SlideShare a Scribd company logo
Seth Familian
Founder + Principal, Familian&1
WORKING WITH
BIG DATA
FOLLOW ALONG!
familian1.com/wwbd
INTRODUCTION
SETH FAMILIAN
FOUNDER + PRINCIPAL, FAMILIAN&1
2
Corporate Strategy User Experience Design
Creative ProcraftinationTeaching + Education
Growth Hacking
AGENDA
‣ Context: What’s big data?
‣ Building dashboards
‣ Useful tools
‣ Inferring segments
‣ Final thoughts
3
WORKING WITH BIG DATA
CONTEXT:
WHAT’S BIG DATA?
4
CONTEXT: WHAT’S BIG DATA?
WELCOME TO DATA OBESITY!
5
http://www.datasciencecentral.com/profiles/blogs/basic-understanding-of-big-data-what-is-this-and-how-it-is-going
CONTEXT: WHAT’S BIG DATA?
HOW BIG IS BIG?
6
http://www.domo.com/blog/2013/05/the-physical-size-of-big-data/
in 1 year!
creates enough data to fill
CONTEXT: WHAT’S BIG DATA?
BIG IN GROWTH, TOO.
7
http://www.infosysblogs.com/brandedge/2013/04/20130419Infographc.html https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
CONTEXT: WHAT’S BIG DATA?
9 SOURCES
8
https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
CONTEXT: WHAT’S BIG DATA?
6 TYPES
9
{
"created_at": "Thu Sep 15 16:29:08 +0000 2016",
"id": 776457834095644700,
"id_str": "776457834095644672",
"text": "I love @glip because it makes me more productive and
reliant on far fewer tools! #gliplove #goglip #gliptastic :)",
"truncated": false,
"entities": {
"hashtags": [
{
"text": "gliplove",
"indices": [
82,
91
]
},
{
"text": "goglip",
"indices": [
92,
CONTEXT: WHAT’S BIG DATA?
6 TYPES
10
CONTEXT: WHAT’S BIG DATA?
6 TYPES
11
CONTEXT: WHAT’S BIG DATA?
THE FOUR V’S
12
http://www.slideshare.net/gschmutz/ukoug2013-big-datafastdata
9 Data Sources
6 Data Types
CONTEXT: WHAT’S BIG DATA?
COHESIVE ASSESSMENT
13
https://datafloq.com/read/understanding-sources-big-data-infographic/338
WORKING WITH BIG DATA
BUILDING DASHBOARDS
FROM BIG DATA
14
BUILDING DASHBOARDS
SMALL DATA EXAMPLE
15
via
USEFUL TOOLS
SPLUNK
16
SPLUNK.COM
FOR ANY MACHINE DATA
Raw 

“Header”

File
BUILDING DASHBOARDS
BIG DATA VISUALIZATION
17
26Mrows
250Kaffiliate IDs
28sub-channels
“Long” Data
BUILDING DASHBOARDS
THE “OLD SCHOOL” APPROACH
18
Raw 

“Header”

File
2
1
Affiliates
Lookup File
Update Loop
Summary Index
saved
searches
scheduled
searches
TRANSFORM
Instantly

Generates
EXTRACT LOAD
Channel

Dashboards
BUILDING DASHBOARDS
ANATOMY OF A SPLUNK SEARCH
19
BUILDING DASHBOARDS
SCHEDULED SEARCHES + INDICES
20
by sourceoforder by af_type
by af_source by af_name
by af_name 

