Data-driven digital marketing is a critical discipline for senior marketers in their efforts to engage today’s demanding omnichannel consumers. But with such a wide array of channels and choices, what are the specific areas of digital marketing that marketers are focused on, how successful are they and what are they looking to improve in the future? In this webinar Ruth Gordon, Director of Digital Marketing at Teradata, will explore the results of the 2014 Digital Marketing Insight survey into how marketing managers are using customer data, analytics and personalisation to achieve their goals, plus the benefits and difficulties they are experiencing along the way. Gareth Powell, Head of Web Analytics at leading internet and catalogue retailer JD Williams, will then give examples of how their brands, including SimplyBe, Jacamo and High and Mighty, are harnessing customer data and analytics to drive business value and enhance the customer experience.

1. Brought to you by In association with
Digital Marketing in 2014:
How to Harness your Customer Data
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2. Today’s Speakers
How to Harness your Customer Data
Gareth Powell
Head of Web Analytics
JD Williams
James Lawson
Consultant Editor
Marketingfinder.co.uk
Ruth Gordon
Director Digital Marketing - International
Teradata

3. Interact with us
How to Harness your Data in 2014
Follow the conversation on twitter #HarnessYourData

4. Business Challenges:
Consumer Expectations Are Rising Fast
The ‘always on…
always connected customer’
demands an engaging, relevant and
seamless 1:2:1 experience
The ability to listen and piece
together a single customer view as a
customer interacts across all
touchpoints is key
New data = a new approach
Discovery: a journey into the
unknown.
Take appropriate actions, increasingly
in real time

5. - Survey in November 2013
by MyCustomer.com, an
online community of customer
focused marketing, customer
service, and CRM
professionals
- Cross industry, European
survey (UK, France &
Germany) of mainly
Marketing and Analytical
roles
- 115 respondents
European Digital Marketing Research
How are Marketers Dealing with the Challenges?

6. Email, search and social are highest priorities but big data is a
priority for 27%
Key Finding #1:
Digital Marketing Priorities

7. Storage and integration (36%) and data quality (23%) are the most
commonly reported challenges preventing digital marketers from
capitalising on digital data
Key Finding #2:
Challenges to Utilising Customer Data

8. Aggregated web data is the primary source of customer data
Key Finding #3:
Customer Data Collected

9. Key Finding #4:
Single Customer View

10. Web analytics is the tool most commonly used (72%) with other
analytics having far less penetration
Key Finding #5:
Analytics Undertaken

11. Quick Poll
Digital Marketing in 2014:
How to Harness your Customer Data

12. Key Finding #6:
Analytics Benefits

13. Level of personalisation varies by platform, highest for email where
only 8% don’t personalise versus a third for web or mobile.
Key Finding #7:
Personalisation Channels

14. Digital marketers are using a range of data to support
personalisation, with online behavioural data being the most
common data source
Key Finding #8:
Personalisation Data

15. Key Finding #9:
Personalisation Importance & Real-Time

16. Quick Poll
Digital Marketing in 2014:
How to Harness your Customer Data

17. The increasing importance being placed on personalisation reflects
the broad range of benefits being delivered
Key Finding #10:
Personalisation Benefits

18. We see visitors, but not
customers Business is unable to
maximise its leading channels
due to an incomplete
understanding of customers’
online and offline behaviours
and the interaction between
the two
We want to respond to
the customer whilst they
are interacting with us
Key Requirement for Modern Marketing
Ability to Analyse Digital Channel Data

