A/B Testing, which is sometimes referred to as split testing, is the simplest form of multivariate testing. A multivariate test makes use of more than one variable in an experiment designed to determine the best possible outcome from a complex set of choices. An ABtest is limited to testing only two variables at a time, A and B, to yield faster results with a lower degree of complexity.
For example, let’s suppose you are trying to decide which route will get you from your home to your office the fastest. There may be many different choices available: the subway, the train, three different routes by car or the bus. With AB testing you would choose two options and test them against each other. Perhaps taking the bus each day for a week to record your transit times, and then taking the subway each day for a week to compare those transit times and see which route is actually faster.
In the simplistic example above we have a huge amount of variability because we don’t know if one of those weeks happens to have less traffic than the other, or if one of those days was a day where the subway happened to be delayed more than usual. Did weather play a part in the results? For that reason, quality split tests require many attempts at each choice and a series of incremental changes to those choices in order to eliminate extraneous information and one-time occurrences. They are also done intermittently instead of one after the other, as if you drove different parts of the way to work and took the bus for other parts of the route on the same day.
Imagine aggregating millions of attempts to go to work by bus from your house to your office, with those attempts happening at every possible time of day, each day of every week over the course of ten years in a row. Matched against corresponding data over the exact same period for attempts via the subway route. As the amount of data increases, so does the accuracy of the results. Unfortunately, when attempting to calculate millions of results from an AB test, trying to choose the winner manually can be an extremely time and resource depleting endeavor. That is where the power of a machine-learning algorithm becomes immediately apparent.
A machine learning algorithm like the proprietary code that powers Khepri.tech is capable of calculating the winner of complex multivariate tests from a massive amount of data in real time. That means, instead of having millions of attempts to sift through by hand and only determining a winning option long after the result has become stale, you can finally get the answers you need right now and dynamically make better decisions immediately to reach better results.
Let’s say for example you have a webpage that is performing pretty well with a million visitors arriving each day, but you want to know whether having a green payment button or a red one will improve your conversion ratios? You also aren’t entirely sure whether the words “Buy Now” or “Get It Now” would work best with your target audience as button text.
By running your own A/B Testing experiments you can easily gather data from that million visits each day by displaying each a variation of the button intermittently, like a green Buy Now button, red Buy Now, green Get It Now and red Get It Now. The result will be a mountain of information, and that mountain grows exponentially as you add more variables to your multivariate testing.
Instead of sitting down with a pen and paper to try to surmise which set of variables works best, you can easily feed all of that AB Test information into Khepri.tech using a simple API, and Khepri will tell you in real time which options are getting you the best results. The answers come so quickly that you can act on them during the testing period by ruling out a result that is obviously weak while shifting a greater percentage of the test iterations and traffic to a result that seems very strong from the outset to verify it is the best solution.
The goal of all AB Multivariate Testing is to find the best and most profitable action from the results of the test while eliminating inefficiencies and expediting the speed of your reaction to the secrets discovered from the data. Khepri uses a proprietary algorithm exclusively created for the purpose of getting you the best choice with the highest degree of accuracy. That’s what empowers you to earn more by putting your data to use in real time to answer virtually any question that offers more than one possible path toward your goals.