Tracking positive and negative experiences in an app allows you make decisions that help in the overall growth of you app.

Let’s take an example of asking for ratings on the app store for your app.

Asking for app store ratings for your app

It makes sense for app publishers to encourage users to provide favorable ratings, simply because the likelihood of users downloading an app is much higher for apps with high ratings.

There are millions of apps on the app store, but the number of users who give a rating or review on an app store are amazingly low.

  1. On an average, more than 58% of the 3+ million of apps have zero reviews.
  2. Of the users who used your app at least two times, only 0.2% to 1.4% rate an app.

To weed out bad apps, iOS originally used to ask for reviews when a user deleted an app.  This obviously lead to a bunch of negative reviews on the app store. AppiRator (circa 2009) attempted to circumvent and ask for reviews earlier in the life cycle of the app and is widely used by a number of apps.  Users are prompted to rate right away, be reminded to rate later in a few days, or skip rating the current version.  Prompting is delayed till the app has been used for a few days and a few times.

AppiRator uses alerts which are considered very obtrusive and super annoying, and does not account for immediate user’s experience before asking for a rating. For instance, you don’t want to ask for a rating if your app just crashed, the backend is not responsive or the app took a long time to load.

Screen Shot 2016-04-30 at 8.55.41 PM

User psychology, attitudes and behavior

Every positive,  and timely managed negative experiences help with app growth.

Even simply asking for ratings at the right time can go a long way in getting favorable ratings, but more importantly such actions communicate the app publishers’ commitment to providing delightful and engaging app experiences.

A happy user is more likely to rate favorably.  But how do you determine that the user is happy?

Conventional wisdom suggests simply asking the user if they are happy.  This is making a decision based “what people say“.

Attitudinal targeting techniques

The purpose of attitudinal research is usually to understand or measure people’s clearly expressed beliefs, which is why attitudinal data is used excessively in marketing departments, customer-, partner- and employee- satisfaction market research surveys.

What people say” vs. as “what they do” may be different. Further, it is not always convenient to ask questions in an app as it always disrupts the workflow of an app user.

Behavioral targeting techniques

Actions derived from “what people do” in your app and “how they use it” are always better indicators of user’s experience in your app.  So, using the example topic of asking for ratings, how can you determine a good time to ask for ratings?

A good time to ask for ratings is when user does something positive in your app, like win a difficult game, invites other users or advocates about your app.

 

crash

Images failed to load. It is not a good idea to ask for a rating when user is not having the best experience.

And similarly determining when not ask for a rating.

You don’t want to ask for a rating if your app just crashed, the backend is not responsive, a resource failed to load or the app took a long time to load.

Once you determine it is a good time to ask for rating you may still want to ask the user if they would like rate your app simply to get permission to redirect to app store – but you can personalize the message.

Let’s say you noticed a user solved a puzzle faster than the average, and the user has used your app more than 3 times in the last 7 days.  It is time to ask for a rating with a personalized message:  “Wow! You are a great strategist and solved the puzzle in under 7 minutes!  Would you help us out and rate us on the app store?”

You noticed a user stated to share content to slack 3 times but never successfully finished it. Time to redirect to support.  “Seems like you are having trouble with sharing content from the app.  Would you like to see a 90 second video and/or let us know how we can be helpful?”

Here are some positive and negative experiences you can look for in your app.

Representative Positive experiences

feed

User made a repeat purchase. Asking for a rating while avoiding alerts.

  • user does something positive in your app like wins a difficult game
  • user reaches Wall of Fame
  • user finishes a task faster than what average time to finish task
  • user finishes a task successfully
  • user invites other users to join your app via SMS, message or email, from you within your app
  • user advocates or shares your app on social media
  • user opts in for Push notifications
  • user shares her phone number with your app
  • user opts in to share contacts, geo location
  • user shares content on social media
  • uses your app more consecutively in last 3 days
  • 7D user stickiness (number of times user used app in last 7 days) is 3
  • 7D user stickiness is 5
  • 30D user stickiness app (number of times user used app in last 30 days) is between 10 and 14
  • user makes an in app purchase

Representative Negative experiences

  • app crashed first time
  • app crashed second time with an hour
  • app crashed third time in last 7 days
  • an image resource did not load
  • it took longer than 3 seconds to load page on WiFi
  • it look longer than 5 seconds to load page on cellular
  • save operation failed
  • backend is not responsive
  • user started to share content but never successfully finished
  • user declined Push Notifications
  • user declined access to contacts
  • clicked on area of screen where there is no action associated or is confused by the UI
  • an engaged and active user has never used a specific screen

Tracking positive and negative experiences

Some app publishers have built an extensive experience tracking system that counts positive and negative behavioral experiences.  They also aggregate the difference between these experiences for different versions of the app.

 currentPositiveExperiences: 34
 currentNegativeExperiences: 28
 aggregateExperiences: {
 "v5.0.4": "-2"
 "v5.1.0": "0"
 "v5.2.0": "+1"
 "v6.0.0": "+6"
 }

Each positive or negative experience has  a weighted count.

  • First crash is a “-1”
  • Second crash within N days and / or within X app starts is “-3”
  • First time app used for 4 days in a row is a “+3” and so on.
  • user invites others to download app is “+2”

The app uses the difference between positive and negative experiences to make multiple decisions, including whether to ask for a rating, recommend user update to a newer version of app, redirect to support, or recommend installing other apps from same app publisher etc.

Posted by Dickey Singh

Dickey Singh is the CEO and co-founder at Pyze and has over two decades of experience in mobile, Big Data and SaaS. He started Pyze to help app publishers engage, retain and grow their mobile users using automation. https://twitter.com/DickeySingh Get Pyze: https://pyze.com