What is A/B testing? But while A/B testing has done much for the marketing discipline, it doesn't go far enough on its own to meet the needs of modern marketing professionals. A/B testing can get better. Members test each version to determine which works best. But they continue to tweak the home page by testing different headlines, CTA button colors or sizing and content promotions. That's how you find the winning experience for each person. Savvy marketers think about A/B testing with both types of personalization experiences: segments (groups of people) and individuals (one-to-one). Your homepage could display one headline to visitors from small businesses and a different headline to users from large enterprises. Rather than testing different generic versions of the home page to find the one experience that works best for most people, you can test different versions of the homepage tailored to each of your target audiences. Combining this type of one-to-one personalization with A/B testing actually seeks to test the algorithm itself.
If you’re not combining your tests with personalization efforts, you’re doing it wrong.
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Successful startups today try new things quickly. Founders don’t spend years investing time and money to build a business only to find the market doesn’t want the product or the model doesn’t scale. Smart entrepreneurs fail fast. They limit the damage and move on to the next potentially winning idea.
This mentality is prevalent in marketing, too, due primarily to A/B testing. The practice has taken much of the guesswork out of marketing, helping transform it into one of the most measurable business-development disciplines. But the way marketers have approached A/B testing over the years doesn’t cut it in today’s world. Here’s why, and how you can fix it.
What is A/B testing?
An internal debate springs up whenever a business takes on a new marketing initiative. This headline or that one? This subject line or that one? This image or that one? Each team member has an opinion. In the past, groups needed to reach agreement. Members could select only one option for each element, and achieving that consensus often took time.
A/B testing changed the dynamic. You no longer must commit to one version of anything — you can test a few different approaches in small batches. An A/B test displays two (or more) experiences to your audience so you can measure the impact of each and statistically determine which was most successful.
Imagine you plan to send an email, and you’re torn between two subject lines. A/B testing enables you to assign a Subject Line A and a Subject Line B to an initial, smaller list of recipients. You’ll want to answer questions such as:
- Which had a higher open rate?
- Which had a higher clickthrough rate?
- Which ultimately drove more conversions?
Obviously, you’ll use the winner when you send the rest of the messages to the larger list of remaining email addresses. You can apply the same approach to test different home page experiences, calls to action (CTAs), ad copy, blog titles and other components. Each test helps you refine your strategy for future efforts.
This data-driven model clearly improves on the guessing method. But while A/B testing has done much for the marketing discipline, it doesn’t go far enough on its own to meet the needs…