Targeted Marketing: How Marketing Companies can use Big Data to Target Customers Based on Location & Certain Demographics
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This presentation will show how an outdoor advertising company used the Oracle Big Data environment to provide real time statistics and high-value insights to their customers. Using data from providers such as Pinsight Media and Perconix data from Axciom, they are able to accurately show the demographics of the consumers in the viewshed of their billboards and other digital assets. With the need to get useful information out of the terabyte of data they were receiving, our client use Oracle BDCS, specifically, Hive to create external tables connecting to flat files or MongoDB and Impala to analyze the data. The data was then loaded into Oracle DBCS for to be accessed by OACS for further analysis and dashboarding.
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Targeted Marketing: How Marketing Companies can use Big Data to Target Customers Based on Location & Certain Demographics
- 1. Targeted Marketing How Marketing Companies can use Big Data to Target Customers Based on Location & Certain Demographics
- 2. Confidential – Restricted Slide 2 Who @m I?
- 3. Confidential – Restricted Slide 3 Who we @re? BA, short for Business Analytics, is the technology used by companies to investigate business performance by analyzing their data. BA encompasses three major areas Business Intelligence (BI), Enterprise Performance Management (EPM) and Big Data Analytics. BA in this instance indicates that we will go beyond EPM and also address topics/technologies that pertain to BI and Big Data. Wikipedia describes soapbox as a raised platform on which one stands to make an impromptu speech, often about a political subject. The term originates from the days when speakers would elevate themselves by standing on a wooden crate originally used for shipment of soap or other dry goods. I want this is be not only a place for me to share tips and tricks, but also a platform where industry practitioners can come to share their views and expertise. I am not an expert! I say this because I have had the pleasure through the years of working with people I would consider experts in specific products. Instead, I see myself as a data integration specialist or enthusiast if you will. I am someone who is passionate about the field and have made considerable investment in the space. If you are like me or find the thought of maintaining a website a bit daunting, this may be your opportunity to share your thoughts and educate others. So if you have a topic that you would like to contribute, please let me know at BASoapbox.com.
- 4. Confidential – Restricted Slide 4 The Abstract This presentation will show how an outdoor advertising company used the Oracle Big Data environment to provide real time statistics and high value insights to their customers. Using data from providers such as Pinsight Media and Perconix data from Acxiom, they are able to accurately show the demographics of the consumers in the viewshed of their billboards and other digital assets. With the need to get useful information out of the terabyte of data they were receiving, our client use Oracle BDCS, specifically, Hive to create external tables connecting to flat files or MongoDB and Impala to analyze the data. The data was then loaded into Oracle DBCS for to be accessed by OACS for further analysis and dashboarding. In the session, I will: Go over the overall objective of the project Go over some of the key Hive and Impala queries Show how ODICS was instrumental with the automation, but also with the interaction with MongoDB Go over some of the lessons learned
- 5. Confidential – Restricted Slide 5 The Big Question Can we identify by audience size and segment the consumer population exposed to a billboard by time of day and day of week? 12:00 AM 11:59 PM7:00 AM 12:00 PM 5:00 PM Out of the billion of aggregated anonymized observed devices in a geographical location per day: 1. We need curated assets (billboards) 2. We need to get a total count of the devices we observe within the viewshed of our assets (billboards) 3. We need to take a look at home and secondary locations to define the segmentation of the consumers near our assets (billboards)
- 6. Confidential – Restricted Slide 6 Viewshed Impressions Once a viewshed is constructed, an impression then becomes the intersection of consumers with the asset. Wikipedia describes viewshed as the geographical area that is visible from a location. It includes all surrounding points that are in line-of-sight with that location and excludes points that are beyond the horizon or obstructed by terrain and other features (e.g., buildings, trees).
- 7. Confidential – Restricted Slide 7 A Few Key Terms… Geofence as Polygon Pivot Dwell Time Route Lat/Lon Asset (Digital Billboard) x Lat/Lon Lat/Lon Lat/LonLat/Lon Lat/Lon Lat/Lon Visit Term Definition Pivot When a user cross a polygon or geofence boundary (i.e. New Jersey to New York City). Dwell Time When a user cross a polygon or geofence boundary. New Jersey to New York City. SoHo to LES. Outside to Inside. Visit dwell time at a location that is material to intent Geofence A designated boundary around a geometry that, if crossed, initiates a notification. Geofences are often used in real-time route Web applications. Polygon On a map, a closed shape defined by a connected sequence of x,y coordinate pairs, where the first and last coordinate pair are the same and all other pairs are unique.
