Hello there, I'm George Hall and welcome back to another one of our Analytics Anonymous sessions,
where we take a small, bite-sized look at data, insights, analytics and more.
I'm joined today by Zoe Hitchens, an Analytical Manager here at Good Growth. Zoe, how are you?
I'm all good, George. Thank you for having me.
Good, good. Now, Zoe, I know you've been incredibly busy lately with all sorts of projects going on,
so I'm excited to hear what you've got for us today.
So we're going to be diving into sort of the key to good data.
So having a little look at sort of the key principles that we sort of like to look at,
and just understanding the key to good data.
Perfect. And we say good data there. What does good data mean in the context of
digital growth and then in the wider e-commerce industry?
So it has probably a few different layers to it, but ultimately good data needs to be sort of
fit for purpose. So whether that be for the individual, whether it be for the business,
it should probably be quite like clean, structured, easy to use. But ultimately,
businesses need to be able to sort of analyse that data so they can understand their performance
against their sort of set business objectives and requirements. They need to have that confidence
around their data, whether that be their architecture of their data and the strategy.
Otherwise, how are they sort of expected to make those informed business decisions?
And then that data point bleeds into insight, which I know is one of the main things that you
work on. And without the insight, you can't really optimise your testing and your delivery.
Yeah, absolutely. So sort of that quality of that data means that businesses might make sort of
ill-informed decisions if they don't have that trust behind it. So using poor quality data or
incorrect data even, yeah, might mean that sort of businesses or teams within a business make some
sort of questionable assumptions about their performance or incorrect assumptions about
their reporting. And that could form the basis of some quite big business decisions. It might be
that they're seeing a channel converting really well, so they're putting a lot of money behind it
and actually, in essence, is not performing any better than any of the other channels. So
it can really affect the bottom line, sort of rolled up. So yeah, it's really important that
they have a good base for their reporting. Also in terms of sort of organisational efficiency as
well. So if you're sort of the raw format in which you're getting this data is quite messy,
it needs a lot of processing and cleaning, then this is not wasted effort, but effort that could
be better spent elsewhere if there was a sort of more formalised process already in place,
more automated process for collecting that data. So it's in that ready-to-use format.
Because if the data is hard to analyse, you should probably look to make some changes there.
And speaking about good data, what are the key characteristics of what good data actually is?
I mean, are there principles that you use or the wider Good Growth team use to differentiate the
data between good and bad? So within Good Growth, we have sort of five principles, we like to call
them, for what we deem as sort of good data. These are accurate, trended, explainable,
reproducible and unbiased. If we were to try and make a fun acronym out of that,
we struggled. A true sounding like a sneeze was the best we could come up with for that one,
but I'm not going to coin that one. I don't think we use that one internally.
But yeah, if we just quickly go through that list. So accuracy of data, we briefly touched on the
collection of the data there, really ensuring that when you're going through and setting up
your sort of traffic analysis, your event structure, even things like campaign strings and stuff like
that, you want to be quite rigorous and meticulous and just make sure these things are correct.
You want an easy life, you want things to be pulling through correctly, you don't want to
have to worry about negating the double counting of events in certain areas of the page or
making sure that actually some traffic that arrived from here actually is coming from here.
And all the sort of caveats that come in between, you ultimately want an easy life and to be able
to share that data with other people in the business as well without all of those caveats.
And then when stitching together data sets, I think that's quite a common pitfall where people
start to come a little bit unstuck in their data quality. So you want to sort of use as many sort
of automated processes and scheduling as possible and essentially just reduce that human input.
It avoids the risk of sort of human error. Everyone's human. We might have a typo every
now and then. So yeah, avoid those sort of manual imports. If we go into sort of trending data,
I think was the next one down the list. This can be obviously looking at trends within a
short period of time, looking at comparisons sort of year on year. You ultimately want to understand
if there's any sort of seasonality affecting your data, any sort of macroeconomic factors,
but also just within a sort of maybe an A-B testing window, if you're seeing a significant
impact for one of your say control or challenges, you want to know that that's been a consistent
impact throughout the duration of your test. If it's just one day that's sort of skewing the data,
you know that there's probably not enough rigor behind it to say, okay, we've got the confidence
to say that that actually has a true impact. And then that feeds into the sort of explainable
other data point there. So if you're seeing that data isn't particularly trended, you're seeing
some quite odd behavior, whether that be in sort of traffic conversion rate, you ultimately want
to know what's driving this. And this is where sort of data and insight come together. You see,
yeah, spike in sales on day X is due to, okay, that was because our marketing department sent
an email out that day with a 50% discount code, you want to be able to sort of marry up all the
different other activity happening with the business. So you know what has driven those impacts.
