Digital Marketing in 2014 - How to Harness Your Customer Data.

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Ruth Gordon, Director of Digital Marketing at Teradata International & Gareth Williams of catalogue retailer JD Williams look at how digital data driven marketing requires a different approach to more traditional CRM which throws up some new and exciting challenges for Marketeers. The results of the Celebrus & Teradata Digital Marketing Insights survey with Mycustomer.com looks at how businesses are capturing and using their customer data for analytics and personalisation and whether this is enabling them to achieve their goals, plus the difficulties and benefits along the way.

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  • I’m Ruth Gordon, Director of Digital Marketing at Teradata International, and over the next 20 minutes I’m going to look at how digital data driven marketing requires a different approach to more traditional CRM which throws up some new and exciting challenges for Marketeers. The results of of our Digital Marketing Insights survey with Mycustomer.com looks at how businesses are capturing and using their customer data for analytics and personalisation and whether this is enabling them to achieve their goals, plus the difficulties and benefits along the way.Teradata customers are really beginning to see the value of data in engaging their customers and we’re very pleased to have Gareth Powell from JD Williams who is going to talk about their successes.
  • OLD NOTES:Business challenge #1Businesses are finding that their ‘always on…always connected’ customers have high expectations.They want things faster because they’re having to squeeze more into less timeAnd they want things their way and on their termsThey’re looking for new experiences that will excite them and content that is relevant to them, Something has to really stand out to capture their attentionThey value the opinions of others but also like to be heardAnd they expect to experience a seamless multichannel, regardless of the channel their shopping inBusiness challenge #2:Knowing how an individual customer interacts across all your touchpoints is becoming critical to enaging your customers but the ability to continually listen, then pull this together into a single customer view is a huge challenge for today’s digital marketers.These interactions represent vast quantities of data that are highly variable, dynamic and multi-structured and often in a format that is aggregated from data sources such as web analytics tools.Business challenge #3:And it’s this diverse nature of digital data which makes it difficult for most traditional technologies to capture, store and analyseThis new world has different requirements and needs a new approach…The data is most useful at it’s lowest level because that’s where the value lies, not in aggregated statsIt’s vast so the technology needs to be scalableIt requires advanced analytics like pattern analysis to enable discovery of new insightIt requires tying together datasets from various sourcesIt’s value decays rapidly so we need to be able to have fast timely processingBusiness challenge #4:The next challenge is to meet customer expectations by utilising these new insights to deliver an individual experience at a true 121 level.It’s the ability to interpret those signals and take appropriate actions, increasingly in real time, across any device, channel or location that’s the key to success.
  • OLD NOTE:In November last year Teradata and Celebrus (KH addition) carried out a Digital Marketing survey with Celebrus, our partner who enabless the tagless capture of web data and Mycustomer.com, the online customer forum.The survey was conducted across the UK, Germany and France. It was cross industry and aimed at Marketing and CRM professionals.The aim was to understand the importance of a data driven approach to digital marketingThe questions focused on how digital marketers are using customer data, analytics and personalisation to achieve their goals and the benefits and difficulties they are experiencing.The report is available to download from Mycustomer .com but I’ve just pulled out some of the highlights.
  • The digital landscape is rapidly evolving and modern marketers face an increasingly tough job to juggle the sheer volume of trends and channels that demand their attention.Email arises as the most popular priority at 58% of those surveyed, despite the emergence of other digital channels,, closely followed by search marketing at 57%.Surprisingly, given its relative immaturity & continuing questions about its measurability, social marketing was the 3rd most nominated priority at 52%Big data was a priority for 27% of respondents, alongside mobile marketing.