+ ppc_s
SUMMARY INDEX INDEX_MAIN_SOURCES INDEX_A.COM INDEX_AFFILS INDEX_PAID_SEARCH INDEX_SHOPPING_ENGINES
12M+ TRANSACTIONSFULL DB
METRICS
SAVED SEARCHES
DATA DELTAS
METRICS DATA DELTAS
METRICS DATA DELTAS
METRICS DATA DELTAS
METRICS DATA DELTAS
BUILDING DASHBOARDS
NESTED CHARTS + SMALL MULTIPLES
21
BUILDING DASHBOARDS
SEGMENT
22
SEGMENT.COM
FOR DATA ROUTING
BUILDING DASHBOARDS
CLEARBIT
23
CLEARBIT.COM
FOR DATA ENRICHMENT
BUILDING DASHBOARDS
SEGMENT-CLEARBIT ENRICHMENT
24
RAW DATA EXTRACT
Core Website
Enrichment of
email addresses
USEFUL TOOLS
MIXPANEL
25
MIXPANEL.COM
FOR USER-EVENT DATA
USEFUL TOOLS
MIXPANEL
ANALYTICS
26
VIA INSIGHT-SCREENSHOT REPS
MARKETING + OPS
INTERCOM
27
A BETTER CRM
INTERCOM.IO
BUILDING DASHBOARDS
AUTOMATED BIG DATA FLOW (EXAMPLE 1)
28
RAW DATA EXTRACT LOADTRANSFORM
Traffic Sources 

& Session Stats
RAW DATA
Behavioral Segments,
Funnels, Retention &
LTV
EXTRACT
Additional aggregation
and data refinement
Core Website
Social Engagement Footprint
Unified social

footprint metrics
Enrichment of
email addresses
CRM data store for
easy segmentation +
analysis
Additional context 

on Twitter followers
More flexible segments,
funnels + retention
metrics
BUILDING DASHBOARDS
AUTOMATED BIG DATA FLOW (EXAMPLE 2)
29
LOAD
Custom
dashboards
synced with
70+ APIs
Traffic Sources 

& Session Stats
Realtime (RT)
TRANSFORMRAW DATA EXTRACT
Core Website
Social Engagement Footprint
heavy-duty query tools already in place
App Databases
Custom aggregation scripts
Postgres or
Redshift DB
Daily
Pull
Internally-reported metrics
summarized for triangulation
Daily CSV
Behavioral segmentation 

+ in-app messaging
RT
Behavioral Segments,
Funnels, Retention & LTV
RT
Unified social

footprint metrics
Unified app downloads
& ratings metrics
App Store Activity
email address
enrichment
BUILDING DASHBOARDS
AUTOMATED BIG DATA FLOW (EXAMPLE 3)
30
LOAD
Custom
dashboards
synced with
70+ APIs
Traffic Sources 

& Session Stats
Realtime (RT)
TRANSFORMRAW DATA EXTRACT
Core Website
MongoDB
Custom aggregation scripts
MySQL
Presence Table
Internally-reported metrics
summarized for triangulation
Weekly
CSV
Behavioral segmentation 

+ in-app messaging
RT
Behavioral Segments,
Funnels, Retention & LTV
RT
Unified app downloads
& ratings metrics
App Store Activity
email address
enrichment
Daily Dump
Instant dashboards
for Intercom
BUILDING DASHBOARDS 31
MORE
ADVANCED
AUTOMATION
AUTOMATION
ZAPIER
32
ZAPIER.COM
FOR COMPLEX ACTIONS
MARKETING + OPS
TYPEFORM
33
GORGEOUS + POWERFUL SURVEYS
AUTOMATION 34
EXAMPLE WITH INTERCOM + TYPEFORM
ZAPIER
AUTOMATION 35
MIND MELTED YET? LET’S TAKE 15.
BREAK TIME!
36
‣ Stretch your legs
‣ Hydrate or grab a snack
‣ We’ll start again in 15 mins!
Seth Familian
Founder + Principal, Familian&1
WORKING WITH
BIG DATA
FOLLOW ALONG!
familian1.com/wwbd
WORKING WITH BIG DATA
USEFUL TOOLS
FOR BIG DATA
38
USEFUL TOOLS
A BUSY
LANDSCAPE
39
USEFUL TOOLS
LET’S
SIMPLIFY
40
USEFUL TOOLS
AND LET’S
REFRAME IT
41
EVENT-BASED ANALYTICS
+TEXTUAL
VISUAL
ANALYTICS + INSIGHT
PROCESSING + NORMALIZATION
DATA TRANSFORMATION (ETL)
ACTIVITY
MODALITY
DATA DISPLAY + DASHBOARDING
STATISTICAL ANALYTICS VISUAL ANALYTICS
USEFUL TOOLS
POWER
PLAYERS
42
EVENT-BASED ANALYTICS
+TEXTUAL
VISUAL
ANALYTICS + INSIGHT
PROCESSING + NORMALIZATION
VISUAL ANALYTICSSTATISTICAL ANALYTICS
DATA TRANSFORMATION (ETL) DATA DISPLAY + DASHBOARDING
ACTIVITY
MODALITY
USEFUL TOOLS
SPLUNK
43
SPLUNK.COM
FOR ANY MACHINE DATA
USEFUL TOOLS
SPLUNK
44
SPLUNK.COM
FOR ANY MACHINE DATA
USEFUL TOOLS
CHARTED
45
CHARTED.CO
FOR SUPER SIMPLE CHARTS
USEFUL TOOLS
C3.JS
46
C3JS.ORG
FOR CUSTOM CHARTING
USEFUL TOOLS
TAGUL
47
TAGUL.COM
FOR GORGEOUS WORD CLOUDS
USEFUL TOOLS
QUID
48
QUID.COM
FOR UNSTRUCTURED ANALYSIS
USEFUL TOOLS
QUINTLY
49
QUINTLY.COM
FOR SOCIAL MEDIA DATA
USEFUL TOOLS
GOOGLE 

