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While user tracking with WebTrends, comScore, Google Analytics etc. is a de-facto standard in the online world, tracking visitors in the real world is still fragmented. From a wide perspective, potential tracking data is produced by various sensors. With a real ‘bricks and mortar’ store, one could figure out possible sensors they could use: customer frequency counters at the doors, the cashier system, free WiFi access points, video capture, temperature, background music, smells and many more. For many of those sensors additional hardware and software would be needed, but a few sensors already have solutions available, e.g. video capturing with face or even eye recognition. The most interesting sensor data that doesn’t require additional hardware and software could be the WiFi access points. Especially given that many visitors will have WiFi enabled mobile phones. This talk demonstrates how WiFi access point log files can be used to answer different questions for a particular store.
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How do we answer these questions?
Before we started designing a blueprint solution we first of all asked ourselves:
1 Who would be asked to answer questions like this?
2 Who is this person?
3 What tools does this person expect to use?
4 And what is a typical skill set of this person?
5 How do they work?
Preparation
May
21,
2013
8
So, how do we answer these questions as a Data Scientist?
From a high level of abstraction the
answer is simple. We need a data
management system with three pieces:
ingest, store and process.
Traditional Data Management System Approach
May
21,
2013
Data
Source Data
Ingestion
Data
Processing Data
Storage
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So, how do we answer these questions as a Data Scientist?
We take this basis architecture and replace the generic terms while mapping it onto the Hadoop ecosystem.
With this Hadoop architecture a Data Scientist should be able to answer the questions without any programming environment. He/she can also use familiar BI, analysis and reporting tools as well.
Blueprint for a Data Management System with Hadoop
May
21,
2013
Data
Source Flume
HIVE,
Impala HDFS
BI/Analysis/R
eporting
10
Ingrediants
1 2 WiFi access points to simulate two different stores with
OpenWRT, a linux based firmware for routers, installed
2 Flume to move all log messages to HDFS, without any
manual intervention (no transformation, no filtering)
3 A 4 node CDH4 cluster (2GB RAM, 100GB HDD)
4 Pentaho Data Integration‘s graphical designer for data
transformation, parsing, filtering and loading to the
warehouse
5 Hive as data warehouse system on top of Hadoop to
project structure onto data
6 Impala for querying data from HDFS in real time
7 MS Excel to visualize results
Setup
May
21,
2013
11
How it Works
Analytics System
May
21,
2013
Flume Hive
Impala
OpenWRT
00:A0:C9:14:C8:28
Syslog Server
Flume
Source
Sinks to
HDFS Loads Raw CSV
Hadoop/HDFS
M/R
Pentaho
UDP
CC 2.0 by Qi Wei Fong | http://flic.kr/p/7w8vfq
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Visits for stores number one & two
The plot indicates that about 85% of the visits were detected in store
number one and about 15% in store number two. One might draw the
conclusion that store number one is in a much better location with more
occasional customers.
But let’s gain more insights by analysing the number of unique visitors.
Analysis Result
May
21,
2013
14
Unique visitors
This plot gives us more details about the customers. It turns out that
the 135 visits in store number one were caused by just 9 unique
visitors while store number two encountered 5 unique visitors.
Analysis Result
May
21,
2013
15 This plot indicates that we have more returning than new users in both
stores. In store number two we didn’t see a new user over the past 4 days at
all.
It’s probably a good idea to start a marketing campaign which aims at new
customers, e.g. to give out vouchers for the first purchase.
New vs. returning users
Analysis Result
May
21,
2013
16 The plot for the last 4 days vividly visualizes that the visit duration in store number one was evenly distributed while the distribution in store number two shows some peaks.
We can also see that visitors tend to stay in shop number one much longer.
Visit duration over the past 4 days
Analysis Result
May
21,
2013
17 There is a lot of useful information that can be derived from this plot.
1. There is a repeating pattern of step-ins and step-outs within a short period of time.
2. There was a step-out of store number one and a step-in into store number two within just 28 seconds.
Avg. Duration Between Visits of one particular user
Analysis Result
May
21,
2013
May 21, 2013
CC 2.0 by Aurelien Guichard | http://flic.kr/p/cjg9yw
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CCAH Course in ZH
• Cloudera Administrator Training for
Apache Hadoop (CCAH)
• June 26th – 28th 2013
• Limmatstrasse 50, Zurich
• More info's: http://www.ymc.ch/training
Announcement
May
21,
2013
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Links
1 Presentation, Video and Post Series
• http://bitly.com/bundles/cguegi/1
2 http://www.bigdata-usergroup.ch
3 http://about.me/cguegi
4 http://www.ymc.ch/training
May
21,
2013
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