Brought to you by In association with
Digital Marketing in 2014:
How to Harness your Customer Data
The webinar will begin ...
Today’s Speakers
How to Harness your Customer Data
Gareth Powell
Head of Web Analytics
JD Williams
James Lawson
Consultant...
Interact with us
How to Harness your Data in 2014
Follow the conversation on twitter #HarnessYourData
Business Challenges:
Consumer Expectations Are Rising Fast
The ‘always on…
always connected customer’
demands an engaging,...
- Survey in November 2013
by MyCustomer.com, an
online community of customer
focused marketing, customer
service, and CRM
...
Email, search and social are highest priorities but big data is a
priority for 27%
Key Finding #1:
Digital Marketing Prior...
Storage and integration (36%) and data quality (23%) are the most
commonly reported challenges preventing digital marketer...
Aggregated web data is the primary source of customer data
Key Finding #3:
Customer Data Collected
Key Finding #4:
Single Customer View
Web analytics is the tool most commonly used (72%) with other
analytics having far less penetration
Key Finding #5:
Analyt...
Quick Poll
Digital Marketing in 2014:
How to Harness your Customer Data
Key Finding #6:
Analytics Benefits
Level of personalisation varies by platform, highest for email where
only 8% don’t personalise versus a third for web or m...
Digital marketers are using a range of data to support
personalisation, with online behavioural data being the most
common...
Key Finding #9:
Personalisation Importance & Real-Time
Quick Poll
Digital Marketing in 2014:
How to Harness your Customer Data
The increasing importance being placed on personalisation reflects
the broad range of benefits being delivered
Key Finding...
We see visitors, but not
customers Business is unable to
maximise its leading channels
due to an incomplete
understanding ...
Today It’s About the CUSTOMER
...not the Web
How did they arrive
on site?
Tells us which
channels drive
traffic & conversion
Are they interested
in what other
people t...
• Using demographics +
transactional history
• Segmenting, recognising
patterns and predicting
behaviour
• LTV
• Loyalty
•...
Personalised emails
triggered by behaviour
Personalised web content by
visitor profile & behaviour
Channel & offer engagem...
•Journey improvements:
•Churn
•Complaint investigation
•Site & basket
abandonment
•Web failures
•Omni-channel behaviour
•S...
•Advanced spend attribution
•Fraud detection
•Lead generation
•Compliance and mis-selling
•Behavioural based pricing
with ...
25
Online Analytics
Gareth Powell,
Head of Web Analytics, JD Williams
26
30-45 45-65 65+
AB
DE
JD Williams Introduction:
Our Key Brands by Age & Social Group
• 4.0M customer accounts have placed an order in the last year
• Average customer age is 60
• 81% of customers are Female
...
• Head of Web Analytics: Gareth Powell
• Customer Journey Team: 5 Analysts
• Site Operations / MVT Team: 3 Analysts
• Seni...
• Teradata
• Celebrus
• Coremetrics – used to understand site / promotion
performance, CRO
• Google Analytics – ties in wi...
Analytics @ JD Williams:
Core Business Analytics Landscape
Modelling and Data Mining
Reporting and Insight (Offline and On...
• Celebrus - entry into big data for N Brown
• Bottom-up approach: deeper-drive analytics tool for SQL experts
• Ability t...
• Teradata Integrated Channel Intelligence (ICI) Layer
- Captures low level interaction data
- Can still analyse this unde...
•WURFL
•Mobile / Tablet device data at individual session level in Teradata. Over 1K combinations
of Models and Operating ...
Products
Abandoned
Entry
Method
Payments
62 Tables
Products
Added to
Bag
Filters
e.g. price
Products
Removed
from Bag Prod...
Filters –> Mailing Selections –
Accounts selecting ‘shoes’
Products Abandoned –>
Abandoned Bag Email
Products Viewed –> Br...
What have people been searching?
Are they
an existing
or new
customer?
Do people scroll
down the page or
look at what is f...
• Predicts how likely a
customer is to visit our
websites within a month
• Gives a rank from 0 to 19
based on how engaged ...
Segment Name Behaviours Potential Actions
Value Hunters Customer who consistently
clicks / visits Sale area of site.
Custo...
WEB_SESSION_ID DATE_AND_TIME_OF_LHN_CLICK LHN_CONTROL_USED LHN_VALUE_SELECTED
629999510 24/01/2014 14:39 home kitchen & co...
• Cosmetic Testing – placement, colours, type of CTA
• Fundamental business questions – Cash / Credit, Re-directs
• Some o...
Week
LW TW
Product Products Viewed Product Conversion Products Viewed Product Conversion
KNOT MAXI DRESS 1,195 1.0% 1,305 ...
• Exploiting unstructured data. Opportunities of pattern detection
through big data tools such as Teradata Aster
• Attribu...
• Over £4M incremental revenue benefit delivered last Financial
Year from Web Analytics initiatives
• We are all at a crit...
44
Practical Next Steps
Gareth Powell
• When integrating Celebrus we worked out what data is
important to the business. This is an evolutionary process
• Develo...
Gareth Powell
Head of Web Analytics
JD Williams
James Lawson
Consultant Editor
Marketingfinder.co.uk
Ruth Gordon
Director ...
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 ‘Dig...
Thank You
Brought to you by In association with
Digital Marketing in 2014:
How to Harness your Customer Data
Upcoming SlideShare
Loading in...5
×

Digital Marketing in 2014: How to harness your Customer Data

121

Published on

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.

Published in: Marketing
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
121
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Digital Marketing in 2014: How to harness your Customer Data"

  1. 1. Brought to you by In association with Digital Marketing in 2014: How to Harness your Customer Data The webinar will begin shortly Listen via your computer speakers or on the phone UK: +44 (0) 20 7151 1875 Access Code: 736-940-702
  2. 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. 3. Interact with us How to Harness your Data in 2014 Follow the conversation on twitter #HarnessYourData
  4. 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. 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. 6. Email, search and social are highest priorities but big data is a priority for 27% Key Finding #1: Digital Marketing Priorities
  7. 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. 8. Aggregated web data is the primary source of customer data Key Finding #3: Customer Data Collected
  9. 9. Key Finding #4: Single Customer View
  10. 10. Web analytics is the tool most commonly used (72%) with other analytics having far less penetration Key Finding #5: Analytics Undertaken
  11. 11. Quick Poll Digital Marketing in 2014: How to Harness your Customer Data
  12. 12. Key Finding #6: Analytics Benefits
  13. 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. 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. 15. Key Finding #9: Personalisation Importance & Real-Time
  16. 16. Quick Poll Digital Marketing in 2014: How to Harness your Customer Data
  17. 17. The increasing importance being placed on personalisation reflects the broad range of benefits being delivered Key Finding #10: Personalisation Benefits
  18. 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. 19. Today It’s About the CUSTOMER ...not the Web
  20. 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. 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. 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. 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. 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. 25 Online Analytics Gareth Powell, Head of Web Analytics, JD Williams
  26. 26. 26 30-45 45-65 65+ AB DE JD Williams Introduction: Our Key Brands by Age & Social Group
  27. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 44 Practical Next Steps Gareth Powell
  45. 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. 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. 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. 48. Thank You Brought to you by In association with Digital Marketing in 2014: How to Harness your Customer Data

×