Big Data Analytics 1: Driving Personalized Experiences Using Customer Profiles
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The document discusses driving personalized experiences using customer profiles. It outlines the benefits of personalization and the high-level personalization process, which includes creating customer profiles, enriching them with public data, capturing customer activity, performing clustering analysis to define customer personas, tagging customer profiles with personas, and personalizing interactions. MongoDB is recommended for personalization due to its document model, high performance, scalability, rich querying capabilities, and ability to integrate with Hadoop/Spark. Examples are provided of how customer data like interests, visits, and purchases can be collected and stored in profiles to enable real-time personalization based on persona matching.
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Big Data Analytics 1: Driving Personalized Experiences Using Customer Profiles
- 1. Driving Personalized Experiences Using Customer Profiles Matt Kalan Sr. Solution Architect MongoDB, Inc. @matthewkalan matt.kalan@mongodb.com
- 2. 2 Big Data Analytics Track 1. Driving Personalized Experiences Using Customer Profiles 2. Leveraging Customer Behavior to Enhance Relevancy in Personalization 3. Machine Learning to Engage the Customer, with Apache Spark, IBM Watson, and MongoDB
- 3. 3 Agenda For This Session 1.Benefits of Personalization 2.High level process 3.Data capture steps 4.Data analysis steps 5.Real-time personalization 6.Summary 7.Q&A
- 4. 4 You Notice When Content is Personalized When it looks like this outside Left: from www.johnbyronkuhner.com via Google Images Right: from www.steinmart.com via Google Images Is this the best ad to show you?
- 5. 5 Or Better This When it looks like this outside Left: from www.johnbyronkuhner.com via Google Images Right: www.linkedin.com/pulse/20140729161519-34678510-take-note-time-to-move-beyond-personalization-to-contextualization More relevant
- 6. 6 Personalization Pays – Conversion Rates
- 7. 7 Personalization Pays – ROI Impact
- 8. 8 High Level Personalization Process 1. Profile created 2. Enrich with public data 3. Capture activity 4. Clustering analysis 5. Define Personas 6. Tag with personas 7. Personalize interactions Batch analytics Public data Common technologies • R • Hadoop • Spark • Python • Java • Many other options 4 & 5 performed much less often than tagging
- 9. 9 Why MongoDB for Personalization? • Document model => customer profiles are rich structures perfect for documents • High throughput => profiles are read/written every page so high performance is critical • High scalability => high performance must scale easily for any data size & request volume • Rich querying & indexes => often only portions of the profile are queried for and especially ad hoc marketing requires rich querying capabilities. Geospatial indexes critical for mobile • Real-time analytics => can analyze directly on MongoDB or prepare aggregated results for external analysis with the aggregation framework • Strong consistency => want profile changes & tracking to take effect immediately • Hadoop/Spark integration => can run distributed analytics on data in MongoDB or copy it to HDFS to run there both with the MongoDB Hadoop Connector • Low TCO => Low cost enterprise software license, commodity hardware, & management
- 10. 10 Customer Example: Scratchpad • Records all activity in researched trips • Needed – Document model – Dynamic schema – Rich querying – Easy scaling
- 11. 11 And Many Other Customers Personalizing with MongoDB • Sailthru • Sitecore • Adobe (AEM) • Expedia • ADP • Foursquare • Otto • Chico’s and 100s more…
- 12. Data Capture
- 13. 13 Anonymous user Might just start with this if no cookie { "ipAddress" : "216.58.219.238", "referrer" : "google.com" } Pretty useless, right?
