Tips & Tricks for Getting Things Done Using Analytics Data

Tips & Tricks:
Doing the right
stuff with
Analytics
bit.ly/AnalyticsDone
@OptimiseOrDie @CharlesMeaden

“A very common theme that runs through all our
investments is really around data and the merger of
human and machine. How do we process that data in
order to make the world a better place? To make people
happier, to enrich their lives, to provide better products
and services. It doesn’t matter if it’s to enable drug
discovery or trying to make food delivery more efficient,
data runs across almost every one of our companies.”
Jeffrey Housenbold, Softbank
(1 Trillion Dollar fund)

© Optimal Visit 2d018© Optimise Or Die & Digital Nation Ltd.
INTRODUCTION
YOUR PRESENTERS TODAY:1.
Charles Meaden
@CharlesMeaden
bit.ly/2HeHAt6
Craig Sullivan
@OptimiseOrDie
linkd.in/pvrg14

66.249.64.135, 10.2.0.36, 10.2.0.62 - -
[25/Jan/2018:07:24:16 +0000] "GET /webapp/wcs/stores/
servlet/qs_product_tbp?catId=168216&catalogId=10051&fi
lters=2a7&productId=513678 HTTP/1.1" 200 19351 "-"
"Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.
google.com/bot.html)"sctest.thebookpeople.co.uk2_443
66.249.76.132, 10.2.0.36, 10.2.0.61 - -
[25/Jan/2018:07:24:28 +0000] "GET /webapp/wcs/stores/
servlet/qs_product_tbp?catId=168216&catalogId=10051&fi
lters=2a7&productId=501281 HTTP/1.1" 200 19345 "-"
"Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.
google.com/bot.html)"sctest.thebookpeople.co.uk2_443

WHY ASK US?
• 40 years working with Analytics
• Nearly 500 Analytics setups
• Mistakes are good!
• Failure is a part of analytics work
• Our pain is your gain

TECHNOLOGY AGNOSTIC
• This isn’t a Google specific talk
• Actionable information
• But not about geeky reports (aww!)
• It’s about Analytics Strategy
• Competitive Advantage
• Employee Productivity

EXAMPLES ARE REAL
• We care about handling client data
• We can’t always show real data
• We will show you models or concepts

The Right Stuff:
1. Data Collection (CS)
2. Data Skew (CM)
3. Data Pollution (CS)
4. Data Enrichment (CM)
5. Data Automation (CS)
6. Data Presentation (CM)
7. Data Ownership (CS)
8. Training & Investment (CS)

“Out of the entire lifecycle of
analytics measurement, there is
only one place guaranteed to
pollute everywhere else and that’s
the data collection layer.”
Simo Ahava

11
What’s the problem?
Data Collection is Broken for Most Companies
Thanks to
@Simoahava

480 client configurations
95% had high priority issues
100% had medium priority
3/480 had no tracking issues
<1% had solid analytics
Analysis of Site Audits
@OptimiseOrDie

@OptimiseOrDie
Why is Google Analytics free?Wiring Analytics to Idiosyncratic Business Signals:

@OptimiseOrDie
You wouldn’t do out of the box for….
• Ecommerce software
• Credit Card Processing backend
• Security systems, Tills, Wifi in retail
• Computer Networking
• Accounting Software
• Stock Management
• Nuclear power station management software...

@OptimiseOrDie
Even FREE tools are not FREE of effort:
• FREE simply means that you get a basic system
• It does not mean that it RUNS for free
• A car needs correct setup, oil, fuel and maintenance
• It doesn’t run on air and neither does analytics
• Investment in analytics is #1 weakness in my clients
• How can you race with others, if your dash is broken?
• How can measure self performance if it’s flawed?
• How can your team do their jobs, if they can’t measure too?

@OptimiseOrDie
Hello Mate! Got some cheap tills for ya!

@OptimiseOrDie
Knackered Tills = Crap Data
• Would they work out of the box?
• What would you need to do?
• Set up Categories
• Set up all the buttons
• Refunds?
• Lunch Money?
• Gift Certificates?
• Supplier cash payouts?
• Till operator signon?
• Cash, Credit, Account payments?
• Fraud detection?
In other words, the till is an empty
vessel until wired into the context of
your business operations and
reporting!

