Ask Users To Repeat Their Email Address

The reason why an extra field for the email address is so popular among product developers is apparent: Every company wants to minimize the risk of hard bounces (non-deliverables caused by invalid email addresses). Unfortunately, following this approach doesn’t guarantee that you’ll get a valid address. Users often copy and paste their address from one field to another.


Avoid asking users to retype their email address.


Avoid asking users to retype their email address. (Large preview)

Provide “Show Password” Option

Duplicating the password input field is another common mistake among product designers. Designers follow this approach because they believe it will prevent users from mistyping a password. In reality, a second field for a password not only increases interaction cost, but also doesn’t guarantee that users will proceed without mistakes. Because users don’t see what they’ve entered in the field, they can make the same mistake twice (in both fields) and will face a problem when they try to log in using a password. As Jakob Nielsen summarized:

Usability suffers when users type in passwords and the only feedback they get is a row of bullets. Typically, masking passwords doesn’t even increase security, but it does cost you business due to login failures.

Instead of duplicating the password field, provide an option that allows users to view the password they have chosen to create. Have an icon or checkbox that unmasks the password when clicked. A password preview can be an opportunity for users to check their data before sending.


Show password' option


Not being able to see what you’re typing is a huge issue. Providing a ‘Show password’ option next to the password field will help to solve this problem. (Large preview)

Don’t Slice Data Fields

Do not slice fields when asking for a full name, phone number or date of birth. Sliced fields force the user to make additional taps to move to the next field. For fields that require some formatting (such as phone numbers or a date of birth), it’s also better to have a single field paired with clear formatting rules as its placeholder.


“Full name” field


Avoid splitting input fields; don’t make people jump between fields. Instead of asking for a first name and last name in two separate fields, have a single ‘Full name’ field. (Large preview)

Avoid Dropdown Menus

Luke Wroblewski famously said that dropdowns should be the UI of last resort. Dropdowns are especially bad for mobile because collapsed elements make the process of data input harder on a small screen: Placing options in a dropdown requires two taps and hides the options.

If you’re using a dropdown for selection of options, consider replacing it with radio buttons. They will make all options glanceable and also reduce the interaction cost — users can tap on the item and select at once.




(Large preview)

Use Placeholders And Masked Input

Formatting uncertainty is one of the most significant problems of form design. This problem has a direct connection with form abandonment — when users are uncertain of the format in which they should provide data, they can quickly abandon the form. There are a few things you can do to make the format clear.

Placeholder Text

The text in an input field can tell users what content is expected. Placeholder text is not required for simple fields such as “Full name”, but it can be extremely valuable for fields that require data in a specific format. For example, if you design search functionality for tracking a parcel, it would be good to provide a sample tracking number as a placeholder for the tracking-number field.




(Large preview)

It’s vital that your form should have a clear visual distinction between the placeholder text and the actual value entered by the user. In other words, placeholder text shouldn’t look like a preset value. Without clear visual distinction, users might think that the fields with placeholders already have values.

Masked Input

Field masking is a technique that helps users format inputted text. Many designers confuse field masking with placeholder text — they are not the same thing. Unlike placeholders, which are basically static text, masks automatically format the data provided by the user. In the example below, the parentheses, spaces and dashes appear on the screen automatically as a phone number is entered.

Masked input also makes it easy for users to validate information. When a phone number is displayed in chunks, it makes it easier to find and correct a typo.

Masked input for a phone number. (Image: Josh Morony)

Provide Matching Keyboard

Mobile users appreciate apps and websites that provide an appropriate keyboard for the field. This feature prevents them from doing additional actions. For example, when users need to enter a credit card number, your app should only display the dialpad. It’s essential to implement keyboard matching consistently throughout the app (all forms in your app should have this feature).

Set HTML input types to show the correct keypad. Seven input types are relevant to form design:

  • input type="text" displays the mobile device’s normal keyboard.
  • input type="email" displays the normal keyboard and ‘@’ and ‘.com’.
  • input type="tel" displays the numeric 0 to 9 keypad.
  • input type="number" displays a keyboard with numbers and symbols.
  • input type="date" displays the mobile device’s date selector.
  • input type="datetime" displays the mobile device’s date and time selector.
  • input type="month" displays the mobile device’s month and year selector.



When users tap into a field with credit card number, they should see a numerical dialpad — all numbers, no letters. (Large preview)

Use A Slider When Asking For A Specific Range

Many forms ask users to provide a range of values (for example, a price range, distance range, etc.). Instead of using two separate fields, “from” and “to”, for that purpose, use a slider to allow users to specify the range with a thumb interaction.


Sliders are good for touch interfaces because they allow users to specify a range without typing.


Sliders are good for touch interfaces because they allow users to specify a range without typing. (Large preview)

Clearly Explain Why You’re Asking For Sensitive Information

People are increasingly concerned about privacy and information security. When users see a request for information they consider as private, they might think, “Hm, why do they need this?” If your form asks users for sensitive information, make sure to explain why you need it. You can do that by adding support text below relevant fields. As a rule of thumb, the explanation text shouldn’t exceed 100 characters.


A request for a phone number in a booking form might confuse users. Explain why you are asking for it.


A request for a phone number in a booking form might confuse users. Explain why you are asking for it. (Large preview)

Be Careful With Static Defaults

Unlike smart defaults, which are calculated by the system based on the information the system has about users, static defaults are preset values in forms that are the same for all users. Avoid static defaults unless you believe a significant portion of your users (say, 95%) would select those values — particularly for required fields. Why? Because you’re likely to introduce errors — people scan forms quickly, and they won’t spend extra time parsing all of the questions; instead, they’ll simply skip the field, assuming it already has a value.

Protect User Data

Jef Raskin once said, “The system should treat all user input as sacred.” This is absolutely true for forms. It’s great when you start filling in a web form and then accidentally refresh the page but the data remains in the fields. Tools such as Garlic.js help you to persist a form’s values locally until the form is submitted. This way, users won’t lose any precious data if they accidentally close the tab or browser.

Automate Actions

If you want to make the process of data input as smooth as possible, it’s not enough to minimize the number of input fields — you should also pay attention to the user effort required for the data input. Typing has a high interaction cost — it’s error-prone and time-consuming, even with a physical keyboard. But when it comes to mobile screens, it becomes even more critical. More typing increases the user’s chance of making errors. Strive to prevent unnecessary typing, because it will improve user satisfaction and decrease error rates.

