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Big data and Marketing by Edward Chenard

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Big data and Marketing by Edward Chenard

  1. 1. Edward Chenard Big Data and Marketing How Big Data is Becoming a Marketing Tool STAV Data
  2. 2. According to Gartner 85% of Fortune 500’s are not doing it. According to Accenture, of those who are doing it, 75% are failing. Few can describe it and even fewer know how to do it. What is Big Data?
  3. 3. 1. Big Data Collection (HDFS) 2. Big Data Processing (Hadoop) 1. Data Mining at Scale (Hive) Breaking down the IT of Big Data
  4. 4. Big Data Tools Words you May Hear BlinkDB CassandraHive Python Pig Stinger HadoopGiraph Spark GraphX MLbase You don’t need to be an expert in these tools, but knowing how they are used goes a long way Impala
  5. 5. Image Unstructured Semi Structured Structured • Click Streams • Social Streams • RSS feeds • XML Documents • Spreadsheets • Relational Databases Data ecosystem, what is it, how to understand it. Unstructured data is the goldmine, it is growing while structured data is shrinking. But to make big data work for you, you need to structure of the unstructured
  6. 6. Image Structured Unstructured First understand what kind of data you have to work with. How to Make Data a Marketing Tool
  7. 7. How we Personalize Big Data and Marketing in Use Combine the strengths of Google and Facebook’s methods with psychograph techniques. Listen, Adapt, Respond Services co-created with customers and are interpedently with wider service networks. Benefits People will log in more Higher conversion and AOV Better emotional bond between company and customer Psychograph Self Facebook Self Google Self Clash between Today and Future Aspirational You Present You 1-1
  8. 8. Sentiment Expressed as positive, neutral, or negative, the prevailing attitude towards and entity Behavior These signals identify persistent trends or patterns in behavior over time Event/Alert A discrete signal generated when certain threshold conditions are met Clusters Signals based on an entity’s cohort characteristics Correlation Measures the correlation of entities against their prescribed attributes over time Rate of Change (Slow or Fast) Quality (Predictive or Descriptive) Sensitivity (Sensitive or Insensitive) Frequency (High or Low) All signal types have certain qualities that describe how quickly signals can be generated (frequency), how often the signals vary (rate of change), whether they are forward looking (quality), and how responsive they are to stimulus (sensitivity) Signals have attributes depending on their representation in time or frequency domain can also be categorized into multiple classes Signal Types
  9. 9. Timing/ Recency Measure the freshness of the data and of the insight Source Measure sources’ strength: originality, importance, quality, quantity, influence Content Derive the sentiment and meaning from tracking tools to syntactic and semantics analysis Context Create symbol language to describe environments in which the data resides Clickstreams Social Articles Blogs Tweets For each dimension, develop meta-data, ontology, statistical measures, and models High quality signals are necessary to distill the relationship among all the of the Entities across all records (including their time dimension) involving those Entities to turn Big Data into Small Data and capture underlying patterns to create useful inputs to be processed by a machine learning algorithm. Finding Signals in Unstructured Data
  10. 10. Behavioral Patterns 1 to 1 Marketing Product/Service Compatibility Market Trends Social How the Data Becomes Customer Experiences Crowd based user actions drive recommendations Personalized email marketing Recommendations based on products Use machine learning algorithms to predict trends Small world network communication Algorithms analyze data Data Capture Points, Experience Delivery Points, Metrics Data Capture Ecosystem
  11. 11. The Data, Insights, Action Gap The Data Insights Gap Data to insights can often fall short for a number of issues - Difficulties in defining areas of focus for external data - Only gradual adoption of exception analytics and automated opportunity seeking - Example (P&G / Verix Systems) - Opportunity seeking business alerts - Value share alerts - Out of stock alerts - New Launch alerts The Insights Action Gap Processes and systems designed prior to big data thinking Examples: - CRM - Pricing: Buy now in-store pricing - Supply chain and logistics - Prevalence of operational , internal metrics - Complex new concepts: “Intents”
  12. 12. Image Activity Based Thinking
  13. 13. Human Motion Graphs Human motion graphs help understand movement of customers and helps to predicts timing of marketing activities
  14. 14. Image Tracking How People Respond
  15. 15. Image Data Discoverers Data Discoverers are setting the trend in what will be common place in just a few short years. More people will want to use their data and the consumerization of data and technology will continue. As this trend goes, only organization that learn to merge the various disciplines of strategy, analytics and IT, will be successful Data as a Lifestyle
  16. 16. Real-Time Firehose Services Apps Multimedia Places Internet of Things Our Data Sources are Changing
  17. 17. Search On-sites Sensors Re-marketing Customer Feedback Signals Hub Social Personalization Products Customer Service Digital Marketing In-store Creating Customer Signal Hubs
  18. 18. Where we are Going How we organize our data is getting more customized and real-time for real bottom line improvements 0% 5% 10% 15% 20% 25% Vendors Hadoop Customized Customized Realtime Big Data Technology Evolution Personalization Technology Evolution
  19. 19. How to Take Advantage of Data
  20. 20. data visualization strategy / review technology implementation analytics “The STAV Cycle” “gaining insight and telling stories with data” © 2014 STAV Data www.stavdata.com
