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.

1. CC 2.0 by Mr. T in DC | http://flic.kr/p/7khrin

2. CC 2.0 by Franck BLAIS | http://flic.kr/p/cwVnSy

3. CC 2.0 by John Steven Fernandez | http://flic.kr/p/a8uTzz

4. CC 2.0 by Ian Carroll | http://flic.kr/p/6NWoGm

5. CC 2.0 by Perry French | http://flic.kr/p/8wDMJS

6. CC 2.0 by John Mitchell | http://flic.kr/p/5UaPg8

7. 7
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. 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

9. 9
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,
ImpalaHDFS
BI/Analysis/R
eporting

10. 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. 11
How it Works
Analytics System
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21,
2013
Flume
Hive
Impala
OpenWRT
00:A0:C9:14:C8:28
Syslog Server
Flume
Source
Sinks to
HDFSLoads
RawCSV
Hadoop/HDFS
M/R
Pentaho
UDP

12. CC 2.0 by Qi Wei Fong | http://flic.kr/p/7w8vfq

13. 13
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
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21,
2013

14. 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. 15This 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. 16The 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. 17There 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

18. Ma
y
21,
201
3
CC 2.0 by Aurelien Guichard | http://flic.kr/p/cjg9yw

19. 19
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

20. 20
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