Here are a few things you can do to achieve this goal:

Autocomplete

Most users experience autocompletion when typing a question in Google’s search box. Google provides users with a list of suggestions related to what the user has typed in the field. The same mechanism can be applied to form design. For example, a form could autocomplete an email address.

This form suggests the email host and saves users from typing a complete address. (Image: GitHub)
Autocapitalize

Autocapitalizing makes the first letter a capital automatically. This feature is excellent for fields like names and street addresses, but avoid it for password fields.

Autocorrect

Autocorrection modifies words that appear to be misspelled. Turn this feature off for unique fields, such as names, addresses, etc.

Auto-filling of personal details

Typing an address is often the most cumbersome part of any online signup form. Make this task easier by using the browser function to fill the field based on previously entered values. According to Google’s research, auto-filling helps people fill out forms 30% faster.

Address prefill. Image: Google

Use The Mobile Device’s Native Features To Simplify Data Input

Modern mobile devices are sophisticated devices that have a ton of amazing capabilities. Designers can use a device’s native features (such as camera or geolocation) to streamline the task of inputting data.

Below are just a few tips on how to make use of sensors and device hardware.

Location Services

It’s possible to preselect the user’s country based on their geolocation data. But sometimes prefilling a full address can be problematic due to accuracy issues. Google’s Places API can help solve this problem. It uses both geolocation and address prefilling to provide accurate suggestions based on the user’s exact location.

Address lookup using Google Places API. (Image: Chromatic HQ) (Large preview)

Using location services, it’s also possible to provide smart defaults. For example, for a “Find a flight” form, it’s possible to prefill the “From” field with the nearest airport to the user based on the user’s geolocation.

Biometric Authorization

The biggest problem of using a text password today is that most people forget passwords. 82% of people can’t remember their passwords, and 5 to 10% of sessions require users to reset a password. Password recovery is a big deal in e-commerce. 75% of users wouldn’t complete a purchase if they had to attempt to recover their password while checking out.

The future of passwords is no passwords. Even today, mobile developers can take advantage of biometric technologies. Users shouldn’t need to type a password; they should be able to use biometric readers for authentication — signing in using a fingerprint or face scanning.


eBay took advantage of the biometrics functionality on smartphones. Users can use their thumbprint to login into their eBay account.


eBay took advantage of the biometrics functionality on smartphones. Users can use their thumbprint to login into their eBay account. (Large preview)

Camera

If your form asks users to provide credit card details or information from their driver’s license, it’s possible to simplify the process of data input by using the camera as a scanner. Provide an option to take a photo of the card and fill out all details automatically.

Let users scan their identity card, instead of having to fill out their credit card information manually. (Image: blinkid)

But remember that no matter how good your app fills out the fields, it’s essential to leave them available for editing. Users should be able to modify the fields whenever they want.

Voice

Voice-controlled devices, such as Apple HomePod, Google Home and Amazon Echo, are actively encroaching on the market. The number of people who prefer to use voice for common operations has grown significantly. According to ComScore, 50% of all searches will be voice searches by 2020.




How people in the US use smart speakers (according to comScore) (Large preview)

As users get more comfortable and confident using voice commands, they will become an expected feature of mobile interactions. Voice input provides a lot of advantages for mobile users — it’s especially valuable in situations when users can’t focus on a screen, for example, while driving a car.

When designing a form, you can provide voice input as an alternative method of data input.




Google Translate provides an option to enter the text for translation using voice. (Large preview)

Field Labels

Write Clear And Concise Labels

The label is the text that tells users what data is expected from them in a particular input field. Writing clear labels is one of the best ways to make a form more accessible. Labels should help the user understand what information is required at a glance.

Avoid using complete sentences to explain. A label is not help text. Write succinct and crisp labels (a word or two), so that users can quickly scan your form.

Place The Label And Input Close Together

Put each label close to the input field, because the eye will visually know they’re tied together.


A label and its field should be visually grouped, so that users can understand which label belongs to which field.


A label and its field should be visually grouped, so that users can understand which label belongs to which field. (Large preview)

Don’t Use Disappearing Placeholder Text As Labels

While inline labels look good and save valuable screen estate, these benefits are far outweighed by the significant usability drawbacks, the most critical of which is the loss of context. When users start entering text in a field, the placeholder text disappears and forces people to recall this information. While it might not be a problem for simple two-field forms, it could be a big deal for forms that have a lot of fields (say, 7 to 10). It would be tough for users to recall all field labels after inputting data. Not surprisingly, user testing continually shows that placeholders in form fields often hurt usability more than help.


Don’t use placeholder text that disappears when the user interacts with the field.


Don’t use placeholder text that disappears when the user interacts with the field. (Large preview)

There’s a simple solution to the problem of disappearing placeholders: the floating (or adaptive) label. After the user taps on the field with the label placeholder, the label doesn’t disappear, it moves up to the top of the field and makes room for the user to enter their data.

Floating labels assure the user that they’ve filled out the fields correctly. (Image: Matt D. Smith)

Top-Align Labels

Putting field labels above the fields in a form improves the way users scan the form. Using eye-tracking technology for this, Google showed that users need fewer fixations, less fixation time and fewer saccades before submitting a form.

Another important advantage of top-aligned labels is that they provide more space for labels. Long labels and localized versions will fit more easily in the layout. The latter is especially suitable for small mobile screens. You can have form fields extend the full width of the screen, making them large enough to display the user’s entire input.




(Large preview)

Sentence Case Vs. Title Case

There are two general ways to capitalize words:

  • Title case: Capitalize every word. “This Is Title Case.”
  • Sentence case: Capitalize the first word. “This is sentence case.”

Using sentence case for labels has one advantage over title case: It is slightly easier (and, thus, faster) to read. While the difference for short labels is negligible (there’s not much difference between “Full Name” and “Full name”), for longer labels, sentence case is better. Now You Know How Difficult It Is to Read Long Text in Title Case.

Avoid Using Caps For Labels

All-caps text  —  meaning text with all of the letters cap­i­tal­ized  —  is OK in contexts that don’t involve substantive reading (such as acronyms and logos), but avoid all caps otherwise. As mentioned by Miles Tinker in his work Legibility of Print, all-capital print dramatically slows the speed of scanning and reading compared to lowercase type.