  21. 21. Phase Typical Issues Recommended Approach Strategy / Review Define goals / outcomes / expectations in the form of business benefit / customer benefit; form hypotheses / build business case; Evaluate whether expectations for the current cycle were met; identify opportunities for improvement; set expectations for the next cycle Ignored / under-emphasized Increase emphasis Establish formal methodology Build capability Technology Implementation Identify the tools needed to accomplish the business goal; define the technical path for accomplishing the business goal; establish development schedule Over-emphasized Initiated too early Inadequate skill set Decrease emphasis Employ proof-of-concept Use external services Build skill set gradually / incrementally Analysis Analyze the data collected by the IT implementation – find the gems; a function for data scientists or traditional BI – not an IT function; data science = 80% data analysis / data cleaning, 20% algorithm creation Descriptive orientation (business intelligence) Dis-integration of business intelligence / data science Organized in IT function / focus on algorithm creation Adopt predictive orientation (data science) Integrate business intelligence / data science Organize in business function / focus on data analysis and data cleaning Data Visualization Tell the story of the patterns in the data; a function for designers – not data scientists; critical to making the analysis useful from a business perspective Located in IT function / performed by data scientists Focus on methodology vs. results Locate in business function – branding or UX Assign to designers “Making the Impossible Possible” “Big Data is good for solving impossible problems; it just makes simple problems more complex” The STAV Cycle will increase the probability of of success for any organization. Implementation of the cycle includes many more details; it needs to be adjusted to each organization and the goals of each project; but the basic framework doesn’t change. If you use this framework, your big data project will be successful. © 2014 STAV Data www.stavdata.com
  22. 22. internal / external (medium investment / medium scope) internal (large investment / broad scope) external (small investment / narrow scope) © 2014 STAV Data www.stavdat a.com Maturity Model / Product Development Life Cycle
  23. 23. Vision & Goals Governance Execution Clearly articulated vision for marketing and data use, precisely defined goals with how to measure. Defined scope of the product. Market strategy, customer segmentation, prioritization, org focus, measurement and incentive systems Production process, flexibility at scale, efficiency, relationship management, benchmarking, metrics, initiatives How work gets structured
  24. 24. Strategy - Define the goals Social Define how to engage IT Assemble the Technology Analytics Make sense of the Data Linguistics Distributed Processing (Hadoop) Algorithms Development Cross team Customer Experience Improvement Data science is a discipline for making sense of unstructured as well as numerous data sets at scale Develop Your Team
  25. 25. Listen •Listen to the data streams Share •Share the data with the rest of the organization Engage •Engage to the data to find the insights Innovate •Innovate new ideas from the insights gained from the data Perform •Perform insightful actions from the data to create better customer experiences Always Remember: Data, Insights, Actions
  26. 26. Print Radio SEO and PPC Social Predictive Marketing Television You Are Here Human History of Marketing Image credit: www.conducthq.com Using Data for Marketing in the Future Predictive Marketing
  27. 27. • Extreme machine learning • Collaborative predictive analytics • Scale-invariant intelligence • Neural networks for machine perception • Real-time interactive big data visualization • Graph all the things • Large scale machine learning cookbooks • Collecting massive data via crowd- sourcing “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer onto a freeway.” Big Data: 2014
  28. 28. • Personalization everywhere • Company and consumer collaboration in service design • Predictive location based selling • Digital Concierges • Real time event networks • Graph and signal hubs merge for better understanding of ad placement • Large scale channel disruptions • Marketing becomes more analytical Big Data visionaries pose existential threats Predictive Marketing: 2016
  29. 29. What’s Next: Combining contextual and analytical approaches provide a more complete picture of how customers interact with the firm Both approaches privilege observation and understanding what people actually do and look for opportunities to fix, improve and innovate. Robin Beers, founder of Business is Human
  30. 30. Location Analysis Graph Analysis App and Device Analysis Customer Feedback Personal Event Networks Social Personalization Digital Concierge Real-time Service Better Ad Performance True Omni Signal hubs will become new centers for data, helping to create better customer insights Predictive Analytics Creating Customer Signal Hubs of the Future
  31. 31. Although IT can build the systems, it will still be left to analyst and marketers of all types to create the actions needed to engage customers How Predictive Marketing is Shaping Up
  32. 32. Web PDS Email ECC Personal Event Network Appt Scheduler Add to Calendar Confirmation Email Add Confirmation and Appt to PDS Using the digital concierge system, we can create easy to use appointment systems, capturing the data and using it for future personalization efforts Appointment setting with a Digital Concierge
  33. 33. Image Engaging millions at a time Data Monetization - Keep it - Sell it - Partner with it - Share it Marketing of a Mass Personalized Scale
  34. 34. Processes are lined, linear chains of cause and effect. A service is different. Processes are designed to be consistent, personalization services are not consistent but individualized and co-created. The differences are not superficial but fundamental. Co-created value requires a relationship Marketing of the Future: Process vs Service
  35. 35. Marketing as a service relies on the ability of an organization to learn from customer’s responses and to listen and adapt to those signals. Causes of success are never revenue, costs, profits, etc.., those are lagging indicators or effects. What matters are the activities that generate the profits, activities that create long or short term value. You can measure that via personalization as it is a leading indicator activity if done correctly. Marketing is about Listening and Learning
  36. 36. An organization’s data is found in its computer systems, but a company’s intelligence is found its biological and social systems --- Valdis Krebs, researcher Linking things changes things: social networks are good at habit building. As behaviors are repeated, they form stronger associations over time. You form strong bongs with people in your life with whom you spend the most time, the same can be said in a social interactive personalization model, customers will form strong bonds with organizations they interact with the most over a given period of time. Small world networks: people banding together to achieve a wide variety of shared objectives. These are the most powerful types of social networks and the way to truly engage customers is to beyond just social network sites and to get into the small world networks as a valuable member of the network. Marketing and Social
  37. 37. Start small, and remember, everyone else is in the same boat Online Resources What You can do now
  38. 38. Thank you