@OptimiseOrDie
Bulls**t flows upwards!
Pastel BS
Dashboard
BS metrics
BS Collection
BS metrics
BS Collection
BS metrics
BS Collection
BS metrics
BS Collection
BS reports BS Reports

@OptimiseOrDie
Get Data Collection Nailed
• Over 50% of setups are ‘plain vanilla’ (GA)
• Even Free Analytics is NOT free – it needs work
• If you screw up collection, you need a Time Machine
• Problems either exist now (Audit catches them) or are introduced (poor
release process for Analytics)
• Always have a TEST and LIVE data view (TEST is for staging/QA/any
non live testing)
• Invest in automation (e.g. link/campaign tracking tools to reduce
tracking errors)
• Invest directly into supporting data collection as part of the project
outcome!

 Data Collection Summary
 Get a regular baseline audit of tracking
 Set up TEST & LIVE data views
 Provide ‘tracking’ support for all stories / tasks
 Test all new changes on staging (before
implementing)
 Have a ‘signoff’ process to validate
 Listen to Simo about Agile Integration
 Don’t do a bolt on – data is part of everything

• Where your data is correct, but
needs to cleaned and untangled,
before you perform any analysis
• Time taken to clean the data
means less time for analysis!
• People start to lose trust in the
data if you don’t fix this…
What is Skewed data then?

SECTION TITLE
SANITY CHECKS FOR DATA HYGIENE
1. Does the data I’m working with make sense to me?
2. Does it make sense to someone else?
3. How clean is the data collection & processing?
4. How much work have I got to fix this?

You have one landing page on your site called:
www.website.com/index.html
But in your analytics system, you have three hundred pages like:
www.website.com/index.html?partnerid=123
Why is this a problem?
Data Skew – Too Many URL’s

• Subdomain / Cross domain tracking
• Payment Gateway / 3rd party redirects
• Campaign Tracking Misconfigured
• Pages with mixed cases (e.g. /Index and /index.html)
Data Skew – Common Sources:

• Google Analytics shows only the top
50,000 items per table
• Everything else goes into a bucket
marked “(other)”
Our client asked 3 questions:
(1) Are we recording everything?
(2) Are we recording too much data?
(3) How do just record what we need?
• Investigations showed 111 different
query strings appearing in the URLs
Data Skew – Too Many URL’s

• Working with the client, we determined which
query strings:
1. Were useful for analysing user
behaviour and should be kept
2. Had no analytical value and should be
removed
• 150 Google Analytics filters were configured to
clean and remove the unnecessary query
strings
• Where possible, useful data passed is via the
Data Layer instead of on URLs!
• The end result - a clean and usable analytics
system without page fragmentation! @OptimiseOrDie @CharlesMeaden
Data Skew Filtering Solution

 Data Skew Summary
 Do the page names and events make sense (to someone
else other than you)?
 Are you capturing data that you don't need for analysis?
 Does your analytics match up with your other systems?
 How have you grouped the data in your systems?
 How many views on your data exists?
 When was the last time you audited your analytics?
 Test every single change that you make!

Your data contains extra stuff (more data) that shouldn’t be there
A 3rd party is adding the data (bot, spam) or you’ve done something wrong!
What is data pollution (vs. skew)?

Our Conversion Rate is Dropping:
Prospects
Customer
Logins
Internal
Logins
Dev & Staging
Philippines
Data Pollution Example
Automated

Prospects
Customers
(trial &
paid)
Internal
& Partner
Dev &
Staging
Philippines
This is how it REALLY should look:
Data Pollution Example
Bot, Spam,
Automated

• Not filtering out 3rd parties (agencies, dev, partners,
employees, contractors)
• Bots and automated services
• Site Performance Tools
• Internal Testing (Dev)
• Internal Monitoring (Dev)
• External Monitoring (Uptime)
• Misconfigured tags
• Double firing tags
Common Data Pollution Sources:

 Data Pollution Summary
 Very common problem but until you check, it’s
usually invisible, unless you look carefully
 External or internal can screw data hygiene
 Impacts trust in data (visible or invisible)
 Worst case we’ve seen was 60% of traffic
 Corrupts key site metrics (CR, Bounce etc.)
 Filter out to ONLY what you explicitly need!

37
Plug and Play Analytics?
Data quality isn’t acquired – it’s earned!
Thanks to
@Simoahava

SECTION TITLE
• Some ‘Out of the box’ analytics definitions are flawed
• You will always need to tune these to fit how your business,
product or service actually works
• Customising, tweaking and enhancing the vanilla analytics
configuration = enriched data
• What can you do to capture more of the important
interactions and events as part of the user journey?