All-capitalized letters


All-capitalized letters are hard to scan and read. (Large preview)

Layout

You know by now that users scan web pages, rather than read them. The same goes for filling out forms. That’s why designers should design a form that is easy to scan. Allowing for efficient, effective scanning is crucial to making the process of the filling out a form as quick as possible.

Use A Single-Column Layout

A study by CXL Institute found that single-column forms are faster to complete than multi-column forms. In that study, test participants were able to complete a single-column form an average of 15.4 seconds faster than a multi-column form.

Multiple columns disrupt a user’s vertical momentum; with multiple columns, the eyes start zigzagging. This dramatically increases the number of eye fixations and, as a result, the completion time. Moreover, multiple-column forms might raise unnecessary questions in the user, like “Where should I begin?” and “Are questions in the right column equal in importance to questions in the left one?”

In a one-column design, the eyes move in a natural direction, from top to bottom, one line at a time. This helps to set a clear path for the user. One column is excellent for mobile because the screens are longer vertically, and vertical scrolling is a natural motion for mobile users.

There are some exceptions to this rule. It’s possible to place short and logically related fields on the same row (such as for the city and area code).




If a form has horizontally adjacent fields, the user has to scan the form following a Z pattern. When the eyes start zigzagging, it slows the speed of comprehension and increases completion time. (Large preview)




(Large preview)

Create A Flow With Your Questions

The way you ask questions also matters. Questions should be asked logically from the user’s perspective, not according to the application or database’s logic, because it will help to create a sense of conversation with the user. For example, if you design a checkout form and asks for details such as full name, phone number and credit card, the first question should be for the full name. Changing the order (for example, starting with a phone number instead of a name) leads to discomfort. In real-world conversations, it would be unusual to ask for someone’s phone number before asking their name.

Defer In-Depth Questions To The End

When it comes to designing a flow for questions you want to ask, think about prioritization. Follow the rule “easy before difficult” and place in-depth or personal questions last. This eases users into the process; they will be more likely to answer complex and more intrusive questions once they’ve established a rapport. This has a scientific basis: Robert Cialdini’s principle of consistency stipulates that when someone takes a small action or step towards something, they feel more compelled to finish.

Group Related Fields Together

One of the principles of Gestalt psychology, the principle of proximity, states that related elements should be near each other. This principle can be applied to the order of questions in a form. The more related questions are, the closer they should be to each other.

Designers can group related fields into sections. If your form has more than six questions, group related questions into logical sections. Don’t forget to provide a good amount of white space between sections to distinguish them visually.




Generally, if your form has more than six questions, it’s better to group related questions into logical sections. Put things together that make sense together. (Large preview)

Make A Long Form Look Simpler

How do you design a form that asks users a lot of questions? Of course, you could put all of the questions on one screen. But this hinder your completion rate. If users don’t have enough motivation to complete a form, the form’s complexity could scare them away. The first impression plays a vital role. Generally, the longer or more complicated a form seems, the less likely users will be to start filling in the blanks.

Minimize the number of fields visible at one time. This creates the perception that the form is shorter than it really is.

There are two techniques to do this.

Progressive Disclosure

Progressive disclosure is all about giving users the right thing at the right time. The goal is to find the right stuff to put on the small screen at the right time:

  • Initially, show users only a few of the most important options.
  • Reveal parts of your form as the user interacts with it.
Using progressive disclosure to reduce cognitive load and keep the user focused on a task. (Image: Ramotion)
Chunking

Chunking entails breaking a long form into steps. It’s possible to increase the completion rate by splitting a form into a few steps. Chunking can also help users process, understand and remember information. When designing multi-step forms, always inform users of their progress with a completeness meter.




Progress tracker for e-commerce form. (Image: Murat Mutlu) (Large preview)

Designers can use either a progress tracker (as shown in the example above) or a “Step # out of #” indicator both to tell how many steps there are total and to show how far along the user is at the moment. The latter approach could be great for mobile forms because step indication doesn’t take up much space.

Action Buttons

A button is an interactive element that direct users to take an action.

Make Action Buttons Descriptive

A button’s label should explain what the button does; users should be able to understand what happens after a tap just by looking at the button. Avoid generic labels such as “Submit” and “Send”, using instead labels that describe the action.




Label should help users finish the sentence, ‘I want to…’ For example, if it’s a form to create an account, the call to action could be ‘Create an account’. (Large preview)

Don’t Use Clear Or Reset Buttons

Clear or reset buttons allow users to erase their data in a form. These buttons almost never help users and often hurt them. The risk of deleting all of the information a user has entered outweighs the small benefit of having to start again. If a user fills in a form and accidentally hits the wrong button, there’s a good chance they won’t start over.

Use Different Styles For Primary And Secondary Buttons

Avoid secondary actions if possible. But if your form has two calls to action (for example, an e-commerce form that has “Apply discount” and “Submit order”) buttons, ensure a clear visual distinction between the primary and secondary actions. Visually prioritize the primary action by adding more visual weight to the button. This will prevent users from tapping on the wrong button.




Ensure a clear visual distinction between primary and secondary buttons. (Large preview)

Design Finger-Friendly Touch Targets

Tiny touch targets create a horrible user experience because they make it challenging for users to interact with interactive objects. It’s vital to design finger-friendly touch targets: bigger input fields and buttons.

The image below shows that the width of the average adult finger is about 11 mm.




People often blame themselves for having “fat fingers”. But even baby fingers are wider than most touch targets. (Image: Microsoft) (Large preview)

According to material design guidelines, touch targets should be at least 48 × 48 DP. A touch target of this size results in a physical size of about 9 mm, regardless of screen size. It might be appropriate to use larger touch targets to accommodate a wider spectrum of users.

Not only is target size important, but sufficient space between touch targets matters, too. The main reason to maintain a safe distance between touch targets is to prevent users from touching the wrong button and invoking the wrong action. The distance between buttons becomes extremely important when binary choices such as “Agree” and “Disagree” are located right next to each other. Material design guidelines recommend separating touch targets with 8 DP of space or more, which will create balanced information density and usability.