- 8. Confidential – Restricted Slide 8 Final Answer 9 AM to 10 AM Work Learn Shop & Eat Play Audience Identity Audience Behavioral Graph Audience Size Time • Friday 9 AM to 10 AM Audience Size • 10,432 impressions Audience Segmentation • 42% college crowd • 38 % mobile mixers • 20% city mixers For impression seen at certain time, say between 9 AM to 10 AM, we are able to travel backward and forward in time to understand home location type and secondary location type. This all informs audience segmentation.
- 9. Confidential – Restricted Slide 9 Primary Data Sources • Owned by Sprint • Parquet file representing the observed population pivoting through Assets’ viewsheds. • Contains FIPS/Block Group data for each viewshed impression FIPS / Block Group • Data which will provide personal characteristics such as demographics, income, ethnicity, etc. to give us profile of the users that are passing through each asset. • PersonicX is a segmentation system which places U.S. households into one of 70 distinctive segments and 21 life stage groups based upon specific consumer behavior and demographic characteristics. • Asset inventory are maintained in a MongoDB application called GSP • GSP holds Point of Interest (POI) data, asset data (e.g., location of asset, latitude, and longitude), geospatial data, real estate, wireless, transit, and taxonomy • Output data from Big Data Cloud Service (BDCS) will be loaded back into GSP
- 10. Confidential – Restricted Slide 10 Data Flow +Big Data Manager ODI to orchestrate Big Data Manager (BDM). BDM will be used to move Pinsight data from AWS S3 to BDCS +Big Data Manager ODI to orchestrate Big Data Manager (BDM). BDM will be used to move files from Acxiom to BDCS. ODI to orchestrate mongodump and mongoimport functions to load Asset information from MongoDB to BDCS.
- 11. Confidential – Restricted Slide 11 Big Data Manager – Data Pull from S3 to HDFS
- 12. Confidential – Restricted Slide 12 Oracle Data Integrator (ODI) • ODI was used in the following ways on the project: • As a means to orchestrate certain processes • Performing E-L-T type integrations • The use of ODI made it easier to not only streamline, but automate certain processes.
- 13. ODI Process Orchestration • This insert shows how we can have ODI perform the follow orchestration, in a streamlined and automated fashion: 1. Call the EDQ Workflow that will do the IMS data cleansing. 2. If the process fails, then email the responsible parties. Otherwise, call the EDQ Workflow that will export the IMS (cleansed) data from EDQ to staging DB or flat file format 3. Email responsible parties that all processes ran successfully
- 14. Confidential – Restricted Slide 14 The Data Lake Big Data Cloud Service (BDCS) was used as a central data repository, divided into ponds for raw data, canonical model and semantic layer. Large cellular data from Pinsight, consumer insights from Acxiom and asset data from MongoDB were loaded into the Hive table in the raw data pond. Data Ingestion Pond Data is stored as-is Recent History available on HDFS Older file moved to Oracle Object Storage Common Meanings Additional standardized field and values Optimized storage format Ideal layer for exploratory data analysis (EDA) Business Ready Finalized Model Data ready for consumption Conforming to the business’ needs Acxiom MongoDB Pinsight
- 15. Confidential – Restricted Slide 15 Big Data Cloud Service (BDCS)
- 16. Confidential – Restricted Slide 16 Pinsight Sample File
- 17. Confidential – Restricted Slide 17 Acxiom Sample File (Demographics) FIPS / Block Group
- 18. Confidential – Restricted Slide 18 Acxiom Sample File 1. ROW FORMAT SERDE "org.apache.hadoop.hive.serde2.RegexSerDe" Since the file has a fixed format, we use a SerDe to tell hive to use the RegexSerDe class to serialize and deserialize. 2. "input.regex" is used to deserialize the rows read from the table data. So this regex pattern is applied to the row value read from the file to split up into different columns defined in the meta data for this hive table. 3. "output.format.string" is used to serialize the rows being written out to this table data. This value is used as a format to generate a row value (from its column values) that is to be written back to the output file for this hive table. Regex Description . any character except for line break {n} n number of repetition of code that precedes it
- 19. Confidential – Restricted Slide 19 Insert Query
- 20. Confidential – Restricted Slide 20 Impala Query
- 21. Confidential – Restricted Slide 21 Loading Fact Table p_acxperconicsfact
- 22. Confidential – Restricted Slide 22 Output to MongoDB
- 23. Confidential – Restricted Slide 23 The Final Output
Editor's Notes
- FIPS county code. The Federal Information Processing Standard Publication 6-4 (FIPS 6-4) was a five-digit Federal Information Processing Standards code which uniquely identified counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. The FIPS State Code is a unique two-digit numeric code used to identify the 50 states, the District of Columbia, and the outlying areas of the United States. The FIPS County Code is a three-digit numeric code that, when used with the FIPS State Code, provides a unique identifier for each county and statistically equivalent entity of the 50 states, the District of Columbia, and the possessions and associated areas of the United States. Census tracts are the basic county subdivision used by the Census Bureau for census collection purposes and contain an average of 1,500 households. The tract code is made up of a 4-byte prefix and a 2-byte suffix (the next element) often separated by a decimal. Here, the tract element is provided as one 6-byte element. A subdivision of a census tract, a block group is another county subdivision used by the Census Bureau for census collection purposes and refers to an area that contains an average of 500 households.