ANALYTICS
50
ANALYTICS.GOOGLE.COM
FOR WEBSITE TRAFFIC
USEFUL TOOLS
GOOGLE SHEETS
51
SHEETS.GOOGLE.COM
USEFUL TOOLS
GOOGLE SHEETS
52
SHEETS.GOOGLE.COM
USEFUL TOOLS
GECKOBOARD
53
GECKOBOARD.COM
USEFUL TOOLS
KLIPFOLIO
54
KLIPFOLIO.COM
WORKING WITH BIG DATA
INFERRING SEGMENTS
FROM BIG DATA
55
INFERRING SEGMENTS FROM BIG DATA
Frequency (F)
Ranking
Recency (R)
Ranking
Monetary (M)
Ranking
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
1. all customers are independently ranked into equal-sized “tiles” three times over
56
1,164,927 customers
2. M scores are multiplied by 100 and F scores are multipled by 10 to create unique ranking values
100 200 300 400 500 10 20 30 40 50 1 2 3 4 5
3. MFR scores are added up for each customer to yield 125 unique MFR segments:
111 121 131 141 151 112 122 132 142 152 155
211 221 231 241 251 212 222 232 242 252 255
511 521 531 541 551 512 522 532 542 552 555
Most recent,
frequent, and
highest-value
customers Least recent,
frequent, and
lowest-value
customers
RFM SCORING
INFERRING SEGMENTS FROM BIG DATA 57
WHY QUANTILES?
NORMAL DISTRIBUTION SKEWED-DISTRIBUTION
INFERRING SEGMENTS FROM BIG DATA 58
X11 X21 X31 X41
X12 X22 X32 X42
X13 X23 X33 X43
X14 X24 X34 X44
X15 X25 X35 X45
X51
X52
X53
X54
X55
High Frequency
High Recency
Low Frequency
Low Recency
Still Loyal
Once Loyal
New
Old
F + R = LOYALTY INSIGHTS
INFERRING SEGMENTS FROM BIG DATA
$0
$1,500
$3,000
$4,500
$6,000
59
Average total spent ($) by new MFR quantiles rerun for non-outlier M1 + M2 customers
M1 M2 M3 M4 M5
percent: top 20% of
M1+2
2nd 20% 3rd 20% 4th 20% Bottom 20%
segment
size:
93,134 93,139 92,861 93,406 93,143
avg. $
spent:
$3,337 $1,137 $642 $412 $276
total $
spent:
$345,234,826 $105,573,528 $59,348,459 $38,398,553 $25,537,936
% of total
revs:
53% 32% 18% 11% 8%
High-Value
Customers
Low-Value Customers
M = VALUE 