- 14. 14 More Than Just What You Collect IP Address Referrer Information Broker Location Company Weather Avg Income Interests Possible Interests e.g. Kay Jewelers, Dick’s Sporting Goods Budget Indication e.g. Barney’s Search term
- 15. 15 Often User Creates a Profile { "_id" : ObjectId("553ea57b588ac9ef066428e1"), "ipAddress" : "216.58.219.238", "referrer" : ”kay.com", "firstName" : "John", "lastName" : "Doe", "email" : "johndoe@gmail.com" }
- 16. 16 Even Email Unlocks Useful Info
- 17. 17 Available Early in Relationship { "_id" : ObjectId("553e7dca588ac9ef066428e0"), "firstName” : "John", "lastName” : "Doe", "address” : "229 W. 43rd St.", "city” : "New York", "state" : "NY", "zipCode" : "10036", "age" : 30, "email" : "johndoe@gmail", "gender" : "male” }
- 18. 18 Often Users Even Volunteer Preferences
- 19. 19 Easy to Store in Profile { "_id" : ObjectId("553e7dca588ac9ef066428e0"), "firstName” : "John", "lastName” : "Doe", "address” : "229 W. 43rd St.", "city” : "New York", "state" : "NY", "zipCode" : "10036", "age" : 30, "email" : "johndoe@gmail.com", "gender" : "male”, "interests" : [ ”dumplings", ”board games", ”rooftop", ”ginger beer", ”ahi tuna", ”healthy food" ] }
- 20. 20 In Return, User Gets Relevant Info
- 21. 21 Customer Activity Valuable to Track { "_id”: ObjectId("553e7dca588ac9ef066428e0"), "firstName : "John", "lastName” : "Doe", "address” : "229 W. 43rd St.", "city” : "New York", "state" : "NY", "zipCode" : "10036", "age" : 30, "email" : "johndoe@gmail.com", "gender" : "male”, ... "visitedCounts" : { "watches" : 3, "shirts" : 1, "sunglasses" : 1, "bags" : 2 } } From gilt.com
- 22. 22 Purchases Are Usually Even More Valuable { "_id”: ObjectId("553e7dca588ac9ef066428e0"), "firstName : "John", "lastName” : "Doe", "address” : "229 W. 43rd St.", "city” : "New York", "state" : "NY", "zipCode" : "10036", "age" : 30, "email" : "johndoe@gmail.com", "gender" : "male”, ... "purchases" : [ { "id" : 1, "desc" : "Power Oxford Dress Shoe", "category" : "Mens shoes" }, { "id" : 2, "desc" : "Striped Sportshirt", "category" : "Mens shirts" } ] } From gilt.com
- 23. 23 Data Capture – Simple to Sophisticated { "_id" : ObjectId("553e7dca588ac9ef066428e0"), "firstName" : "John", "lastName" : "Doe", "address" : "229 W. 43rd St.", "city" : "New York", "state" : "NY", "zipCode" : "10036", "age" : 30, "email" : "john.doe@mongodb.com", "twitterHandle" : "johndoe", "gender" : "male", "interests" : [ "electronics", "basketball", "weightlifting", "ultimate frisbee", "traveling", "technology" ], "visitedCounts" : { "watches" : 3, "shirts" : 1, "sunglasses" : 1, "bags" : 2 }, "purchases" : [ { "id" : 1, "desc" : "Power Oxford Dress Shoe", "category" : "Mens shoes" }, { "id" : 2, "desc" : "Striped Sportshirt", "category" : "Mens shirts" } ] } Additional behavior tracking • How long on each page (e.g. publishing)? • What is reaction to pop-up promotions? • Looks at cross-sold items on page? • What categories are clicked on? • Does a certain price point drive buying? • Purchases at certain times of year?
- 24. Data Analysis
- 25. 25 Clustering Overview • Think of each of your customers or users of your site as a data point • How can we group users into like sets for marketing, cross-sell, etc. similarly • K-means is a common algorithm for clustering Image from: http://pypr.sourceforge.net/kmeans.html Clustered DataOriginal Unclustered Data
- 26. 26 Clustering Process for Personalization Customer Profile Documents Map to Vectors [1, 3, 0, …] Clustering Algo Vectors Iterate on inputs Define Personas Clusters of customersUpdate profiles with persona Tag Profiles with Personas Clusters of customers
- 27. 27 Mapping Profile to Vector Input { "_id" : ObjectId("553e7dca588ac9ef066428e0"), "firstName" : "John", ... "visitedCounts" : { ”Mens watches" : 3, ”Mens shirts" : 1, ”Mens sunglasses" : 1, ”Mens bags" : 2 }, "purchases" : [ { "id" : 1, "desc" : "Power Oxford Dress Shoe", "category" : "Mens shoes" }, { "id" : 2, "desc" : "Striped Sportshirt", "category" : "Mens shirts" } ] } Mens shirts Mens pants Mens shoes Mens ties Mens Sunglass Mens Watch … 11 0 10 0 1 3 [ 11, 0, 10, 0, 1, 3, ...] (example vector) e.g. 1 purchase = 10 visited counts