Notes de l'éditeur

  • How is big data used? How is it helping?
  • The core of big data
  • Expanding a marketers knowledge of big data, what you need to know.
  • Data ecosystem, what is it, how to understand it.
  • Most of what is needed to make marketing better is still to be explored.
  • Understand how to see your customer online
  • Signals help to make sense of the various types of data so you can use them in new ways.
  • How to understand signals in unstructured is not the same as structured data.
  • Take data, understand it, process it, extract value, visualize, communicate, measure
  • The Action Gap is still a big problem for many companies, understanding the cap will reduce the learning curve
  • A shift from systems and forecasts to activities as our center of design needs to take place.
    Data alone can’t predict an unpredictable social animal known as the human being
    Focus on the activities of people, not so much predicting them because we can’t do that good of a job with what we have.
  • The Human Motion Graph is emerging as a new way of understanding customers movements and how they relate to your product or service.
  • Fitbit as an example of data discoverers
    Data as the new self discovery tool
    Leads to consumerization of IT IT needs to adapt to be social
    This means teaming up with marketing and letting marketing joining the conversation with data
  • How is big data used? How is it helping?
  • Signal Hubs are the Future of understanding customers
  • Realtime and customization are the future, faster response times.
  • How to set up your data practice.
  • How is big data used? How is it helping?
  • Different stages of maturing a company goes through.
  • Set up a structure
    Understand the vision, make it clear
    Governance: have structure around the tasks
    Execution: Know how to get it done and why

    Leadership plays a very important role in defining the vision and goals along with how they wish to see the program governed. Most people don’t understand personalization so having the right structure in place helps to ensure a good foundation for growth.
  • How is big data used? How is it helping?
  • How is big data used? How is it helping?
  • How is big data used? How is it helping?
  • How is big data used? How is it helping?
  • How is big data used? How is it helping?
  • How is big data used? How is it helping?
  • How is big data used? How is it helping?
  • The product is an intermediate step, not an end in itself, even after the customer buys, there is still a relationship after the sale that can take place beyond the product. With a process, this isn’t the case, once the final step is complete, you are done.

    A process has one customer, the person who receives the final results, a service at its core a relationship between the served and server. At every point of interaction the measure of success is not a product but the satisfaction, delight, or disappointment of the customer.

    “Most corporate systems were not built with customer delight in mind.” Fred Reighheld, Fellow, Bain and Company
  • Learning organizations evolve with the customer and personalization helps you understand how to evolve.

    Ritz Carlton, the staff is trained to listen for guest preferences, not always stated in the form of a direct request. The staff is trained to look for intent and then act upon it. This is why the Ritz Carlton’s service is legendary, they have learned how to perfect personalization in the physical space and is a model to follow for Best Buy and can be done with our own personalization efforts.

    Continuous improvement is natural!
  • Anatomy of a social network:

    Brokerage: A person or group that connects different clusters together.

    Closure: Building trust within a cluster, the closer you are the stronger the trust.

    Betweeness: Critical linking member between other nodes in the cluster.

    Closeness: How easily a person can make connections

    Degree: Number of connections

    Developing a social aspect of personalization requires a high degree of network fluency, situational awareness, influence, compatibility and a fair amount of luck.

  • How is big data used? How is it helping?
  • How is big data used? How is it helping?