How to build an Analytics team.

2. Oh shit,Analytics.
Huge
October 13, 2015

3. About Huge.

4. What we do:
We help transform brands and grow
business.

5. Our Culture:
Make something you love.
Hire the best people.
Work on incredible projects.
Have fun.

6. 1,200 employees worldwide.
Our offices.

7. Research & analytics.
Focus groups & surveys
Ethnography & usability studies
Social listening & trends spotting
Website, social & campaign analytics
Data platforms.
Strategy & planning.
Brand planning & marketing
Product strategy & business consulting
Communications planning
Search strategy (SEO, SEM)
Discipline specialists
(e.g., CRM, mobile)
User Experience.
Content strategy
Information architecture
Interaction design
Mobile experience
Creative.
Visual Design
Copywriting & editorial
Campaign development
Mobile, emerging & in-store digital
Technology.
Ecommerce
Technical leadership/architecture
User interface development
Mobile & emerging platforms
Social platforms
Rapid & enterprise development
Quality assurance & deployment
Social engagement.
Content strategy
Information architecture
Interaction design
Mobile experience
Studio.
Photography
Motion
Video creation & production
Animation & illustration
Production
Media.
Media planning
Media buying
Campaign management,
optimization & analysis
Media partnership development
Program Management.
Project management
Planning & roadmap development
Resource management
Risk mitigation

9. Enough shameless
self promotion.

10. What is analytics?

11. What is analytics? Data.

12. Data is the right experiencefor the right people
time
place

13. Data is the right experiencefor the right people
time
place
ROI

14. A small piece.

15. But let’s step back.

16. 1. Business Goals.
Why does this experience exist?
2. Objectives.
Specific strategies utilized to accomplish business
objectives.
4.Metrics.
Numbers.
3. KPIs.
How are we doing against objectives?
Good relationships start with good planning.

17. Measure
Analyze
Socialize
Test
Refine
InsightImpact

18. Collect.
Integrate online and offline data
collected throughout the user
journey.
Store.
Implement best of breed data
stores and data management
platforms.
Model.
Execute goal and need driven
segmentation and predictive
algorithms.
Target &
Personalize.
Optimize the ecosystem to
drive positive KPIs.
Visualize.
Creating interface and
user experiences through
data.
Communicate.
Build a CRM based
communication loop to
improve customer value.
Analyze.
Drive business insight
based on user behavior.
Syndicate.
Expose API to increase
secondary monetization
opportunities.

19. REACH.
LAND.
LEAD.
CONVERT.
SCORED RETARGETING
INCREASE TRAFFIC
Attribution
Marketing analytics
SEO Optimization
INCREASE ENGAGEMENT
Testing
Web analytics
Content & offer optimization
INCREASE SALES
Lead scoring
CRM analytics /data mining
Email testing and optimization
Buy-flow optimization
LOOKALIKE TARGETING
ENGAGE.
PERSONALIZATION
DATA
ANALYTICS
CALL
CENTER.
CRM
OUTBOUND EMAIL
PERSONALIZATION
3rd PARTY
DATA

20. Help me know.

21. That’s all well and good Jon…but now I have
these guys sitting in my office.

22. You and your analyst..
(Feed and caring directionsfor your new pet).

24. You don’t know what you’re
talking about.
We don’t know what we’re
talking about.

25. You?

26. Us.

27. Us.

28. You kind of know what we’re
talking about.
We kind of know what you’re
talking about.

29. Find common ground through beauty.
We’re all here for the same reason. It is very easy for data folks to prejudge creatives’ decisions as opinion-based and without
any empirical basis. Similarly, creative teams look — often rightfully — at analysts as mathematicians who want to optimize the
soul out of their ideas. The truth is that analysts that end up at creative agencies are there because they like creativity. Analysts
need to remember that they’re there to help create make beautiful experiences, not just a perfectly A/B tested wireframe. And
creatives need to remember that optimization need not mean losing their vision any more than a tailor destroys the vision of a
beautiful suit: The goal is to create a perfect fit for the end user.

30. Creative instincts are analytical
hypotheses in the making.
OK, basic rule: No patronizing each other. Creative teams, instead of asking for a specific analysis, ask a question you need
answered and make it clear why it’s so important. Asking for specific analysis constrains in the analyst’s own creative instincts
(yes, these do exist). Analysts need to keep in mind that there are no stupid questions in a creative process. You might think that
the answer to something is self-apparent: It’s not, or your colleague wouldn’t be asking about it. There are connections between
things like design and UX it’s hard for analysts to see because they tend to removed from the creative process. And while it’s an
analyst’s job to be objective, it can also make it easy to get all judgmental about the simplicity (or lack there of) of a specific need.

31. Don’t get pissed off.
No one wants to be told that their baby is ugly. That is equally true for both creatives and analysts. It is very easy to be defensive
if someone criticizes your work, particularly if that criticism is coming from someone with a fundamentally different worldview. In
the early part of projects, we build hypotheses built on instinct and experience. The job of an analytics team is to test those ideas
through a formalized process (we do love a good process formalization). Sometimes those hypotheses need modification in
order to adhere to user needs that might be unanticipated. It’s important to give yourself the allowance to incorporate challenges
to your core assumptions. Analysts need to keep in mind that data does always tell the full story. Not even close. Your ideas and
analysis are no more precious than those of your creative peers. Accept that the role of creative teams is to take your analysis
into account alongside multiple other inputs. My rule of thumb is that typically one-half to one-third of my most brilliant analysis
will ever be used in a specific project. Yeah, it sucks to spend hours in your data-crunching package of choice and then find out
that much of it gets left on the cutting-room floor. You know what? That happens to creatives, too.

33. Three
people.
28 people.
3 years ago. Now.

34. What happened?
More clients.
New types of work.
Deeper relationships.
More offerings.


35. Relationships not projects.

36. DataScienceofferings.
Digital& media analytics:
Planning,implementing,using,reporting.
Personalization&Targeting:
Planning, platforming,algorithms,execution,testing.
Enterprisedata systems:
Strategy,platforming, analysis,visualization, modeling.

37. Some lessons from mistakes.

38. Unless you are super cheap analytics
cannot be sold as a stand alone service.

39. Shoot big to get little.

40. Your clients need to know that no one owns
data.

41. Alot more money is made on strategy than
analysis.

42. Someone will call you on your bullshit. But not
that often.

43. But seriously, it’s a
big deal.

45. On to the discussion....