A view on AB testing tools of the future.
How it works:
- You plugin the AB testing tool to your application and say: optimize page A, on the measurable goal X (for example downloads).
- The tool by itself: creates new UI variation -> tests it -> analyses results -> makes it default -> creates new UI variation -> tests it -> etc… This goes ad eternum… Much like natural evolution, keeps experimenting/mutating, until it finds the UI that works best for the defined goals.
New UI variations do not need(shouldn’t even) be 100% random, they should use smarter techniques like: genetic(and other search/optimization) algorithms + tried out design heuristics + branding guidelines(avoid color A, use font B, etc..) + (sampled)user filtering + some amount of randomness + etc..
Knowledge Base: Build a Database with the test results, that collects knowledge of what worked and what didn’t (for a given context). Just as Pandora collects user input for building its recomendation system, this accumulated knowledge would serve as input for the task of generating the new UI variations. Note: The amount of data is key; the bigger the amount of test results, the closer to all possible variations thus the closer to all the best optimizations. With a large amount of test and tried out results quicky we would get the perfect UI rules.
Page Flow: Tool should optimize not only the page itself, but also navigation along pages, customizing content depending on the flow For example, forward the user to a different page, depending on the keyword used in a search engine when arriving at the website.
Personalized UI: What works for user A might not work for B. A 16 years old likes different things than a 50 years old. Even for a unique user, his tastes changes over over time: winter vs summer, week vs weekend, working hours vs non-working hours etc… So the perfect interface might need to be changing over time(? Don’t assume, experiment and see if it works…).
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