In our last post, we introduced the Slice API and shared with you a bit of the journey that inspired us to offer a public API for developers to retrieve purchase history from their users who have opted in. We touched on a few of the use cases, but didn’t go into much depth on what we see as the potential reach of our technology. So today, we’re going to describe a few of the most compelling concepts that we’ve seen for our data integrations that we’ve seen at hackathons or mocked up internally and haven’t had time to build ourselves, but we would love to see somebody else let loose on them!
So without further ado, here are some of our favorite ideas.
Idea 1: Lyft and Uber Spending Report
Every few months we hear a story about somebody who sold their car and now relies entirely on Lyft and Uber for transportation. The stories often include a detailed breakdown of the person’s spending, and the claim that ride-sharing services are more cost-effective than car ownership.
Imagine an app that uses Slice data to analyze the total amount spent on ride-sharing services and car rentals on a monthly basis, and compares it against the costs of owning different types of cars (car payment, insurance, parking, etc.). This could be helpful for somebody who doesn’t have a car but is considering buying one. As a bonus, integrate with Automatic to build a mash-up of ride-sharing data from Slice and personal driving habits. This way, you can help predict the monthly transportation costs for somebody who already owns a car but is considering getting rid of it.
This is just one of countless apps that we enable to help users make smart purchasing decisions. You can imagine it applied to Amazon Prime, Spotify Premium, and any number of other subscription services as well.
Idea 2: Personal Shopper and Recommendation Engine
Product recommendation engines typically work by identifying relationships among users and products, e.g. identifying purchasing habits among groups of similar users. One of the most valuable – and elusive – data sources for recommendation engines is a users’ past purchasing behavior.
Imagine an app that uses Slice data to build a profile of a user and that makes suggestions for additional items that the user may want or need to purchase. For example, your app might notice that a user recently bought a suit, and recommend some matching ties to go with it. You could monetize using affiliate links so that you get a percentage of every sale that you drive. For richer profiles, consider incorporating other data streams as well, such as Facebook likes and even FourSquare check-ins; or explore making mobile recommendations based on a user’s GPS location.
Off to the races!
Our hope is that these ideas show why we’re so excited about our technology, and get you thinking about the power of item-level purchase data.
If you want to take a crack at one of these ideas, or if you have another idea for how you can use this data, we want to help you get started! You can find the Slice API on Mashape.
In our next post, we’ll do a deep dive into one of our early integrations and explore how one of our partners is leveraging our data to enrich their user experience.