One of the testing strategies is experimenting with the path people take towards their final conversion/purchase. This is referred to as a split-path test or alternative path test.
This could give you the needed lift when nothing seems to move the needle anymore.
What Is a Split-Path Test?
A split-path test is defined as “a type of A/B/n test where, instead of just altering a single page, you change multiple sequential pages.”
Split-path testing sends users down a different path instead of showing different page variations. It includes things like multi-step checkout paths, multi-page forms, and product recommendation wizards. As Chris Goward explained in You Should Test That!:
While split-path testing isn’t talked about as often as your regular A/B/n tests about button colors, it isn’t a new concept. Bryan Eisenberg and John Quarto von Tivadar wrote about this a while ago in Always Be Testing:
“This is different in that you’re testing the performance of grouped pages against other grouped pages. For example, you could test a checkout process by splitting it into two variations, one with four steps (or pages) and another with only three steps.”
How User Flow Affects Conversions
A major factor affecting your conversions is user flow. It’s the path a user follows through your website interface to complete a task (make a reservation, purchase a product, subscribe to something). It’s also called user journey or conversion path.
Here’s an example of a typical conversion path you might have:
Facebook update –> Landing page –> Shopping cart sequence –> Payment funnel –> Pre-bill order confirmation page –> Billing page –> Confirmation page
Split-path testing is one way of optimizing your conversion path. As for why it’s important to do so, here’s how HubSpot explains it:
Just as it’s vital to make sure you’re maximizing the output of each individual page on your website, it’s incredibly important to understand how all of your pages work together. Which pages of your website make you the most money? Which ones generate the most leads? Which ones are preventing your prospects from advancing through your sales funnel? On a relay team, you can put together the greatest four runners in the world. But if they drop the baton at every handoff, you’ll never finish a race. Similarly, if a reader moves from one page to the next and gets confused, the only button they are likely to click is “back”.
Testing alternative conversion paths can open up new opportunities to boost conversions that cannot be obtained by simply changing page elements.
How Split-Path Testing Can Optimize Conversion Paths
Setting up split-path tests is similar to your regular old A/B/n tests in that you’re tracking the same goal. Each group of pages is treated as variation to be tested against the control, and each variation has the same conversion goal page – probably the order confirmation or thank you page.
You need to analyze the data the same way as a regular A/B test, with the same statistical rigor.
In addition, be careful with selection bias in split-path testing. As Andrew Anderson explains, many people think that you should only split-path test with new users, which creates a biased population:
Remember also that site navigation isn’t usually linear. That means that gravity doesn’t pull visitors very cleanly down your funnel, no matter how much you wish that were the case. Linear assumptions of data is common, and it’s a bias that can be detrimental to any of your optimization efforts.. As Eisenberg and Quarto-vonTivadar explained:
“Although this can be an easy way to test strongly linear scenarios, you should keep in mind that few visitors navigate your site in a truly linear fashion (even though you might wish they did for analytics-tracking purposes!). Persuasion instead tends to occur along nonlinear paths, although that is a subject far beyond the scope of this book. Further, the longer the “click distance” between testing page and conversion page, the greater the likelihood that some visitors will wander off to other portions of your site, which Website Optimizer may record as a failed conversion for testing purposes, even though such a result doesn’t reveal visitor intent.”
For that reason, split-path tests are most appropriate on sections of your site that do follow a linear path, like your checkout flow and registration forms.
Split-Path Testing is An Innovative Test
Split-path testing is considered an ‘innovative test’ – as opposed to an incremental test, split-path testing offers offers the opportunity for much greater lifts. That said, it’s riskier as well for multiple reasons.
As Andrew Anderson mentioned, “Perception is not always reality however as you may just be exponentially adding bias on top of bias and allowing yourself to take more time and opening yourself up to even more local maxima.”
If you’ve hit a glass ceiling (i.e. a local maximum) and need to do something innovative and creative to bust out of it, split-path testing is a possible but highly cost inefficient solution.
Let’s Talk About The Local Maximum
What is the local maximum?
It’s when, after a progressive period of A/B/n testing, your gains start to slow down. You’re experiencing diminishing returns even though you’re testing a bunch of iterations. It’s frustrating, but it happens to everyone.
What can you do about it?
There are many solutions, but most of them center on shifting your paradigm and getting a bit creative. In other words, swinging for the fences with some innovative tests, trying to change customer behavior/experience to increase conversions.
Though, as Andrew Anderson explains, split-path testing is not a silver bullet solution to the local maximum problem:
Split-Path Testing Subscription Pathways
Marketing Experiments wrote about a few tests they ran optimizing the subscription funnel of some large publications. They performed split-path testing to simplify the funnels. Here’s what the first test looked like:
In this case, reducing the steps from 9 to 3 increased conversion rates by about 300%. As they wrote, “Reducing the number of steps is probably the single most-significant improvement you can make to your subscription path.“
In their second example, they employed a similar strategy: reduce steps to subscription. This is the flow that they started with (resulting in zero registrations in this case):
They decided to chop a few of those pages off and pair it with an email capture form on the initial offer page for the next test:
Finally, they removed another page from the sequence while retaining the same offer page:
Of course, their sample size is too small to draw any conclusions between the four page and three page examples, but it seems they are moving the flow in the right direction.
