Posted by willcritchlow

Much has been written and
about the interplay of SEO and CRO, and there are a lot
of reasons why, in theory, both ought to be working towards a
shared goal. Whether it’s simple pragmatism of the business benefit
of increasing total number of conversions, or higher-minded
pursuits such as the ideal of Google seeking to reward the best
user experiences, we have many things that should bring us

In practice, though, it’s rarely that simple or that unified.
How much effort do the practitioners of each put in to ensure that
they are working towards the true shared common goal of the
greatest number of conversions?

In asking around, I’ve found that many SEOs do worry about their
changes hurting conversion rates, but few actively mitigate that
risk. Interestingly, my conversations with CRO experts show that
they also often worry about SEOs’ work impacting negatively on
conversion rates.

Neither side weights as highly the risks that
conversion-oriented changes could hurt organic search performance,
but our experiences show that both are real risks.

So how should we mitigate these risks? How should we work

But first, some evidence

There are certainly some SEO-centric changes that have a very
low risk of having a negative impact on conversion rates for
visitors from other channels. If you think about changing meta
information, for example, much of that is invisible to users on the
page—- maybe that is pure SEO:

And then on the flip side, there are clearly CRO changes that
don’t have any impact on your organic search performance.
Anything you do on non-indexed pages, for example, can’t change
your rankings. Think about work done within a checkout process or
within a login area. Google simply isn’t seeing those

But everything else has a potential impact on both, and our
experience has been showing us that the theoretical risk is
absolutely real. We have definitely seen SEO changes that have
changed conversion rates, and have experience of major CRO-centered
changes that have had dramatic impacts on search performance (but
more on that later). The point is, there’s a ton of stuff in the
intersection of both SEO and CRO:

So throughout this post, I’ve talked about our experiences,
and work we have done that has shown various impacts in different
directions, from conversion rate-centric changes that change search
performance and vice versa. How are we seeing all this?

Well, testing has been a central part of conversion rate work
essentially since the field began, and we’ve been doing a lot of
work in recent years on SEO A/B testing as well. At our recent
London conference, we announced that we have been building out new
features in our testing platform to enable what we are calling

full funnel testing
which looks simultaneously at the impact of
a single change on conversion rates, and on search performance:

If you’re interested in the technical details of how we do the
testing, you can
read more about the setup of a full funnel test here
. (Thanks
to my colleagues Craig Bradford and
Tom Anthony
for concepts and diagrams that appear throughout this post).

But what I really want to talk about today is the mixed
objectives of CRO and SEO, and what happens if you fail to look
closely at the impact of both together. First: some pure CRO.

An example CRO scenario: The business impact of conversion rate

In the example that follows, we look at the impact on an example
business of a series of conversion rate tests conducted throughout
a year, and see the revenue uplift we might expect as a result of
rolling out winning tests, and turning off null and negative ones.
We compare the revenue we might achieve with the revenue we would
have expected without testing. The example is a little simplified
but it serves to prove our point.

We start on a high with a winning test in our first month:

After starting on a high, our example continues through a bad
strong — a null test (no confident result in either direction)
followed by three losers. We turn off each of these four so none of
them have an actual impact on future months’ revenue:

Let’s continue something similar out through the end of the
year. Over the course of this example year, we see 3 months with
winning tests, and of course we only roll out those ones that come
with uplifts:

By the end of this year, even though more tests have failed than
have succeeded, you have proved some serious value to this small
business, and have moved monthly revenue up significantly, taking
annual revenue for the year up to over £1.1m (from a £900k
starting point):

Is this the full picture, though?

