For me, when it comes to thinking how my app can benefit from AI, I find most of my ideas come from my background in e-commerce and SaaS industries.
Below are some of my ideas.
Conversion rates and cart abandonment
When I worked for a retail company, we spent a lot of time thinking about how to improve the conversion rate. Conversion rate in an e-commerce app is the ratio of shoppers who browse the app that also end up buying something.
In an ideal world, each person who launches an app and starts browsing would end up making a purchase, and that would mean a conversion rate of 100%.
In reality the conversion rates are much lower than 100%; our objective is to increase them by improving the shopping experience, so that everyone wins.
Cart abandonment rate is almost the opposite of conversion rate. This is the ratio of people who put items in their online shopping cart, but don’t make the purchase. It’s usually measured over a period of time; let’s say, if an item is in someone’s cart for a week or more with no activity then we can consider that cart abandoned.
We could use AI to predict how likely a user is to abandon their cart by looking at their demographic data and the data on the shopping journey, such as items viewed, items in cart, how long they’ve been using the app, have they returned to the shop, and other similar data points.
Using this information, we can prompt a shopper identified as likely to abandon their cart with a personalized limited-time discount.
When a customer is contemplating making a purchase, it’s also common to suggest items that might go well with that particular item, so that you sell two items instead of just one. That’s called cross-selling.
You can use AI to find out which items to recommend, based on how likely a shopper is to buy those items when they’re offered in a cross-selling setting.
Predicting user churn
Moving from e-commerce examples and onto SaaS examples, churn is always a popular topic with SaaS companies. These companies are selling subscriptions for their services, which bring recurring revenue each month or year. Netflix or Spotify are both examples.
Churn is the event when a customer cancels their subscription, and companies are looking to minimize it.
Using AI, you could predict which customers are likely to churn in the coming months, and then use incentives to retain them as customers.
These are just a few examples that come to mind when looking at my personal experience. There are obviously many more excellent use cases for AI.
I’d love to hear some of your thoughts. Do these use cases apply to your work as well, or do you have other good AI use cases to share?
Understanding what folks care about when it comes to AI will definitely help us when building a product that works for you, and knowing what guides, tutorials, and sample content to provide for them.