Not to be confused with: EPPO Global Database, or Eppo Experimentation Platform, or European Plant Protection Organization, or European Public Prosecutor’s Office
What product management best practices can help you build valuable and delightful products for your customers? Check out our list!
Whether you’re an aspiring product manager or an already established product professional, our list will help you reflect on your processes and hopefully improve them.
Read more: https://userpilot.com/blog/product-management-best-practices/
Many product and data pros understand that having deep insight into every user’s journey is a critical reason to invest in analytics. Creating this holistic customer profile requires effective identity resolution—which is also vital to experimentation.
Identity resolution (ID resolution) is the process of identifying and linking multiple pieces of information to a single individual, ensuring that different data points—like email addresses, phone numbers, or device IDs—are tied to the same person so you can understand their behavior.
In experimentation, identity resolution requires two key components: gathering accurate and reliable data and ensuring users have smooth and consistent experiments. In essence, identity resolution is the backbone of reliable experimentation.
Read more: https://amplitude.com/blog/identity-resolution-in-experimentation
Let’s start with the burning question: Why should you bother experimenting with your product and in-product experiences? Well, the answer is simple—experimentation is the secret sauce that can take your product from good to spectacular! By experimenting with something new, you get validation of your idea, and when you go a step further by testing and iterating, you get even more valuable insights into what resonates with your users and what falls flat. It’s like being a mad scientist, but without the lab coat and crazy hairdo (unless that’s your thing).
Read more: https://www.appcues.com/blog/guide-to-in-product-experimentation
Due to the substantial traffic volume on Square’s pages, any modifications to them can have a significant impact on downstream metrics and multiple cross-functional teams that depend on these pages for seller acquisition and lead generation. Thus experimentation is one of key focus areas in our team, to provide visibility on the impact of changes to stakeholders, assess risk and enable data driven decision making. Here are some of the key learnings from the dozens of experiments our team has run on the website traffic:
Read more: https://developer.squareup.com/blog/lessons-learned-from-running-web-experiments/
Ever wonder how Netflix serves a great streaming experience with high-quality video and minimal playback interruptions? Thank the team of engineers and data scientists who constantly A/B test their innovations to our adaptive streaming and content delivery network algorithms. What about more obvious changes, such as the complete redesign of our UI layout or our new personalized homepage? Yes, all thoroughly A/B tested.
In fact, every product change Netflix considers goes through a rigorous A/B testing process before becoming the default user experience. Major redesigns like the ones above greatly improve our service by allowing members to find the content they want to watch faster. However, they are too risky to roll out without extensive A/B testing, which enables us to prove that the new experience is preferred over the old. And if you ever wonder whether we really set out to test everything possible, consider that even the images associated with many titles are A/B tested, sometimes resulting in 20% to 30% more viewing for that title!
Read more: https://netflixtechblog.com/its-all-a-bout-testing-the-netflix-experimentation-platform-4e1ca458c15
One way to facilitate experimentation at scale — within a large business with large data assets — is by centralising some of the infrastructure that can be used to deploy, monitor, and analyse experiments.
So, at SEEK we have been working on building an online experimentation platform that can be used to experiment on our AI products and services.
The overall objective of our experimentation platform is that it will help reduce friction for teams that want to run A/B tests, whilst also helping improve the robustness, consistency, and efficiency with which we experiment.
Read more: https://medium.com/seek-blog/10-lessons-from-building-an-experimentation-platform-ded851715683
A/B testing is no longer a new field. Finding a proper A/B testing tool isn’t the problem anymore. Now, the problem is choosing the right one.
Google has announced that it will shut down Google Optimize in September 2023, putting an end to one of the most used A/B testing tools ever.
If you work in conversion optimization—whether at an agency, in-house, or as a consultant—you almost certainly run A/B tests, and Google Optimize sunset will probably affect your tech stack.
Read more: https://cxl.com/blog/ab-testing-tools
A/B testing stands for “Always Be Testing,” right? It should!
Once you start testing various elements of your marketing campaigns – from PPC text ads to landing pages to email subject lines – you realize “A/B testing best practices” are only a rough guideline. You never know what’s going to work with your audience until you A/B test it.
Read more: https://www.wordstream.com/blog/ws/2012/09/25/a-b-testing
For businesses to flourish in today’s highly competitive market and hit that conversion rate figure they’ve always aimed for, they must build a scalable experimentation culture.
SEO, content marketing, and paid advertising may dramatically help drive more traffic to a website, but it’s the magic of A/B testing and the repeatability of wins brought about by a culture of experimentation that can get more conversions and high revenues on the plate.
Read more: https://vwo.com/blog/ab-testing-examples/
The need to use predictive analysis and modeling in forecasting the growth of data has been brought about by how great the volume and variety of data there currently is. According to Gartner, “Data preparation is an iterative and agile process for exploring, combining, cleaning, and transforming raw data into curated datasets for self-service data integration, data science, data discovery, and business intelligence/analytics.”
Read more: https://www.datasciencecentral.com/what-is-trustable-data-why-do-you-need-it/
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