#FridayFeeling: The End of A/B Testing

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#FridayFeeling_ The End of AB Testing

A/B testing is a relic of a time when we couldn’t personalize at scale. Find out why A/B testing is going away, what we’re all doing wrong in our thinking about testing, and what will replace it in this video.

#FridayFeeling: The End of A/B Testing

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In today’s Friday feeling I have a feeling we are heading towards the end of a b testing

will explain what I mean

a B testing is

a an idea that

there’s a right answer

that given two choices between a red button, a blue button or a subject line with emoji, or without emoji, that there’s a right answer. Like, this is the answer.

The reality is most A B tests are not. When you look at the data, most A B tests are not like an overwhelming land sites like 99% of this and 1% of this, there’s usually like 6040, maybe 7030 fair amount of time 5050,

when you use something like Google optimized Google optimized will say, Hey, there was no clear answer on this test that happens more than you think

what that means is that

there’s more than one right answer. and that in turn means that a B testing as marketers traditionally approach it, which is, we’ve got to find the right answer. We got to find the

answer. Well know there may be many answers

up until now, up until the last couple of years, we went with a B testing because we wanted to try and optimize for

the answer that was easiest for us as marketers to to do so. Okay, red button versus blue button. Well, 60% of people like the red button. So we’re going to put the red button up

and we’ll hopefully that will get us more conversions.

Okay, what about the people who like the blue button as 40% of them? Do you just flip the burden to 40% of your audience say,

I’m sorry, guys, your choices don’t matter. We’re going with the majority.

Here’s why. I think we’re heading towards the end of a b testing

systems and software

combined with machine learning and automation

give us the ability to create personal profiles and personalization at scale in a one to one way. So if you click on the blue button, we run a test, you click on the blue button, but you don’t click on the red button. We save that information and say, okay, you like the blue button. So let’s make future buttons for you. Blue.

Somebody else who clicks on the red button, we save their profile data,

guess what? Now that person sees

red,

you open subject lines with emojis. Somebody doesn’t open subject lines without emoji.

We save that information. We’ve helped build that profile so that

ultimately you get what you want.

There is no wrong answer. It’s the answers that you like best.

And we can ask for some of those answers. Certainly,

do you prefer red buttons? Or blue buttons? Do you prefer mode? You’re not emoji? Do you like social shares with cute puppy pictures, whatever. The thing is, whatever you you want,

our software should give to you.

And we do this over and over and over again. Until you build a solid profile of what works for you what ads you click on what links you click on,

that’s taking a B testing to its logical conclusion, which is give you what you want

now requires marketers

to think

and say, Well, you know, what, we need to have machinery and, and, and algorithms and support that say, yeah, whatever this person wants, they get within reason. I mean, there’s, we’re not going to, if you sign up for, for example, my personal newsletter, you’re not going to get a newsletter, it’s customized to be entirely Pokemon links, right, even if you love Pokemon, because that’s, that’s not the point of the newsletter. So there are guard rails, but

we should start being able to offer that level of personalization. Our software has to do it. We can’t do it as, as marketers, there’s no way we can do one to one personalization, unless you literally have like two customers, if you only have two customers. And yet, you can do one one personalization very easily. But if you have a mailing list one, five people on it,

you need machinery to do that. But once you do,

then it gets interesting, then it gets then we start being able to engineer systems that can

that can serve the needs of the person and get away from this idea of there is a right answer.

Because every person who

makes a choice is giving us the answers that work best for them.

Now, what else this gives us is a lot more insight, a lot more behavioral insight.

One of the things that a lot of marketers are struggling with right now is like, what do we do in the wake of California consumer privacy law GDPR and this and that, and, you know, we can’t collect this data anymore. And, well, no, you don’t need that data.

And a lot of cases, not for machine learning. One of the greatest quotes I heard from a recent IBM event was that the more specific a piece of data, the less useful it is for modeling,

knowing that my name is Christopher Penn, you can’t build a model on that, knowing that I’m male, knowing that I’m

in my 40s, knowing that I live in the northeast, those are things that are more useful data points because they’re bigger data sets and then can build better models with them.

But even that did is not as interesting is what I do behaviorally. If you sign up for say, a an email newsletter, and you say, I want to see more topics, more content about AI, and I want to see less content about politics.

newsletter software will eventually know what to send, you will send you a version that contains less of one more of the other but now with retargeting audiences and things I can send out and say, Yep, I

I want to make sure that this person

gets has their preferences in the system, or the system collects preferences for them. Behavioral preferences based on what they click on, on what they express.

And now we’ve got a new I know new ebook on politics,

don’t send it to this person, they’re not going to enjoy it,

send it to this person who like people, that’s all they want. And so it’ll help us make more sales and better sales by directing content and goods and things to where people need them. And it’s also free market research. If you set up

trainable machine learning based personalization systems, you get to look at your audience say, Okay, what are they interested in and something See, wow, everybody over here is really, really interested in I don’t know, the internet of things for dairy farms.

And you’re like, well, that’s a substantially

larger portion of our audience than I thought it was going to be. Why is this a thing with our audience now, you know,

now, you know, and I have that market insight, instead of saying, AB test, throw away those people who like dairy farms IoT, because that’s not what we do. Now, you know, now you have that market research. And you can go,

all right, maybe we need to offer

some kind of product or service or something there for those people because they will, they will want it they’re interested in it and the paying attention to it.

So a lot to think about a lot of stuff to think about

personalization one to one marketing with machine learning, doing the heavy lifting for you is the logical conclusion on the end of a b testing. So what we have to do is think differently about is a B testing something we should be doing or it should we be focusing on one to one segmentation I think that’s the future I feel like that’s the future and and a good way to pivot our thinking from the right answer to the answer suits each customer individually. As always, please subscribe to the YouTube channel in the newsletter and I’ll talk to you soon. Take care

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