steps3UX

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Author: Rishi Sethi, Dylan Dullea, Nikolaos Georgantas

Behavioral AnalyticsThe

Cookbook

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Contents

Introduction

Determine Your Objectives

Tracking Events

Comparing Data Point Changes Over Time

Utilizing Cohorts

Utilizing the Magic Genie Approach

Mapping Objectives to Data Points

Funnel Analysis

Correlation Analysis

Step 1: Determining Your Objectives

Step 2: Configuring Analytics

Conclusion

Glossary

Step 3: Using Behavioral Analytics to Analyze Data

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34

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12

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17

29

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: 86% of users never return after initially downloading the appIntroductionWe have written this e-book for product managers, designers and business executives who want to get their hands dirty with data in order to achieve their business objectives.

User experience (UX) is the overall experience of a person using a product such as a website or application, especially in terms of how easy or pleasing it is to use.

Digital products must have a compelling user experience in order to compete with the multitude of apps and services available in the market today. Apps and websites with an intuitive UX immediately achieve virality, growth and engaged users. Unfortunately, the vast majority of apps have very poor experiences for their users. According to Appcues1, “86% of users never return after initially down-loading your app.”

Most digital product managers and designers strive for a better UX. But unfortunately, they rarely use data for optimization.

Successful user experience requires extensive testing and data analysis to identify bottlenecks and increase conversions. Using data effectively will increase revenue, growth and virality without requir-ing your team to spend a single dollar on marketing. Behavioral analytics is the study of how and why users use an application, and forms the cornerstone of user experience optimization.

At Method Mill, we have extensive experience using data to drive UX, and we will outline our process here. This book focuses on behavioral analytics for mobile apps and web apps, but these principles for UX optimization can be used for any medium.

Key Takeaways:1. User experience is the overall experience a person gets when using a website.2. Behavioral analytics is the study of how and why users use an application.

http://www.appcues.com/blog/3-cringeworthy-stats-on-customer-retention/1

Introduction

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Step 1: Determine your objectives and figure out what data points to track

Increasing ConversionsConversions vary depending on the type of app, but almost every app has an action that constitutes a conversion. Some examples of conversions could be buying an item, check-ing the weather or sharing a photo.

Increasing EngagementThis goes hand and hand with increasing con-versions, but examples of increased engage-ment could be more pictures shared, videos watched or levels in a game played.

Increasing ViralityVirality is the tendency of a digital item to be shared by a user. Virality forms the holy grail of growth. Users who discover an app virally engage much more frequently than users who are acquired through other means and churn at a significantly lower rate.

Reducing ChurnChurn is the rate at which users stop using an application. Churning users presents one of the largest problems for app developers. Us-ers typically will leave an app at an alarmingly high rate if they do not derive value or enjoy-ment quickly.

Increasing Retention RateRetention Rate is a measure of how many us-ers come back to your app after a certain time period. If your day 7 retention rate is 30%, then 30% of your users come back to your app in 7 days or later (this would be quite high given that 86% of users only use an app once!).

5 Common Optimization Objectives for mobile and web apps:

Determine Your ObjectivesJumping into a data set without any idea of what you want to achieve almost guarantees that you’ll drown in a sea of data. In order to optimize your user experience, first determine your objectives.

Formulating Questions That Map Your ObjectivesOnce you have determined these objectives, formulate analytics questions that you’d like to answer in order to achieve objectives. Use the “Magic Genie” approach, which basically says, “If you have a magic genie that can answer any question about your data, what would you ask and why?”

Let’s Take a Look at an Example of Formulating Questions Using the Magic Ge-nie Approach

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3 Examples of formulating analytics questions:Objective: Increase Revenue by 20% • What products are being bought? If we know this then we can place these products in the recommended section in our app.

• How successful is our product recommendation engine at driving sales?If a large percentage of sales are driven from the recommendation engine, we should feature these recommendations in a prominent place. If not, we should improve the algorithm for recom-mendations.

