Is There an Alternative to A/B Testing?
The simple answer is yes. The not-so-simple answer is that Loops’ "Release Impact" offers a proven alternative to A/B testing.Let me elaborate.
We're all aware that effective A/B testing demands a considerable investment in terms of traffic, time, and other resources. Though strategies exist to lessen these challenges (as mentioned in my previous post), achieving robust A/B testing can still be an uphill task.
In various B2B scenarios, due to the complexities associated with A/B testing, many product teams opt to sidestep it...
proceeding without an accurate measure of the impact of their changes.
However, an alternate solution to navigate the hurdles of A/B testing is to employ causal inference.
By utilizing causal inference in conjunction with specialized algorithms, simulating an A/B test becomes feasible. This method filters out various noise factors like seasonality, marketing shifts, alterations in user demographics, and prevailing trends. This allows you to assess the impact of your initiatives without resorting to conventional A/B testing. At Loops, we term this feature "Release Impact."
Sounds intriguing, right? You might wonder, "How dependable is this ‘Release Impact’ feature?”
It's a frequent query we face, particularly during initial engagements with our clients. The response?
While A/B testing remains the gold standard for accuracy, our models are not far behind.
But don't just take our word for it.
We've consistently subjected our models to stringent tests, juxtaposing them with A/B tests previously carried out by our clients. The outcomes from Release Impact have time and again showcased that with the appropriate algorithms and causal models, it's entirely possible to derive insights akin to those from an A/B test without actually implementing one.
Moreover, the model has proven its mettle even in tough scenarios.
For instance, a B2B company with a relatively modest user base of 400 used the Release Impact and saw similar results to an A/B test that took them several months to run.If you're curious to see this in practice and have past A/B tests to refer to, provide us with the details and the corresponding data. We'll run it through "Release Impact" for you to evaluate the outcome. We've undertaken this exercise multiple times to great acclaim.
