Don't be like Frank: musedata lessons from House of Cards

Angie Judge, CEO Dexibit DIGITAL ACADEMY @ AUCKLAND MUSEUM MUSEUMS AUSTRALSIA 2016 #MA16NZ #MUSETECH #AKLTECHWEEK

BIG DATA AND ANALYTICS A new age of data driven insight

What can we learn about musedata from Frank Underwood?

GOOD: Analytics is a 10x return strategy for competitive advantage, when we’re competing for visitor attention

BAD: Don’t lock your data scientist in the basement and ask him to win you the presidency. That’s not how life works.

Let’s start with one question. What’s yours?

DON’T BOIL THE OCEAN

Analytics is a foundation, not just a cherry on top: 1.  Beginning digital transformation?

Establish a benchmark, start a data history

2.  During change What do the numbers say? Analyse cause and effect.

3.  Evaluating project success What was the return on investment? What health checks do we need? How do we continue to improve? What next?

The beginning, middle and end ANALYTICS AND YOUR DIGITAL JOURNEY

Leading by the numbers DON’T GUESS, KNOW.

A quick leader’s checklist: n  Data is a strategic imperative n  Move hippos to data driven decisions n  Understand where data comes from and its integrity n  Always base in context – you know your museum n  Encourage data as a communication tool n  Make life easier with less reporting, not more n  Ask questions of analytical enquiry

The process of data YOUR PLAN FOR DATA LEADERSHIP

Ask a question

Talk to the

people

Highlight key data

driven decisions

Establish metrics

and goals

Trace your data sources

Know your

data’s journey

Leverage an

analytics solution

Examples: 1.  Why is visitation trending up/down?

What are the most common influences?

2.  What impacts visitor retention and how can we increase repeat visitation?

3.  What parts of the museum experience drives visitor engagement and activation?

Starting with a question IF YOU DO ONE THING, DO THIS…

Data personas WHO IS DRIVING DATA?

Board members

Director or executive

Marketing and membership

Visitor services

Front line staff

Education and learning

Exhibitions and experience

Curatorial and collections

Finance and operations

Digital and Technology

CHAM

PIO

NS

OTH

ER U

SERS

AMBASSADORS

Data driven decisions WHAT DECISIONS ARE WE MAKING?

Visitor

How are our campaigns working?

How can we increase visitor retention

How can we optimise onsite versus online

balance?

Collection

What should we display next? Where?

What content should we invest in?

What should we acquire next?

Venue

How can we drive up sales?

What should we push, up or cross sell?

How should we manage external influences?

Key metrics THE TOP 30: VISITATION, COLLECTION AND VENUE

Total members

Social followers

Email subscribers

32,497 40,321 64,132

Visitor satisfaction

Average visit (min)

Educational visits

4.9 137 116

Onsite visitation

Digital visitation

Repeat visitation

26,011 27,339 1.3

Content readership (%)

Comment interactions

Positive sentiment (%)

23 122 64

On display (%)

Available online (%)

Total access (%)

13 34 38

Collection objects

Content articles

Content cover (%)

394,617 201,922 32

Average temp (°C)

Average humidity (%)

Average exposure (hr)

18 NO DATA NO DATA

Retail sales ($)

Ecommerce sales ($)

Donations received ($)

310,499 93,144 4,312

Sold tickets

Scheduled events

WiFi connections

19,388 14 322

53,450 TOTAL VISITATION

596,539 TOTAL ITEMS

25,490 TOTAL TURNOVER ($)

VISITORS COLLECTION VENUE

Where does data come from? INTEGRATION, SYNC OR ENTRY

What should we do with it? WHAT HAPPENS WITH YOUR DATA

INTEGRATE

INGEST

STORE

MODEL

VISUALISE

< This bit is important:

Know what assumptions, variables and

treatments are being applied to

your data

Have you got:

§  Integration requirements, (especially in real time)?

§  A need to treat data, or blend multiple data sources?

§  Wide user access and mobility?

§  Lower user confidence?

§  Big data storage needs?

When does a spreadsheet not cut it? ANALYTICS KICKS IN

X

WAIT! What is big data?