• Client had lots of useful information –
which was added to the query strings on
the page URL
• When they launched their new site, they
implemented analytics with ‘clean urls’
and a vanilla analytics implementation
• All the useful data that used to be there,
was no longer being recorded
Data Enrichment – Data Going Missing

• We created a plan to explicitly review all
the data required and identified which
stuff was useful
• We then made sure the important
signals that were really needed by the
client, were put back!
• Instead of being on every URL, the data
is pushed to GA 360 using Custom
Dimensions & Events
Data Enrichment – Capturing The Useful Stuff

• Departure and arrival airports
• Dates of travel
• Size of group
• Hotels viewed
• Whether search used
• Quote created
• Availability for each hotel
• Existing customer or prospect
Example Custom Dimensions

42
Genuine
Prospect
Internal
Dev
QA
Classic
Classic
downgrade
Standard VIP
Standard
downgrade
Premium
Prospects, Customers, Staff (subscriptions)

Analytics Data Enrichment

 Data Enrichment Summary
 Are you capturing all the key information you need to
analyse the customer journey
 Maximise use of your analytics tool to store additional
information – you’ll be glad that you did in a years
time (love note!)
 Think about what you need to capture every short and
long term customer goal reached
 Investigate how to integrate or blend additional tool
data (e.g. VOC, Session replay) with your analytics tool

• If it takes longer than 15 mins/week
• AUTOMATE IT
• Do you know how much time is wasted?
• Not without doing user research!
• Do you know the life of data?
• Why don’t you follow it sometime?
• Remove Report Monkey Wastage
Data Automation Foundations:

• Removal of manual reporting tasks
• Making it 1 click, auto email or dynamic
• Saving multiple repeat efforts
• Allows for pushback / redefinition
• Reduces ‘ad hoc’ report processing
• Needs OVER resourcing to work
• Won’t save time – saves PRODUCTIVITY
Data Automation Definition:

• SQL extracts
• Spreadsheet or Google Sheets pull
• Automation / Reporting / Analysis tools
• Analytics Package Auto Reporting
• Dashboards
• Emailed Reports
• Work back from outcomes!
Data Automation Execution:

@OptimiseOrDie
Craig’s Favourite Models
A. 1 Hour Research
B. Bounce Layer
C. Landing & Next Page Path
D. Traditional Funnel
E. Ring Model
F. Horizontal Funnel
G. Top of Funnel Drivers
H. Page Groups
I. Intent Model
J. Multi Goal Groups
K. Temporal or Lifecycle
L. Ecommerce Grid
M. Dimensional Grid Pull

Funnel success
Funnel Step 2
Funnel Step 1
Sales
Ecommerce
Behaviour
@OptimiseOrDie
Traffic
Get a funnel for all these:
• New & Returning Visitors
• Channel breakdown
• Country breakdown
• iPhone Models
• Android Models
• Windows Browsers
• Mac Browsers
• Device type & Category
• Landing page(s)
• Content page(s) viewed
• Demographics
• Days to conversion
• Path Length
Want something? Get in touch!
Dimension Grid Pull

@OptimiseOrDie
• Rapid Blockage Identification
• Landing Layer Issue
• Site Depth Engagement
• Funnel Loss
• Bugs
• Measurement issues
• This one grid cube on the left
started 1 hour of work that
nailed a 2M per month issue
Dimension Grid Pull (app.profitgrid.io)

@OptimiseOrDie
Dimension Grid Pull (app.profitgrid.io)

@OptimiseOrDie
Dimension Grid Pull

Grid Tool app.ProfitGrid.io
Download Deck bit.ly/2nySVXd
XDO – Full Article bit.ly/XDOfull
Build your own device lab bit.ly/MobileDeviceLab
Google Analytics Templates bit.ly/XDOTemplates
Mobile Checklist for XDO bit.ly/MobiCheck
Excel Grid and Dimensions bit.ly/TheGridLayout
Feedback & Suggestions sullivac@gmail.com
pieter@zetom.nl
@OptimiseOrDie
4L Grid Resource Pack

@OptimiseOrDie
Automating Models and Data Integration:
Dimensions, Metrics, Query Explorer:
Very useful for forming queries and getting to know the API:
https://developers.google.com/analytics/devguides/reporting/core/dimsmets
https://ga-dev-tools.appspot.com/query-explorer/
Guide to getting started with the API:
https://www.optimizesmart.com/how-to-use-google-analytics-api-without-any-coding/
Free Google Sheets Addon:
Google Sheets Addon:
https://developers.google.com/analytics/solutions/google-analytics-spreadsheet-add-on