- 28. 28 Aggregation Framework for Filtering Profiles //Adds up the visited counts (vc) and purchases to filter out those below 20 counts db.profiles.aggregate( [ {$project: { vc: "$vc", purchases: "$purchases", total: {$add: [ {$ifNull: ["$vc.mShirts", 0]}, {$ifNull: ["$vc.mPants", 0]}, {$ifNull: ["$vc.mShoes", 0]}, {$ifNull: ["$vc.mTies", 0]}, {$ifNull: ["$vc.mSunglass", 0]}, {$ifNull: ["$vc.mWatch", 0]}, {$ifNull: ["$vc.mBags", 0]}, {$multiply: [ {$size: "$purchases"}, 10 ]} ]} } }, {$match: {total: {$gte: 20}} } ])
- 29. 29 Input/Output for K-Means Algo Clustering Algo Iterate on inputs Clusters of customers Vectors: [ [11, 0, 10, 0, 1, 3, ...], [ 0, 5, 10, 3, 0, 0, ...], ... ] K = # of clusters Driven by marketing effort or data analysis N = # of iterations { Centers: [ {name: C1, vector:[..] }, {name: C2, vector:[..] }], ... ] Clusters: [ {C1: [[11, 0, 10, 0, 1, 3, ...],...]}, {C2: [[ 0, 5, 0, 0, 10, 0, ...],...]}, ... ] } Vectors
- 30. 30 Clustered DataOriginal Unclustered Data Choosing Personas • Each cluster would usually map to one persona you can identify, name, and target • Common to name personas to be memorable, e.g. shoe fanatic, bargain hunter, researcher, etc. C1 C2 C3 Shoe Fanatic?
- 31. 31 Mapping Customer Profile to Persona { Centers: [ {name: C1, vector:[..] }, {name: C2, vector:[..] }], ... ] Clusters: [ {C1: [[11, 0, 10, 0, 1, 3, ...],...]}, {C2: [[ 0, 5, 0, 0, 10, 0, ...],...]}, ... ] } { "_id" : ObjectId("553e7dca588ac9ef066428e0"), "firstName" : "John", ... "visitedCounts" : { ”Mens watches" : 3, ”Mens shirts" : 1, ”Mens sunglasses" : 1, ”Mens bags" : 2 }, "purchases" : [ { "id" : 1, "desc" : "Power Oxford Dress Shoe", "category" : "Mens shoes" }, { "id" : 2, "desc" : "Striped Sportshirt", "category" : "Mens shirts" } ], "persona" : "shoe-fanatic" } Loop through each vector in cluster, map to customer, and tag customer with persona
- 33. 33 Easier with a Rich Customer Profile to Personalize { "_id" : ObjectId("553e7dca588ac9ef066428e0"), "firstName" : "John", "lastName" : "Doe", "address" : "229 W. 43rd St.", "city" : "New York", "state" : "NY", "zipCode" : "10036", "age" : 30, "email" : "john.doe@mongodb.com", "twitterHandle" : "johndoe", "gender" : "male", "interests" : [ "electronics", "basketball", "weightlifting", "ultimate frisbee", "traveling", "technology" ], "visitedCounts" : { "watches" : 3, "shirts" : 1, "sunglasses" : 1, "bags" : 2 }, "purchases" : [ { "id" : 1, "desc" : "Power Oxford Dress Shoe", "category" : "Mens shoes" }, { "id" : 2, "desc" : "Striped Sportshirt", "category" : "Mens shirts" } ], "persona" : "shoe-fanatic” }
- 34. 34 Example Images from Target.com Can cross-sell based on current page Also to the real person
- 35. 35 Many Personalization Techniques to Mix & Match • Related content • Content history • Next best offer • Trigger-based • Threshold • Last behavior • Time & event • Offer matching • Filter-based • Crowd-sourcing • Voice of customer • User-directed • Persona matching Source: http://semphonic.blogs.com/semangel/2014/03/strategies-for-personalization-delivering-an-extra-unexpected-treat-.html
- 36. 36 Alternatives Give Less Capabilities Activity Logs Customer Profiles (no activity) Application Option - separate weblogs Customer Profiles with Activity Tracking Application Better option Tag with Persona Marketing Clustering & Analytics Can market: • On activity today • With rich & specific queries
- 37. 37 Better Option Enables Real-time Persona Matching 1. Profile created 2. Enrich with public data 3. Capture activity 4. Clustering analysis 5. Define Personas 6. Tag with personas 7. Personalize interactions Batch analytics Public data Can even match customer to a persona while customer is engaged Logic is to calculate the distance to each cluster center and tag with the closest one’s persona
- 38. Summary
- 39. 39 Personalization Pays – ROI Impact
- 40. 40 High Level Personalization Process 1. Profile created 2. Enrich with public data 3. Capture activity 4. Clustering analysis 5. Define Personas 6. Tag with personas 7. Personalize interactions Batch analytics Public data Common technologies • R • Hadoop • Spark • Python • Java • Many other options 4 & 5 performed much less often than tagging
- 41. 41 Big Data Analytics Track Driving Personalized Experiences Using Customer Profiles 2. Leveraging Customer Behavior to Enhance Relevancy in Personalization 3. Machine Learning to Engage the Customer, with Apache Spark, IBM Watson, and MongoDB
Editor's Notes