What happens when we add in the impact on organic search
performance of these changes we are rolling out, though? Well,
let’s look at the same example financials with a couple more
lines showing the SEO impact. That first positive CRO test?
Negative for search performance:

If you weren’t testing the SEO impact, and only focused on the
conversion uplift, you’d have rolled this one out. Carrying on,
we see that the next (null) conversion rate test should have been
rolled out because it was a win for search performance:

Continuing on through the rest of the year, we see that the
actual picture (if we make decisions of whether or not to roll out
changes based on the CRO testing) looks like this when we add in
all the impacts:

you remember how we thought we had turned an expected £900k of
revenue into over £1.1m? Well, it turns out we’ve added less than
£18k in reality and the revenue chart looks like the red line:

Let’s make some more sensible decisions, considering the SEO

Back to the beginning of the year once more, but this time,
imagine that we actually tested both the conversion rate and search
performance impact and rolled out our tests when they were net
winners. This time we see that while a conversion-focused team
would have rolled out the first test:

We would not:

Conversely, we would have rolled out the second test because it
was a net positive even though the pure CRO view had it neutral /

When we zoom out on that approach to the full year, we see a
very different picture to either of the previous views. By rolling
out only the changes that are net positive considering their impact
on search and conversion rate, we avoid some significant drops in
performance, and get the chance to roll out a couple of additional
uplifts that would have been missed by conversion rate changes

The upshot being a +45% uplift for the year, ending the year
with monthly revenue up 73%, avoiding the false hope of the pure
conversion-centric view, and real business impact:

Now of course these are simplified examples, and in the real
world we would need to look at impacts per channel and might
consider rolling out tests that appeared not to be negative rather
than waiting for statistical significance as positive. I asked CRO
expert Stephen Pavlovich from for his view
on this and he said:

Most of the time, we want to see if making a change
will improve performance. If we change our product page layout,
will the order conversion rate increase? If we show more relevant
product recommendations, will the Average Order Value go up?

But it’s also possible that we will run an AB test not to improve
performance, but instead to minimize risk. Before we launch our
website redesign, will it lower the order conversion rate? Before
we put our prices up, what will the impact be on sales?

In either case, there may be a desire to deploy the new variation
— even if the AB test wasn’t significant.

If the business supports the website redesign, it can still be
launched even without a significant impact on orders — it may
have had significant financial and emotional investment from the
business, be a better fit for the brand, or get better traction
with partners (even if it doesn’t move the needle in on-site
conversion rate). Likewise, if the price increase didn’t have a
positive/negative effect on sales, it can still be

Most importantly, we wouldn’t just throw away a winning SEO
test that reduced conversion rate or a winning conversion rate test
that negatively impacted search performance. Both of these tests
would have come from underlying hypotheses, and by reaching
significance, would have taught us something. We would take that
knowledge and take it back as input into the next test in order to
try to capture the good part without the associated downside.

All of those details, though, don’t change the underlying
calculus that this is an important process, and one that I believe
we are going to need to do more and more.

The future for effective, accountable SEO

There are two big reasons that I believe that the kind of
approach I have outlined above is going to be increasingly
important for the future of effective, accountable SEO:

1. We’re going to need to do more testing generally

I talked in a recent Whiteboard Friday about the surprising
results we are seeing from testing
, and the increasing need to
test against the Google black box:

I don’t see this trend reversing any time soon. The more ML
there is in the algorithm, and the more non-linear it all becomes,
the less effective best practices will be, and the more common it
will be to see surprising effects. My colleague Dom Woodman talked about this
at our recent SearchLove London conference in his talk
A Year of SEO Split Testing Changed How I Thought SEO

2. User signals are going to grow in importance

The trend towards Google using more and more real and implied
user satisfaction and task completion metrics means that
conversion-centric tests and hypotheses are going to have an
increasing impact on search performance (if you haven’t yet read
fascinating CNBC article that goes behind the scenes on the search
quality process at Google
, I highly recommend it). Hopefully
there will be an additional opportunity in the fact that
theoretically the winning tests will sync up more and more —
what’s good for users will actually be what’s good for search
— but the methodology I’ve outlined above is the only way I can
come up with to tell for sure.

I love talking about all of this, so if you have any questions,
feel free to drop into the comments.

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