• What do users find irritating about the checkout process? This would allow us to increase the conversion rate of the checkout process.

Objective: Increase shares in our app by 25%• How many users currently use the share functionality, and how often on average do they share? If we understand that few users are sharing many posts then we should simplify the sharing pro-cess and make it more discoverable. If many users are sharing few posts than we need to make the sharing experience more enjoyable so users will want to share again.

• Is any specific content being shared? Knowing this would enable us to change our news feed to promote the most viral content.

• Do users who share content have any other similarities? For example, do they tend to use a similar version of the app or are they all of the same demo-graphic? There are lots of ways we could use this information, but one example could be if we determine shares are lower on desktop, we could optimize the UX there.

Objective: Have all active users collectively upload 5 items each on average• How many items is the average user uploading now?We need to know this number in order to figure out if we have to do a drastic redesign of the app or just tweak the UX. If the average active user shares one image now UX tweaks may not be enough to have them share 5x more, and we will need a full redesign in order to achieve our objective. However, if the average active user shares 4.5 images we can make a few tweaks in the app to boost shares 10%.

• Do users upload lots of items in a short period of time and then leave the app, or do they upload few items slowly?

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If they are uploading over a short period of time, we should re-engage users to upload again. If they are uploading slowly, we should compel them to upload more during each session.

• What are the most popular uploaded items? We could recommend these items to users who have not uploaded them yet.

Now that you have determined both your objectives and what questions you need to answer in order to achieve them, map your questions to data points.

Use a simple table like the one below to map your objectives, analytics questions and data points.

Note #1: These questions mainly focus on what analytics questions we want answered about user ex-perience, but the Magic Genie approach could easily be used to generate questions that we would want answers to improve user acquisition or other marketing efforts.

Note #2: We only picked three questions for each example objective, but when doing this with your own app, formulate dozens of questions that map to your objectives. Be as exhaustive as possible with your list of questions. Remember, don’t worry how you are going to answer them!

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Objective Question Data Point Lies Where

Increase revenue by 20%

Increase revenue by 20%

Increase revenue by 20%

Increase revenue by 20%

Increase revenue by 20%

Increase revenue by 20%

What products arebeing bought?

Products Purchased Application databaseEvent data

How successful is our recommendation engine?

% of unique users purchas-ing products from engine over time

Application databaseEvent data

How successful is our recommendation engine?

% total Products Purchased from engine over time

Application databaseEvent data

What do users find irritat-ing about the checkout process?

Number of users that complete each step of the checkout process

Event data

What do users find irritat-ing about the checkout process?

Average time spent at each step of the checkout pro-cess

Event data

What do users find irritat-ing about the checkout process?

Qualitative data from user testing about the checkout process

Qualitative

Note #1: There should be a one to one mapping for objectives to questions to data points, but the same data points can live in multiple places. This means that each field in the table above can only have one value except for the last column.Note #2: Event data is data that users create when using your app like a swipe, tap or click. Appli-cation data is data that powers your app. We’ll cover terminology for common places data can lie in the next section.Note #3: As mentioned above, we are just giving a sample of some data point mappings. Typically, this spreadsheet is at least a few hundred rows because it contains every data point that you’ll need to track.Note #4: Qualitative data can take many forms. It can be a simple survey, a video or just a single user’s opinion.Note #5: Unfortunately, you will most likely not be able to find data that maps to every single ques-tion you asked during the Magic Genie stage, which means that you may not be able to answer all of your questions. This is completely OK! The Magic Genie framework will still generate many ques-tions that you can answer with data.

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When you align business objectives to data, you ensure that you:

a) Will not drown in data b) Have a comprehensive list of all the data points you need to track and where they lie

Using this mapping technique sets the foundation for successful UX optimiza-tion.

Key Takeaways:

1. Determine the business objectives for your product.2. Use the Magic Genie approach to determine what questions you’d like to answer. Don’t worry about how a data point actually trackable.3. Map your questions to data points.

