A/B Testing And Multivariate Analysis For Live Chat Services
Live chat promises to better fulfill customer needs and increase conversions, average order size, and sales. Effective live chat services need to meet the needs of existing and potential customers. A/B Testing and Multivariate Testing are two processes companies can use to test their live chat services on a regular basis and ensure continuous improvement in standards. The aim must be to optimize consistency and responsiveness of live chat agent-customer interactions, and to make sure that live chat services deliver effective results to the brand and the customer. To do this, testing cannot be taken lightly.
You may know that A/B Testing and Multivariate Testing are useful for web page optimization, so how can they contribute to live chat optimization? Is one of these processes superior? What are the pros and cons? There is an ongoing debate in the live chat industry between the proponents of Multivariate (MVT) Testing and A/B Testing (aka, Split Testing or A/B/n Testing, where the “n” stands for any number of variations in a test) over which method yields better results. Before comparing which testing method is more advantageous for live chat optimization, we first need to understand A/B testing and MVT.
A/B Testing, which is also widely known as Split Testing, is a method of website optimization that compares conversion rates for two versions of a page (version A and version B) using live web traffic. Site visitors are directed to one of the two versions. By tracking the way visitors interact with the page – the videos they watch, the buttons they click, or whether or not they sign up for a newsletter – companies can determine which version of the page is effective. For example, the current version of a company’s home page might have in-text calls to action, while the new version might eliminate most text but include a new top bar advertising the latest product. After enough visitors have been funneled to both pages, the number of clicks on each page’s version of the call to action can be compared. It’s important to note that even though many design elements are changed in this kind of A/B test, only the impact of the design as a whole on each page’s business goal is tracked, not individual elements.
Multivariate Testing uses the same core mechanism as A/B Testing, but uses a higher number of variables and reveals more information of how these variables interact with each other. The purpose of multivariate test is to measure the effectiveness of each design combination in relation to the overall goal. Once a site has received enough traffic to run the test, the data from each variation is compared to find not only the most successful design, but also to reveal which elements have the greatest positive or negative impact on a visitor’s interaction. The most commonly cited example of Multivariate Testing is on a page in which several elements are up for debate — for example, a page that includes a signup form, some kind of catchy header text, and a footer. To run a multivariate test on this page – rather than to create radically different designs to A/B test – you might create two different lengths of sign-up form, three different headlines, and two footers. Next, you would funnel visitors to all possible combinations of these elements. This is also known as full factorial testing, and is one of the reasons why multivariate testing is often recommended only for sites that have a substantial amount of daily traffic. The more variations of a page that need to be tested, the longer it takes to obtain meaningful data from the test.
It’s important to A/B test live chat services to ensure that the design implementation and live chat agent behavior provide quality service to customers. With A/B testing, you can divert groups of users to different versions and precisely measure the impact of live chat on conversions. You can, then, assess your chat provider and check how they compare with their competitors, in addition to optimizing the design and conditions of how the chat is triggered. With A/B testing the following data can be gathered:
- Conversion rate, how many visitors who chatted bought the product, or requested a callback, or signed up for a newsletter?
- Average time live chat agent spends on chat?
- Ease of use for visitors
- Top queries asked
- Average order value
- Customer satisfaction scores
On the other hand, Multivariate Testing provides sophisticated level of control that lets you test multiple variations of buttons, windows, invitations, and invite rules or rule sets simultaneously. With multidimensional segmentation variables (first time vs. repeat visitors, geo-location, device, page or referrer URL, page view count, etc.) and defined control group sizes and durations, businesses have the tools they need to run experiments and gain valuable insight into their live chat agent strategy. With multivariate testing, the following data can be gathered:
- Measuring the incremental effect of having chat versus not having chat
- Comparing multiple variations of button types (e.g., static and floating), styles, and positions to see which generates more conversions
- Testing alternative proactive invitation rules to measure engagement uplift
Live chat services are becoming an increasingly effective way to engage with customers. To ensure you’re taking advantage of the critical opportunities for live chat agent engagement, it’s essential to test multiple approaches. With A/B Testing and Multivariate Testing, you’ll be able to optimize your live chat implementation quickly and easily.
Speak with the team of Data Scientists at RapportBoost.AI to learn more about our live chat agent training solutions.
Authored by Tushar Pandit, Data Science Advisor to RapportBoost.AI.