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A marketers guide to data analytics marketing finder webinar 17 july 2013

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    • 1. Brought to you by In association with A Marketer’s Guide to Data Analytics: Gaining Real Insight from Big Data The webinar will begin shortly Listen via your computer speakers or on the phone UK: +44 (0) 207 151 1875 Access Code: 182-031-422
    • 2. Today’s Speakers A Marketer’s Guide to Data Analytics Colin Linsky Worldwide Predictive Analytics Leader IBM SPSS James Lawson Consultant Editor marketingfinder.co.uk
    • 3. Interact with us A Marketer’s Guide to Data Analytics Follow the conversation on twitter #AnalyseData
    • 4. © 2012 IBM Corporation Agenda  Analytics – Time to Rethink  Predictive Analytics – The Competitive Advantage  Using Data Analysis to Increase Sophistication  Turning Insight into Action – The Path to Personalization 4#AnalyseData
    • 5. © 2013 IBM Corporation 1. Analytics – Time to Re-think? #AnalyseData
    • 6. © 2013 IBM Corporation Thinking big about data presents huge opportunities for marketers Billions of consumer preference and satisfaction indicators available across call centers, web sites, transaction logs, face-to-face interactions and social media 1 in 5 minutes online is spent on social networks only 6.8% of marketers believe social media is integrated into strategy 5 billion+ mobile phones globally 49% of consumers use two or more technologies to shop, and 53% of active adult social networkers FOLLOW A BRAND Big Data Data in many forms Data in doubt Terabytes and zettabytes of data Variety Batch and streaming Velocity Veracity Volume 400 million tweets are sent daily 7.6% of budget in marketing is allocated to social media 55% use their mobile phone for price comparison 34% scan QR codes #AnalyseData
    • 7. © 2013 IBM Corporation Outperformers are twice as good at deriving value from data – key to engaging customers as individuals Outperformers strongly differentiate their organizations in three key areas Outperformers Underperformers Draw insights from data 54% Translate insight into action 57% 54% 26% 26% 31% 108% more 108% more 84% more Access to data Source: Q22 ―How good is your organization at driving value from data?[Today]‖ (Global n=631 to 636) (Retail n=103 to 104) #AnalyseData
    • 8. © 2013 IBM Corporation Analytics-driven organizations are distinguished by their ability to leverage: All perspectives Past (historical, aggregated) Present (real-time, scenarios) Future (predictive, prescriptive) At the point of impact All decisions Major and minor Strategic and tactical Routine and exceptions Manual and automated All information Transaction/POS data Social data Click streams Surveys Enterprise content External data (competitive, environmental, etc.) All people All departments Front line, back office Executives, managers Employees Suppliers, customers and consumers Partners #AnalyseData
    • 9. © 2013 IBM Corporation Customer Analytics – an increasing requirement Organisations are trying to determine how to keep the best customers, increase loyalty and demonstrate customer commitment while reducing costs AND increasing margin. Intensifying Competition Soaring Customer Expectations Channel Proliferation and Complexity Social Networking Shrinking Wallet Share Decreasing Loyalty #AnalyseData
    • 10. © 2013 IBM Corporation Increasing sophistication of Customer Analytics means:  Improvements in consumer relationships, brand loyalty, increased customer satisfaction, and development of a continuous dialog with top consumers  Analyzing consumption data to understand product affinity, infer behavior and deliver the right action/offer at the right time  Maximize marketing spend and improve effectiveness  Improve financial performance, and drive increased profit and margins  Up-sell and cross-sell Attract, retain and grow consumers with Customer Analytics  Purchase history  Browsing habits  Loyalty program details  Social Media  Historic campaign responses  Price sensitivity  Current interaction  Customer demographics  Call centre records  Communication preferences  Advanced customer segmentation  Product affinity  Propensity to purchase  Market basket analysis  Social media analytics  Targeted promotions  Next best action  Deeper customer engagement  Campaign insight & optimization  Life time profitability analytics  Price management  Channel performance  Reporting and analysis  Advanced analysis and predictions  Scorecarding & dashboarding  Planning, budgeting and forecasting  Business rules and optimization  Real-time decisions  Scenario and what-if modeling Customer Data Analytics Capabilities Insight into Action #AnalyseData