Add Registration Steps
You can also take the opposite strategy and add in more steps to the checkout funnel. Here’s Andrew Anderson’s take:
So, let’s say you’re running a SaaS startup and want to increase the amount of paid signups you have. Your optimization efforts have leveled out – value proposition tweaking is no longer bringing any results. So you think that perhaps you can increase quality leads by changing the checkout flow itself.
You go from: Home page → Sign up → Confirmation page
To: Home page → Features → Sign up → Confirmation page
You hypothesize that if customers are to view your features page before being asked for their credit card, they’ll be more likely to sign up. Perhaps they’ll be higher quality leads. They may have a lower attrition rate. So, you can split-path test this to be sure.
Change The Order of Steps in a Funnel
Sometimes, changing the order of the flow but keeping the same content can provide a lift (Andrew estimated that he’s seen anywhere from 5-45% lifts and for some reason the scale tends to go up with larger sites.”
So imagine you’re selling a subscription product and your checkout flow looks like this:
Home page → Sign up → Payment Page → Create Account → Thank You
You could try changing the order to switch the payment and the create account pages, like this:
Home page → Sign Up → Create Account → Payment Page → Thank You
Similarly, if you ran an eCommerce site, you could experiment with running the billing address before the shipping, or vice versa. You get the picture.
Andrew Anderson told me about a Malwarebytes example where they ran a test changing their entire shopping cart user flow, going from multiple pages to different orders and page layouts. They tested a large variety of examples and found that moving from a 3 page flow to a one page flow and introducing the purchaser information fields much earlier results in a dramatic uptick in overall user RPV.
Test Low Touch vs High Touch Paths
Another split-path test one could run is sending users down a fully online purchase path or a live representative quote or demo path.
Picture that you’re running a SaaS company that sells form analytics software. You send 50% of traffic down your traditional funnel where they can pay online and get started on their own, and you send 50% down a “get a quote” or “get a demo” path and have a sales rep take it from there.
In this scenario, you need to factor in the increased cost that accommodates the increased workload for your sales team. Here’s how Andrew Anderson put it:
In general, the sales rep/quote approach tends to work better with more complicated or more expensive products (this is the norm for many enterprise software products).
In this case, as in the others, there’s always a chance it won’t work and you’ll have spent a large opportunity cost. In addition, mind your sales cycles and your churn later on down the line with either path.
Warning: Heavy Development Resources Might Be Required
Split-path testing has promise of a huge lift but also the possibility of a huge waste of time.
For instance, coding these variations is much more costly and time consuming than testing page elements. Developers need to code outside of their testing tool and usually run split url tests to accomplish this (could also run multi-page tests for some checkout flows, but that’s theoretically different from the type of split-path test we’re talking about).
Dave Gowans, Head of Conversion at Conversion.com, gave the following example:
And as Andrew Anderson said, “You almost always need a unique user flow path or a cookie based system to control server side implementation. In many cases you will not even use the testing tool for data tracking.” It’s a minor issue, but one to consider with split-path testing.
Just imagine that you need to have a developer work on your variation full time for two weeks. How much does that cost you in time and opportunity cost (what else could they have built in that time)? Andrews says you can mitigate some of this cost by never wasting the time on 1 alternative experience.
According to him, “2 wasted weeks as a 10% success rate is far worse than 6 weeks at a 75% success rate and a 50-100% increase in expected scale of outcome. If you are going to go this big and take the time, the math always shows that you need to do even more experiences. The cost does not exponentially grow but the expected outcome does.”
Another issue: the only data you have is current funnel performance, meaning that existing analytics (correlative data) is basically useless. It tells you what did happen, no what should happen.
A Possible Split-Path Testing Strategy
Being that there is great risk with split-path testing, here’s a possible strategy to consider:
Start by improving your existing funnel. Use the ResearchXL model to figure out all friction, distraction, and other issues. When you begin to hit diminishing returns, then it may be time to experiment with split-path testing. By testing whole flows you can actually get better data, in that you’re able to find real problems and distinguish them from data anomalies and/or biased perceptions.
Finally, when considering split-path testing, keep in mind how you’re prioritizing tests. With this type of test, you’re balancing the impact with a much higher cost of implementation. Therefore, with the higher risk associated, you always need to increase the expected outcome. Dave Gowans from Conversion.com echoed this sentiment:
Split-path testing runs on the same statistical models as a regular A/B/n test, but is different in that it fundamentally changes a user experience.
It is both a potential huge win but also an opportunity to get even more caught up in your own biases. It is the definition of swinging for the fences, but if you do it with discipline and with an understanding of opportunity cost and efficiency, it can be a huge win for your entire organization.
This could be good, in that you can achieve bigger uplifts, or this could be bad in that you spend a lot of money and time and development resources on a test that doesn’t move the needle at all.