Post on 10-Apr-2015
description
Dr. Richard Hackathorn
Bolder Technology, Inc.
May 14, 2009
Unlock Your Customer Data
with Data Visualization and
Customer Segmentation
Sponsor
Speakers
Richard HackathornPresident and Founder,
Bolder Technology, Inc.
Andrew CardnoChief Technology Officer,
BIS2
R.D. HackathornR.D. Hackathorn
Unlock Your Customer Data
With Data Visualization and
Customer Segmentation
Richard Hackathornrichardh@bolder.com
Slide 5R.D. Hackathorn
A Tough Business Problem
• Knowing your customer Understanding the behavior of your customer
Aligning your products/services to this behavior
• Each customer is different ...whether a person (B2C) or a company (B2B)
...changing with the seasons and phases of the moon
...depending on many unobvious factors
• Customers are not rational but ...
they are predictable
Slide 6R.D. Hackathorn
Predictably Irrational by Dan Ariely
• Do people make rational decisions? Deep question! What is rationality?
• Our brain tricks us constantly Recent events weight more than past events
A random event that is positive will lead to bad habits
• Customer behavior is a summation of these
irrational decisions
Slide 7R.D. Hackathorn
A Tough Business Problem
• How can you ‘assist’ your customer to... Recognize your product/service, brand, logo?
Match your product/service with their needs/wants?
Decide whether your product/service is worth it?
Make a purchase of your product/service?
• Would it be of value if you knew about: Growth opportunities in customer base
Effectiveness of marketing campaigns
Frequency of visits to revenue and profits
Customers who were ‗overdue‘ for a visit
...and so on
Slide 8R.D. Hackathorn
An Example – Telecommunications
• Facing cost reductions, evolving technology
alternatives and shifting market niches
• Customer retention
• Service up-selling
Slide 9R.D. Hackathorn
A Tough Business Problem
• If you knew your customer, you could: Develop customized marketing programs
Highlight specific product features
Establish various service options
Design an optimal distribution strategy
Determine appropriate product pricing
Prioritize new product development efforts
Design of new product strategies (packaging, pricing)
• Impacts many functions and levels
across the entire enterprise
Slide 10R.D. Hackathorn
Customer Segmentation
• Segmenting Compile demographics, etc about customers
Cluster customers based on similarity of attributes
Treat customers within a cluster as the same
• Targeting Choosing segment to target with specific marketing
• Positioning Designing marketing for a specific segment
• Problems with this approach Incomplete and inconsistent data
Clustering algorithms with complex interpretations
Large number of segments
Slide 11R.D. Hackathorn
It feels like...
You are looking through a keyhole
Slide 12R.D. Hackathorn
It feels like...
...into a room with
lots of activity
your
customers
Slide 13R.D. Hackathorn
It feels like...
...but
you really
want to
see like
this!
Slide 14R.D. Hackathorn
Need for a New Paradigm
• How do you distill meaning out of data? Lots of data, powerful query/report tooling
• Computational-centric analytics Analysis comes from processing algorithms
Such as data mining tools, predictive analysis...
• Visual-centric analytics Analysis comes from visual perception
...But a new generation of visual tools
Slide 15R.D. Hackathorn
Toward Visual-Centric Analytics
data warehouse
visualization
computational-centric
computation
Slide 16R.D. Hackathorn
data warehouse
Toward Visual-Centric Analytics
data warehouse
computation
visualization
computation
computational-centric visual-centric
Slide 17R.D. Hackathorn
Toward Visual-Centric Analytics
• Achieving the balance
Slide 18R.D. Hackathorn
Cross-Levels and Cross-Functions
data warehouse
strategic
tactical
operational
cross-levels
computation
Slide 19R.D. Hackathorn
Cross-Levels and Cross-Functions
data warehouse
computation
strategic
tactical
operational
data warehouse
computation
cross-levels cross-functions
marketing
sales
manufacturing
Slide 20R.D. Hackathorn
Visualizing Cross-Enterprise
data warehouse
computation
typical
Slide 21R.D. Hackathorn
Visualizing Cross-Enterprise
data warehouse
computation
data warehouse
computation
typical depth across levels
Slide 22R.D. Hackathorn
Visualizing Cross-Enterprise
data warehouse
computation
data warehouse
computation
data warehouse
computation
typical depth across levels breadth across functions
Slide 23R.D. Hackathorn
The Power of Seeing
• Seeing the business value Closely coupled to the business value chain
Naked data that shows business dimensions
• Seeing the whole picture Data in context with overview and detail
High-density content in shape, texture, color...