- P2P10 Driving Personalized Experiences Using Customer Profiles This session covers the end-to-end process of personalization and demonstrates a great example of combining operational data for an application in MongoDB with the ability to analyze that data and operationalize the results. We will discuss storing rich customer profiles in MongoDB, using clustering to develop a customer segmentation, and leveraging that as a filter for valuable personalization of your application. You'll walk away with a good idea of how to drive targeted experiences to customers for more relevant engagement and how personalization is accessible to companies large and small.
- This session is broad end-to-end, then deeper in next 2 session. Goal is for everyone here to believe personalization is achievable to build into your applications
- Explain who did the survey and who was asked questions. Actually easy to get value incrementally from starting small and adding more complex personalization
- Actually easy to get value incrementally from starting small and adding more complex personalization
- Mention other parts of track will cover the technologies used for batch analytics
- 70% of marketers said user preferences give high ROI
- 68% said user behavior
- Point out schema design might be different depending on requirements and how using profile info Probably have a separate collection for order info but relevant info stored with profile
- 2 dimensions might be how many shoes bought vs. how many tops (forgetting the axes). In reality can be many more dimensions
- Might filter out any counts lower than 20 or some number, only run on customers with enough information (frequent customers) Could have a different part of the vector for purchasing.
- There is a choice of what vectors to send. Might just choose counts larger than e.g. 5 or only for those customers with at least 20 counts because you judge you have enough samples
- Marketing might decide they want to focus on 5 personas to start, or through data analysis, you find one cluster really exhibits very different behavior within it and you want to break it up (could mention the technology products that can use for clustering, e.g. spark, ML, language libraries)
- Explain how k-means works at high level, iteratively moving the centers to define the nearby clusters e.g. if the two axes were shoes vs. clothes, then green might be high frequency buyer of everything, red is high shoe buyers, and blue is little of everything Might name it by the cluster center, especially focusing on how it is different from other cluster centers Over time, you would learn whether these personas are stable or not or change frequently, in which case you might not focus on those, e.g. patterns in the month before Christmas (buying patterns very different).
- A lot of work just for that little tag, but that tag represents a fast way to characterize that person and add to personalization rules
- Even counts and therefore persona very helpful. A good problem to have is too much information to personalize with – start simple, measure, and add
- Great juxtaposition of two approaches. Even though I’m looking at a woman’s dress, it uses Feature Products to market to me personally. Other sections cover related items to this dress so best of both worlds Featured products could be items commonly bought for my persona, or trending today by persona More advanced Could track products selling by persona today Figuring out whether things are gifts (e.g. clothes for women and I’m a man)
- Many of these are useful by themselves but many made better when you add a persona Beyond just personalizing from customer profiles, rules-based Suggest based on what already in the cart what page visited for a while weather in the area this weekend responds to discounts
- Can do ad hoc marketing & promotions, e.g. Who looks at the swimwear or shoe category a lot Who shopped last year on Black Friday Who shopped a lot right before spring last year Who bought a suit and bought or looked at ties
- Most importantly can identify a persona while the person is shopping (once browsing enough) instead of waiting until next time they come to your app Mention other parts of track will cover the technologies used for batch analytics
- Actually easy to get value incrementally from starting small and adding more complex personalization
- Mention other parts of track will cover the technologies used for batch analytics
- If time, can ask people what algorithms they are using for personalization