Why small tweaks and split testing don't work, and what to do instead
cortes.design
cortes.design
I am no longer with the company, but around 10 years ago, I turned a failing consultancy into an SEO SaaS company. Turning revenues from -40k/month to 90+k/month after laying off more than half the staff when we lost our last client and deciding to take our destiny into our own hands building a suite of SEO products.
We ran split tests on almost every design change. They weren't always tiny, some were entire page layouts. But even on things like changing the header text saw measurable differences.
I built the ability to split test into every facet of our framework. We had the ability to split test pages, transactional emails, marketing emails, and we would tweak everything all the time. If we noticed group a wasn't performing as well as b, we would drop a.
There were some tiny things that made major differences:
- Email "From" being a person's name and not just the service bumpped onboarding but nearly 20%.
- The onboarding subject "Almost Done..." performed about 10% better than "Activate your account"
- Having the video auto play converted visitors almost 50% more often than them having to click the play button (it may be evil, but it makes money).
- Having the video be an animation vs just a spokesperson talking into a webcam converted 5% better with the same script (+2% better with just reading the text from the page).
We had a large audience after we launched our first product free of charge for a limited time (linklicious.co -- formerly linklicious.me -- formerly lts.me), then used that momentum to built out a new product every month or so for the next couple of years. It was a great job in a terrible industry. 80% of my job was inventing new ideas.
Check this tools[0] and this article [1]
[0] https://www.evanmiller.org/ab-testing/sample-size.html#!20;8...
[1] https://www.evanmiller.org/how-not-to-run-an-ab-test.html
Most of these numbers were measured after at least a week of leaving the experiments running.
We never tested more than experiment at a time per visitor, but our framework had the ability to run multiple tests simultaneously, while cookie-ing a visitor to a single test.
b) That's assuming you are using statistics right. It's quite hard to use statistics right.
Did you know, in experimentation programs run by Microsoft, Google and Amazon, roughly two thirds of their ideas have no impact or hurt the metrics they were designed to improve? And yet rookie web Devs or marketing assistants "know" better.
Source: https://www.google.com.au/url?sa=t&source=web&rct=j&url=http... (Read section three of this paper by a Microsoft distinguished engineer)
I'll point out a major difference, though: Microsoft, Google, and Amazon are already highly optimized. They've had millions of man-hours put into optimizing everything from product design to UI to UI element positioning to wording to colors to fonts. When you get a new, beneficial result in a change to Google, it's usually because the environment has changed while you weren't looking, and the user is different.
That doesn't apply to a new business that's just learning how to sell their product. In a new business, by definition, you've spent zero time micro-optimizing your message & product. You can get really big wins almost by accident, if you happen to have stumbled into a product category where users actually want what you're building.
If you end the test the moment the data reaches 95% significance, it will show a difference about 50% of the time for the same email. Many people make this mistake.
A 95% confidence interval doesn't mean much if you dont follow good statistical practices.
A small sample size would lower your power to detect meaningful differences, which the original scenario doesn't have (by definition).
(If distributional assumptions, etc, are violated, then that's a different story!)
https://heapanalytics.com/blog/data-stories/dont-stop-your-a...
The point I was trying to make was you can decide to run a test with a very small sample (e.g. n = 5), and it will still have the level of type 1 power you set if you chose a significance level of .05.
> You need to make sure you have enough samples in order to know if you rejected the null hypothesis by chance.
You do this when you decide the significance level (e.g. .05). The value needed to reject, given a significance level, is a function of sample size.
The definition of Type 1 error on wikipedia has a good explanation of this:
What you're saying is perhaps correct but it's not how AB testing is done in the real world. I think many tests show a difference for random reasons.
Obviously some segments are going to open/click/spam at higher rates, but that's fine and controllable with a customer rating. So when you split your users, ensure an even makeup of customers, and your tests will be fine.
Because the ones who weren't going to convert never came back
If anything, the biggest issue I've seen with A/B testing is that it biases organizations to things that are easy to measure (i.e. shopping conversion) while sometimes at the expense of things that are longer term/harder to measure (like brand reputation). I'd be most interested to hear how some companies have dealt with that shortcoming.
But all that said, one of the biggest benefits of a culture around A/B testing is that it gets companies away from the endless back-and-forth around opinions (like this article) and builds a culture of "OK, we'll try your suggestion and see if it succeeds or fails".
If you noticed I included a section explaining when a split test makes sense and you can tell that Booking.com fit it that criteria as they were already starting to grow a lot.
Your article points to 2-3x industry average conversion rates (for existing traffic), but says nothing about the more important factors of acquisition or inventory.
The whole point being that the way Booking (and other) companies act is the exact opposite of a cargo cult. Everything needs to be backed up with data and challenging conclusions is part of the culture.
All that said, a common complaint of Booking is that it has a ton of "dark UI" patterns, so it will be interesting if there is any long term blowback against short term A/B winners that erode the goodwill to the brand over time.
At this point, all these articles get me thinking about is why they keep getting written. Were these designers abused by bad product managers? Are they ego-driven and don't like having their creativity reduced to quantitative values? I don't know, but I do know that designers who talk this way tend to be toxic to a productive culture. I've experienced that firsthand.
Controversy is great advertising: they get all the folks who hate them to help spread the word about their services.
It's the same thing with software methodologists, gurus, and architects. Roughly daily there's a new blog post about how [common practice] is now considered harmful, and you need [proprietary expertise held by consultant] to implement some other replacement instead. These posts may or may not be helpful to your software engineering efforts, but they are certainly helpful to the poster's bottom line.
For many types of businesses, once a website design is good enough, it is really hard to gain meaningful improvements from further changes. Chances are that changing the color or position of button is not going to make a difference.