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Tip #1: In order to identify bottlenecks across the entire user experience, most apps must track their entire conversion funnel from start to finish. To do this, sketch out your conversion funnel and de-termine all the events that need to be tracked. You will probably be tracking most of the events that occur in your app. Here is an example of what your conversion funnel could look like.

Tip #2: User experience data is typically optimized through analyzing usage data (data collected about user events). However, we do not recommend such a monolithic approach to user experience. If your goal is to increase virality, track what content users share! (This type of data is known as ap-plication data, or data that lives on your server and powers your application). Everything related to the user matters to UX!

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Tip #3: As mentioned in tip #2, the techniques of mapping objectives to discover what data points to track can be used for almost any type of optimization analysis. Use this framework to optimize user acquisition, email marketing or even sales.

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Step 2: Configuring Analytics

Event data forms the cornerstone of behavioral analytics. We’ll cover event tracking here, but make sure that all your other sources of data (see the box below) are configured correctly. Following the mapping technique outlined in step one makes tracking data points straightforward.

When tracking events, you will most likely use an event tracking provider. Some of the most com-mon are:

• Google Analytics• Mixpanel• Kiss Metrics• Amazon Mobile• Flurry• Localytics• Amplitude• Indicative• Heap

If you are unsure what vendor to pick, Segment and mParticle are data integration providers that allow you to track your event data once and then send it out to many different providers.

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How to track eventsIn step 1, you outlined what data points you need tracked. The next step involves physically tracking the data point in your app using your event tracking provider.

Tracking providers display two types of data: lifecycle metrics which are useful for high level report-ing, and event data, which is useful for both reporting and optimization. Tracking lifecycle metrics does not require any configuration, but tracking events typically requires that you determine what events you’d like to capture beforehand.

The best way to configure event data involves visually determining which events you want to track and then creating a corresponding spreadsheet describing your visualizations. This ensures that ev-eryone knows how events and actions correspond. Here is an example:

3 common sources of data other than event data:

Application dataApplication data powers your app. Application data consists of information about users, items that people are buying, and any piece of information that lives in the cloud. Common application databases are MySQL, Postgres and MongoDB.

User Acquisition data User Acquisition data consists of data generated from user acquisition campaigns. Data from Ad-Words, Facebook, Twitter or LinkedIn ads allows you to monitor and optimize your user acquisition efforts.

Email Marketing dataEmail Marketing data is collected from email marketing campaigns. Open rates, click rates and unsubscribe rates allow you to monitor and maximize your email marketing efforts.

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Event # Event NameData Point

Properties Tracking Code

1

3

5

2

4

6

7

Home_clicked / ga( ‘Send’, ‘Event’,‘Buttons’, ‘Click’, ‘Home’);

Help_clicked / ga( ‘Send’, ‘Event’,‘Buttons’, ‘Click’, ‘Help’);

Become_host / ga( ‘Send’, ‘Event’,‘Buttons’, ‘Click’, ‘Become_host’);

Sign_up / ga( ‘Send’, ‘Event’,‘Buttons’, ‘Click’, ‘Sign-up’);

Log_in / ga( ‘Send’, ‘Event’,‘Buttons’, ‘Click’, ‘Log_in’);

Play

Search

/

Location, Check in dateCheck out date, # of Guests

ga( ‘Send’, ‘Event’,‘Videos’, ‘Play’, ‘Marketing_video’);

ga( ‘Send’, ‘Event’,‘Buttons’, ‘Click’, ‘Search’);

Spreadsheet Tags

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Using this methodology simplifies implementation, and communicates the tagging nomenclature to the rest of the team. A tag is a piece of code that marks where an event occurs.

Event tracking providers have different guidelines on the exact format of a tag, but as an analyst, you have to communicate what events to tag to your development team. Using the system we outlined above ensures that developers will know exactly how to implement tags, and that your whole team will understand your tagging schema and nomenclature.

Tip #1: Give very clear names to events and properties. All events should be verbs.

