Everyone does this predicting thing: financial analysts, business people, product guys, politicians, you name it. They do this for various purposes.
For app makers, predictive analytics can be that magic crystal ball that lets them see which users will remain by their side and which will churn eventually. And since no app maker/marketer wants to see a single user churn, they will get proactive in re-engaging those users and not letting them go.
So, what is predictive analytics anyways?
OK, if we were ever to define it, we would most probably say that it’s generally the use of machine learning to analyze current and historical data to be able to make predictions about future trends.
Have you ever been shopping on an online store and seen a list of items “you might like?” These product recommendations are being picked for the user with the help of predictive analytics. It offers the user something related to their purchase, something that they will most probably be eager to purchase but would not do that otherwise.
This works pretty straightforward; once the user adds an item to their shopping cart, the predictive analytics engine offers something that other shoppers usually purchase alongside this item.
There are other types of data that predictive analytics engines can collect in order to “decide” what to show the users. For example, they might collect the user’s geographic location (this one is especially easy to do if the user is on their mobile device) or their browsing data (e.g., through cookies).
A predictive analytics engine can collect such user data as friends, likes, hashtags, subscriptions, group membership, interests, etc. Then, it can offer suggestions based on user behavior. This works pretty well on social media apps.
I am sure you have been suggested by Facebook to join a group or to “like” a page at least once in a lifetime.
And the fact is that this really works. People often “like” pages that are similar to those they have already liked. And they start spending more time in an app and start interacting with it more intensively as a result. This all makes them more engaged users for the app.
Guess what? The guesswork will be gone with a predictive analytics tool. Well, most of the time because predictions cannot be 100% true. However, this is much better than playing the blind game, right?
Whether it’s by using mathematical algorithms or machine learning, predictive analytics tools can identify user behavior patterns in the vast amounts of data that app makers collect. These patterns are used to create “models.” Models help predict an outcome from new data that becomes available. So, if app usage data shows that particular users have churned, predictive analytics engines can predict which users are most probably going to churn in the future.
As soon as you predict who might churn, you can take the necessary steps to stop them from churning. Here is why predictive analytics eliminates the guesswork and helps you come up with a good strategy and keep your users by your side.
What’s more important?
As an app maker, you always want to know what your users are doing in your app, how they are feeling about it, whether they are enjoying it or not. And that’s fine. That’s what app makers usually care about.
So, it’s critical to have the right tools at hand. Inapptics is one of those tools that is powered by machine learning and helps you predict user churn. Want to try it? Get started for free today!
Source link https://uxplanet.org/how-predictive-analytics-can-improve-your-mobile-app-eb604065e4f6?source=rss—-819cc2aaeee0—4