Fam1 Big Data + Visualization

PROPRIETARY + CONFIDENTIAL © 2012-16 Familian&1, All Rights Reserved
insights + interfaces
for digital business
an overview of
Big Data + Visualization
trends · tools · techniques
Presented by Seth Familian // Founder + Principal
415-226-9615 // seth@familian1.com // @sethfam1

PROPRIETARY + CONFIDENTIAL © 2012-2016 All Rights Reserved
Growth Hacking
A bit about me
2
corporate strategyCorporate Strategy
User Experience Design
Product Management Creative Procraftination
Teaching + Education

‣ Big Data Context 
How did we get here?
‣ Tools + Services 
For visualizing + analyzing big data
‣ Automated Dashboards 
From high volume data ﬂows
‣ Loyalty Segmentation 
From customer transaction data
What we’ll cover this afternoon
3

an overview of 
Part 1
Big Data Context
how did we get here?

Welcome to data obesity!
5http://www.datasciencecentral.com/proﬁles/blogs/basic-understanding-of-big-data-what-is-this-and-how-it-is-going

How big is big?
6http://www.domo.com/blog/2013/05/the-physical-size-of-big-data/
in 1 year!
creates enough data to ﬁll

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/

9 Sources
8
http://www.infosysblogs.com/brandedge/2013/04/20130419Infographc.html

6 Types
9
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"text": "I love @glip because it makes me more productive
and reliant on far fewer tools! #gliplove #goglip
#gliptastic :)",
"truncated": false,
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6 Types
10

6 Types
11

The 4 V’s
12http://www.slideshare.net/gschmutz/ukoug2013-big-datafastdata
9 Data Sources
6 Data Types

Putting it all together
13https://dataﬂoq.com/read/understanding-sources-big-data-infographic/338

an overview of 
Part 2
Tools + Services
for visualizing + analyzing big data

A busy landscape
15

Let’s simplify
16

And reframe it
17
EVENT-BASED ANALYTICS
+
TEXTUAL
VISUAL
ANALYTICS + INSIGHT
PROCESSING + NORMALIZATION
DATA TRANSFORMATION (ETL)
ACTIVITY
MODALITY
DATA DISPLAY + DASHBOARDING
STATISTICAL ANALYTICS VISUAL ANALYTICS

Power players
18
EVENT-BASED ANALYTICS
+
TEXTUAL
VISUAL
ANALYTICS + INSIGHT
PROCESSING + NORMALIZATION
VISUAL ANALYTICSSTATISTICAL ANALYTICS
DATA TRANSFORMATION (ETL) DATA DISPLAY + DASHBOARDING
ACTIVITY
MODALITY

Splunk for any machine data
19
SPLUNK.COM

Charted for super simple charts
20
CHARTED.CO

Tagul for simple unstructured data
21
TAGUL.COM

Quid for complex unstructured data
22
QUID.COM

Quintly for social media data
23
QUINTLY.COM

Google Analytics for website traffic
24
GOOGLE.COM/ANAYTICS

Mixpanel for user event data
25
MIXPANEL.COM

Mixpanel in practice with headline-screenshot construction
26
MIXPANEL.COM

Intercom for powerful customer engagement
27
INTERCOM.IO

Skitch for rapid screenshot markup
28
SKITCH.COM

Google Sheets for simpler data aggregation
29
SHEETS.GOOGLE.COM

30
GECKOBOARD.COM
Geckoboard for complex data aggregation

Geckoboard + Klipfolio for complex unified dashboards
31
GECKOBOARD.COM KLIPFOLIO.COM

an overview of 
Part 3
Automated Dashboards
for high volume data ﬂows

The “Old School” approach
33
“Long” Data
Raw  
“Header” 
File 2
1
Affiliates
Lookup File
Update Loop
Summary Index
saved
searches
scheduled
searches
TRANSFORM
Instantly 
Generate
EXTRACT LOAD

Segment for data processing + routing
34
SEGMENT.IO

How Segment Works
35
RAW DATA EXTRACT
Core Website
Enrichment of
email addresses

LOAD
Interconnected SaaS Tools
36
TRANSFORM
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

Making room for “old school” approaches
37

The beneﬁts of interconnected tools
38

an overview of 
Part 4
Loyalty Segmentation
from customer transaction data

Scoring Top Customers via RFM
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. are independently ranked into equal-sized “tiles” three times over
40
All Customers
NORMAL DISTRIBUTION SKEWED-DISTRIBUTION

Scoring Top Customers via RFM
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. are independently ranked into equal-sized “tiles” three times over
41
All 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

R + F = loyalty insights
42
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

$0
$1,500
$3,000
$4,500
$6,000
M = value  
43
Average total spent ($) by new MFR quantiles rerun for non-outlier M1 + M2 customers
M1 M2 M3 M4 M5
percent: top 20% of 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

RF + M = 8 loyalty/value segments
44
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

Categorical Segmentation
45
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+

Crossing RFM + Categorical Segments
46
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

an overview of 
Part 5
Final Thoughts
on big data + visualization

A new type of knowledge worker
48
http://www.doclens.com/87922/think-issue-7-2014/

An incredibly valuable skill
49

The precipice of a daunting future?
http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html 50
Where we are: How it feels:

Start here
51
CHARTED.CO

Or here
52
TAGUL.COM

Or here
53
SEGMENT.COM

Or here
54
FAMILIAN1.COM

Or here
55
HTTPS://GENERALASSEMB.LY/INSTRUCTORS/SETH-FAMILIAN/4813

Or here
56
SEGMENT.COM/INTEGRATIONS
ZAPIER.COM/ZAPBOOK

an overview of 
And scene.
Thank you!
let’s keep in touch
seth@familian1.com // @sethfam1

Fam1 Big Data + Visualization

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