  • Respondents told us that a number of obstacles are preventing their digital marketing efforts from capitalising on customer data, with the storage and integration of customer data reported by 36% and data quality by 23%
  • Of the many data sources available to marketers today aggregated web data is the primary source of customer data, with less than 40% collecting online behavioural or social data.Dominant use of web analytics/aggregated data could be one cause of data quality & actionable data challenges as the data is not designed to be actionable for some modern marketing such as one-to-one personalisation and communications. (KH addition)
  • The big headline for data collection and storage is that within the next 2 years, 57% of respondents expect to achieve a single customer view…a huge increase from the 21% that are achieving this now.Not surprising when you see the benefits that people are reporting…70% say better customer insights and 60% say improved targeting
  • Analytics is becoming an increasingly sophisticated discipline with- in marketing and one which, if done well, can significantly enhance marketing effectiveness and profitability of any organisation. A growing number now acknowledge the significant advantage that an analytical view across multiple sources of data and multiple channels could deliver to businesses and marketing departments in particular. Unsurprisingly, web analytics is the tool most commonly used by those questioned as part of the survey—almost three quarters (72%) report that they actively use it to support their digital marketing efforts. Other analytics endeavours have far less penetration according to the findings, with around a third of our sample using the likes of Voice of the Customer (36%), customer journey analysis (35%) and segmentation (34%). Once again, efforts that have a ‘social media’ bent are poorly represented in the results. Analytics to measure engagement and influence (20%) and sentiment (8%) both having limited adoption. Also of note from our findings is that while attribution is increasingly an issue for marketers—with a growing combination of media assisting sales across multiple touchpoints on the path to purchase meaning that first or last touch is not good enough—less than half (41%) of respondents told us that they use campaign attribution. It’s interesting to see that there’s a relatively low% of individual customer analytics @ just 31% needed to meet today’s challenges that we looked at earlier. (KH addition)
  • Organisations are reporting significant benefits from their analytics…and want to do more.Time, structural issues and skills and expertise are seen as the biggest barriers to achieving better analytics.But respondents are prepared to invest in dedicated in house analytics teams – a jump form 40% today to 51% within 2 years
  • Many respondents have a degree of personalised activity although this varies according to platform.Email is significantly the most personalised with only 8% of respondents doing no personalisation. Although name is the most popular at 45%, over a third are also personalising marketing based on historical data and profile.33% are not personalising their websites today and 30% are not personalising their mobile. Interestingly only 8% are using location data to personalise.
  • Online behavioural data is by far the most commonly used data source for personalisation (59%) followed by onsite search (40%). Only 37% are using CRM data.
  • The results on personalisation are very encouraging.Most respondents are doing personalisation to some degree, even if its just using name in email…but 51% currently say that this is either very important or critical and this rises to a huge 80% within 2 years.Those respondents who are personalising are seeing the benefits in terms of improved conversion, retention and customer experienceNo suprisingly, data quality is the biggest barrier to achieving personalisation.The intention to do personalisation in real time is clear…with 78% of respondents predicting that they will be doing this within 2 years.
  • Analytics is becoming an increasingly sophisticated discipline with- in marketing and one which, if done well, can significantly enhance marketing effectiveness and profitability of any organisation. A growing number now acknowledge the significant advantage that an analytical view across multiple sources of data and multiple channels could deliver to businesses and marketing departments in particular. Unsurprisingly, web analytics is the tool most commonly used by those questioned as part of the survey—almost three quarters (72%) report that they actively use it to support their digital marketing efforts. Other analytics endeavours have far less penetration according to the findings, with around a third of our sample using the likes of Voice of the Customer (36%), customer journey analysis (35%) and segmentation (34%). Once again, efforts that have a ‘social media’ bent are poorly represented in the results. Analytics to measure engagement and influence (20%) and sentiment (8%) both having limited adoption. Also of note from our findings is that while attribution is increasingly an issue for marketers—with a growing combination of media assisting sales across multiple touchpoints on the path to purchase meaning that first or last touch is not good enough—less than half (41%) of respondents told us that they use campaign attribution. It’s interesting to see that there’s a relatively low% of individual customer analytics @ just 31% needed to meet today’s challenges that we looked at earlier. (KH addition)
  • The increasing importance expected to be placed on personalisation reflects the broad range of benefits that personalisation efforts are delivering to businesses…
  • We are seeing from our customers that the ability to analyze digital data has become a key requirement
  • Need the tools and data designed to give you the highly granular data you need to make this transition – very hard is relying on yesterday’s technologies – too complex, time to data too slow, not granular enough, can’t piece together individuals across channels & devices