Slide 24R.D. Hackathorn
The Power of Seeing
• Seeing in a glance Intuitive perception...once trained
Stimulating the ‗a ha‘ moments
• Seeing as a group Decisions made across functions and across levels
Resulting in coordination across the enterprise
Slide 25R.D. Hackathorn
A Tough Business Problem – Wrap-up
• Knowing your customer Understanding the behavior of your customer
Aligning your products/services to this behavior
• Seeing your customer behavior Perform a value-based customer segmentation
...Using visual-centric analytics
...Gaining insights into behavior of specific customers
• Telecommunications... Finding the good customers who will probably flip
Offering the right service to the right customers
26
TM
Unlock Your Customer Data
With Data Visualization and
Customer Segmentation
Andrew CardnoCTO, BIS2
27Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Introduction to Super Graphics
SPATIAL TEMPORAL PIVOTAL
QUARTAL INSPATIAL
28Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
EDW
CRM, 3rd Party BI Tools & Other
Applications
XML
Access the vizbybis2 GUI or use
vizbybis2 as a mashup
Directly access the data e.g.
• Regular queries
• Spatial queries & functions
Example Super GraphicsTM
Easily identify patterns and meaningful
relationships in the data
Present Visualizations
Query DataMillions of customers:across Thousands of branches / locationsdoing Millions of transactionswith Billions of interactionsbuying Thousands of products / servicessupplied by Thousands of vendorsdue to Thousands of Promotionsserviced by Thousands of employees
Present the results visually
using Super Graphics.
vizbybis2 is a highly
configurable tool.
Allows you to directly
query the database.
EDW friendly because it
works on the data in place.
Introduction to Super Graphics
29Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Enhancing Your Customer Data
Demographic & other data
+zip codes, roads,
polygons etc.
BIS2 SpatialXchange
Mashup on context maps (such as GoogleTM Maps which
accelerate deployment and are continuously updated).
Spatially visualize your database!
30Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Using Industry Solutions to
Unlock Value
retailVizTM
…see and understand: customer bahaviors, instore management, channels, campaigns, SCM…
insuranceVizTM
…see and understand: customer profitability, claims, risk factors, cross-sell / upsell, agent performance, lapses…
telVizTM
…see and understand: customer revenue, lifetime value, churn, network performance, campaign effectiveness…
entertainmentVizTM
…see and understand: customer preferences, distribution management, promotions…
manufacturingVizTM
…see and understand: demand, costs, operational efficiency, resource management, production/supply risks…
moneyVizTM
…see and understand: customer profitability, financial risk, customer retention, competition, costs, customer interactions…
gameVizTM
…see and understand: how to optimize revenue, factors that influence what customers play, rate of play, when, how long, where…
31Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Good Customer Segmentation
is the Key to Unlocking Value
Dimension Reduction
Identify Noise Clusters Cluster Remainder
DA
TA Clustering
32Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Good Customer Segmentation
is the Key to Unlocking Value
Dimension Reduction
Identify Noise Clusters Cluster Remainder
DA
TA Clustering
Visualize from WHOLE to PART
Use Clusters to Build
Super Graphics
Understand All
• Marketing Results
• Responders
• Related to Physical Location
VIS
UA
LIZ
AT
ION
Clusters of results
Dimensional Analysis
Cyclical patterns of clusters
Deep customer insights of clusters
Inside customer interactions based on clusters
33Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Key Business
Performance
Drivers (BPDs)
Percent (%) of whole or other
variable – where are the
variations from the whole
For example, A map showing
percent (%) of responders
against whole
Understanding through Super Graphics to develop insightsINS
IGH
T
Good Customer Segmentation
is the Key to Unlocking Value
Dimension Reduction
Identify Noise Clusters Cluster Remainder
DA
TA Clustering
Visualize from WHOLE to PART
Use Clusters to Build
Super Graphics
Understand All
• Marketing Results
• Responders
• Related to Physical Location
VIS
UA
LIZ
AT
ION
Clusters of results
Dimensional Analysis
Cyclical patterns of clusters
Deep customer insights of clusters
Inside customer interactions based on clusters
34Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Spatial – Spatial Xchange (Story 1)
BIS2‘s Spatial
Xchange provides
key geospatial
data for BIS2 and
Partner
customers.
