Combining Data Sets for Better Customer Insights
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Slides for May 19 Meetup http://www.meetup.com/Seattle-Marketing-Analytics-Group/events/221239307/
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Combining Data Sets for Better Customer Insights
- 2. Listening to the voice of the customer 15 15 16 17 18 20 22 24 0 2 3 4 6 8 10 14 1995 1996 1997 1998 1999 2000 2001 2002 Sales Forecast in 1999 Traditional Cameras Digital Cameras 15 15 16 17 18 15 13 10 0 2 3 4 6 10 14 18 1995 1996 1997 1998 1999 2000 2001 2002 What Really Happened Traditional Cameras Digital Cameras
- 3. Retail has a multitude of devices that generate petabytes of potential insights Monitoring and mining social media data enables retailers to enhance customer insights Open data sources and internal sources enable retailers to better understand customers Democratization of data
- 4. What WalMart Knows About Customers‘ Habits • Backed by the trillions of bytes' worth of shopper history that is stored in Wal-Mart's computer network, the company could "start predicting what's going to happen, instead of waiting for it to happen“ • The experts mined the data and found that the stores would indeed need certain products - and not just the usual flashlights "We didn't know in the past that strawberry Pop-Tarts increase in sales, like seven times their normal sales rate, ahead of a hurricane. And the pre-hurricane top-selling item was beer"
- 6. Where pictures are taken in London by tourists (red) vs. by locals (blue) Real Time Instagram pictures in Seattle
- 8. How A Retailer Figured Out A Teen Girl Was Pregnant Before Her Father Did Shopping Patterns Reflect Life Changes Data from Sales Correlated to Birth Registry to determine patterns Attempt to get better customer insights & predict what customers want before they tell you
- 9. Correlations using Credit Card Data Identify Areas where Customers Come From based on credit card spending patterns Build Profile using credit card patters. Identify churn patterns.
- 10. • Cellphone data from 2007 shows how the population density of coastal towns like Biarritz and Bayonne shoots up by 50 or 60 per cent on public holidays. • On normal weeks, France pulses like a beating heart, its people clustering into Paris from Monday to Friday, before heading out into the countryside on weekends. • accurate, up-to-date data on population movement across the nation could help public transport agencies plan services, or help event planners manage large crowds that flock to concerts • Understanding such movement in countries like Sierra Leone, for instance, would let epidemiologists halt the spread of disease, or get help to where it is needed in disasters.
- 11. Digilant: Above and beyond the data Creative Commons by Charis Tsevis • lawsuit called attention to academic research that suggests that Netflix indirectly exposed the movie preferences of its users by publishing anonymized customer data • a second set of information such as comments on the popular Internet Movie Database could help third parties triangulate the identity of the “anonymous” Netflix customers. The Creepy Factor
- 12. • Analyzed 74 billion data points logged from GPS-tracking devices installed in 552,000 vehicles owned by small businesses across the country. • Examined whether data on the movement of goods-carrying trucks can be corralled to provide a timely view of the direction of consumer spending, business investment and other economic fundamentals. • Charted the vehicular activity of small businesses alongside historical retail sales recorded by the U.S. Census Bureau since 2011, and found that the numbers are so closely correlated that they could be used as real-time economic indicators. Predicting Retail Sales from Movement of Goods Carrying Trucks
- 13. Filling Gaps in Customer Insight There are only four people/organizations in the world who know my location at all times: my wife (because I tell her), Apple (because Siri), the NSA (because NSA), and now Uber. Since the service Uber has built is so convenient, and increasingly essential to my life, Uber knows where I live, where I work, where I eat, where I travel, where I stay/visit and when I do all these things. -Ron Hirson, Forbes
- 16. Business users access results from anywhere, on any device Advanced Analytics • HDInsight • SQL Server VM • SQL DB • Blobs and tables Devices Applications Dashboards Data Microsoft Azure Machine Learning Storage space Integrated development environment for Machine Learning ML Studio Business problem Business valueModeling Deployment • Desktop files • Excel spreadsheets • Other data files on PC Cloud Local Data to model to web services in minutes http://studio.azurem l.net Web Clients API Model is now a web service Monetize this API
- 17. Machine Learning scenarios for retail •Assortment •Inventory •Out of stock/overstock •Price optimization Demand Analytics •Online recommendations •Call center •Assisted selling Recommendation •Customer segmentation •Cognitive intelligenceChurn Analytics •Targeted marketing •Media mix modelling •Channel mix marketing •Search engine marketing Marketing Analytics •Employee theft •Video analytics •Web transaction analytics Fraud Analytics
- 18. Build Reuse Consume AzureML Studio AzureML Gallery AzureML Marketplace Retail Forecasting Churn Prediction Market Basket Analysis Online Fraud Detection Build from scratch or Templates R Modules Python Modules Retail Forecasting Churn Prediction Market Basket Analysis Online Fraud Detection
- 19. Data Science Challenges Predict how sales of weather- sensitive products are affected by snow and rain
- 20. We are especially pleased that our analysts can focus on the results and not worry about the complex algorithms behind thescenes Andrew Laudato Pier 1 Imports Objectives • Give customers a better experience and selection • Understand what customers are looking for based on online search Tactics Combine online and in- store transactional and behavioral data to predict what products customers would be most likely to purchase next Results • Customers have more personalized choices • Targeted campaigns • Better inventory forecasts Delight customers with the right offers Use technology to determine what customer would purchase next
- 22. We are using Azure to make our UX smarter and truer to its purpose: enhancing the guest experience. Kevin Mowry Chief Software Architect Ziosk Objectives • Give guests a personalized experience • Understand what customers are looking for based on user engagement data Tactics Deliver mobile experience at every table and use profile and engagement data to personalize experience Results • Personalized experience for users • Better and real-time customer insights Personalizing the guest experience Use technology to personalize guest preferences
- 23. With Azure Machine Learning, the wow factor is huge. Customers are amazed that we can predict so accurately what they need. Mushtaque Ahmed COO JJ Food Service Objectives • Make recommendations to customers based on demand patterns • Improve ordering process by predicting what customers would order Tactics Use predictive analytics to determine what customers would need based on patterns. Use recommendations online as well as in call centers Results • Quicker and easier ordering process for customers • Better inventory management Predicting what customers will order Use technology to streamline the ordering process
- 25. Observation Pattern Theory Hypothesis What will happen? How can we make it happen? Predictive Analytics Prescriptive Analytics What happened? Why did it happen? Descriptive Analytics Diagnostic Analytics Confirmation Theory Hypothesis Observation Two Approaches to Information Management for Analytics: Top-Down + Bottoms-Up
- 26. Implement Data Warehouse Physical Design ETL Development Reporting & Analytics Development Install and Tune Reporting & Analytics Design Dimension Modelling ETL Design Setup Infrastructure Understand Corporate Strategy Data Warehousing Uses A Top-Down Approach Data sources Gather Requirements Business Requirements Technical Requirements
- 27. The “data lake” Uses A Bottoms-Up Approach Ingest all data regardless of requirements Store all data in native format without schema definition Do analysis Using analytic engines like Hadoop Interactive queries Batch queries Machine Learning Data warehouse Real-time analytics Devices
- 28. Data Lake + Data Warehouse Better Together What happened? What is happening? Why did it happen? What are key relationships? What will happen? What if? How risky is it? What should happen? What is the best option? How can I optimize? Data sources
- 29. “The era of ambient intelligence has begun, and we are delivering a platform that allows companies of any size to create a data culture and ensure insights reach every individual in every organization.” Satya Nadella – SQL Server 2014 Launch, 4/15/2014
- 30. Big Data Stories on