4. the analytics alphabet…
…in numbers
75 man hours
14,654 words written
73 tips
63 pages
8 categories
3 levels
2 people
#passion
5.
6. the guide
fundamentals
If you’re using analytics, you should at least be aware of
all these features and tips
For regular users of analytics who want to get that little
bit more into the detail
Key insights for the direct response marketer – taking
conversion analysis that little bit further
intermediate
advanced
setup
ecommerce
content
metrics / KPIs
audience
insight
traffic sources
goals / conversions
18. filters
Should always have two analytics profiles (Views) setup for any website
1) Completely un-touched (no filters)
2) One with company IPs filtered out
Why bother?
Testing
100% data capture
Prevent internal visits from skewing
site performance and decision making
19. New visit(or)s
• If you have a website, like an
industry blog, which has a high % of
new visitors you have to review why
it’s so high, because ideally you
would want returning visitors to
dominate.
• If you have an Ecommerce
website with a low % of new
visitors but you’re not generating
enough sales revenue, you should
assess you traffic sources and
campaigns to see how you can drive
more new visitors.
28. experiments
Test different versions of landing pages (up to 5) to
improve performance in a controlled environment.
In an experiment you can control:
• the % of visitors who visit your test page(s) vs.
control page
• objective of the test – goal / ecommerce /
site usage
• test duration
• traffic sources entered into the experiment
(controlled by the URL).
29. trends
Comparisons give you context:
• look at the same data point over two time periods
• look at 2 data points in parallel
…to understand if the trends you are seeing are replicated across all metrics on your
site.
Data segmentation can provide reasons:
• identify which traffic source / campaign / keyword / device caused a shift
33. conversion (goal) paths
Useful views for interpretation include:
• Looking at how often different traffic source
patterns emerge.
• Find out:
• How many visits come from the same source
• Which traffic sources drive more first visits or
last
• Discovering what keywords tend to overlap with
another traffic source or with other keywords
(either before or after each other).
37. model comparison tool
Last interaction – any last action visit to the site (any source can be credited)
– the default setting.
Last non-direct click - any last action visit to the site, except if the last action
was a direct visit. This is useful if you believe direct visits wouldn’t have
happened without other digital channel support.
Last Adwords click – when the last action was an Adwords click (useful to
compare against last action to understand the % of last clicks which were via
Adwords)
First Interaction – any first action visit to the site (any source can be
credited). This is useful if you’re running awareness driving campaigns and want
to attribute credit for this effort.
Linear – all actions in the user journey are given equal credit for the
conversion. This is useful if you believe each touch point in a user journey is
equally important.
Get a show of hands to find out who uses analytics regularly, and who is confident in it
Run through the intention and purpose of the analytics alphabet and refer to the handout
This is bad – retail should have 20%-40% bounce ideallyDirect visits are good bounce rateEmail bounce rate @ 80% would suggest email not targeted properly and affecting the whole site due to the sheer volume of visits
60-80% bounce rate is ‘OK’ – it’s a blog, single piece of content – not needing to go elsewhere, although we would like them to…
95% bounce rate seems really bad… but we don’t promote other pages on the site to keep them perusing content…Leads nicely on to duration… how engaged is someone with content
Practical examples – get the audience to participate and discuss
Selling advertising – this would be POOR (need to sell impressions to make money)
Answering questions – don’t need to venture further and they’ve given me the information I need in one “hit”
Early introduction to attribution modelling
Early introduction to attribution modelling
Nikki
Nikki
Nikki
Nikki
Nikki
Nikki
Nikki
Nikki
Nikki
Nikki
Could people share the common questions they ask about their results?
Nikki
Included assisted conversions/ multi-channel funnels /
Included assisted conversions/ multi-channel funnels /
Included assisted conversions/ multi-channel funnels /
Included assisted conversions/ multi-channel funnels /
Included assisted conversions/ multi-channel funnels /
Included assisted conversions/ multi-channel funnels /
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?
Exercise – give a scenario and a tool from which to judge performance – who would win?