@OptimiseOrDie
Automating Google Sheets:
https://medium.com/@jev/how-to-import-data-from-google-analytics-into-google-sheets-with-
google-apps-script-5174a10b24d8
ProfitGrid:
Simple tool to pull a multi-dimensional grid from any GA setup:
http://app.profitgrid.io
Analytics Edge:
Very flexible excel tool for pulling API data. Free version available. Bristling with options:
http://www.analyticsedge.com/simply-free/
Supermetrics:
Free GA plugin for Excel and Google Sheets. Multiple data sources available:
https://supermetrics.com/product/supermetrics-data-grabber/
https://supermetrics.com/product/supermetrics-for-google-drive/

@OptimiseOrDie
PowerBI:
Allows you to pull GA data directly - was PowerPivot. If you want a multi-dimensional API pulled
horizontal funnel you can segment on-the-fly, this is what you need. A very powerful way of
viewing and pivoting cubes of GA data:
https://powerpivotpro.com/get-the-software/
Next Analytics:
Free and paid options available:
https://www.nextanalytics.com/excel-google-analytics/
Analytics Canvas
Integration, extracts, multiple accounts, automation and scripting. Paid only.
http://analyticscanvas.com/google-analytics/
Scitylana:
Integration, Hit level datastream, Extracts. Free option available.
https://www.scitylana.com/

@OptimiseOrDie
Integrating GA with R:
http://code.markedmondson.me/googleAnalyticsR/
http://cran.r-project.org/web/packages/RGoogleAnalytics/
https://github.com/skardhamar/rga
http://cran.r-project.org/web/packages/RGA/index.html
R for Analysts:
http://www.eanalytica.com/r-for-web-analysts/
Simo Ahava Recommends:
"I wrote these two a while ago. First is for validating a GA account setup, with focus on
Custom Dims too, and the second is for mass updating Custom Dimensions"
https://chrome.google.com/webstore/detail/google-analytics-
validato/nmjiiaaejkhpegmcpfaehmbijgoilimo?utm_source=permalink
https://chrome.google.com/webstore/detail/google-analytics-custom-
d/ogcaloflfbimfnpkkfpfddocaegdmgkk?utm_source=permalink

@OptimiseOrDie
• Learn how to craft useful and reliable models from data directly
• Make your own ‘on the fly’ ‘User’ or ‘Session’ funnels for anything!
• Understand how the Analytics Package data model works
• Understand your data collection – how it works
• Learn about common collection and configuration biases
• Improve Event and Interaction tracking
• Learn how to integrate or transform data sources
• Ability to target/analyse cohorts rather than just ‘average’ or ‘mixed’ traffic
• Automating and iterating models, grids and funnels
Core Skillz & Benefitz

@OptimiseOrDie
• Browsers & Devices – how do they work (for analytics)
• Tagging and Javascript – how it works (and tool proficiency)
• Filters and Regex – for quickly hacking or grouping models
• Data Model – familiarity with how GA collects and stores data
• Advanced Segments – a stepping stone to mastering API pulls
• Modelling – imposing your abstraction upon a dataset
• Audit & Configuration – knowing exactly what’s broken or reliable
• Visualisation – Presenting actionable, useful and simple truths
• Automation – API pulls, Integration, GTM, Machine Learning
Core Skills – MUST have

@OptimiseOrDie
Automated Ecommerce Models
A. Customer Segmentation
B. New vs. Existing customer
C. Basket analysis (various)
D. Returns analysis
E. Site Search analysis
F. Boston Grid Quadrant
G. Profit Analysis
H. Yield Analysis
I. Product Interaction Layer analysis
J. Out of stock analysis
K. First purchase / anchors
L. Discount impact model
M. Cart abandonment analysis
N. Live chat or textual analysis
O. Merchandising reporting (various)

 Data Automation Summary
 To save, you have to spend
 One hour of automation = Days saved
 Increases Data Literacy and ‘art of the possible’
 You don’t need to know how to code
 Or create SQL queries
 If you can use Excel, you can Automate
 Restructure analytics work around removing
duplicated or senseless work
 90/10% monkey/thinking vs. 10/90

SECTION TITLE
ASK THESE 3 QUESTIONS WHEN PRESENTING
1. Can people clearly see surfaced and actionable issues?
2. Do people know what actions they can or should take?
3. What does your presented data cause your audience to think?

