The word, tradition, and its variants can be a bit tricky because of what they imply: something long-established that comes with special significance. Because of this, efforts to change something that’s viewed as “traditional” are usually met with resistance. While it’s true that some traditions should definitely be kept alive, this doesn’t stand for traditional brands. No, to stay alive in today’s digital ecosystem, brands need to be modernised and adapt to the changing times. For example, traditional marketing methods like telemarketing and putting out adverts on magazines, newspapers, television and radio shows need to be updated to their online counterparts. Similarly, a physical storefront needs to be supplemented with a website that acts as the brand’s face on the internet.
Given today’s saturated mobile marketplace, it was a significant step forward when Google launched their Store Listing Experiments tool to help developers A/B test app store creatives and identify which combinations of art and messaging drive the most installs. But, just how effective is this tool? Our latest post aims to answer that question by addressing several misconceptions about the tool, specifically what it can help you accomplish and where it lacks in being a sufficient, standalone app store optimization (ASO) platform.
You need to run experiments. The one who runs the most experiments wins. BUT – most marketing experiments are done wrong. What’s missing is hypothesis driven testing across all inter-business disciplines. For each experiment create a document that contains the list items below: Hypothesis (What you expect to happen and the change to be made) Dependent variables (What are the test outcomes?) Methodology (What type of statistical test will you use on the data and why?) Sample size (How will you calculate sample size, and what is the expected number?) Analysis Plan (Which segments of data will you be analyzing? Which metrics?) Test Execution Plan (How will this test be run? Where and when?) Dependencies (What sort of resources will be needed for this test? Budget? Staff?)
Optimization is the process of testing and refining websites, mobile applications, landing pages, email campaigns and marketing and growth efforts with the goal of increasing performance around desired outcomes. Optimization includes A/B testing, which is the process of testing one thing against another version of the same thing. A/B testing and optimization is an important part of growth marketing. These articles feature the best strategies on how to optimize your growth efforts and provide new ideas to help inform your A/B testing and optimization plans.
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