19. Today It’s About the CUSTOMER
...not the Web

20. How did they arrive
on site?
Tells us which
channels drive
traffic & conversion
Are they interested
in what other
people think?
Reviews are
important in future
communications
What products or
services are they
interested in & are
they looking at new
categories?
Informs future
promotional , cross-
and up-sell strategy
What are they
interested in but
not buying?
Identifies pre-
purchase intent
Are they socially
active?
Helps determine
influence & brand
advocacy
Are they viewing
help pages?
Do they need
support?
Do they search for
cheap products and
sort by price
descending?
Informs ‘price
sensitive’ future
offers
Where did they
leave the site?
Determine customer
experience issues
How long are their
sessions, how
frequent & how
many pages do they
view?
Determines contact
strategy & channels
How often do they
click through from
email?
Determines contact
& message strategy
What device are
they using?
Ensures that
messages render
correctly
What promotions
are they browsing?
Informs promotional
strategy
What content are
they interested in?
Informs future
communications
Are they logging on
to multiple
accounts from the
same IP?
Identifies potentially
fraudulent activity
Individual-level Digital Channel Data
Example Data & Uses

21. • Using demographics +
transactional history
• Segmenting, recognising
patterns and predicting
behaviour
• LTV
• Loyalty
• RFM
• Targeted Marketing
• Using individual customer
browsing behaviour of
prospects & customers
• Segmenting, recognising
patterns and predicting
behaviour, text mining
• Personalisation
• Engagement
• Campaign Attribution
• Product affinities
• Customer journey
• Using individual social
media detail like social
graph or twitter feeds
• Enriching with declared
actions, preferences or
intentions across 1 or
more social channels
• Market knowledge
• Advocacy & Influence
• Sentiment
• Purchase Intent
CRM eCRM sCRM
Beyond CRM:
Integrating Digital & Social Intelligence

22. Personalised emails
triggered by behaviour
Personalised web content by
visitor profile & behaviour
Channel & offer engagement
determine contact strategy
Location based offers
Improved online product
recommendations
Transforming Customer ENGAGEMENT:
Relevant & Timely Offers Via Preferred Channels

23. •Journey improvements:
•Churn
•Complaint investigation
•Site & basket
abandonment
•Web failures
•Omni-channel behaviour
•Site usage reporting by
customer
•Improved MVT reporting
•Process improvements
Enhancing the Customer EXPERIENCE:
Through Deep Customer Analytics

24. •Advanced spend attribution
•Fraud detection
•Lead generation
•Compliance and mis-selling
•Behavioural based pricing
with telematics
•Channel optimisation e.g.
Paper removal
Improving Business EFFICIENCIES
Through Analytics & Optimisation

25. 25
Online Analytics
Gareth Powell,
Head of Web Analytics, JD Williams

26. 26
30-45 45-65 65+
AB
DE
JD Williams Introduction:
Our Key Brands by Age & Social Group

27. • 4.0M customer accounts have placed an order in the last year
• Average customer age is 60
• 81% of customers are Female
• 76% are dress size 16+
• Over 40 transactional websites with the ability to carry your bag
across sites
• In the last year 56% of our sales have been online
• 43% of website traffic now arrives via Smartphone or Tablet
(35% of online sales)
• Store Portfolio expansion and International growth
• £785M Revenue in previous Financial Year
• Operating Profit of £102.2M in previous Financial Year
JD Williams Introduction:
Our Business

28. • Head of Web Analytics: Gareth Powell
• Customer Journey Team: 5 Analysts
• Site Operations / MVT Team: 3 Analysts
• Senior Business Process Manager: 1 Analyst
• Part of a wider team of 30 in Marketing (Customer Analytics)
Analytics @ JD Williams:
Current Online Analytics Team

29. • Teradata
• Celebrus
• Coremetrics – used to understand site / promotion
performance, CRO
• Google Analytics – ties in with AdWords very well so
Advertising teams use heavily
• Other Data Sources / Website Applications
Analytics @ JD Williams:
Current Online Analytics Tools

30. Analytics @ JD Williams:
Core Business Analytics Landscape
Modelling and Data Mining
Reporting and Insight (Offline and Online Customer Data)
Campaign Execution
Web Analytics - Coremetrics
Account # Propensity to Buy
24149080 £288
99218880 £56
63978660 £11
R² = 0.9198
-2
0
0 2 4 6
ln(odds)
gd
Celebrus