(Large preview)

Disable Buttons After Tap

Forms actions commonly require some time to be processed. For example, data calculation might be required after a submission. It’s essential not only to provide feedback when an action is in progress, but also to disable the submit button to prevent users from accidentally tapping the button again. This is especially important for e-commerce websites and apps. By disabling the button, you not only prevent duplicate submissions, which can happen by accident, but you also provide a valuable acknowledgment to users (users will know that the system has received their submission).

This form disables the button after submission. (Image: Michaël Villar)

Assistance And Support

Provide Success State

Upon successful completion of a form, it’s critical to notify users about that. It’s possible to provide this information in the context of an existing form (for example, showing a green checkmark above the refreshed form) or to direct users to a new page that communicates that their submission has been successful.

Example of success state. (Image: João Oliveira Simões)

Errors And Validation

Users will make mistakes. It’s inevitable. It’s essential to design a user interface that supports users in those moments of failures.

While the topic of errors and validation deserves its own article, it’s still worth mentioning a few things that should be done to improve the user experience of mobile forms.

Use Input Constraints for Each Field

Prevention is better than a cure. If you’re a seasoned designer, you should be familiar with the most common cases that can lead to an error state (error-prone conditions). For example, it’s usually hard to correctly fill out a form on the first attempt, or to properly sync data when the mobile device has a poor network connection. Take these cases into account to minimize the possibility of errors. In other words, it’s better to prevent users from making errors in the first place by utilizing constraints and offering suggestions.

For instance, if you design a form that allows people to search for a hotel reservation, you should prevent users from selecting check-in dates that are in the past. As shown in the Booking.com example below, you can simply use a date selector that allows users only to choose today’s date or a date in the future. Such a selector would force users to pick a date range that fits.




You can significantly decrease the number of mistakes or incorrectly inputted data by putting constraints on what can be inputted in the field. The date picker in Booking.com’s app displays a full monthly calendar but makes past dates unavailable for selection. (Large preview)

Don’t Make Data Validation Rules Too Strict

While there might be cases where it’s essential to use strict validation rules, in most cases, strict validation is a sign of lazy programming. Showing errors on the screen when the user provides data in a slightly different format than expected creates unnecessary friction. And this would have a negative impact on conversions.

It’s very common for a few variations of an answer to a question to be possible; for example, when a form asks users to provide information about their state, and a user responds by typing their state’s abbreviation instead of the full name (for example, CA instead of California). The form should accept both formats, and it’s the developer job to convert the data into a consistent format.

Clear Error Message

When you write error messages, focus on minimizing the frustration users feel when they face a problem in interacting with a form. Here are a few rules on writing effective error messages:

  • Never blame the user.
    The way you deliver an error message can have a tremendous impact on how users perceive it. An error message like, “You’ve entered a wrong number” puts all of the blame on the user; as a result, the user might get frustrated and abandon the app. Write copy that sounds neutral or positive. A neutral message sounds like, “That number is incorrect.”
  • Avoid vague or general error messages.
    Messages like “Something went wrong. Please, try again later” don’t say much to users. Users will wonder what exactly went wrong. Always try to explain the root cause of a problem. Make sure users know how to fix errors.
  • Make error messages human-readable.
    Error messages like “User input error: 0x100999” are cryptic and scary. Write like a human, not like a robot. Use human language, and explain what exactly the user or system did wrong, and what exactly the user should do to fix the problem.
Display Errors Inline

When it comes to displaying error messages, designers opt for one of two locations: at the top of the form or inline. The first option can make for a bad experience. Javier Bargas-Avila and Glenn Oberholzer conducted research on online form validation and discovered that displaying all error messages at the top of the form puts a high cognitive load on user memory. Users need to spend extra time matching error messages with the fields that require attention.




Avoid displaying errors at the top of the form. (Image: John Lewis) (Large preview)

It’s much better to position error messages inline. First, this placement corresponds with the user’s natural top-to-bottom reading flow. Secondly, the errors will appear in the context of the user’s input.


eBay uses inline validation.


eBay uses inline validation. (Large preview)

Use Dynamic Validation

The time at which you choose to display an error message is vital. Seeing an error message only after pressing the submit button might frustrate users. Don’t wait until users finish the form; provide feedback as data is being entered.

Use inline validation with real-time feedback. This validation instantly tells people whether the information they’ve typed is compatible with the form’s requirements. In 2009, Luke Wroblewski tested inline validation against post-submission validation and found the following results for the inline version:

  • 22% increase in success rate,
  • 22% decrease in errors made,
  • 31% increase in satisfaction rating,
  • 42% decrease in completion times,
  • 47% decrease in the number of eye fixations.

But inline validation should be implemented carefully:

  • Avoid showing inline validation on focus.
    In this case, as soon as the user taps a field, they see an error message. The error appears even when the field is completely empty. When an error message is shown on focus, it might look like the form is yelling at the user before they’ve even started filling it out.
  • Don’t validate after each character typed.
    This approach not only increases the number of unnecessary validation attempts, but it also frustrates users (because users will likely see error messages before they have completed the field). Ideally, inline validation messages should appear around 500 to 1000 milliseconds after the user has stopped typing or after they’ve moved to the next field. This rule has a few exceptions: It’s helpful to validate inline as the user is typing when creating a password (to check whether the password meets complexity requirements), when creating a user name (to check whether a name is available) and when typing a message with a character limit.
Reward early, punish late is a solid validation  approach. (Image: Mihael Konjević)

Accessibility

Users of all abilities should be able to access and enjoy digital products. Designers should strive to incorporate accessibility needs as much as they can when building a product. Here are a few things you can do to make your forms more accessible.

Ensure The Form Has Proper Contrast

Your users will likely interact with your form outdoors. Ensure that it is easy to use both in sun glare and in low-light environments. Check the contrast ratio of fields and labels in your form. The W3C recommends the following contrast ratios for body text:

  • Small text should have a contrast ratio of at least 4.5:1 against its background.
  • Large text (at 14-point bold, 18-point regular and up) should have a contrast ratio of at least 3:1 against its background.

Measuring color contrast can seem overwhelming. Fortunately, some tools make the process simple. One of them is Web AIM Color Contrast Checker, which helps designers to measure contrast levels.