If you have 50 customers a month, you are not going to have enough data measure 5% improvement in conversion even if you let the test run for a year.
If a business has 50,000 customers are month, then you can measure a 5% improvement in conversion in a few days and testing is a valuable tool.
Until you are at that scale where testing becomes feasible, you are better off just following best practices in design and focus on other areas to drive more business. If you are going to test something, it has to be big bold changes so you are able to measure the significance with a small sample size.
In the same way, some designers want to sculpt an ultra-minimalist flat typographic fantasyland with no user cues when the product is a straightforward ecommerce shopfront. Let's call these people "design wankers".
Fortunately, split testing can prove that the "wankers" are wrong. This makes them write angry blog posts about how testing is bullshit and Steve Jobs and skeuomorphism and building a better horse.
Can someone now please solve the "astronaut" problem for me?
[0] https://www.theguardian.com/technology/2012/jul/16/marissa-m...
In a previous life I ran a Conversion Rate Optimization as a Service business and optimized $100s of millions of transactions for customers across categories.
The amount of research on this topic is staggering and a lot of it dates years back like Walmart's seminal review in 2012 (1) showing the devastating impact of speed on conversion rate.
In case that's not enough for you Google set up not one, but TWO separate page speed tools to help webmasters fix this chronic problem before they just threw in the towel and took over the job for you with their AMP effort. (2)
Oh and by the way did you know that if your site speed is slow Google will penalize your SEO? And drop your Page Quality Score index resulting in higher PPC charges?
The best technical guide on the internet to addressing this problem is here - http://httpfast.com/ - and a comprehensive monitoring service built by the author here - https://www.machmetrics.com/.
(1) http://www.webperformancetoday.com/2012/02/28/4-awesome-slid...
(2) https://testmysite.thinkwithgoogle.com/ and https://developers.google.com/speed/pagespeed/insights/
A/B testing small changes is something that really big sites like Amazon can do because they have enough volume to justify it. It's kind of like blood doping in sports. If you're already at the top of your game it will make enough of a difference to be significant, but if you're just some average person who can't run a mile... blood doping is the last thing you should do.
He said it with a grin because it is a huge amount in the context of a competitive time trial -- like the difference between first place and 10th or 20th place perhaps. But as a percentage difference it's roughly a 1.4% increase in speed (assuming you can maintain 50 km/h). It's practically nothing in real terms.
In competitive cycling, though, the margins are super small. You might win the Tour de France by 2 minutes, which seems like a pretty big lead, until you realise that's 2 minutes in 80 or 90 hours of cycling. This is why Team Sky's approach of "marginal gains" is so successful -- the difference between first and second place is something like 0.04% performance.
We've got this idea that we need to optimise performance (in terms of SEO, etc, etc), but I've never seen anybody quantify the margin of "victory" required. How much better do you need to get to push you over the edge? Because that's what's going to dictate what strategy you need to pursue.
The start-up failed for many reasons and this was one.
"When you focus too much on increasing the conversion of a certain page you'll either not get any positive results or just push the problem to the rest of the funnel, none of which, will increase your revenue btw..."
And then they launch into their "How to redesign a SaaS website in 3 weeks" solution with:
"#1 - Focus on the money pages. Why would you focus on redesigning pages that barely no one visits or that is not related to a conversion goal?!
You need to focus on the pages that are part of the buyer's journey from landing on your website the first time to completing a goal..."
Am I reading this wrong or is the author contradicting himself?
At what point do you consider an experience "optimized" and say we just can't squeeze anything else out of this?
Rule #1: Perfection is attainable
Rule #2: The universe is not constant: Screen sizes change, browsers, screen types, technology, new competitors come up changing people's expectations, people's tastes change, you expand into new demographics/demos/sales channels which react differently than existing demographics/sales channels, etc.
Ideally this work is done with a marketing/programming/operations team all in one, so all aspects are considered, with their trade offs, to forward on a collective understanding of solving user pain points.
At a certain point your offering is it's offering, people found the maximum value out of it, and making it easier to use or testing different ways of delivering that functionality is just adding on lipstick.
At what point does it make sense to pursue other revenue models or strategies rather than squeezing out another 1-3%
I am bringing this up for a reason -- my company is going through this very thing
I think you should always be critical of what would be better use of your time: optimizing vs getting more (targeted) traffic ;)
I think if you think small tweaks are all you have left that you need to consider how can you make your website specific to each persona you have on or even dig through which type of people could benefit from your product even outside of what your currently targeting.
Optimising revenue is not desirable. Making enough revenue to achieve your goal is what is desirable. Once you've done that, you should concentrate on new goals. However, just like refactoring code, you may find that you need to go back and revisit something in the future. "Not now" does not necessarily mean "Not ever". It just means that I'm putting this aside until it makes business sense to do it.
To complicate things, though, just like programmers have to be the ones calling the shots about whether or not something needs to be refactored (they are the ones who have the required vision and experience to know), marketing people need to be the ones calling the shots about these kinds of things. If you don't trust your programmers to make appropriate decisions, then you have a huge problem. The same is true of marketing people.
Fixing that trust issue (either by placing your trust in the people, or replacing people who are not worthy of trust) is hyper difficult, though. It's one of the reasons organisations run around doing ineffective things instead of fixing this kind of root problem.
The corollary, I suppose, is that once you have some customer traction, looking at where those costs are is where you want to spend time with optimization. However, this begs some questions:
If I'm spending $500 for a $5000 customer - should I change anything at all to drive down that $500?
...which leads to: At what CAC:Cusotmer Value ratio should I start trying to drive down that cost relative to other costs in the biz?
Anyone have any real experiences from their biz' they can share on this subject?
To answer your question that totally depends because in the end what you'll need is cash flow to maintain that reinvesting process and that's something (finances/management) I'm not an expert in :P