There are countless ways to add machine learning into your app development. As listed in the case study, machine learning can predict conversion KPIs, churn, in-app purchases, build personas, and prevent affiliate fraud. That’s only the tip of the ML iceberg (I’ve been wanting to use that saying). This post will be building off that case study and show you where and how you can insert ML into your app growth. Let’s get into it. Step 1: Machine Learning in App Ideation Besides branding, ideation is your the most creative step in developing your app. Before launching, you want to get a lay of the competitive landscape and identify user needs and pain points. As usual, the ML process starts with data. The data you want to analyze or make predictions from may include app store data to identify genre gaps and how competitive your space will be. Other valuable data points may include CrunchBase data, review data, language availability data, version history data. You can even go as far as app icon data to begin branding. The purpose of collecting this data is to discover what mistakes your competitors are making, what your potential users are looking for in an app, what will be the complexity of your potential app? Say you’re looking to build a music streaming app to compete with Apple Music and Spotify. You would take a dataset kind of like this: Here you analyze how many music apps are in the app store, see what factors are related to high user ratings, and identify the average price of music apps. You can even predict how an app with similar characteristics will perform. You can even merge multiple datasets to get more accurate analytics. Just remember, with (accurate) data, anything is possible to predict. Since we’re a nocode platform, we won’t give you coding advice on how to build predictive algorithms. It’s much simpler than that. You can just upload data, ask questions, and get outputs without the traditional ML process. Types of Questions You Can Ask: Average price of music apps? Min/Max of size bytes? What is the probability of rating counts for music apps? What music apps are greater than, equal to 5 language numbers? Step 2: Machine Learning in App Designing/Development User testing is a vital, vital, vital, part of building your app. We suggest the best way to gather data is user surveys in numerical values. If you can get your app page features, button positions, or copy in numerical value, you can see what factors are related with customer behavior. For Example: Friendly CTA copy = 1, Direct CTA Copy = 2 Button positioned at the bottom of the screen = 1, Button positioned in the top right = 2 Number the pages to see what number a user exits the app. These are just some of the ways you can get insight quickly from machine learning and predict customer behavior before you launch. If you have this data, some of the questions you can ask your dataset include: What CTA copy converted the best? What button position is best for in-app purchases? What page number did the user spend the most time on? The type of data you collect isn’t just limited to UX. You can also rate overall satisfaction in numerical value or usefulness to get an idea of the overall effectiveness of your app. Step 3:  Launching Your App With Machine Learning Marketing your app is the final step in making a successful app. With user data, you can begin to build personas and identify possibly high value customers and build your marketing resources around them. ‍ Once you gather user data, you can begin to segment customers and personalize their messaging differently for increased engagement and brand loyalty. For example : Additionally, you can also predict what types of customers will spend the most time on your app and optimize the UX for them. Strategically using your data to predict customer behavior will cause your app to grow exponentially as your app and product marketing will be optimized for your users. Now You Don’t Have an Excuse Not To Use ML When Creating an App You have the tools to insert machine learning into your app development and a step-by-step guide to creatively predict user behavior to optimize. (Originally published ) here

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