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The A-Z of useful analytics
Kherrin Wade
Digital Media Account Director,
Adido
@digiKherrin
#letsdodigital
introduction to analytics
analytics alphabet concept
fundamentals
intermediate
advanced

agenda

questions
the analytics alphabet
the analytics alphabet…
…in numbers
75 man hours
14,654 words written
73 tips
63 pages
8 categories
3 levels
2 people

#passion
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
fundamentals
bounce rate
“The percentage of visitors who only visit one page during a session”
good or bad bounce rate?

All visits

60% bounce rate

Direct visits

25% bounce rate

Email visits

80% bounce rate
good or bad bounce rate?

All visits

68% bounce rate

Organic visits

68% bounce rate
Twitter visits

86% bounce rate
good or bad bounce rate?

All visits

95% bounce rate
Time on page
10:13
duration (avg. visit)

What is the purpose of
your website?
Does performance vary
by traffic source or
visitor type?
positive or negative
interpretation?

Time on site

5 minutes (High)

Avg. page views

7 (High)
positive or negative
interpretation?

Time on site

3 minutes (High)

Avg. page views

2 (Low)
positive or negative
interpretation?

Time on site

30 seconds (Low)
Avg. page views

1 (Low)
direct

Direct will only be credited with the goal
conversion (in GA standard reports) if it is
the only channel in a user’s journey to
conversion.
direct
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
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.
intermediate
annotations
devices
devices
devices
devices
devices
devices
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).
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
y?
advanced
attribution
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).
conversion (goal) paths
conversion (goal) paths
conversion (goal) paths
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.
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?
who gets the credit?

1. Last interaction?
2. First interaction?

3. Last Adwords click?
4. Linear?

And, would you turn off
any channel?
questions?
Thank you
Kherrin Wade
Digital Media Account Director,
Adido
@digiKherrin
kherrin.wade@adi.do

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LDD Southern Summit 2013 - Adido -A to Z of analytics

  • 1. The A-Z of useful analytics Kherrin Wade Digital Media Account Director, Adido @digiKherrin #letsdodigital
  • 2. introduction to analytics analytics alphabet concept fundamentals intermediate advanced agenda questions
  • 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
  • 8. bounce rate “The percentage of visitors who only visit one page during a session”
  • 9. good or bad bounce rate? All visits 60% bounce rate Direct visits 25% bounce rate Email visits 80% bounce rate
  • 10. good or bad bounce rate? All visits 68% bounce rate Organic visits 68% bounce rate Twitter visits 86% bounce rate
  • 11. good or bad bounce rate? All visits 95% bounce rate Time on page 10:13
  • 12. duration (avg. visit) What is the purpose of your website? Does performance vary by traffic source or visitor type?
  • 13. positive or negative interpretation? Time on site 5 minutes (High) Avg. page views 7 (High)
  • 14. positive or negative interpretation? Time on site 3 minutes (High) Avg. page views 2 (Low)
  • 15. positive or negative interpretation? Time on site 30 seconds (Low) Avg. page views 1 (Low)
  • 16. direct Direct will only be credited with the goal conversion (in GA standard reports) if it is the only channel in a user’s journey to conversion.
  • 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
  • 30. y?
  • 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.
  • 38. who gets the credit?
  • 39. who gets the credit?
  • 40. who gets the credit?
  • 41. who gets the credit?
  • 42. who gets the credit?
  • 43. who gets the credit?
  • 44. who gets the credit?
  • 45. who gets the credit?
  • 46. who gets the credit?
  • 47. who gets the credit?
  • 48. who gets the credit?
  • 49. who gets the credit? 1. Last interaction? 2. First interaction? 3. Last Adwords click? 4. Linear? And, would you turn off any channel?
  • 50.
  • 52. Thank you Kherrin Wade Digital Media Account Director, Adido @digiKherrin kherrin.wade@adi.do

Notas del editor

  1. Get a show of hands to find out who uses analytics regularly, and who is confident in it
  2. Run through the intention and purpose of the analytics alphabet and refer to the handout
  3. 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
  4. 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…
  5. 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
  6. Practical examples – get the audience to participate and discuss
  7. Selling advertising – this would be POOR (need to sell impressions to make money)
  8. Answering questions – don’t need to venture further and they’ve given me the information I need in one “hit”
  9. Early introduction to attribution modelling
  10. Early introduction to attribution modelling
  11. Nikki
  12. Nikki
  13. Nikki
  14. Nikki
  15. Nikki
  16. Nikki
  17. Nikki
  18. Nikki
  19. Nikki
  20. Nikki
  21. Could people share the common questions they ask about their results?
  22. Nikki
  23. Included assisted conversions/ multi-channel funnels /
  24. Included assisted conversions/ multi-channel funnels /
  25. Included assisted conversions/ multi-channel funnels /
  26. Included assisted conversions/ multi-channel funnels /
  27. Included assisted conversions/ multi-channel funnels /
  28. Included assisted conversions/ multi-channel funnels /
  29. Exercise – give a scenario and a tool from which to judge performance – who would win?
  30. Exercise – give a scenario and a tool from which to judge performance – who would win?
  31. Exercise – give a scenario and a tool from which to judge performance – who would win?
  32. Exercise – give a scenario and a tool from which to judge performance – who would win?
  33. Exercise – give a scenario and a tool from which to judge performance – who would win?
  34. Exercise – give a scenario and a tool from which to judge performance – who would win?
  35. Exercise – give a scenario and a tool from which to judge performance – who would win?
  36. Exercise – give a scenario and a tool from which to judge performance – who would win?
  37. Exercise – give a scenario and a tool from which to judge performance – who would win?
  38. Exercise – give a scenario and a tool from which to judge performance – who would win?
  39. Exercise – give a scenario and a tool from which to judge performance – who would win?
  40. Exercise – give a scenario and a tool from which to judge performance – who would win?
  41. Exercise – give a scenario and a tool from which to judge performance – who would win?