That’s not big data, this is big data UMMM… WHAT IS BIG DATA?

Big data has: 1.  Volume And not just created by people 2.  Variety Multiple sources, structured and

unstructured 3.  Velocity A massive and continuous flow in real time 4.  Volatility Data has a use by date 5.  Veracity The risk of noise and dirty data 6.  Value Drives business insight 7.  Validity The right data for the right decision

Dashboards as a quick win HOW ARE YOU COMMUNICATING DATA?

Opportunities for numbers:

n  Formal reports

n  Board room and department meetings

n  Administration areas

n  Atrium

n  On staff devices

Recommendations POWERED BY BIG DATA: FROM NETFLIX TO THE MUSEUM?

Understanding identity means we can provide: 1.  Offers

Events, exhibitions, souvenirs, hospitality…

2.  Recommendations Others who enjoyed X liked Y, something you haven’t seen before…

3.  Loyalty Repeat visitor rewards, VIP treatment, points, utility marketing…

What else?

WAIT! So what?

Telling a story with data HOW ARE YOU COMMUNICATING DATA?

n  Keep it raw: be objective and uncensored n  Don’t jump in: use a narrative

n  What’s going on? Set the scene. n  What does it all mean? Discuss cause and effect. n  What have we done already? What next?

n  Tailor to your audience n  Executive level or grass roots? n  What is the audience time and patience? n  What medium do our audience understand best?

Lean sigma CONTINUOUS IMPROVEMENT USING METRICS

Plan

Do Study

Act 1.  Define

What is the business problem? 2.  Measure

What are the metrics saying? 3.  Analyse

What is the cause and effect? 4.  Improve

How do solutions compare? 5.  Control

How do we continuously improve?

Continuous improvement Data & Insights presentation at https://goo.gl/eN5nKr

Step by step:

n  Goals

n  Strategies

n  Tactics

n  Measure

n  Benchmark

What is an A-B test? BONUS POINTS

Making change? Test the comparison to know if it’s a good one.

OK. Where do I start?

Visitor behaviour Using visitor presence

With visitor presence, discover:

n  Visitation footfall

n  Zone activation

n  Dwell time engagement

n  Trail route experience

n  Repeat visit retention

(with your WiFi network, or a gallery accessory device)

Cultural enrichment Understanding context

Collection:

n  Accessioned

n  On display

n  Available online

n  Content activation

n  Lifetime engagement

Commercials:

n  Ticketing

n  Upsell, cross sell

n  Souvenir

n  Hospitality

n  Parking

n  Membership

And more:

n  Weather feeds

n  Events in your town or city

n  How your venues compare (if multi site)

Digital analytics Data & Insights presentation at https://goo.gl/eN5nKr

Understanding the funnel:

n  Who is visiting online?

n  Where are they coming from? (paid or natural search, sites, direct from email, online ads, other)

n  What are they visiting?

n  How are they converting or engaging?

Google Analytics Data & Insights presentation at https://goo.gl/eN5nKr

What are we trying to do?

n  Segment

n  Optimise

n  Create relationships

n  Customise content

n  Test, test, test!

Privacy DOS AND DON’TS

Do:

ü  Include data governance in leadership structure

ü  Have a clearly agreed policy

ü  Think about security

ü  Be transparent with visitors

ü  Explain the value you’re creating

Don’t: ✗  Collect data you don’t need ✗  Assume privacy laws are static ✗  Forget to explain privacy

implications to staff ✗  Have inconsistencies between

onsite and online policies ✗  Get creepy with personalisation

Respectful data use means:

#musedata What next?

Where next? MUSEDATA

DATA AND INSIGHTS SPECIAL INTEREST

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ARTS ANALYTICS SUPPORT GROUP

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#MUSEDATA

TAKEAWAYS DON’T BE LIKE FRANK! 1. Don’t lock data scientists in the basement 2. Don’t tell them to ‘win you the presidency’ 3. Don’t boil the ocean

DATA DRIVEN INSIGHTS FOR CULTURAL INSTITUTIONS