Do Not Rely On Color Alone To Communicate Status

Color blindness (or color vision deficiency) affects approximately 1 in 12 men (8%) and 1 in 200 women in the world. While there are many types of color blindness, the most common two are protanomaly, or reduced sensitivity to red light, and deuteranomaly, or reduced sensitivity to green light. When displaying validation errors or success messages, don’t rely on color alone to communicate the status (i.e. by making input fields green or red). As the W3C guidelines state, color shouldn’t be used as the only visual means of conveying information, indicating an action, prompting a response or distinguishing a visual element. Designers should use color to highlight or complement what is already visible. Support colorblind people by providing additional visual cues that help them understand the user interface.


Use icons and supportive text to show which fields are invalid. This will help colorblind people fix the problems.


Use icons and supportive text to show which fields are invalid. This will help colorblind people fix the problems. (Large preview)

Allow Users To Control Font Size

Allow users to increase font size to improve readability. Mobile devices and browsers include features to enable users to adjust the font size system-wide. Also, make sure that your form has allotted enough space for large font sizes.


WhatsApp provides an option to change the font size in the app’s settings


WhatsApp provides an option to change the font size in the app’s settings. (Large preview)

Test Your Design Decisions

All points mentioned above can be considered as industry best practices. But just because something is called a “best practice” doesn’t mean it is always the optimal solution for your form. Apps and websites largely depend on the context in which they are used. Thus, it’s always essential to test your design decisions; make sure that the process of filling out a form is smooth, that the flow is not disrupted and that users can solve any problems they face along the way. Conduct usability testing sessions on a regular basis, collect all valuable data about user interactions, and learn from it.

Conclusion

Users can be hesitant to fill out forms. So, our goal as designers is to make the process of filling out a form as easy as possible. When designing a form, strive to create fast and frictionless interactions. Sometimes a minor change — such as properly writing an error message — can significantly increase the form’s usability.

his article is part of the UX design series sponsored by Adobe. Adobe XD tool is made for a fast and fluid UX design process, as it lets you go from idea to prototype faster. Design, prototype and share — all in one app. You can check out more inspiring projects created with Adobe XD on Behance, and also sign up for the Adobe experience design newsletter to stay updated and informed on the latest trends and insights for UX/UI design.

Smashing Editorial
(al, yk, il)


Excerpt from: 

Best Practices For Mobile Form Design

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What Happens After The Conversion? How To Optimize Your Marketing Campaigns For Higher Quality Leads

How excited would you be if you doubled the number of leads your marketing campaign was generating in less than a month? What if you found out that the improvement wasn’t an improvement at all, because as lead quantity went up, lead quality was going down? That’s exactly what happened with a campaign I ran once. I can assure you – it’s not fun! One survey of B2B marketers found that their #1 and #2 challenges were generating high quality leads and converting leads into customers: Your Landing Page Conversion Rate Is Only Half Of The Story Converting visitors to leads…

The post What Happens After The Conversion? How To Optimize Your Marketing Campaigns For Higher Quality Leads appeared first on The Daily Egg.

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What Happens After The Conversion? How To Optimize Your Marketing Campaigns For Higher Quality Leads

How To Internationalize Your WordPress Website

On September 30th, 2017, the international WordPress community united for 24 hours to translate the WordPress ecosystem. For the third time, #WPTranslationDay fused an all-day translating marathon with digital and contributor day events designed to promote the value of creating accessible experiences for global users, better known as “localization”.
As an open-source community, we should all strive to localize our open-source contributions. Before you can transcribe your digital assets though, you have to internationalize your codebase.

Read More:  

How To Internationalize Your WordPress Website

Product Marketing Month: Why I’m Writing 30 Blog Posts in 30 Days

alt : https://unbounce.com/photos/30-in-30.mp4https://unbounce.com/photos/30-in-30.mp4

We Have 1.06 Products. Do we suck at product marketing?

I wrote that statement on a whiteboard at the start of a website brainstorm session.

What does 1.06 products mean?

1.06 sums up my frustration at the adoption rate of our new products. Yup, Unbounce is now more than just a landing page builder. We released two new products, namely “overlays” and “sticky bars”, and we grouped them together under an umbrella term “Convertables”.

The number 1 represents our flagship industry-leading landing page product (100% of our customers have adopted it), and the .06 represents the tragic adoption rate of our new products (6%).

And yes, you’d be correct if you noted that “Convertables” isn’t a real word, but then neither is Unbounce, so we went with it after a notable amount of company-wide polling, and general corporate groupthink. More on that later.

So, how does this scenario result in me writing 30 blog posts about product marketing?

Rewind to October 5th: I was in a meeting with fellow co-founders Rick, Carl, and Carter, openly expressing my frustration with the adoption numbers, and Carter interrupted me to ask, “Okay, fine, but what are you going to do about it?”.

Then this video happened…

Awesome, right?! Yeah, it is, until the moment I realized it’s been exactly 301 days since I last wrote a blog post (I’ve been focusing on public speaking), making this level of bravado a tad audacious at best. Aaand, yes I realize I was a little intoxicated in the video.

But, I’ve learned over the years, that being a bit ridiculous in my promises is the only way I really know how to get shit done. When I tell everyone that I’m doing something big, the self-imposed peer pressure is what motivates me to make sure I complete my mission.

Enter Product Marketing Month (PMM): A Product Marketing Journey

This brings me to our blog. We’ve never written much about our products on the blog, in fact, we’ve actively avoided it to let the content speak for itself as an educational pillar of the community, and to remain non-salesy.

I’ve realized though, that it doesn’t make much business sense to be that overtly humble in all marketing communications. There has to be a way to balance exposing people to your product without it detracting from the experience.

It’s my fault in many ways. When I started our blog back in 2009, I had a mission to be different from our competitors, to not come across as a salesperson, and just to provide value and entertaining content that stood out.

We dominated the realm of conversion content for many years, but in an increasingly competitive SaaS martech space, our content is no longer number one, and it’s time that we change our approach.

Which is why we’re doing a blog takeover for the whole of January.

Our goal is to explore a category we’ve not covered before (product marketing), but also to expose a transparent window – transparency is one of our six core values at Unbounce – into our journey as a company, as a marketing team, and myself personally, to become better product marketers.

For me, it’s the first time I’ve ever been involved in product marketing, which will make it a fascinating personal journey reinventing myself as a different kind of marketer.

I’m also cutting the number of speaking gigs I do in 2018 in half, because let’s be honest, in this moment, the success of Unbounce can be more rapidly impacted by me staying home than being on the road.