Tip #2: Whenever possible, minimize events and utilize parameters (also known as properties) or meta information that gives more context about how the event happened. For example, the event “Button Clicked” could have a parameter of “button” so the analyst knows which button was clicked. This will keep your analytics dashboards clean. In example above we included parameters for the last event.

Tip #3: With almost all tracking providers, you cannot retroactively track events. This means that you need to determine what events you want to track before you launch your app. If you do not correctly track an event, you won’t be able to retroactively pull data about how many times it has occurred. Double check your tags carefully before launching.

Lifecycle Metrics:

Lifecycle metrics have their place for high level reporting, but they do not help with optimization. Some of the most common lifecycle metrics include:

• DAU/MAU (daily and monthly active users)

• Rolling Retention

• Sessions launched

• Number of installs

• Most common devices used

As you can see, these metrics give a snapshot of performance, but do not give you inspiration for improving your UX. Knowing that your 7 day rolling retention rate is 25% does not give any guidance for how to improve that number.

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Key Takeaways:1. Event tracking forms the base of user experience optimization, but make sure to correctly track your application, marketing and other data sources.2. Vendor selection plays a crucial role in allowing you to analyze your data. If you’re not sure which vendor to implement, try a data integration layer like mParticle or Segment.3. Make your tags both as a spreadsheet and visually. Visually tagging events makes the tags clear for your team.

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Step 3: Using Behavioral Analysis to Analyze DataThis could be an entire book (or encyclopedia) on its own. We can’t cover all types of analysis here, but we will focus on what’s most important for user experience optimization and go over the main techniques for behavioral analytics.

Remember, you are analyzing data in order to achieve your objectives. One of the most common behavioral analytics mistakes involves taking data out of context. For example, an event tracking provider might report that users collectively shared 500 pictures in your app yesterday. This number means very little without context. How many people typically share pictures? How many users used the app yesterday? How many unique users shared a picture yesterday? Knowing the answers to these questions puts the data point in context. If 15000 pictures were shared the day before, we have a problem on our hands. On the other hand, if 50 pictures were shared yesterday, we have a huge spike in engagement.

In order to put the data we are analyzing in context, we use two techniques:

1. Data visualizationVisualizing data usually goes a long way to interpreting it in context of other data points.

2. Pre-numerical analysisPerforming most of our analysis without filling in data points.

Having a system for analyzing data enables you to figure out exactly what data points you need for your analysis. A good analogy for this is the quadratic equation. Once your derive the formula, you can easily fill in the variables and get your answer.

To reiterate: Do not aimlessly explore data. Develop a plan for contextualizing data before analyzing it.

We will cover four techniques here for using behavioral analytics to achieve your objectives.

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Data point

% total products purchased from engine over time

Number of users that complete each step of the checkout process

Products purchased

% unique users purchasing prod-ucts from engine over time

Average time spent at each step of the checkout process

Factors that could influence this data point to rise or fall

Recommendation engine algorithm, recommendationprominence in app

Active users, recommendation engine, seasons, press,user acquisition campaigns

Recommendation engine algorithm, recommendationprominence in app

Checkout copy, perception of the security of thecheckout process

Length of check out changes, UX and UI of the checkout process

Technique #1: Comparing Data Point Changes over time

Comparing how a data point changes over time is the most rudimentary type of behavioral analytics technique. Typically, a drastic change of a key data point over time signals a change has occurred.In step 1, we mapped our objectives to our data points. Now, we simply determine reasons for why a data point could change over time:

Note #1: As with all of our tables, this list is by no means exhaustive.

We now have a framework for analyzing these data points in context. We have mapped out objec-tives to questions we need answered, and determined factors that could influence the data points to change over time. Now, we simply fill out the data. Since we know where each data source lies, this is quite straightforward.

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Note #1: Make sure your changes are statistically significant (A quick Google search will reveal easy ways to calculate this).