The geospatial
data is ‗load-ready‘
with Super
Graphics from
BIS2.
35Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Spatial – With Customer
Segments (Story 2)
Analysis of revenue and profit by customer segment, product and ZIP Code - Using
the vizbybis2 Spatial Super Graphic, show any predominance for revenue and profit by
ZIP Code. CLUSTER #1
CLUSTER #2
Example questions include:
• Are there any regional variations in consumption? And consumption over time against any national trends? This could be against the norm and/or revenue value per head of population.
• Is there any region which is a leading region? We could use the take-up or churn from any leading segment, what will it look like in five years?
CLUSTER #3
36Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Temporal – Call Patterns (Story 3)
Understanding call volume patterns against network utilization - Using the Temporal
Super Graphic, show call volume patterns against network utilization to better optimize
network utilization and reduce costs.
Example questions include:
• Network utilization by all products and by product line.
• Network utilization by all customers and by customer segment.
• Network utilization by all geographic areas or particular geographic areas.
• Network utilization - % from mean – areas of the network.
• Network utilization – product line or customer segment increase/decrease year-on-year.
Network Utilization –
2001 - 2008
37Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Pivotal – Call Routing (Story 4)
Understanding call routing patterns - Using the vizbybis2 Pivotal Super Graphic, show
call routing patterns to better understand and optimize interconnect pricing and costs.
Inte
rco
nn
ec
t
Ag
ree
me
nt
Example questions include:
• Call routing by all interconnect agreements and specific interconnect agreements.
• Call routing by cost by product line.
• Call routing by cost by customer segments.
• Call routing costs - % from mean.
• Call routing costs – product line or customer segment increase/decrease
year-on-year.
Time or customer segments or product lines (over the network)
38Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
―Old
er‖
Custo
me
rs
―Ne
we
r‖
Custo
me
rs
Rank Average Revenue Per User (ARPU)
Per Year
Split by Revenue
Low ARPU High ARPU
1
32
0
0. Top left:
(Older customers and low ARPU)
Customers who have been with us a long
time and have a low revenue per user.
1. Top right:
(Older customers and high ARPU)
Customers who have been with us a long
time and have a high revenue per user.
2. Bottom left:
(Newer customers and low ARPU)
Customers who have been with us a short
time and have a low revenue per user.
3. Bottom right:
Newer customers and high ARPU)
Customers who have been with us a short
time and have a high revenue per user.
# History means the length of time someone has been a
customer. An ―older‖ customer means that they have been a
customer for a relatively longer period, than a new customer.
Each quadrant is split by an equal amount of revenue.
@ Alternatively, could be split by ARPU.
Ran
k H
isto
ry#
Sp
lit
by R
even
ue
@Quartal – Customer Revenue (Story 5)
Identifying customer segment opportunities:
39Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Quartal – Customer Segmentation
(Story 6)
Cluster 1 Cluster 2
Cluster 4Cluster 3
Rank Revenue Per User (ARPU) Per YearSplit by Revenue
Ran
k H
isto
ry#
Sp
lit b
y R
even
ue@
Differences in customer
behavior—by customer
segment—can easily be
seen when the customer
segments are displayed
in the Quartal Super
Graphic.
The ability to change what is
displayed on the x and y axis
of the Quartal picture makes
the Quartal Super Graphic a
powerful and intuitive
customer segmentation,
selection, and analytical tool.
40Copyright © 2008 - 2009 – Business Intelligence Systems Solutions, Inc.
All Rights Reserved.
May 2009
Wrap-up
• Unlocking business value—fast and
economically.
• Volume, velocity and variety of data requires
new approaches.
• Good customer segmentation is the key to
unlocking value.
• Super Graphics enables you to unlock your
customer data.
Questions?
Speaker Contact Information
• If you have further questions or comments:
Richard Hackathornrichardh@bolder.com
Andrew Cardno, BIS2
andrew.cardno@bis2.net