Transparency

Along the way, I’ll be opening up the Unbounce vault to share our core metrics with you. This will include our churn and product adoption metrics, which we hope to be able to lift in a big way throughout this 30-day experiment. There will be data check-ins throughout, with a halfway report, and then a full “Results Show” at the end.

I’ll also be digging into our analytics to see what the engagement and attribution looks like for every one of the 30 blog posts.

Some of the content will revolve around the learnings and experiences of becoming a better product marketer, and the rest will be an exploration of the ways we’re trying to rethink what our products are, what they mean to our customers, and how we can do a better job communicating their benefits (with some case studies and new ways of thinking – I hope).

I say “I hope” because I’m writing this as you read it. That’s what tends to happen when you commit to something as absurd as 30-in-30.

Follow Along << Mid-Post CTA

I encourage you to follow along by subscribing to our weekly update emails at the bottom of the page. I’m really keen to have our community (that’s you) help us explore how to do this properly, and hopefully, we’ll all learn how to do a better job of marketing our products.

This is a screenshot of the subscribe form at the bottom of the post. Thought you should know.

You can also subscribe by clicking here to launch a popup which contains the subscribe form. << product marketing much?

Aaand I’ve configured it so you’ll see an exit popup when you leave this page. Note, that I’m doing this to show the product in a relevant and hopefully useful manner.

Unbounce Product Adoption Metrics

How do we measure adoption at Unbounce? To understand, it helps to explain a little about how we define a customer. In the old days, a customer was any signup, someone who started a 30-day trial. Over time we learned we should be measuring a little deeper into the customer lifecycle, and decided a customer was someone who paid us twice; once after the 30-day trial, and again after sixty days.

In 2017 we modified this further to someone who pays us three times, giving us a much better sense of true churn numbers.

To be considered a customer who has adopted our products, we have an additional set of app usage criteria:

For landing pages adoption means: a customer who has built and published one or more pages, has set up a custom domain, configured an integration with another tool, and has paid us three times.

For “Convertables” (Overlays & Sticky Bars) adoption means: a customer who has built and published a popup or sticky bar, installed our one-line global Javascript on their website, received at least 10 conversions, and has paid us three times.

Full transparency: 6% adoption for a new product sucks. It sucks really bad.

So what went wrong? Why was adoption so low?

We made some mistakes, namely…

Mistake #1: We called a popup an overlay.
Mistake #2: We created a fictitious umbrella term “Convertables” for only two child products, and for a few months, only one child product.
Mistake #3: We assumed that people would find and use these two products, hidden behind said umbrella term in the app.
Mistake #4: We assumed that the functional user of our landing page product would be the same person who needs to use overlays popups and sticky bars.

How do we un-f*** this problem?

The first thing we’re doing is removing any public-facing mentions of the term “Convertables”. This has excited the marketing team because it’s much easier to market something when you know how to describe it.

Beyond that, the approach I’m taking is a combination of four primary tenets:

  1. First, is a concept I call “Productizing Our Technology” or POT for short. This is about discovering new and novel ways that our platform can be used, that people either haven’t imagined or simply didn’t know was possible.
  2. Second, is exploring the entire Unbounce ecosystem, from the app, to the website, our content channels, and our community, to see how we could do a better job of exposing the benefits of our products to those who can benefit from them.
  3. Third, is using the Product Marketing Month blog takeover to create interactive demonstrations right here on the blog – the goal of which is to reduce the Time to Value (TTV) by creating more obvious ah-ha moments.
  4. Fourth, understanding who the various target personas and functional users of the different products are, and adjusting our targeting and marketing communications to find and speak to those potentially different users.

In regards to #3 the blog takeover, if you take a look at the top of the screen, you’ll see a header bar like this:

Or this one, if you have scrolled down the page:

If you look at the hierarchy of information from left to right, you see: 1) Who we are: logo, 2) What we do: value prop, 3) How to take action: the three big orange buttons.

This is hugely different to the rest of the blog, which retains the navigation of the whole site (I’ve thought that was incongruent for a long time).

My hope is that the new header bar helps more people know what we do, and how our products can help. I’ll be tracking engagement with the 3 CTAs and comparing these 30 posts against our other blog content in terms of its ability to get people to sign up.

Productizing Our Technology: Landing Pages, Popups, & Sticky Bars

I had my own ah-ha moment when I started imagining all the ways that I could hack/modify/extend the ways the Unbounce conversion platform can be used. We have 3 core pieces of product technology (not including our AI/Machine Learning efforts that will power our technology in the future): landing pages, popups, and sticky bars.

By taking our core tech, combining the available features, with new jQuery scripts, CSS, and some 3rd-party integrations, it’s possible to create a plethora of new “mini-products” that if embraced by the community, might inform future product direction.

Take a look at the spreadsheet below. This is my POT matrix. The complete sheet currently houses over 120 new product ideas.

Productizing Unbounce Technology
(Click image for full-size view)

Across the top (in yellow) are the core products, their features (such as targeting, triggers, display frequency), and the different hacks, data sources, and integrations, that can be combined to produce the new products listed in green in the first column.

Each product is flagged as being in one of three states:

NOW: These products are possible now with our existing feature set.
MVP: These products are possible by adding some simple scripts/CSS to extend the core.
NEW: These products would require a much deeper level of product or website development to make them possible. These are the examples that came from “blue sky” ideation, and are a useful upper anchor for what could be done.

I’ll be explaining these use cases in greater detail as the month progresses, and I’ll be building some of them directly into these blog posts as I write them. << FTR this will involve me reverting to my long-extinct coding background to hack the shit out of the blog to show you what I’m talking about.

Please Follow Along

That’s the intro, that’s what happened, and what we’re going to do to try and fix it. Subscribe to the weekly email updates, join the discussion in the comments, and chat directly with me on Twitter.

There is also a full calendar at the bottom of every post that will link to all 30 PMM topics as they roll out.

What’s coming on day 2 of PMM?

Tomorrow’s post is called “55 Simple/Hard/Brilliant Things Your Marketing Team Should Be Doing to Improve Product Awareness & Adoption”. It’s based on the results of rapid-fire brainstorms which exposed quick-win tactics all product marketers can use to expose your products in small and simple ways, to build to a critical mass of awareness.

This should be very relevant to anyone in marketing, and doubly so to those working for a SaaS business.

