Using our event analytics platform for fun

and profitStewart Duncan –Director of Data Science

A little about us…

• 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

Our analytics team a year ago…

Alberto

Daniele Anthony

Imtiaz

Natalie & Emma

How had we got there?

Core OLTP platform migrationSingle tool approach to our

data warehouse..Siloed web analytics

Resulted in…

Which meant we did a lot of this…

Making the case for a new data architecture

Mec

han

ism

Op

po

rtu

nit

y

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 newopportunities

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

Mining our granular event data

Business challenge:

– Shopping sessions can last up to a week

– Customers use many channels in that time

– Paid search becoming increasingly competitive

– First touch attribution hides the impact of nurturing channels

What is the true value of each marketing channel so that we can

allocate budget accordingly?

The exam question…

Zendesk

Chopin App

RabbitFeed

Ruby TrackerOnsite

conversion events

Snowplow EnrichmentClientJavascript

Tracker

Custom Channel Enrichment

Page view events

Redshift

Google AdWords

Clicks and cost reports

Post-process sessionising

Cross-channel ID stitching

ExactTargetEmail

Platform

Telephony Solution

Email eventsCall /

Service events

Using Snowplow to collect data across channels

Preparing data for modelling

Redshift

Marketing path to conversion, tagged by SB channels

Marketing touches to conversion

Now we have:

Customer 1: Partner Affiliate PPC PPC

Customer 2: Natural Search

Customer 3: Display

Customer 4: Partner Email

PPC

Conversion

Conversion

Display Display

… … … … …

Modelling a Bayesian Network based on…

This likelihood function needs to be

calculatedSimply the frequency of

conversions in the data set

What we are looking for

Calculating P(Channel | Conversion)

Count frequencies at which a channel appears in a journey to conversion…

Number of appearances Frequency Probability

11 1 0.10%

10 1 0.10%

9 1 0.10%

8 1 0.10%

7 2 0.20%

6 2 0.20%

5 7 0.71%

4 8 0.81%

3 56 5.69%

2 166 16.85%

1 740 75.13%

PPC AffiliatesNumber of

appearances Frequency Probability

6 2 0.99%

5 1 0.49%

4 2 0.99%

3 10 4.93%

2 21 10.34%

1 167 82.27%

…etc..

Calculating P(Channel | Conversion)

Deriving a probability density function for each channel based on frequencies. Adjust by iteratively adding ‘noise’ to smooth curve and maximise entropy…

And the result…

Channel Propensity

Direct 5.0%

White Label Partner 1 12.0%

PPC 15.0%

Email 19.0%

White Label Partner 2 12.0%

Natural Search 13.0%

Affiliate 8.0%

Partner Landlord 16.0%

Display less than 1%

After inputting into the equation:

Email has 19% influence on

conversion across all journeys

This method:– Weights each channel,

taking into consideration non-converting paths

– Allows us to calculate a more accurate ROAS per channel