    • 11. © 2013 IBM Corporation 1/30/13 ―Our experience is that within broad consumer movements, small groups of users (often overlooked in cursory analyses) actually drive the economics. Achieving a more refined understanding of who is doing what requires a thoughtful segmentation—incorporating data about consumers’ demographics, household characteristics, usage patterns, spending, attitudes, and needs—supported by ―big data‖ analytics." ―iConsumer: Digital Consumers Altering the Value Chain.‖ W. Duncan, E. Hazan and K. Roche. McKinsey & Co. 2013
    • 12. © 2013 IBM Corporation 2. Predictive Analytics – The Competitive Advantage #AnalyseData
    • 13. © 2013 IBM Corporation Business Analytics 13 What happened? Why? What to do next? BI PA From Sense and Respond to Predict and Act #AnalyseData
    • 14. © 2013 IBM Corporation Predictive Analytics – What is it? • A true analytics process is the one that transforms raw data into actionable insights, the true transformation from "So What?" to "Now What?". • Business Analytics is the process that transforms raw data into actionable strategic knowledge to guide decisions aiming to increase market share, revenue and profit. • Drive your business by making informed decisions based insights derived from analyzing one of you most valuable company assets, data. • Analytics takes data and translates it into meaningful, value-added options for leadership decisions. • Actionable, statistically supported insights from data that help drive competitive advantage. • ―By 2014, 30% of analytic applications will use proactive, predictive and forecasting capabilities‖ Gartner Forecast, 2011 http://www.readwriteweb.com/enterprise/2011/01/business-analytics-predictions.php #AnalyseData
    • 15. © 2013 IBM Corporation Key Moments of Truth  Research and Browse  Browsing and cart use  Pre-purchase  Checkout and payment  Delivery  Multi-Channel use  Sign-up to a Loyalty Program  Response to a campaign or promotion  Credit application  Complaint  Claim  Customer Service Request  Warranty registration  Blog/Twitter  Social Media  Product out-of-stock  Destruction of perishables  Low velocity product sales  Demand forecast Attract Grow Retain Fraud Risk #AnalyseData
    • 16. © 2013 IBM Corporation Consolidated Data Sources 16 Single Customer View Loyalty Scheme eCommerce Direct Marketing Catalogue Ordering Customer Service Social Networking Home Delivery 3rd Party Data POS / Transactions Browsing History Purchase / Return History Product Catalogue Store / Channel Landscape #AnalyseData
    • 17. © 2013 IBM Corporation Driving Smarter Business Outcomes Capture DataCollection Enabling a complete view of the customer combining enterprise and social media based data Act Deployment Technologies Deploy predictive analytics within business processes, across access platforms, maximizing operational impact … … Predict Platform Pre-built Content StatisticsText Mining Data Mining Understand customers micro-behavior across channels, predict their next move and make the next best offer RetainUp-sellAttract #AnalyseData
    • 18. © 2013 IBM Corporation Variety Data in Many Forms Structured, unstructured, text, multimedia Velocity Data in Motion Streaming data, milliseconds to seconds to respond Volume Data at Rest Terabytes to Exabytes of existing data to process Veracity Data in Doubt Uncertainty due to data inconsistency & incompleteness, ambiguities, latency, deception, model approximations Worried about Big Data? #AnalyseData
    • 19. #AnalyseData A Marketer’s Guide to Data Analytics: Gaining Real Insight from Big Data Quick Poll
    • 20. © 2013 IBM Corporation 3. Filling the Tank with Fuel #AnalyseData