Reproducibility is just again, validating the results and findings that you've seen,
whether that be you being able to pull that same data again, or other people in the business being
able to get to the same answer. It just makes it a lot cleaner and scalable as well within the business.
And then the final one is around data sort of being unbiased. That's kind of interesting one.
If you're looking at sort of is data not just coming from your internal collect, internally
collected data? Is it coming from an external source? Is your data representative of your entire
user base? Are you sure that you're getting your right demographic split, for example, which is
sort of influencing a lot of decisions based on UX and other product decisions. So having biased data
can essentially generate that sort of false insight, which again, in turn leads to those
bad decisions that can affect that bottom line. And then that unbiased aspect as well as from a
data perspective, as well as the sort of approach that you take to data as well, I think is quite
important. We've seen sort of time and time again with whether the data is coming from the
whether it be sort of our testing clients or clients that we've done some insight work with.
They have these big ideas for change. They want to sort of build it up, wrap it up into an experience
or an A-B test. It's had a lot of eyes on it, whether it be from UX, product, experimentation,
everyone getting involved. So if everyone's sort of had the input to it, it must do well, right?
But actually, by the time it goes live, it's had a negative performance. But if it's sort of that
borderline, no real impact shown, it's not doing any better, it's not necessarily doing anything
worse, there might be that tendency to kind of lean towards, well, if it's not having a negative
impact, it's having a positive impact. So just making sure that you don't have that biased
outlook in terms of viewing the data sets and actually validating any impacts.
Mason Higgins And then out of those five, I mean, they're all obviously extremely important,
but is there one that sticks out to you in terms of, I guess, the golden principle? Or is it
a case of they're all so important because they can't exist without each other?
Kate Inglis I mean, yeah, ultimately, they're all very important. They sort of feed into each other.
So yeah, you wouldn't think of having sort of one without the others. But I'd say the accuracy
aspect is probably quite fundamental. And the reason that I sort of came to that one first,
if you know that there's errors in your data, like how can you be sure that you're able to
use that to make any sort of decisions going forwards? I think I've read something online
that's like good data beats opinion. You can sort of imagine lots of people in a boardroom voicing
their opinions and stuff like that. All it takes is for someone to say, this is what the numbers say.
If you have that confidence and the sort of backing behind what you're seeing, it sort of negates any
need for sort of personal preference or business decision. Mason Higgins And so one of the things,
obviously, we focus on massively at Good Growth is customer failure. How does understanding
how does a business's understanding of their customer failure then lead to, I guess, a better
usage of data and a better application of it when you're looking to drive growth?
Sarah Higgins Yeah, so I say understanding customer
failure, it's very much the heart of what we do at Good Growth. And it's pretty crucial to sort of
understand customer behavior, so businesses can respond to that take action, and ultimately
respond to the customer's needs. So again, this notion of failure can be sort of understood using
qualitative and quantitative methods of data capture. So you might be looking at
some journey mapping, you can see what's causing a dropout at X point in the customer journey,
you can see that from traffic analysis, or you might actually have some more explicit data
on the customer voice through some customer testing or surveying. Once you have this sort
of understanding and acceptance that there are points of failure, no one's perfect, you can
really shape not just the actions that you take to mitigate these failure points. So whether that be
revising some of your user journeys, making some UX changes, really targeting some A-B testing
activity around that one area, you can also then shape those KPIs and success metrics that allow
you to measure that impact. So if you know that you've got an area of the site that's a bit of a
bottleneck in terms of conversion, you've got that kind of success metrics for you that allow you to
be able to understand if you're having any impact and being able to shift the needle at all.
Perfect. And I guess closing things off, and maybe putting you on the spot a little bit here,
but is there a key message that you'd give to a data team and insight team that were looking to
get their data back on track or even in a much better place? What would a key takeaway be for
them from this session? I'd say probably start simple. I think businesses get quite caught up
with how granular they can take the data, which in its own right is very important. But equally,
being able to report at a granular level is only sort of well and good if you're able to action at
that level. So you could be looking at sort of your data, whether it be sort of your traffic
split by channels, but by demographic. Okay. We know that 70% of people come to our website
through mobile first, for example, but you're still designing your website desktop first. So
it's little things that sort of, if you have the data available, make sure that you're making some
meaningful changes based on that. Again, sort of channel specific targeting and demographic
specific, maybe like email campaigns and stuff like that. Use the data wisely, but don't get
too caught up in making sure that you're kind of reporting at that granular level, unless you're
able to have the sort of capacity to be able to action. Well, Zoe, an absolute pleasure as
always to have you on the podcast. Always great to get your insights from the front line. And
thank you very much to all of those of you listening. I hope to see you all again very soon.
Thanks Zoe. Thank you very much. Take care.