capturing online customer data to create better insights and targeted actions using snowplow - sdu...

Presention Snowplow

Meetup

19-05-2016

Capturing online customer data to create better

insights and targeted actions using Snowplow

Snowplow Meetup

Sander Knol & Tamara de Heij

19th May, 2016

Presention Snowplow

Meetup

19-05-2016

Content

• Business context Sdu

• Delivering the Intelligent Platform: Snowplow + Spark

• Creating First Use Cases

• Next steps

Presention Snowplow

Meetup

19-05-2016

Business context Sdu

Presention Snowplow

Meetup

19-05-2016

Who are we?

SDU is a publisher that supplies current information on law and regulations to

lawyers, tax experts, policy makers and other legal professionals

Traditional company in transition

300+ employees

We believe in creating content / product to the wishes of our customers , because

progress is different for everybody

Both off- and online content/products

Presention Snowplow

Meetup

19-05-2016

Why did we want this?

• Ownership data

• Open generic tools (no vendor

lock-in)

• Ability to give support internally

And not be reliable on external

suppliers

IT

• Improving customer journey

• Insights in product use

• Future wish: reacting realtime to

triggers in market

Marketing

• Insights in Acquisition –

development – retention – winback

• Ask and answer business

questions

• Integration of customer behavior in

marketing database

Marketing

intelligence

• Integration offline and online.

• In depth analytical possibilities on

top of google analytics

• Optimal mix of advertising budget

E-commerce

Presention Snowplow

Meetup

19-05-2016

What steps did we take?

Develop Powerpitch

LonglistShortlistChoice

ManagementDecision based

on PAP

Implementation in POC

Transfer to organisation

Proof in use cases

Learning

Presention Snowplow

Meetup

19-05-2016

• Implementing Snowplow in the cloud

• Implementing Apache Spark in the cloud

• Incloud database with all the captured data

• Alignment with Google Universal

Delivering the Intelligence Platform:

Snowplow + Spark

Presention Snowplow

Meetup

19-05-2016

The Delivered Intelligence Platform Using Snowplow and Spark

Behavioral

Data

Click

data

Capture and store data Analyse the data

Presention Snowplow

Meetup

19-05-2016

The Delivered Intelligence Platform – Alignment with Google Universal

Intelligence platform - Snowplow / Spark

• Unlimited external data

• Advanced reporting through tools

• Advanced Machine Learning options

• Customer id + fingerprint + IP

• Full export options

Universal Analytics

• Limited external data

• Slice and dice in frontend user system

• No machine learning options

• Upload a customer id in a dimension

• Limited export options

Presention Snowplow

Meetup

19-05-2016

Planning 6 weeks Proof Of Concept (POC)

Week 1

•Security certificates

•First (generic) tags and triggers in GTM

Week 2

•Second batch of tags and triggers in GTM

•Test of the snowplow data and first EDA

Week 3

• Implementation of Databricks / Spark

•Setting the connection to Snowplow S3 and Redshift

Week 4•Start of use cases

Week 5

•Finalization of use cases

• Budget calculations for future tools (with cloud computing not so straightforward)

Week 6

•Wrap up project

•End presentation

Presention Snowplow

Meetup

19-05-2016

What were our Technical learnings / findings

Security certifications in AWSIT expertise with experience in

network and AWS

Complex Google Analytics

implementation

Completeness of the tracking

Combining off- and online data

Account structure in AWS

Using multiple accounts good

for governance, more complex

in use (whitelisting IP)

Data collection through GTM (=

browser side) is not 100%

complete. Neither is GA.

Implement key in datalayer.

You need web developers

Either start with clean

implementation, or plan

accordingly

Presention Snowplow

Meetup

19-05-2016

Creating First Use Cases

• Case 1: Basket analyses

• Case 2: Service Page Visits

• Case 3: Search Page Usage

Presention Snowplow

Meetup

19-05-2016

Use Case 1: The Correlation Between Site Visits and Products Put in the Basket

• Products (below, right) are visited frequently,

but are not often added to the basket.

• Products (upper left) are not frequently visited,

but are often added to the basket

• Is the price of some products too high or too

low?

• Are pages difficult to find?

• Is there a difference between our high valued

customers vs low valued customers?

Insights

Implications

Information

Presention Snowplow

Meetup

19-05-2016

Use Case 2: Most Frequently Visited Service Pages

• Top 10 of webpages related to service

• The top (detailed) service webpage is

‘abonnement-opzeggen’ (cancel subscription)

• 75% (57% + 19%) of the sessions that visit this

page, continues to the cancellation form.

• In 25% of the sessions the customer uses

another form, i.e. the general contact form

(instead of or on top of the cancellation form)

• Cancellations reach Sdu not in different ways.

Are the forms processed similarly?

Insights

Implications

Information

Cancellation form

No Yes

Contact No 19% 57%

form Yes 5% 19%

Presention Snowplow

Meetup

19-05-2016

Use Case 3: Search Pages

• 6 Distinct clusters, of which ‘zoekers’

(searchers) is a small group with relatively high

revenue

• What can we do to leverage the relatively large

group of visits with no revenue that visits

predominantly in the evening? Are these

private people visiting our site?

• Hypothesis: the searchers have a need for a

specific product. Further research and a/b

testing is advised; specifically on search.

Insights

Implications

Information

Presention Snowplow

Meetup

19-05-2016

Next steps

Presention Snowplow

Meetup

19-05-2016

How are we organized for Snowplow?

Sdu

Marketing & Sales

Marketing Intelligence

- Analyses using SQL(Redshift)and R and

Python (Databricks)

E-commerce

- Google Tag Manager

implementation

IT

Architecture and

infrastructure

- Alignment with current and

future business architecture

- Technical support

Business Analist

- Translating Business needs

into technical design

Presention Snowplow

Meetup

19-05-2016

Which are the next steps for Sdu?

• Duplicates: create a script to deduplicate current and future records.

• Implement server-side tracker as a solution to prevent missing web shop transactions.

• Assess low-cost alternative to the use of the Redshift database (AWS) for the long term.

• Structural solution for security Redshift database (whitelisting IP address of Databricks cluster)

Technical next steps

• Determining KPI’s

• Measuring product use

• Analysing data and determine next action

Supporting lean startup

• Answer Business questions on customer behaviour

• Answer questions not asked

• Tracking product use

Learning

Presention Snowplow

Meetup

19-05-2016

Key take-aways and recommendation

Involve senior management from start

POC of 6 weeks is realistic

Share quick wins / successes for acceptance of theproject

Presention Snowplow

Meetup

19-05-2016

Capturing online customer data to create better

insights and targeted actions

Thank you.

capturing online customer data to create better insights and targeted actions using snowplow - sdu...

Data & Analytics