Digital Marketing In 2014:

How to Harness Your

Customer Data

WEBINAR 19th March 2014James Lawson – Moderator

Ruth Gordon – Teradata

Gareth Powell – JD Williams

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Welcome

James Lawson,Consultant Editor, Marketing Finder

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How to Harness Your Customer DataRuth Gordon Director Digital Marketing, Teradata

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

- 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 ResearchHow are Marketers Dealing with the Challenges?

Email, search and social are highest priorities but big data is a priority for 27%

Key Finding #1:Digital Marketing Priorities

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

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:Analytics Undertaken

Poll #1:Customer Analytics

Who is responsible for customer analytics in your organisation e.g. customer journey, campaign attribution, basket analysis etc?

• Part of what the web analytics team does• Separate customer analytics team• Customer experience team• Part of what the business intelligence team does• Another team• Very limited/no customer analytics undertaken

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 mobile.

Key Finding #7:Personalisation Channels

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

Key Finding #9:Personalisation Importance & Real-Time

Poll #2:Personalisation Channels

Which channels do you struggle to personalise through?• Email• Website• Call centre• Mail• SMS• Very limited personalisation

The increasing importance being placed on personalisation reflects the broad range of benefits being delivered

Key Finding #10:Personalisation Benefits

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 MarketingAbility to Analyse Digital Channel Data

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 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 DataExample Data & Uses

• 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

• Issues

CRM eCRM sCRM

Beyond CRM: Integrating Digital & Social Intelligence

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

•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

•Advanced spend attribution

•Fraud detection

•Lead generation

•Compliance and mis-selling

•Behavioural based pricing with telematics

•Channel optimisation e.g. Paper removal

Improving Business EFFICIENCIESThrough Analytics & Optimisation

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Online Analytics

Gareth Powell,Head of Web Analytics, JD Williams

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30-45 45-65 65+

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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 • 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

• 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

• 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

Analytics @ JD Williams:Core Business Analytics Landscape

Modelling and Data Mining

Reporting and Insight (Offline and Online Customer Data)

Campaign Execution

Web Analytics - CoremetricsAccount # Propensity to Buy

24149080 £28899218880 £5663978660 £11

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Celebrus

• 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

• 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

•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

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

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

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: ENGAGEMENTUsage in Practice

• 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

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Likelihood to return to site

VES Rank

Detailed Online Customer Data: ENGAGEMENTVisitor Engagement Score

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: ENGAGEMENTBehavioural Segmentation

WEB_SESSION_ID DATE_AND_TIME_OF_LHN_CLICK LHN_CONTROL_USED LHN_VALUE_SELECTED

629999510 24/01/2014 14:39home

kitchen & cookware

629999510 24/01/2014 14:40kitchen & cookware

kitchen storage & bins

Detailed Online Customer Data: EXPERIENCELeft Hand Navigation Example

• 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: EFFICIENCYMulti-Variate Testing

WeekLW 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: EFFICIENCYProduct Conversion

• 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

• 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

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Practical Next Steps

Gareth Powell

• 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

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Realising the Data-Driven Vision:Practical Next Steps & Our Key Learnings

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Thank you for listening!

Now over to you for Q & A

• Download our new eBook here:• Watch this video to see Celebrus & Teradata in action:

http://bit.ly/BSIVideoHRP • Visit www.teradata.com/ for more information• Visit www.celebrus.com for more information• Go shopping at www.jdwilliams.co.uk www.simplybe.co.uk

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What next?

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Exit Questions

1. Do you have a single view of your individual customers across devices and channels?

• Yes – already done• Some gaps – not sure how to fill them• Some gaps – plans in place & getting there• Several gaps – but on the roadmap to improve• Several gaps – no immediate plans to fill them• Many gaps – but on the roadmap to improve• Many gaps – no immediate plans to fill them

2. What’s your biggest digital marketing challenge?• Getting the right detailed data to drive customer insight & action• Having access to the data at the time needed to drive highly

responsible marketing• Resources (skills/budget/time) to use the data to deliver insight• Digital marketing not high enough on strategic agenda• Complex cross-functional decision-making group involved