Data point

% total productspurchased from engine over time

Products purchased

% unique users purchasing products from engine over time

Number of users that complete each step of the checkout process

Count from6/1-6/30

Count from7/1-7/30

Statisticallysignificant(Y/N)

%Change

Possible reasonfor change

18%

5400

9.6%

7000 7300

17%

5600

10%

4.2%

-5.6%

3.7%

4%

Y

Y

Y

N

We had many more active users in July after new marketing push

The new recommendationengine is not working

The new on boardingprocess is working

The above examples are simple but powerful. Being able to interpret why a key data point changes over time allows you to make changes to influence that data point and help you achieve objectives. This analysis framework also gives you inspiration for what changes to make next. Since you deter-mined the factors that influence each data point, you can try to influence these factors by making changes to future versions of your app.

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Possible reasonfor change

We had many more active users in July after new marketing push

The new recommendationengine is not working

The new on boardingprocess is working

Technique #2: Funnel Analysis

Funnels allow you to increase conversions by identifying bottlenecks in your UX. In order to achieve a KPI, a user has to go through a journey by completing a series of events. Funnels put event data in context and immediately give you inspiration for which parts of your product to fix.

To create a funnel, map out the events step by step from beginning to end that a user needs to com-plete in order to achieve a KPI. A KPI (key performance indicator) is a key event that is vital to a con-version. KPIs can consist of conversions themselves. KPIs in a social app could be inviting a friend, uploading a photo or commenting on a photo.

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Sign Up Post a photo Invite a friend Add a Bio

Let’s determine some funnels for Instagram:When you use funnels to visualize a user journey, you can clearly see where large amounts of users are dropping off and you immediately get inspiration for where you can fix pain points that prevent users from completing KPIs.

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Sign Up Post a photo Invite a friend Add a Bio

At each step, a certain number of users drop off and do not make it to the next step. Similarly to the process in technique #1, we first create the funnels before filling in the data. We first sketch out the funnels before filing in the data. (Remember, funnel data is filled out over a given time period).

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6 ways to vary funnel presentation:

You can present funnel data in many different ways. Here are some metrics that can be displayed in a funnel:

1. The number of total times an event occurs

2. The number of unique users completing an event (this number is obviously always lower than the total number of times an event occurs)

3. The percent dropoff at each step

4. Funnels by a certain cohort (see explanation of a cohort below)

5. Screenshot funnels, where you show a funnel by step by step screenshots instead of just num-bers. This contextualizes data further and makes for very powerful presentations.

6. Changing the time period of the visualized data

Users dropoff and do not make it to the next event in a funnel for a variety of reasons. Some of these reasons include poor user experience, poor user interface, a software or hardware bug, poor content in the app or the user simply is distracted.

After you discover where users drop off, you can make changes to your user experience to increase conversions.

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Problem: Misleading affordance.

This headphones but-ton doesn’t give the user an idea of what happens when it is clicked.

Here is a closer look at it. Clicking it begins stream-ing audio of a TED talk through the phone speak-er causing user to immedi-ately leave the app.

In behavioral analytics, there are generally three ways to maximize conversions:

1. Tighten up the funnel at a step. Fixes here could involve: changing affordances, design or copy.

2. Delete steps from a funnel. If conversion involves a user navigating from point a to point b to point c, delete step b.

3. Add more possibilities (branches in a funnel) to get to the conversion.

See the following pages for examples of how to utilize these techniques.

Analysts utilize funnels more than any other behavioral analytics tool to contextualize event data, identify bottlenecks and understand the customer journey. You should make conversion funnels for all the KPIs in your app.

Tighten up the funnel at a step:

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Solution: Tighten up the funnel at a given step: Fixes here can involve: chang-ing affordances, design, or copy.

This unclear affordance causes confu-sion for new users.

A solution as simple as stopping the audio track from automatically playing, and offering a first time user a pop up that says “Listen to TED Talks with the touch of touch of a button” helps alle-viate confusion.

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Problem: Long on-boarding process.

Delete steps from a funnel:

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Solution: Delete steps from the funnel.