- FIPS county code. The Federal Information Processing Standard Publication 6-4 (FIPS 6-4) was a five-digit Federal Information Processing Standards code which uniquely identified counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. The FIPS State Code is a unique two-digit numeric code used to identify the 50 states, the District of Columbia, and the outlying areas of the United States. The FIPS County Code is a three-digit numeric code that, when used with the FIPS State Code, provides a unique identifier for each county and statistically equivalent entity of the 50 states, the District of Columbia, and the possessions and associated areas of the United States. Census tracts are the basic county subdivision used by the Census Bureau for census collection purposes and contain an average of 1,500 households. The tract code is made up of a 4-byte prefix and a 2-byte suffix (the next element) often separated by a decimal. Here, the tract element is provided as one 6-byte element. A subdivision of a census tract, a block group is another county subdivision used by the Census Bureau for census collection purposes and refers to an area that contains an average of 500 households.
- Create a data platform in Big Data Cloud Service (BDCS) that will do the following: Data hub for data integration from Pinsight, Acxiom, GSP (MongoDB) and IMS Serve as landing of raw data, enriched and modeled as work in progress, and finalized for consumption Cleanse the data, especially IMS data that is coming from a DB2 database Creating some semblance of Master Data Management Providing beginnings of ad-hoc data model for analytics Large data sets from cellular and GPS sources are needed for audience size by location Large data sets from consumer insight data and social media are needed for audience segment from size and location POI and geofencing data sources are needed to understand origin and destination segmentation Billboard inventory is needed for associating billboard with origin, destination, POI, and neighborhood
- Create a data platform in Big Data Cloud Service (BDCS) that will do the following: Data hub for data integration from Pinsight, Acxiom, GSP (MongoDB) and IMS Serve as landing of raw data, enriched and modeled as work in progress, and finalized for consumption Cleanse the data, especially IMS data that is coming from a DB2 database Creating some semblance of Master Data Management Providing beginnings of ad-hoc data model for analytics Large data sets from cellular and GPS sources are needed for audience size by location Large data sets from consumer insight data and social media are needed for audience segment from size and location POI and geofencing data sources are needed to understand origin and destination segmentation Billboard inventory is needed for associating billboard with origin, destination, POI, and neighborhood
- The FIPS State Code is a unique two-digit numeric code used to identify the 50 states, the District of Columbia, and the outlying areas of the United States. The FIPS County Code is a three-digit numeric code that, when used with the FIPS State Code, provides a unique identifier for each county and statistically equivalent entity of the 50 states, the District of Columbia, and the possessions and associated areas of the United States. Census tracts are the basic county subdivision used by the Census Bureau for census collection purposes and contain an average of 1,500 households. The tract code is made up of a 4-byte prefix and a 2-byte suffix (the next element) often separated by a decimal. Here, the tract element is provided as one 6-byte element. A subdivision of a census tract, a block group is another county subdivision used by the Census Bureau for census collection purposes and refers to an area that contains an average of 500 households.
- FIPS county code. The Federal Information Processing Standard Publication 6-4 (FIPS 6-4) was a five-digit Federal Information Processing Standards code which uniquely identified counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. The FIPS State Code is a unique two-digit numeric code used to identify the 50 states, the District of Columbia, and the outlying areas of the United States. The FIPS County Code is a three-digit numeric code that, when used with the FIPS State Code, provides a unique identifier for each county and statistically equivalent entity of the 50 states, the District of Columbia, and the possessions and associated areas of the United States. Census tracts are the basic county subdivision used by the Census Bureau for census collection purposes and contain an average of 1,500 households. The tract code is made up of a 4-byte prefix and a 2-byte suffix (the next element) often separated by a decimal. Here, the tract element is provided as one 6-byte element. A subdivision of a census tract, a block group is another county subdivision used by the Census Bureau for census collection purposes and refers to an area that contains an average of 500 households.