INSIGHTS
INFERRING SEGMENTS FROM BIG DATA 60
High Value Customers Low Value Customers
Still
Loyal
Once
Loyal
New
Old
M1 M2 M3 M4 M5
212111
121
112
122
113 123
211
221 222
311
321
312
322
411
421
412
422
511
521
512
522
114 124
115 125
213 223
214 224
215 225
313 323
314 324
315 325
413 423
414 424
415 425
513 523
514 524
515 525
131 132
141 142
151 152
231 232
241 242
251 252
331 332
341 342
351 352
431 432
441 442
451 452
531 531
541 542
551 552
133 134
143 144
153 154
135
145
155
233 234
243 244
253 254
235
245
255
333 334
343 344
353 354
335
345
355
433 434
443 444
453 454
435
445
455
533 534
543 544
553 554
535
545
555
1
2
3
4
5
6
7
8
COMBINING
INSIGHTS
INFERRING SEGMENTS FROM BIG DATA
PRICE-POINT
CUTOFFS
61
Best Camera/Lens Purchased
DSLR Body DSLR Lens DSLR Body + Lens Point-and-Shoot
Segment Name
Relationship 

to Photography
Memory
Keepers
Use cameras to record
family memories and
milestones
less than 

$650
less than 

$300
less than 

$950
less than 

$450
Hobbyists
Enjoy the picture-taking
process; understand and
use camera controls
$650 - $1725 $300 - $750 $950 - $2300 $450 - $700
Prosumers
Advanced skills, but do
not make a living from
photography
$1725 - $2750 $750 - $3000 $2300 - $4200 $700 - $2500
Pros
Rely on photography as a
profession $2750+ $3000+ $4200+ $2500+
INFERRING SEGMENTS FROM BIG DATA
CROSSING RFM
W/ CATEGORIES
62
Low Value High Value
Still
Loyal
Once
Loyal
New Old
Still
Loyal
Once
Loyal
New Old
Memory Keepers 1 2 3 4 5 6 7 8
Hobbyists 9 10 11 12 13 14 15 16
Prosumers 17 18 19 20 21 22 23 24
Professionals 25 26 27 28 29 30 31 32
3. Cross-Tabulate 

Top customers and categories to
create behavioral and 

loyalty-based segments
9 

key categories

account for 81% of sales
2. Isolate 

the top customers and
categories by total dollars
spent, frequency, and
recency (RFM) measures
465,683 

top customers

account for 88% of sales

1,164,927 customers 807 categories
1. Aggregate 

72 months of Internet channel
transaction data, organizing by
key variables
2,246,094 Internet Channel transactions
4. Generate

Segment-specific marketing
recommendations which can
be further targeted by brand
YIELDS SOLID TARGETS 

FOR TACTICAL PLANNING
INFERRING SEGMENTS FROM BIG DATA
DASHBOARD
INTEGRATION
63
INFERRING SEGMENTS FROM BIG DATA 64
REPORTING
≠ STRATEGY!
WORKING WITH BIG DATA
FINAL THOUGHTS
65
FINAL THOUGHTS
A NEW TYPE OF
KNOWLEDGE
WORKER
66
http://www.doclens.com/87922/think-issue-7-2014/
FINAL THOUGHTS
AN INCREDIBLY VALUABLE SKILL
67
https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
FINAL THOUGHTS
THE CORNERSTONE OF A DAUNTING FUTURE?
68
https://studentforce.wordpress.com/2013/09/21/umuc-big-data-revolution-is-here/
FINAL THOUGHTS
DATA AS INTERFACE
69
for
using
Made Visual
BACKGROUND TITLES + BUTTONS TEXT + LINES
Your data + brand
up to 

100,000 

objects
Anywhere on the Web
using 

1 line

of code
FINAL THOUGHTS
DATA AS INTERFACE
70
for
using
FINAL THOUGHTS
DATA AS INTERFACE
71
FINAL THOUGHTS
START HERE
72
CHARTED.CO
FINAL THOUGHTS
OR HERE
73
SEGMENT.IO
MIXPANEL.COM
FINAL THOUGHTS
OR HERE
74
HBR.ORG
FINAL THOUGHTS
OR HERE
75
FINAL THOUGHTS
OR HERE
76
DISCUSSION TIME
WORKING WITH BIG DATA 77
QUESTIONS · FEEDBACK · IDEAS · INSIGHTS
THANK YOU
KEEP IN TOUCH!
78
SETH@FAMILIAN1.COM · @SETHFAM1

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