1.
Behind every great idea
is a great data
Dominic Hurst, digital services lead @LJMU
dominichurst.com | @dh_analytics

2.
So lets go back in time
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3.
Stakeholder wants
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4.
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IT build

5.
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Output?

6.
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“And then what?”

7.
@optimiseordie
#Fail

8.
Lets clear it up…
What is data?
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9.
Data isn’t…
• (just) quantitative
• opinions or assumptions
• best practice

10.
Data is…
• insightful and actionable
• a collaboration (what and why)
• your best practice

11.
Slowly, people are
appreciating data

12.
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Data appreciation scale

13.
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Minion

14.
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Kim Kardashian

15.
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Arsenal

16.
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Winning!

17.
Where are you?

18.
However, issues
prevent us moving
forward
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19.
Legacy
• mis-treated by “specialists”
• marketing (or agency) destroyed reputation to SMT
• aggregated, opinionated nonsense
• certainly not insightful or actionable
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20.
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Legacy - you know…

21.
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Legacy - you know…

22.
Language/ Format
• terminology (too much, overlapping)
• un-humanistic
• incorrect (hit data merged with session data,
internal traffic…)
• isolated (quant or qual)
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23.
Not empathic or
memorable
• numbers
• words
• forgetful
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24.
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So lets change it

25.
#Set goals early
• takes time
• focus
• lean
• tell people
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26.
#Set goals early
Business objectives - set by senior management
Crisp goals - set by the owners of the system
KPI’s - thats you (be consistent)
Sound targets - back to senior management to set
Segment - you again for true insights
You now have a model that is bought into by all
kaushik.net/avinash/digital-marketing-and-measurement-model/

27.
interaction-design.org/literature/article/a-simple-introduction-to-lean-ux
#Embed data into process

28.
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Creates and sorts backlog

29.
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Provides understanding

30.
hollidazed.co.uk/2015/07/16/everything-is-hypothesis-driven-design/
Validates the hypothesis
“Because we think [this] is true we think that doing [this] will mean
[that this will happen]. We’ll know that this is true/false when we
see this [key performance indicator change].”
Ben Holliday

31.
medium.com/@optimiseordie/hypothesis-kit-2-eff0446e09fc
Validates the hypothesis
“1. Because we saw (qual & quant data)
2. We expect that (change) for (population) will cause (impact(s))
3. We expect to see (data metric(s) change) within (x business
cycles)”
Craig Sullivan

32.
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Validates experiments

33.
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Aids the build

34.
#Awareness
• publicise (dashboards, personas, crib sheets)
• in boardrooms, stand-ups
• personalise, simple, visual, to the point,
actionable
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35.
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On the wall

36.
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In the board room

37.
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On the screen

38.
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Driving personas

39.
#Team game
• just like UX, its about the team
• show “new” colleagues the potential in your data
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40.
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#Culture

41.
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Takeaways

42.
This time, lets look to the future
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43.
Stakeholder wants
User needs
IT build outputs
Team build outcomes

44.
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1. Data (always)

45.
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2. Visualise

46.
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3. Metrics that matter

47.
Sanity not vanity
• Top pages by £ value not top pages by pageviews
• Completion rates not just completions
• No of tests with uplift not No of tests (per month)
• Affect on primary goal not just CTR on the page
econsultancy.com/blog/66434-vanity-versus-sanity-metrics-in-conversion-optimisation/

48.
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4. Keep going

49.
news.toyark.com
5. Data is your friend

50.
Thank you
Dominic Hurst, digital services lead @LJMU
dominichurst.com | @dh_analytics