Here’s to kicking off 2018 in a blaze of product marketing glory.

Cheers,
Oli Gardner

p.s. Please jump into the comments below to let me know what products you’re currently trying to take to market.

View original article – 

Product Marketing Month: Why I’m Writing 30 Blog Posts in 30 Days

Product Awareness Month: Why I’m Writing 30 Blog Posts in 30 Days

alt : https://unbounce.com/photos/30-in-30.mp4https://unbounce.com/photos/30-in-30.mp4

We Have 1.06 Products.

I wrote that statement on a whiteboard at the start of a website brainstorm session.

What does 1.06 products mean?

1.06 sums up my frustration at the adoption rate of our new products. Yup, Unbounce is now more than just a landing page builder. We released two new products, namely “overlays” and “sticky bars”, and we grouped them together under an umbrella term “Convertables”.

The number 1 represents our flagship industry-leading landing page product (100% of our customers have adopted it), and the .06 represents the tragic adoption rate of our new products (6%).

And yes, you’d be correct if you noted that “Convertables” isn’t a real word, but then neither is Unbounce, so we went with it after a notable amount of company-wide polling, and general corporate groupthink. More on that later.

So, how does this scenario result in me writing 30 blog posts about our products?

Rewind to October 5th: I was in a meeting with fellow co-founders Rick, Carl, and Carter, openly expressing my frustration with the adoption numbers, and Carter interrupted me to ask, “Okay, fine, but what are you going to do about it?”.

Then this video happened…

Awesome, right?! Yeah, it is, until the moment I realized it’s been exactly 301 days since I last wrote a blog post (I’ve been focusing on public speaking), making this level of bravado a tad audacious at best. Aaand, yes I realize I was a little intoxicated in the video.

But, I’ve learned over the years, that being a bit ridiculous in my promises is the only way I really know how to get shit done. When I tell everyone that I’m doing something big, the self-imposed peer pressure is what motivates me to make sure I complete my mission.

Enter Product Awareness Month (PAM)

This brings me to our blog. We’ve never written much about our products on the blog, in fact, we’ve actively avoided it to let the content speak for itself as an educational pillar of the community, and to remain non-salesy.

I’ve realized though, that it doesn’t make much business sense to be that overtly humble in all marketing communications. There has to be a way to balance exposing people to your product without it detracting from the experience.

It’s my fault in many ways. When I started our blog back in 2009, I had a mission to be different from our competitors, to not come across as a salesperson, and just to provide value and entertaining content that stood out.

We dominated the realm of conversion content for many years, but in an increasingly competitive SaaS martech space, our content is no longer number one, and it’s time that we change our approach.

Which is why we’re doing a blog takeover for the whole of January.

Our goal is to explore a blog topic we’ve not covered before, but also to expose a transparent window – transparency is one of our six core values at Unbounce – into our journey as a company, as a marketing team, and myself personally, to become better at marketing our new products.

For me, it’s the first time I’ve ever been involved in product marketing, which will make it a fascinating personal journey reinventing myself as a different kind of marketer.

I’m also cutting the number of speaking gigs I do in 2018 in half, because let’s be honest, in this moment, the success of Unbounce can be more rapidly impacted by me staying home than being on the road.

Transparency

Along the way, I’ll be opening up the Unbounce vault to share our core metrics with you. This will include our churn and product adoption metrics, which we hope to be able to lift in a big way throughout this 30-day experiment. There will be data check-ins throughout, with a halfway report, and then a full “Results Show” at the end.

I’ll also be digging into our analytics to see what the engagement and attribution looks like for every one of the 30 blog posts.

Some of the content will revolve around the learnings and experiences of becoming a better product marketer, and the rest will be an exploration of the ways we’re trying to rethink what our products are, what they mean to our customers, and how we can do a better job communicating their benefits (with some case studies and new ways of thinking – I hope).

I say “I hope” because I’m writing this as you read it. That’s what tends to happen when you commit to something as absurd as 30-in-30.

Follow Along << Mid-Post CTA

I encourage you to follow along by subscribing to our weekly update emails at the bottom of the page. I’m really keen to have our community (that’s you) help us explore how to do this properly, and hopefully, we’ll all learn how to do a better job of marketing our products.

This is a screenshot of the subscribe form at the bottom of the post. Thought you should know.

You can also subscribe by clicking here to launch a popup (using the on-click trigger feature) which contains the subscribe form. << product marketing much?

Aaand I’ve configured it so you’ll see an exit popup when you leave this page. Note, that I’m doing this to show the product in a relevant and hopefully useful manner.

Unbounce Product Adoption Metrics

How do we measure adoption at Unbounce? To understand, it helps to explain a little about how we define a customer. In the old days, a customer was any signup, someone who started a 30-day trial. Over time we learned we should be measuring a little deeper into the customer lifecycle, and decided a customer was someone who paid us twice; once after the 30-day trial, and again after sixty days.

In 2017 we modified this further to someone who pays us three times, giving us a much better sense of true churn numbers.

To be considered a customer who has adopted our products, we have an additional set of app usage criteria:

For landing pages adoption means: a customer who has built and published one or more pages, has set up a custom domain, configured an integration with another tool, and has paid us three times.

For “Convertables” (Overlays & Sticky Bars) adoption means: a customer who has built and published a popup or sticky bar, installed our one-line global Javascript on their website, received at least 10 conversions, and has paid us three times.

Full transparency: 6% adoption for a new product sucks.

So what went wrong? Why was adoption so low?

Well, first, and most importantly, product marketing is really hard.

We also made a few (well intended) mistakes, namely…

Mistake #1: We called a popup an overlay.
Mistake #2: We created a fictitious umbrella term “Convertables” for only two child products, and for a few months, only one child product.
Mistake #3: We assumed that people would find and use these two products, hidden behind said umbrella term in the app.
Mistake #4: We assumed that the functional user of our landing page product would be the same person who needs to use popups and sticky bars.

How do we un-f*** this problem?

The first thing we’re doing is removing public-facing mentions of the term “Convertables”. This has excited the marketing team because it’s much easier to market something when you know how to describe it, and a multi-product value prop is much harder than a single-product value prop.