    • 21. © 2013 IBM Corporation Advanced Affinity Analysis - Propensity to Purchase Transactions from all customers Market basket insights • If A then B • If C then D • If E and F then G • If H, then H then I Special Offer – This Week Only 10% off on any of these combinations: A + B…G + H…. Promotional Display Buy X get Z for only $1.49! 1 Brand Razors Brand Shampoo House brand shampoo House brand hair color Brand Toothpaste Brand Skin care Men’s fragrance Woman’s fragrance House brand sun care Optician Feminine hygiene Online photo service Personal Electrical 2-4-1 Discount 5% Extra Reward Points 2 4 5 6 7 8 9 10 11 12 13 14 15 3 Offers Transactions from this customer • Cardholder since YYYYMM • Average transaction value • Monthly transaction value • Categories purchased • Brands purchased Descriptive • Age • Gender • Family situation • Zip code Interactions • Web registration • Web visits • Customer service contacts • Channel preference Attitudes • Satisfaction scores • Shopper type • Eco score Offer inserts ? ? ? ? 1512 311 Offer inserts 3 13 6 12 + + + #AnalyseData
    • 22. © 2013 IBM Corporation Let’s start with some simple transaction data… #AnalyseData
    • 23. © 2013 IBM Corporation • Create − Time of Day − Day of Week − Week/Month/Season − Time hierarchies − Channel − Discount/Promotion − Returns − Browsing/Researching/Purchasing • Aggregate: Transactions − Value − Quantity • Model − Product Mix (Associations) #AnalyseData
    • 24. © 2013 IBM Corporation Transaction Data #AnalyseData
    • 25. © 2013 IBM Corporation • Create − Product Catalogue Hierarchy  Attributes  Quantity  Value − Basket Margin • Aggregate: Baskets − Quality Diversity − Brand Diversity − Hierarchy Diversity • Model − Basket Mix (Associations)  Across hierarchies  Across attributes Transaction Data Product Data + #AnalyseData
    • 26. © 2013 IBM Corporation • Create − Sequences − First Transaction − Last Transaction − Time horizons − Lapses − Addition of 3rd Party data enrichment (e.g. geospatial, demographic and lifestyle data) − Target attribute flagging • Aggregate: Customers − Temporal summaries − Shopping mission statistics − Longitudinal spend − Cross category/line statistics • Model − Recency Frequency Monetary Value (RFM) − Lifetime Value (LTV) − Category/Line/Product Recommendations − Propensity to Lapse − Propensity to develop Transaction Data Product Data Customer Data + #AnalyseData
    • 27. © 2013 IBM Corporation • Create − Location demographics − Channel operations − Channel/Store format attributes • Aggregate: Channel − Location Assortment Mix − Staffing and Service provision − Contact statistics − Usage (e.g. visitors, footfall, traffic) − Customer profile analysis − Operations statistics • Model − Anomalous Activities − Revenue Protection Indicators Transaction Data Product Data Customer Data Channel Data + #AnalyseData
    • 28. © 2013 IBM Corporation • Create − Macro-economic indicators − Environmental metrics − Identify significant events • Aggregate: Environment − Build useful historic time structures Transaction Data Product Data Customer Data Channel Data Context Data + #AnalyseData
    • 29. © 2013 IBM Corporation • Create − Summarizing interaction history  Offers/recommendations made • Aggregate: Environment − Frequency of responses − Frequency of activity • Model − Campaign Responsiveness − Offer Sensitivity Transaction Data Product Data Customer Data Channel Data Context Data Interaction Data + #AnalyseData
    • 30. © 2013 IBM Corporation • Create − Solidifying into the Single Customer View from all sources − Track changes in metrics, measures and attributes over time • Model − Profiling − Segmentation − Clustering − Anomalous patterns − Affinity Analysis − Forecasting Single Customer View #AnalyseData
    • 31. #AnalyseData A Marketer’s Guide to Data Analytics: Gaining Real Insight from Big Data Quick Poll
    • 32. © 2013 IBM Corporation 4. The Path to Personalization #AnalyseData
    • 33. © 2013 IBM Corporation Turning customer insight into action with analytics 360 View Of Customer Customer Segmentation Predictive Modeling CUSTOMER • Detect patterns • Find preferences • Identify drivers • Predict demand • Score results • Model scenarios • Connect all touch points • Create 360 view • Analyze historic activity • Explore all data Gain a deep understanding of customer segments, micro-segments, individuals #AnalyseData
    • 34. © 2013 IBM Corporation Historic data Present data Suggested future behavior 360 View Of Customer Customer Segmentation Predictive Modeling Reporting and KPIs Real-time Decisions CUSTOMER CONTEXT • Combine history with current activities • Add other relevant external data (weather, demographics) • Add other relevant internal data (stock position, call center) • Identify behavioral drivers • Determine propensity to respond • Scoring – individuals not interested in everything equally! Turning customer insight into action with analytics Add context with historical sales data to understand product affinity, propensity to purchase and market basket analysis #AnalyseData
