Experiments are hard
Some time ago we conducted an experiment to measure the results of a marketing campaign on customer purchases in a mobile app. A customer would receive a marketing message with the call to action being an in-app product purchase. The campaign aimed to lift the total number of in-app purchases as well as the average […]
Announcing the Zalora-Aampe Partnership
“Less than three weeks after deploying the Aampe personalization system into its notification pipeline, the ecommerce platform Zalora increased the rate that targeted users were adding items to their wishlist and starting a checkout in its app by an average of 21% on a daily basis. The result was significant additional revenue generated in just […]
Why you need conditioned experiments
An A/B test is perhaps the most basic form of an experiment: take a group of people, randomly split them into two subgroups, send a different message to each group, and look at the difference in response. A/B tests are easy and widely available. They are also a really easy way to get misleading, and […]
Change is your business
There are many names for the people who keep a company in business. User, customer, buyer, seller, member, player, contributor, among others. We’re going to use “consumer” because a single word makes things simpler to talk about, and consumer is a very broad word: even if you’re using a company to sell your product or […]
A demo of the Aampe Composer
While a lot of our Aampe product involves work in data science, experimentation, and machine learning, we’ve learned from our early customers that generating personalized notification copy is a major problem they face. Most CRM and product marketing teams seem to write their notifications manually. We don’t think that scales to creating truly personalized copy for […]
A walk through our conditioned experimentation process
We explained our approach to designing “conditioned” experiments in a previous post. Briefly: humans are complicated so if you want experiment results that don’t lie, you have to assign your treatments that take that complexity into account. The traditional approach of just randomly assigning treatments only works if you have a huge sample size – […]
Aampe for Food Delivery App Notifications; every app is a content app
I was recently listening to Patrick O’Shaughnessy interview Tony Xu, the founder of DoorDash, on his podcast Invest Like the Best. About 26 minutes into the episode Patrick asked the question: “When you did get to the consumer side and started building that UI, what did you learn then about generating demand?” Tony’s response made […]
Conversion-based product recommendations
What is a recommender system? A recommender system is an incredibly powerful tool — It’s how Netflix decides which videos to suggest, how Amazon decides the next thing they think you want to buy, and how Spotify tries to get new music on your playlist. Types of recommender systems While there are a many different […]
An implementation of Complement Naive Bayes for Google BigQuery
This is a technical post. If your eyes glaze over from talk of supervised learning and class balancing and the variance-bias tradeoff, you should go read something else (we offer a whole lot of choices!). If those more technical topics interest you, however, then this post is for you. Let’s dive in. The field of […]
How user journeys hurt your business (with proof)
The concept of a rigid “User Journey” has been heavily ingrained in our minds — it’s taught in virtually every “best practices” blog and included as a feature in just about every customer messaging platform (see here, here, and here, for just a few examples) — so it’s not uncommon for us to field these […]