Introduce to Common A/B Mistakes
With a plethora of tools and data collection programs to use, A/B testing is a popular and often effective tool used by businesses. The tool can provide quantifiable feedback from consumers without needing to engage them directly, and implemented properly can benefit the company.
But there are plenty of mistakes inexperienced data analysts and marketing directors can make in using and decoding the data received, draining their time and resources for little benefit.
But what is A/B testing and why is it used so prevalently? What are the benefits of using such a tool in your business, and how can you avoid making mistakes? Below, we discuss all of this and delve into the Common A/B Mistakes That Are A Waste Of Time And Money in more detail, so you can avoid making them in the future.
A/B Testing Basics: Background Market ResearchAlso known as split-testing, A/B testing involves having two variants of a webpage and assigning every visitor to just one of them, recording their interactions with the page, and using the data collected to determine which page layout or design is most effective. It’s existed for a while and as a background process, you’ve likely been a participant in split testing without realizing it.
“Large businesses use split testing frequently to assess their target market, determine how best to showcase their products, and boost overall sales. For small businesses, site traffic and interaction can be the difference between breaking even or going into liquidation. As such, using A/B testing to ensure the best experience for your consumers is a lifeline, so long as it’s done correctly,” says Lauren Jakobs, a CRO Expert at Writinity and Last Minute Writing.
Understanding both the tools available and the data collected is vital to using split testing. The easiest and most common ways A/B is incorrectly utilized begins with the tool itself, namely the ineffective deployment of the tools on offer.
A/B Testing Mistakes: Tool Misuse
Results keep business turning, and it is tempting to run an A/B test as quickly as possible to get results and install them permanently, but this in itself can be a detriment. Rapid testing limits sample sized, reduces variation and can skew results disproportionately if the consumer base is all of a particular demographic. A/B testing doesn’t collect the personal data of visitors, just how they use the site, so for a broad range of data you should run an A/B test as long as possible.
Statistical Irrelevance
Knowing which part of your site is instrumental for sales and customer engagement is key, and a common mistake in A/B testing is to run a test on the wrong page. The page in question needs to be relevant to the company, should serve to inform or drive sales and have a pivotal role in guiding consumers. Target popular pages like blogs, or your “about us” declaration page.
“If you’re looking for a target to test, try the front page; it’s the first thing a consumer is going to interact with and it’s an important part of your marketing strategy,” says Hannah Neils, a tech blogger at DraftBeyond and Researchpapersuk .
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Invalid Hypotheses
You need to consider why visitors interact the way they do. If you make very few sales but have plenty of email subscriptions, what could be the cause? Determine what you believe this could be, tailor a page to fix it then run an A/B test and analyse the data with your hypothesis in mind.
Poorly Conceived Tests
Running A/B tests can be informative, but they aren’t always an appropriate use of funds. Don’t waste time and money on trivial details like text colours or fonts; save it for testing marketing analytics, which assist in driving sales and helping your company grow.
A/B test Overlaps
Running more than one test at a time can mess with your results, especially if the changes you offer are inconsistent between pages or drastically different to what was used previously. You’re more likely to get a false positive (see below) than save money.
A/B testing: Data Misuse
False Positives
Overlapping variations can cause false positives, where there seems to be positive feedback when in reality, they’re entirely unrelated. Correlation does not equal causation, but overlapped A/B testing can certainly make it seem so.
Ignoring Small Gains
A statistically relevant change doesn’t need to be large, just significant. If your sales rise by 1% in one layout compared to another that remained statistically stable, it’s still a significant effect on your figures, especially for small companies.
Outside Influences
Assuming your data is without flaw or influence is a fallacy. Something as simple as code being broken for an hour can affect results if it was during peak time or crucial to navigating your site. Other factors include defamation, poor timing, flawed testing tools, and assumptions about your demographic. Look for these effects and take them into account when analyzing your data.
Mother of two and professional writer at Research Paper Writing Services and Gum Essays, Ashley Halsey has been involved in many projects throughout the country who enjoys traveling, reading, and attending business training courses.