    • 35. © 2013 IBM Corporation Historic data Present data Suggested future behavior 360 View Of Customer Customer Segmentation Predictive Modeling Reporting and KPIs Real-time Decisions CUSTOMER CONTEXT COMMUNITY • Social communities • Staff • Influencers • Known / unknown Turning customer insight into action with analytics Integrate social media data to understand purchase influencers and behavior #AnalyseData
    • 36. © 2013 IBM Corporation Historic data Present data Suggested future behavior 360 View Of Customer Customer Segmentation Predictive Modeling Personal Interactions: Actions Offers CONTENT Reporting and KPIs Real-time Decisions CUSTOMER CONTEXT COMMUNITY • Product assortment • Inventory levels • Relevant promotions • Continuous, meaningful dialogue Personalize customer experiences with analytics Customize experience and interactions by understanding what drives, enhances, and shapes purchase decisions #AnalyseData
    • 37. © 2013 IBM Corporation Historic data Present data Suggested future behavior 360 View Of Customer Customer Segmentation Predictive Modeling Personal Interactions: Actions Offers CONTENT Reporting and KPIs Real-time Decisions CUSTOMER CONTEXT COMMUNITY COMMERCE • Conduct transaction • Exchange money for goods or services Personalize customer experiences with analytics Simplify purchase process by enabling anytime, anywhere exchange of money for goods #AnalyseData
    • 38. © 2013 IBM Corporation Historic data Present data Suggested future behavior 360 View Of Customer Customer Segmentation Predictive Modeling Personal Interactions: Actions Offers CONTENT Reporting and KPIs Real-time Decisions CUSTOMER CONTEXT COMMUNITY COMMERCE Measure and analyze all touches, interactions, responses to refine approach Personalize customer experiences with analytics Measure key metrics at every step and use to continuously refine experience #AnalyseData
    • 39. © 2013 IBM Corporation Turning insight into action: Analytic components and processes Path to personalization means leveraging all forms of data, interactions and time horizons BATCH PROCESSING Predictive Modeling Customer Online Browsing and Transactions In-Store Transactions Products Social Media Demographics Reporting and KPIs • Historic data • Present data • Suggested future behavior Business Rules Domain Expertise Predictive Model Scoring Analytical Decision Management Personal Interactions with Consumers REAL-TIME PROCESSING – ONLINE, IN STORE, MOBILE Association Classification Segmentation Propensity Inventory Supply Chain Path to personalization means leveraging all forms of data, interactions and time horizons #AnalyseData
    • 40. © 2013 IBM Corporation 5. Summary #AnalyseData
    • 41. © 2013 IBM Corporation Summary  Today, filtering and selection is not enough – it’s all about finding useful patterns that are in your data  Access, create, aggregate and model  Measures over time, context and changes in circumstances are almost always worth the effort finding  You can never have too much data but start small and grow Data sources and types Data management activities Data analysis techniques  Make analytics a habit and integrate into regular processes  Regularly monitor metrics and effectiveness – it is a dynamic world and you should refresh regularly to spot changes in your customer’s behaviours and attitudes  Always aim to turn insight into action #AnalyseData
    • 42. © 2013 IBM Corporation 1/30/13 For further information please email: spssmktg@uk.ibm.com For demonstrations and event information: http://www-01.ibm.com/software/uk/analytics/spss/events/
    • 43. Your Questions A Marketer’s Guide to Data Analytics James Lawson Consultant Editor marketingfinder.co.uk Colin Linsky Worldwide Predictive Analytics Leader IBM SPSS
    • 44. A Marketer’s Guide to Data Analytics: Gaining Real Insight from Big Data 3 Great reasons to fill out the exit survey 1. You can request more information on data analytics 2. You can give us your feedback 3. You can request a free copy of The Virtual Presenter’s Handbook
    • 45. #AnalyseData Thank You A Marketer’s Guide to Data Analytics: Gaining Real Insight from Big Data Brought to you by In association with