31. • Celebrus - entry into big data for N Brown
• Bottom-up approach: deeper-drive analytics tool for SQL experts
• Ability to visualise at session level and evaluate all interactions
• Tie-in to customer account: can allocate account # to 50% traffic
• Build up a picture of the customer over time/multiple sessions
• Predictive Web Analytics and Modelling/Segmentation
• No tagging involved making life easier
• Teradata
• Single repository for customer and trading data
• Large % of data held at customer account level e.g. contact history,
payments, historical orders, aggregated customer data e.g. lifetime
value
• Majority of data ties back to a single customer account
Analytics @ JD Williams:
Beyond Web to Customer Analytics & Big Data

32. • Teradata Integrated Channel Intelligence (ICI) Layer
- Captures low level interaction data
- Can still analyse this underlying data source
• Production Layer
- Aggregated view of data
- On-going configuration
- New data requirements
- Majority of analytics conducted on this source
Detailed Online Customer Data:
How Celebrus Data is Stored

33. •WURFL
•Mobile / Tablet device data at individual session level in Teradata. Over 1K combinations
of Models and Operating Systems accessing sites. Monitor and optimise accordingly
•BazaarVoice Product Reviews
•Data at customer and product level in Teradata. 200K + reviews
•Hitwise
•Upstream / Downstream website traffic – aggregated numbers
•Fatwire
•Content Management System data embedded in Teradata. Will provide a view on Stock
Availability and Customer Product Type (vs internal ‘BOS’ product view)
•Responsys
•Email Service Provider data embedded in Teradata at email and customer level -
Clicks,Opens,Bounce
•Autonomy / Optimost
•Engine for MVT. Deeper-dive analytics also possible via Celebrus/Teradata
for each test
Other Data Sources &
Website Applications

34. Products
Abandoned
Entry
Method
Payments
62 Tables
Products
Added to
Bag
Filters
e.g. price
Products
Removed
from Bag Products
Viewed
Bounces
Internal
Search
Nav
Interactions
Order
Tracking
Products
Added to
Wishlist
Exit Page
Pages
Viewed
Image
Zooming
External
Search
Page
View
Time
Sorts
Detailed Online Customer Data:
Some of the Things We See With Celebrus

35. Filters –> Mailing Selections –
Accounts selecting ‘shoes’
Products Abandoned –>
Abandoned Bag Email
Products Viewed –> Browse
not Bought Email
Internal Search –> spot
trends e.g. ‘onesie’
Nav Interactions –> e.g.
spotting sale buyers
Image Zooming –> shows
clear interest in product
Exit Page –>Site Improvement
Pages Viewed –> Tailor
Mailings to Preferences
External Search –> focus of
PPC
Sorts –> Price Preference -
Mailings
Drop-offs –> reacting to site issues
Detailed Online Customer Data:
Translating Data into Opportunities

36. What have people been searching?
Are they
an existing
or new
customer?
Do people scroll
down the page or
look at what is first
shown to them?
What time was a
customer’s web
session? Are there
any peak times?
Are customers
going straight
to sale pages?
Where have
customers
come onto
the site
from?
What are
customers
browsing
patterns?
What site
is the
customer
on?
What do people have in their bag?
Detailed Online Customer Data: ENGAGEMENT
Usage in Practice

37. • Predicts how likely a
customer is to visit our
websites within a month
• Gives a rank from 0 to 19
based on how engaged a
customer is with our website
(0- unengaged, 19 – very
engaged)
• Uses 3 months worth of
Celebrus data to allocate
rank
• Used in email selections and
paper reduction tests
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
VES Rank
Likelihood to return to site
Detailed Online Customer Data: ENGAGEMENT
Visitor Engagement Score

38. Segment Name Behaviours Potential Actions
Value Hunters Customer who consistently
clicks / visits Sale area of site.
Customers who sort products
by descending value.
Customers who filter on price.
Ensure early visibility to Online
Sale.
Consider reducing paper strategy
to Value/Sale Catalogues only?
Frequent
Abandoners
High cart abandonment rate.
Removes items from bag
frequently.
Ensure early visibility to Online
Sale.
Offer incentives to encourage
spend e.g. buy one get one free.
Encourage loyalty, perhaps VIP?
On-Trend
Customers
High % spend on new-in.
Searches for specific products.
Regularly visits new in section
of site.
Send aspirational/new in emails.
Ensure they don’t get too many
mailings with similar product but
that they see the new in.
Detailed Online Customer Data: ENGAGEMENT
Behavioural Segmentation