A simple fix involves removing all of the steps us-ers have to perform in order to begin the app and offer them swipe functionality to view features on the same screen as the “Start Editing” button.

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Problem: Confusing navigation.

Add more possibilities to get to the conversion:

The bottom navigation of this weather app scrolls left and right. This prevents access to many parts of the app. Users may not realize that this scrolling functionality exists, and therefore are denied dis-coverability at the onset of using the app.

One of the goals of the app involves users watching weather videos, and the funnel shows that large number of users do not click on the videos button.

Onboarding

Home

Videos clicked

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Solution: Add more branches

Since video is an important goal in the app, one simple fix is to just push it clos-er to the left on the scroll.

Watch video

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Technique #3: Utilizing Cohorts

By using cohorts, you can figure out commonalities between a group of users . For example, do us-ers who sign up through Facebook share things much more? If they do, compel your other users to link their Facebook accounts.

A cohort is a group of users that have one or more characteristics in common. You can create cohorts with almost any characteristic you can think of.

Some common cohorts:

Users who sign up on the same day. Useful for: optimizing marketing/PR efforts, gaging the im-pact of new changes to your app

Users who are acquired through the same marketing campaign. Useful for: optimizing marketing acquisition (particularly targeting)

Users who sign up using the same device or OS. Useful for: discovering if your mobile or web product has a more compelling UX, finding bugs

Users who have linked social media accounts. Useful for: determining the relationship between social and virality

Users who have dropped off (exited your app) before or after completing a certain event. Useful for: finding bugs, identifying bottlenecks

Users of a particular demographic. Useful for: optimizing marketing, recommendation engines

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You can combine cohorts with funnels to figure out why users drop off a par-ticular step. Here is an example:

Initial Funnel

In one particular application we found that out of 8,000 users 6,000 converted to the next step of the conversion funnel. When we cohorted by device we found that those non-converting users were 53% more likely to be a 6+, 6S+ users.

Given that the iPhone 6+ and 6S+ have larger screen sizes, we can start looking into possible design flaws that prevented these users from completing the first event.

8,000 users

Event 1

6,000 users

Event 2

2,000 users with iPhone 6+, 6S+ didn’t convert

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Technique 4: Correlation Analysis

Correlation analysis allows you to answer the age old question, “I wonder if x and y are related.”

Correlation analysis allows you to determine your Aha Moment, identify if certain features are cor-related with retention or revenue, and figure out if certain cohorts of users convert better than the average user. Calculating correlation is a bit involved technically, but can be understood easily with an example.

To use correlation analysis, first determine what you would like to correlate. Then, determine the cor-relation coefficient (a number between -1 and 1 to measure the strength of relationship between two variables).

Calculating the correlation coefficient almost always requires direct SQL access to your event data, and often involves joining your event data with your application data. Once you have SQL access, write a query to put the data you would like to correlate in two separate columns. Then, you can easily calculate correlation between the two columns in Python, R or Excel.

Aha Moments

An Aha Moment is an action or set of actions that a user must undertake to reduce the rate of her churning significantly. Facebook’s aha moment famously was that if a user friended 7 people in the first 10 days of using the product, they were substantially more likely to get hooked.Identifying your Aha Moment takes work, but it can be done easily with correlation analysis.

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Calculating Correlation

An analyst at a music app wants to increase the average lifetime value of his users. He hypothesizes that the correlation between number of songs uploaded in the first week of using the app and life-time value (also known as LTV) are strongly correlated.

Using SQL, he creates a table with two columns. One column has the number of songs uploaded in the first week for all users and the other column has their LTV. Here is a sample from his table:

He then uses Python to run simple correlation analysis and finds that the correlation coefficient be-tween a user uploading songs in the first week and their lifetime value is .7. This is quite high, and the team changes the onboarding process of the app to guide users to upload as many songs as possible in the first week.

Songs uploaded in the first week Lifetime Value

2

3

10

5

1

$0

$15

$25

$0

$30

Note #1: Correlation does not imply causation! In the above example, uploading songs does not cause a user’s LTV to be higher.