• Ecommerce client had suspicions
that their site search tool wasn’t
working effectively
• The person responsible for the
tool also managed 4 other tools
• Data was presented as a table
containing thousands of rows
Data Presentation – High Cognitive Load

Completely Useless Search Report:

@OptimiseOrDie
@CharlesMeaden
Data Enrichment – Making it Useful

 Data Presentation Summary
 Find out what jobs people actually do and then figure out the
information they need to do their job better (or just ask
them)
 Don’t assume that everyone needs to know everything
 The insight should jump out at you, along with either a
followup question or a concrete action
 Watch – “How to Present to Get Results” by Lea Pica
 Buy - Storytelling with Data: A Data Visualization Guide for
Business Professionals

Monthly reports which lack relevance, are rife with generic suggestions unsupported by research
within the context of your business and simply regurgitate last month’s points, failing to show
any new actionable data.
Thanks to
@Simoahava

Ugly and ineffective hacks to get tracking working at the last minute.
A lack of coordination or standards for how business critical tracking should be delivered. No
data layer or cross silo approach.
Thanks to
@Simoahava

Analytics changes are given low priority and deployed infrequently
Fix Funnel
Tracking
Thanks to
@Simoahava
Deploy
new
funnel

Analytics tracking is often ‘added later’ or seen as a ‘Bolt On’ to existing project work because
ownership is unevenly distributed!

How to Integrate Analytics & Agile
Agile Analytics:
www.youtube.com/watch?v=yIJp9s46CF8
Meaningful Data:
www.youtube.com/watch?v=rMrB0bKdOtc

 Data Ownership
 A complicated area but vital
 Dedicated analytics inside project teams
 No Analytics or Report Monkey Silos
 Watch the Simo talks on Agile Integration
 Every Project or Task must include Analytics
 If it doesn’t need any, then great
 Done does not mean Done – without tracking!

“Nobody ever admits being unable to use
Google Analytics properly. We’d all admit to
being no good at Quantum Physics but when it
comes to analytics, most people either don’t
know or they’re lying. ”
Craig Sullivan

@OptimiseOrDie
Train Your Team so they….
• Can be better at their job, through data
• Know how everything works in analytics
• Get data out with less work
• Achieve self servicing for data needs
• Automate repetitive tasks
• Get more proficient at finding value
• Data Noodling!
A Ferrari mechanic without engine training?

@OptimiseOrDie
Invest in Analytics
Even if tools are free, you should:
• Get an Independent Audit
• Invest in a proper release architecture (QA/DEV/STAGING)
• Make tracking part of every project
• Don’t just tread water – improve tracking
• Put >5% of dev time & analytics budget on EXTENDING
• Things like -> Data Collection, Training, Fixing Stuff,
Automation, Enrichment, Tracking Tools
• Validate training – through research (UX)
• Write a love note to yourself…

@OptimiseOrDie
Write a Love Note to your Future Self:
Hey Craig,
All we are is dust in the wind dude. You’re like my future self, so I had
to tell you. I thought it would be most excellent to give you clean
data, so I fixed it, like all the things.
You’ve got like least 3 months of clean stuff and like reports and stuff
like totally makes sense now! Whoa!
Love you, Dude!
Craig from the Past, Dude

 Investment & Training
 Analytics Strategy needs Training Strategy
 Training people does not mean they leave
 Add 5-10% dev & software budget!
 Don’t just tread water – continual improvement
 Over investing leads to automation (explain)
 Automation leads to standardisation (explain)
 Standardisation leads to Productivity (explain)
 Hiring tips – ask us in Q&A

480 client configurations
95% had high priority issues
100% had medium priority
3/480 had no tracking issues
<1% had solid analytics
The Dark Side
@OptimiseOrDie

The Sunny Uplands:

@OptimiseOrDie
Collection Skew Pollution Enrichment
Automation Presentation Ownership
Train &
Invest
SUMMARY GRID

THANK YOU!
• WRITE THAT LOVE NOTE!
• WANT AN AUDIT?
• SKILLS TRANSFER?
• GET IN TOUCH!
CHARLES : bit.ly/2HeHAt6
CRAIG : linkd.in/pvrg14
Optimising the
Optimisation
@OptimiseOrDie
@CharlesMeaden
26TH MARCH 2019
GOOGLE CONVERSIONS, DUBLIN
bit.ly/AnalyticsDone

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