Big data for sales and marketing people


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I volunteered my time to share about big data to those looking to understand the space.

This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.

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Big data for sales and marketing people

  1. 1. Big Data for Sales and Marketing People Fill the gaps companies need in their big data teams
  2. 2. Who am I • Sr Leader of Omnichannel and Innovation at Best Buy • Experience building and scaling big data projects that include data science and data visualization teams – first in the midwest and retail • Tekne finalist for software innovation • Marketer at heart – Group Alum
  3. 3. What is Big Data • Ask 5 people, get 5 answers • Often defined by the V’s – Volume – how much data – Variety – how many kinds of data – Velocity – how fast data moves – Viability – how useful is the data – Value – what value will the data add ??
  4. 4. Big Data and Your Career Mckinsey Report on Big Data
  5. 5. Framing Big Data 5 Big Data Value: Improved Customer Experience Data Science: Analytics Technology: What Tools and Why Data Strategist - Measurable Results - Multi-Channel Case Studies - MapReduce, Hadoop - Cassandra, The Cloud - Pig, Hive, - HDFS - Solve Customer Painpoints - Develop competitive strategy - Alignment with Analytical Infrastructure - Speed to Market - Privacy Considerations - Data Scientist + Statistician - Where to find talent? - Discovery Analytics - Deep data insights Big Data: Data becomes your core asset. It realizes its value when you know how to do what.
  6. 6. The Hadoop Vendor Ecosystem
  7. 7. Big Data is beginning to generate some returns What businesses are saying about big data: Improved Business Decisions: 84% Improved Current Revenue Streams: 43% Also Support of New Revenue Streams: 31% Not Leveraged for Revenue Growth: 27% However, Businesses are still seeing some gaps: 1. Going from Data to Insights 2. Taking Insights to Action 3. Creating big ideas from Insights. Source: Avanda Inc. 2012 Big Data Survey
  8. 8. How Sales and Marketers Fit into Big Data The world of big data is changing. As more companies move to real time, they are starting to realize that a tech driven strategy will not give them the better business performance or customer experience they crave. That’s where sales and marketers come in or the new data strategists.
  9. 9. Data Management Framework • Holistic approach to understand the information needs of the enterprise & its stakeholders • Consistency for planning & process development • 10 major functional areas, including governance • Aligns data with business strategy (above) and technology (below) • Takes into account the data lifecycle – creation through destruction • Internationally recognized through Data Management Association International (DAMA)
  10. 10. Signal Types Signals have attributes depending on their representation in time or frequency domain can also be categorized into multiple classes 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) Rate of Change (Slow or Fast) Quality (Predictive or Descriptive) Sensitivity (Sensitive or Insensitive) Frequency (High or Low) 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
  11. 11. Finding Signals in Unstructured Data 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. For each dimension, develop meta- data, ontology, statistical measures, and models 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
  12. 12. 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”
  13. 13. New Solutions Must Aid Human Insight Big Data + Personalization + Amplified Human Intelligence Last Decade - Structured Data - Conclusive Dashboards - Small scale / sampling A data architect built a view to reach a specific conclusion Next 5 Years - Any data, from anywhere - Intuitive exploration - Making sense of it at scale Business users easily find, explore, visualize and navigate insights
  14. 14. Human Motion Graph 19
  15. 15. New Tools Same Solutions We have new data sets to help us engage customers, the technology can’t solve the customer experience issues. Companies want marketers with an understanding of Tech
  16. 16. Case Study: Rent the Runway • Rent the Runway rents high end dresses to women, similar to the model of renting tuxedoes to men. • RTR collects many data points on users experience the same items. • Hundreds of women rent the same style, site average of 300 orders per dress up to 1000. • 1/6th of customers have written at least 1 review. • Women are willing to provide information to help others make decisions, 50% of reviewers share their weight, 60% share their bust size. • Seeing a photo review increases the likelihood of renting by 200%
  17. 17. • RTR wanted to create a better personalization system for women searching for the right dress. • How many data points do we need to accurately find other women in our user base like you? • Start basic: Same size, demographics. • Expand: Similar taste • Evaluate: Clickstream updating
  18. 18. RTR: Calculating Sameness • Even with only 4 points of comparison (size, age, height, bust) over 100,000 possible combinations. • Too much detail narrows the results set too far • Slow to compute, large to store. • Simplify, create buckets per characteristic – Height: Petite, Short, Average, Tall – Bust: Small, med, large – Age: Demographic group – Result: 864 vectors that accurately capture the range of women shopping the site.
  19. 19. RTR: Future of Fashion Retailing • The future of fashion retailing is data driven • Crowdsourcing of fit and style matching will become more widespread. • As confidence in the business model grows, so will positive experiences with customers.
  20. 20. What is Data Science Data science is a discipline for making sense of unstructured as well as numerous data sets at scale Disparate Data - News - Web - Email - Research - Clickstream - Various external data sets Interpret Deep processing of data structured and unstructured Resolve Assemble, organize, and relate Reason Uncover relationships, compare and correlate Machine Learning Distributed Processing (Hadoop) Alignment with Business Goals Cross team Customer Experience Improvment
  21. 21. What is Data Visualization Data Visualization is the discipline of telling the story of what the data is saying via visuals Disparate Data - News - Web - Email - Research - Clickstream - Various external data sets Interpret After data science finds insights, create the story Resolve Challenges of story telling Reason Express large complex data in easy to understand visuals Data visualization tools Graphic Arts Light coding Understand human interaction
  22. 22. What is Data Strategy Data strategy is a discipline that managed the customer experience via the understanding of what data says about the customer experience Disparate Data - News - Web - Email - Research - Clickstream - Various external data sets Interpret How the customer experiences products Resolve Pain points and business objectives via technology Reason Uncovers what motivates customers Marketing and Sales High level understanding of technology tools Understands how to use visualization to sell Customer’s advocate for a better experience
  23. 23. How to Get Started • Meetups • Online Classes • Conferences • Read, Read and Read some more.
  24. 24. Meetups We have several great Meetup groups locally that are free to attend: • Data Visualization: Visualization-Group/ • Hadoop: Hadoop-User-Group/ • Big Data Developers: in-Minneapolis/
  25. 25. Classes There are free classes available locally and online you can take: • Big Data University: • Coursera:
  26. 26. Conference There are free classes available locally and online you can take: • Minneanalytics: • Minnebar:
  27. 27. Read Plenty of free blogs, sites and Linkedin groups to join now: • The Connected Company, Dave Gray • The Intention Economy, Doc Searls
  28. 28. Companies Need you More companies understand the need for the business skills to be added into the big data mix. Most need help now! 2 years ago hardly anyone was doing this work, now, hardly anyone isn’t. • Your skills are transferable and needed!