  • Web interaction data can reveal so much new insight about customers…the ability to see what individual customers are doing on the website and tie this to what you already know about them
  • For many years now businesses have been using customer demographic and transactional data to support CRM activity though segmentation and predictive modelling.Now we are seeing customers start to capture and integrate individual browsing behaviour and use this to support eCRM activities like personalisation of web and email based on customer behaviour, enagagement led contact strategy, campaign attribution, product recommendations and customer journey optimisation.And marketeers are now starting to see the value of social data to support Scrm activity. Social data,enriches your customer view with customer preferences, interests and intentions which enables…I’m now going to go through some examples of the types of activities that our customers are doing using digital data
  • The first examples are around ensuring that you say the right thing to the right customer at the right time every timeDetecting customer behavioural triggers whilst they interact with your website, like repeat buying an item but not buying, abandoning a basket but not buying offline, making a purchase, browsing a new category are all opportunities to send individually personalised emails and these are many times more effective than broadcast offers.Many of our customers are now personalising their websites using not just in session behaviour like search but previous onsite behaviour and crm data with substantial lifts in conversion. Gareth will talk about JD Williams. This ability to interact real time and not just with customer that you know, is a powerful tool for engaging with customersUnderstanding how customers engage with different channels and offers can be used to determine future contact strategy, so simple segmentations like online engagement based on how often customer browse, how long for, how many products they view, whether they review products can be used to decide whether to send them paper or not, whether they need to be re-engaged etcCustomers like Macy’s are using ibeacon technology to target 121 offers to customers mobiles as they walk through their storesAnd using product affinity analysis to determine which products are browsed and bought together can really increase the effectiveness of product recommendations
  • Many of our customers are doing some really interesting things with advanced analytics to get better insights into their viewer behaviour.They use path analysis, which makes it possible for you to identify the most common paths for any event, from the many hundreds of different possibilities and identify patterns leading these events. They also use affinity analysis which enables yu to identify the products that are browsed and bought together,They are using these new insights to enable journey improvements e.g. by detecting paths to churn, site and basket abandonment, omnichannel behaviour for site usage reporting, but at a customer level, and to improve MVTCanal Plus, the entertainment provider have been using path and affinity analysis to make viewer recommendations, resolve issues, define channel packages, segment customers and for ad placement.Journey analysis is also being used to optimise processes By determining how customers flow through processes you can understand bottlenecks, repeated steps, inefficiencies like web interactions followed by customer phone queries and significant drop out points. By re-engineering processes customer experiences can be significantly improved leading to improved retention and cost efficiencies
  • There are also many opportunities around improving ROI and driving cost efficiencies.Spend attribution is a very hot topic. The ability to look at customer journeys and understand the importance of every interaction in the final purchase highlights the inadequacies of using first or last click and we have customers who are driving 30% budget savings by optimising paid search bidding and affiliate payments.Identifying fraud as it happens was a big success for one of our online customer. They used web interaction data to discover multiple accounts were being accessed from the same IP address. This led to millions in savings as fraudulent orders were prevented.Behavioural based pricing is also a hot topic, with insurers using individual driver performance from telematics data to determine pricing. They use the driving pattern data alongside pricing and vehicle type to create a risk segment customer . They use this for risk messaging and to determine individual pricing
  • Too complex?
  • Online PushPostcard instead of mailing for high scorersSend mailing to low scorers
  • Behavioural Segmentation is a new project for us which is in the early stages. Knowing how I shop personally, I always filter on price so I can’t be tempted to buy more expensive products. Behavioural segmentation aims to use this type of behaviour to influence a customer’s email and mailing strategy. For example, if a customer only ever views the sale online, is it worth sending them a full margin catalogue?
  • Digital Marketing in 2014 - How to Harness Your Customer Data.

    1. 1. Digital Marketing In 2014: How to Harness Your Customer Data WEBINAR 19th March 2014 James Lawson – Moderator Ruth Gordon – Teradata Gareth Powell – JD Williams 1
    2. 2. 2 Welcome James Lawson, Consultant Editor, Marketing Finder
    3. 3. 3 How to Harness Your Customer Data Ruth Gordon Director Digital Marketing, Teradata
    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. 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
    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. Poll #2: Personalisation Channels Which channels do you struggle to personalise through? •Email •Website •Call centre •Mail •SMS •Very limited personalisation
    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 = 0.919 -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. 46 Thank you for listening! Now over to you for Q & A
    47. 47. • 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 47 What next?
    48. 48. 48 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

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