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Tip #1: Pulling these metrics in providers can be challenging. Not all providers give access to all these metrics, and not all points will be trackable. Don’t worry if you can’t track everything, and try to get direct SQL access to your data (see tip #2).

Tip #2: Get direct SQL access to your data if possible. Writing SQL queries gives you almost unlimit-ed expressive power for queries or questions you have about your data, and they allow you to easily figure out what events are correlated to success. Some providers have direct pipelines that push event data to a SQL database, or you can build your own pipeline if necessary.

Tip #3: You should be analyzing your entire conversion funnel, from start to finish. This involves joining your marketing data as well as backend data to your event data, and gives you unparalleled insight into where users may be dropping off.

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Good UX should always be defined quantitatively. You or your team may feel like your UX is “good,” but data does not lie: if users do not convert and you do not achieve your KPIs or objectives, you have poor UX. Similarly, your team may have disagreements about what changes to make to your product.

Fortunately, behavioral analytics allows you to iterate your app and achieve a good UX. Data is also the great equalizer, and proper use of behavioral analytics will settle any internal product dispute.

Tracking provider technology gives you unparalleled insight as to how users engage with your app, and utilizing the method of data analysis above enables you to optimize your UX and achieve your business objectives.

Conclusion: “Data does not lie”

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Glossary:

Affordance- The properties of an object that signal the function of this given object to a user

Aha Moment- An action or set of actions that a user must undertake to reduce the rate of her churn-ing significantly

Application Data- Data that lives on your server and powers your application. Application data con-sists of information about users, items that people are buying, and any piece of information that lives in the cloud

Behavioral Analytics- The study of how and why a users use an application

Churn- The rate at which users stop using an application

Cohort- A group of users that have one or more characteristics in common

Conversion Funnel- A type of funnel which tracks the entire user lifecycle, from install until conver-sion

Conversions- A key action that gages the success of an app. Examples of conversions are buying an item, checking the weather or sharing a photo

Correlation Analysis- A technique which enables you to determine if two or more variables are re-lated

Correlation Coefficient- A number between -1 and 1 that allows you to measure the strength of a relationship between two variables. If the correlation coefficient is close to -1 there is an inverse re-lationship between the variables (meaning if one variable increases the other variable decreases), if it’s 0 there is no relationship and if it’s close to 1 there is a positive relationship.

Event Data- Data collected from users when using an app such as swipes, taps or clicks

Event Tracking Provider- A piece of software that tracks event data

Funnels- A type of visualization which enables you to see the user journey between two events. Fun-nels enable you to identify bottlenecks in your user experience

KPI- A key performance indicator, or a metric related to the success of your app

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Lifecycle Metrics- Metrics collected from an application that give a snapshot into app performance. Lifecycle metrics are typically not useful for optimization

Lifetime Value- (known as LTV) A measure of how much revenue an app earns over the entire lifecy-cle of a user using the app

Magic Genie Approach- A methodology that enables you to form questions that map your objec-tives to data points

Parameters- Meta information that gives more context about how an event occurred

Qualitative Data- Non-quantifiable information that can be collected from surveys, videos, inter-views or a user’s opinion

Retention Rate- A measure of how many users return to your app after a given time period

Tag- A piece of code that marks where an event occurs

Usage Data- Data collected about user events

User Experience- The overall experience a person gets when using a website or application

Virality- The tendency of a digital item to be shared by a user

Juiced Juiced is a leading digital agency in New York City. We utilize data at every step of development

process to build apps that accomplish client’s objectives.

Contact usinfo@juicedapps.com

Method Mill Method Mill makes the process of data integration easier and cheaper than ever before with our proprietary data pipelining solution. Using our product enables analysts to integrate all their data

and aliminate data silos in minutes.

Contact usinfo@methodmill.com

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Contact usinfo@juicedapps.com

Contact usinfo@methodmill.com