39. WEB_SESSION_ID DATE_AND_TIME_OF_LHN_CLICK LHN_CONTROL_USED LHN_VALUE_SELECTED
629999510 24/01/2014 14:39 home kitchen & cookware
629999510 24/01/2014 14:40 kitchen & cookware kitchen storage & bins
Detailed Online Customer Data: EXPERIENCE
Left Hand Navigation Example

40. • Cosmetic Testing – placement, colours, type of CTA
• Fundamental business questions – Cash / Credit, Re-directs
• Some of our MVTs require on-going measurement to understand
downstream customer behaviour
• Celebrus is perfect for this as we are able to visualise the sessions
and discriminate between Test and Control creatives served
• Enables us to tap into the web behaviour further as well as allowing
us to incorporate product returns, gross margin and financial income
Detailed Online Customer Data: EFFICIENCY
Multi-Variate Testing

41. Week
LW TW
Product Products Viewed Product Conversion Products Viewed Product Conversion
KNOT MAXI DRESS 1,195 1.0% 1,305 0.5%
FUR TRIM PARKA 758 0.9% 768 0.5%
Detailed Online Customer Data: EFFICIENCY
Product Conversion

42. • Exploiting unstructured data. Opportunities of pattern detection
through big data tools such as Teradata Aster
• Attribution Modelling (Sales / campaign assessment) including
Econometrics
• PPC Bid Management. Plus Lifetime Value / Credit Reject
Rate by keyword
• Personalisation. Very successful trial delivered with Celebrus
Real-Time
• Closer alignment with Ecommerce Development – using
analytics to help dictate website priorities
• Multi-channel and Omni-channel analytics. What do our
customers need when and where
• Integration with other channels - Application of Web Data in
Call Centre e.g. Outbound opportunities for customers leaving
a poor product review
Always Learning & Improving:
Going Forwards

43. • Over £4M incremental revenue benefit delivered last Financial
Year from Web Analytics initiatives
• We are all at a critical point with data
• It is a challenge as data is increasing exponentially. Getting the
balance between investigative analytics and managing tactical
business questions is key but a challenge
• Trying to see the wood through the trees is hard when you are
data-rich. It is important to be posing the right questions
• The big data piece is an opportunity but we should not forget
about the small data i.e. what could you be doing better with
what you already have
If you’re not willing to utilise online customer data
you WILL get left behind
Always Learning & Improving:
Results To Date

44. 44
Practical Next Steps
Gareth Powell

45. • When integrating Celebrus we worked out what data is
important to the business. This is an evolutionary process
• Developed IT Web Analytics team to support and develop
Celebrus and Coremetrics. You need to take data seriously
• Close engagement required with stakeholders to help turn
data and insight into £notes
• Develop a test and learn mentality. Not every analytical
project is going to be a success so you need to embrace a fail
fast philosophy
• Develop a strategy for projects as once you have vast data at
your fingertips business questions can overwhelm
45
Realising the Data-Driven Vision:
Practical Next Steps & Our Key Learnings

46. Gareth Powell
Head of Web Analytics
JD Williams
James Lawson
Consultant Editor
Marketingfinder.co.uk
Ruth Gordon
Director Digital Marketing - International
Teradata
Your Questions
How to Harness your Customer Data

47. 3 Great reasons to fill out the exit survey
1. You can give us your feedback
2. You can request your free copy of the ‘Digital
Marketing Insights for 2014 and beyond’
3. You can request your free copy of ‘The
Virtual Presenters Handbook’

48. Thank You
Brought to you by In association with
Digital Marketing in 2014:
How to Harness your Customer Data