Beyond that, the approach I’m taking is a combination of four primary tenets:

  1. First, is a concept I call “Productizing Our Technology” or POT for short. This is about discovering new and novel ways that our platform can be used, that people either haven’t imagined or simply didn’t know was possible.
  2. Second, is exploring the entire Unbounce ecosystem, from the app, to the website, our content channels, and our community, to see how we could do a better job of exposing the benefits of our products to those who can benefit from them.
  3. Third, is using the Product Awareness Month blog takeover to create interactive demonstrations right here on the blog – the goal of which is to reduce the Time to Value (TTV) by creating more obvious ah-ha moments.
  4. Fourth, understanding who the various target personas and functional users of the different products are, and adjusting our targeting and marketing communications to find and speak to those potentially different users.

In regards to #3 the blog takeover, if you take a look at the top of the screen, you’ll see a header bar like this:

Or this one, if you have scrolled down the page:

If you look at the hierarchy of information from left to right, you see: 1) Who we are: logo, 2) What we do: value prop, 3) How to take action: the three big orange buttons.

This is hugely different to the rest of the blog, which retains the navigation of the whole site (I’ve thought that was incongruent for a long time).

My hope is that the new header bar helps more people know what we do, and how our products can help. I’ll be tracking engagement with the 3 CTAs and comparing these 30 posts against our other blog content in terms of its ability to get people to sign up.

Productizing Our Technology: Landing Pages, Popups, & Sticky Bars

I had my own ah-ha moment when I started imagining all the ways that I could hack/modify/extend the ways the Unbounce conversion platform can be used. We have 3 core pieces of product technology (not including our AI/Machine Learning efforts that will power our technology in the future): landing pages, popups, and sticky bars.

By taking our core tech, combining the available features, with new jQuery scripts, CSS, and some 3rd-party integrations, it’s possible to create a plethora of new “mini-products” that if embraced by the community, might inform future product direction.

Take a look at the spreadsheet below. This is my POT matrix. The complete sheet currently houses over 120 new product ideas.

Productizing Unbounce Technology
(Click image for full-size view)

Across the top (in yellow) are the core products, their features (such as targeting, triggers, display frequency), and the different hacks, data sources, and integrations, that can be combined to produce the new products listed in green in the first column.

Each product is flagged as being in one of three states:

NOW: These products are possible now with our existing feature set.
MVP: These products are possible by adding some simple scripts/CSS to extend the core.
NEW: These products would require a much deeper level of product or website development to make them possible. These are the examples that came from “blue sky” ideation, and are a useful upper anchor for what could be done.

I’ll be explaining these use cases in greater detail as the month progresses, and I’ll be building some of them directly into these blog posts as I write them. << FTR this will involve me reverting to my long-extinct coding background to hack the shit out of the blog to show you what I’m talking about.

Please Follow Along

That’s the intro, that’s what happened, and what we’re going to do to try and fix it. Subscribe to the weekly email updates, join the discussion in the comments, and chat directly with me on Twitter.

There is also a calendar at the bottom of every post that will link to all 30 PMM topics as they roll out.

What’s coming on day 2 of PMM?

Tomorrow’s post is called “50 Creative Ideas Your Marketing Team Can Use to Improve SaaS Product Adoption & Awareness”. It’s based on the results of rapid-fire brainstorms which exposed quick-win tactics all product marketers can use to expose your products in small and simple ways, to build to a critical mass of awareness.

This should be very relevant to anyone in marketing, and doubly so to those working for a SaaS business.

Here’s to kicking off 2018 in a blaze of product marketing glory.

Cheers,
Oli Gardner

p.s. Please jump into the comments below to let me know what products you’re currently trying to take to market.

Original article: 

Product Awareness Month: Why I’m Writing 30 Blog Posts in 30 Days

7 Ways to Ensure Your Next Webinar is a Success

perfect webinar

Webinars are one of the most popular tools used by marketers for lead generation. Not only are they great for generating demand but they’re also a less pushy way of nurturing cold leads. The reason is that you are offering to provide information that your audience will value in your webinars. You can also demonstrate your expertise and showcase your knowledge of the industry and domain using webinars. However, webinars can be truly beneficial for your company if they are planned and implemented well. Here, we’ll take a look at some of the things you need to do to ensure…

The post 7 Ways to Ensure Your Next Webinar is a Success appeared first on The Daily Egg.

View this article:  

7 Ways to Ensure Your Next Webinar is a Success

The Numbers Behind Content Marketing: Essential Statistics for 2017

content marketing 2017

What’s the state of play in content marketing? What content should you be creating, how should you be distributing it, where should you be promoting it? There’s a million opinions out there. But here, there’s a million stats instead. Well, OK, there’s actually 101, which I swear is a coincidence. But if you want solid data on emails, SEO, strategy and whole lot else besides – we got ‘em. Dive in! 1. The Market For Content Marketing Importance How important is content marketing to marketers? It’s probably the most important marketing tool for most businesses. 89% of B2B organizations are…

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The Numbers Behind Content Marketing: Essential Statistics for 2017

Why Spending $1,000 on Instagram Influencer Marketing is Worth It

instagram influencers

You might have noticed that influential Instagrammers are doing a lot of promotions for brands relevant to their niche. Being such a visual platform, Instagram may be the best platform for you to run your influencer marketing campaign. Maybe you’re tempted to try it out, but you’re not too keen on investing in a platform that you’re not sure would deliver results. So let’s take a look at some of the reasons why it’s worth it to spend your marketing dollars on Instagram influencer marketing. You’ll Reach a Massive Audience While Facebook still dominates social media usage, Instagram is the…

The post Why Spending $1,000 on Instagram Influencer Marketing is Worth It appeared first on The Daily Egg.

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Why Spending $1,000 on Instagram Influencer Marketing is Worth It

8 Things You Need to Know to Improve Your Influencer Marketing Campaign

influencer

You’re going to start your very first influencer marketing campaign, and you want to make sure it’s a success. Or maybe you’ve executed a few campaigns before, and you want the next one to deliver better results. Either way, knowing how to manage your campaign effectively is crucial if you want influencer marketing to work for you. While it’s not always easy to manage influencer marketing campaigns, you’ll find it much easier if you remember the following steps: 1. Set Up a Goal You should always start with a defined goal, regardless of whether it’s influencer marketing or any other…

The post 8 Things You Need to Know to Improve Your Influencer Marketing Campaign appeared first on The Daily Egg.

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8 Things You Need to Know to Improve Your Influencer Marketing Campaign

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