Revamping Your Data Approach to Enable the Pace & Flexibility Needed to Make Timely Business Decisions This slide deck is from Stewart Duncan of Simply Business and Zach Taylor of Looker. They discussed how Simply Business revamped their data approach to enable the pace and flexibility they needed to make timely decisions based on their data. Simply Business offers a B2B web product that simplifies the experience of selecting and purchasing business insurance. With over 300,000 policyholders, Simply Business is the largest SME insurer in the UK. Removing friction for users as they complete the insurance quote process is critical to providing a hassle-free user experience and driving continuous improvements in conversion rates. Learn how data analytics helps them strive for these improvements. Get ideas on the following: - Using data for rapid A/B testing and to analyze user journeys to inform product development. Transitioning from a traditional data architecture to a modern cloud-based stack integrating MongoDB, AWS Redshift, Hadoop and Looker. - Designing a data platform and organizational process that drives data-driven behavior across an organization. - Empowering product and marketing teams to get into granular online engagement and attribution data which has removed the bottleneck to making informed decisions.

1. Stewart Duncan
Director of Data Science, Simply
Business
Revamping Your Data
Approach

2. Introduction to Simply Business
● Amongst the largest business insurance providers in the
UK
● Almost 300,000 customers (and growing fast)
● Using tech to make insurance simpler, easier and more
personalised
● Customer service is our beating heart
● Building a data-driven culture

3. The Simply Business Product
● Tailored quotes from a panel of 20 insurance providers
● 4 verticals and over 1000 trades
● Online and offline channels
● White label partnerships with large UK aggregators
● Proprietary rating engine technology
● Dynamic online journey - highly specific to a customer
● Highly agile technology
Unique in our market niche, but fast moving and therefore challenging to analyse

4. Our analytics team a year ago…
Alberto
Daniele
Anthony
Imtiaz
Natalie &
Emma

5. How had we got there?
Core OLTP platform
migration
Single tool approach to our
data warehouse..
Siloed web analytics
Resulted in… Which meant we did a lot of this…

6. You can’t have it all
Volume Accuracy
Speed

7. Revamping Data Capabilities
MechanismOpportunity
Use data to optimise
existing processes
Run The
Business
Use data to optimise
the creation of new
business processes
Change The
Business
Explore data to
identify new
opportunities
Find New
Business
Best practice data
warehouse
Scalable data exploration platform
Unified event processing framework
Automated event enrichment and loading
Twitch analytics for
product owners
Analyst toolkit for
discovery
Data syndication
(in & out)
External analytics
applications
Leverage
Position
Use data to
leverage position
in market

8. Make data visible to users as early as possible

9. Zendesk
Using Event Data to Inform Product Decisions
Zendesk
Chopin
App
RabbitFeed
Ruby
TrackerServer
events
Snowplow EnrichmentClient
Javascript
Tracker
Custom Channel
Enrichment
Page view
events
Redshift
Google
AdWords
Clicks and cost
reports
Post-process
sessionising
Cross-channel
ID stitching
ExactTarget
Email
Platform
Telephony
Solution
Email events
Call /
Service
events
LookML
Looker
UI
API
Excel
KPIs
Aggregated Visitor
Metrics
SQL
Server
Mongo
DB
Budgets
Financial Transactions

10. Keeping tabs on our event system

11. A/B Testing

12. Empowering Teams
• Not just the data team
• Gold, Silver, Bronze events
• Events or it didn’t happen
• Easy to send into infrastructure
• LookML easily extended

13. Adding a new Silver Event in LookML
Leveraging derived tables
Redshift window functions to
easily traverse event stream
Basic counts for simple
engagement metrics
Logic can be promoted back
into ETL when embedded

14. Incorporated into more complex models
New Silver Event

15. Empowering Teams
New events added to simple Explores, with
more complicated funnel events incorporated
using derived tables.

16. Roadmap
• Incorporating more events from different systems
• Developing a customer identity service
• Moving to real-time data enrichment for feeding back into core application
• Experimenting with Looker API for data syndication to other systems

17. Zach Taylor
Product Marketing, Looker
Revamping Your Data
Approach

18. 1
1 “Custom” Metrics
2 Centralize Data
3 Decentralize Access
3 Thoughts on
Data Strategy

19. “Custom” Metrics

20. Too broad Too Much
Noise
Not
Actionable
The Problem with Vanity Metrics
Individuals are not
empowered
Hard to isolate specific
outcomes/behaviors
Don’t inform better
decisions

21. What to
Measure
Quantify
with Data
Rally
Around
Better
Metrics
“Custom” Analytics Instead
Identify what exactly
you want to know
Use data to quantify
and measure specific
behavior
Empower individuals
by tracking their impact

22. Venmo’s Product Redesign
Before: After:

23. Whoops…

24. Using Custom Analytics
“It was clear some
users were
accidentally paying
instead of charging,
but it wasn't clear
how widespread
the problem was
and whether it was
worth prioritizing a
fix”

25. Centralize Data

26. Merge Disparate Sources
Marketing Data
Attribution channel
Adwords
Transactional Data
User details
Size of purchase
Event Data
Duration on the site
Flow through app
Redshift
Unified
model
Integrated Model:
• How much should we pay for a
landlord insurance click vs business
user click?
• Are our marketing campaigns
delivering quality users?

27. “Our internal system
showing adoptions and
upgrades from Twitter
ads. This wouldn’t be
possible without tying
attribution data to our
engagement data in a
central database.”
Funnels at Asana
Source: https://eng.asana.com/2014/11/great-data-
delivery/

28. Decentralize Access

29. —TODD LEHR
SVP Engineering,
Dollar Shave Club
“We have a developer
name Juan and any
reports we needed
would flow through
him.”

30. —TODD LEHR
SVP Engineering,
Dollar Shave Club
“When he got
backlogged, our team
didn’t have access to
the data
immediately.”

31. Domain
expertise
Allow
them to
explore
Create a
unified
model
Give Everyone Access
People with questions
are running the
business
As much data from as
many vectors as
possible
So that people can
rally around agreed-
upon data

32. Model-Based Workflow
Unified
LookML
model
Data Team
Product
Marketing
Support
Unified model of the data Interface to the data

33. Find metrics
that drive
decisions
Centralize
disparate
data
sources
Provide
data access
to your full
organization
Key Takeaways

34. Look & Tell London
What: Hear how our other
innovative customers are
leveraging their data
When: May 13, 6:30 pm
Where: Ace Hotel
Shoreditch

35. More Questions?
@lookerdata
@zachtaylor25
zach@looker.com
www.looker.com