How artificial intelligence and machine learning can strengthen mobile application
The mobile application industry is growing rapidly. A lot of experts and analysts suppose AI to be the next huge step of its development in near future. The main reason why is a great variety of purposes AI can be applied.
It is the simplest and most effective application of AI in mobile apps. It help to provide relevant content to continuously engaged users. By monitoring the choices users made and inserting them into a learning algorithm, application provide recommendations that users are likely to be interested in. This is a powerful source of customer experience in building application strategy around the needs of individual customers.
The steps, models & algorithms to provide recommendation:
- Identify target segment -> segmentation algorithms
- Identify product/category -> market basket analysis
- Determine optimal proposal -> elasticity model
- Select preferred channel of information -> channel optimization model
- Select time of offer -> time optimization model
How works recommendation engine:
- Analyze Data
- Create Segments / Groups
- Choose Statistical Models
- Send and Measure Offers
- Recalibrate the Model
- Reissue Offers
Learning behavior patterns
It is the way when you improve your application not only based on user acceptance and A/B tests, but also based on in-depth insight user behavior. To find obvious and simple issues it is often enough BI tools like KISSmetrics or Mixpanel. But when the issue depends for example on unobvious data parameters or on specific consequence of steps here comes data mining. With user activity data and data mining algorithms like anomaly detection (one-class support vector machine), clustering (enhanced k-means, orthogonal partitioning clustering and others), association (apriori and others) you can find hidden disturbance in customer’s journey and improve your application significantly. As well, the results of this work will go in recommendation engine rules.
Some examples of recommendation and behavior application:
- Your potential client left the application but, an hour later, opened it again. Your recommendation engine immediately determines whether they are interested (spending a while on the page, for example) or not really (only opened the app for 5 seconds, probably by mistake) and make proper recommendation.
- Your potential client opened the app from a location near one of your offline target place. It immediately pulls information about the place capabilities and make proper recommendation.
- It looks like customers spend longer time on some pages or sections. They interact with it and it seems they are interested or confused. Data mining steps automatically pull more information about that section from your application, previous customer activity, and customer profile, make proper customer segmentation, behavior association to get the question answered: who are the users (all or specific), what they have in common, and suggest the proper improvement or enhancement.