How to get started in extracting business value from big data 1 of 2 oct 2013
Network Conference LMS Big Data Final 1.24.14
1. What’s the Big Deal About Big Data?
How Insights & Analysis Will Drive Your Fundraising Future
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2. Session Concepts
• Overview
• What is Big Data?
• Who is using Big Data and how
• Big Data in the non profit market
• Creatively using data, big and small
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3. My Background
• Not a data scientist, statistician
or technology guru but …
• a 25+ year database marketer
with specific focus on turning
data insights into actionable
program strategies.
• Agency background serving
broad range of non profit clients
including size, niche, regional
and national programs.
• All engagements have been
grounded in data-driven decision
making and a donor-centric
approach.
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4. 4
Circa 80s Circa 90s - 2007 Current
A personal retrospective on data sources and tools
5. Forecasting the future of information – 2020 Vision
• 1991 book co-authored by Stan Davis and Bill Davidson
• Companies should ‘informationalize’ their business
• create products and services on basis of information
• use ‘information exhaust’ to grow offerings
• 20+ years ago stock market, airlines leading the way
• Today, online companies including Amazon, Google, Facebook
leading the way in giving customers information, making
decisions easier.
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SOURCE: Thomas H. Davenport Analytics 3.0 Harvard Business Review December 2013
7. Big Data is …
vast volumes of unstructured fast moving data
from many resources.
- Thomas H. Davenport Analytics 3.0 Harvard Business Review December 2013
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8. What is structured and unstructured data?
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Structured data resides in fixed
fields within a record or file. SQL
databases and spreadsheets,
other tools contain structured
data.
Unstructured data has no
identifiable structure, can be
many types of information like
images, text, objects, emails.
9. Where is Big Data coming from?
Many places!
• Web browsing data trails
• Social network communications
• Sensor and surveillance data, etc.
Per IBM, 90% of the world’s data has been created in the past two
years
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SOURCE: Rob Petersen 6 case studies show Big Data is helping decision making Biznology December 2012
10. What type of data is being gathered?
All kinds of things!
• Traditional personal identification information like name, address, e-
address, phone numbers
• Secured information like social security, driver’s license, credit cards
• Using smart code logic, detailed promotions and transactions
• Demographic information (age, income, presence of ____), etc.
• Customer survey, satisfaction feedback
• Interests, opinions, preferences
• Friends, family, relationships
• Images
• Actions, inactions
• There’s even data about data (metadata)
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11. Where and how is Big Data being gathered and
analyzed?
• Structured and unstructured data is being collected from
disparate systems and consolidated into
• Data warehouses, NoSQL databases, Hadoop clusters
• In today’s environment multiples of the above
• On analytics and data insights front
• Machine-learning, embedded analytics integrating data into day-to-
day decision making
• More emphasis on prescriptive analytics v. descriptive, predictive
• New processes, organizational structures and functions including
data scientists, chief analytics officers
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13. ORION- on road integrated optimization and
navigation
• 107 year-old company, delivers 16.3m packages per day,
manages 39.5m tracking requests daily
• 1980s began tracking package movement, transactions
• Today have telematics sensors on 46,000 trucks monitoring
• Speed, braking, direction, vehicle performance
• Incoming data monitors performance, informs route redesigns
• Uses online map data and algorithms
• 2011 cut 85 million miles out of drivers’ routes, saved 8.4 million
in fuel
• Moving forward, UPS will use ORION to effect ‘real-time’
reconfigurations
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SOURCE: Thomas H. Davenport Analytics 3.0 Harvard Business Review December 2013
14. Big Data & Analytics
• IBM youtube clip AD http://www.youtube.com/watch?
v=xJfP_o_fANA
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SOURCE: IBM.com/big data
16. Most non profits on the Big Data and analytics
continuum
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Mid 1950s, early analytics
SOURCE: Thomas H. Davenport Analytics 3.0 Harvard Business Review December 2013
17. Why only 1.0 in the non profit market?
• We are behind on technology
• Even our technology is behind on technology
• Online constituents and revenues still lag far behind commercial
marketplace
• Per Charity Navigator, $2.1 billion donated in 2012
• Per Inquisitr website, $39 billion in holiday sales alone in 2012
• Operationally, financially and from a marketing perspective our
industry is not fully integrated
• Budget limitations, technology and change is not cheap
• In many ways and for many reasons, still not fully focused on
our donors and constituents’ experience
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18. What we can, should and/or are doing in this phase
• Clean up our data base(s)
• Set, adhere to business rules regarding data storage, maintenance
• Ensure smart source coding structure
• Consolidate marketing data into an accessible, flexible system
• Carry over impactful donor and marketing information
• Collect/append more donor-centric information like interests,
affinity, demographics to help build comprehensive donor profiles,
better understand potential value to you
• Set treatment and messaging plan, investment levels
• Become not only data savvy, but data creative
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21. Essential Reports
Dashboard-based reporting will enable varying constituents to view
meaningful results in a timely fashion. Consolidated, accessible,
‘clean’ data translates to insightful analysis and in turn solid business
decisions, innovative and winning strategies.
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22. How data (even ‘smaller’ data) can be leveraged to drive
relevant communications and program growth
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23. Relevance driven by donor
behavior and relationships
Relationships
Behaviors
24. Engaging younger constituents
Age 45-54 - Top Channels
Channel Donors Gifts Revenue Value/Member
Direct
Marketing 58,390 98,054 $7,021,583 $ 120.25
Special Trips 77 99 $320,577 $ 4,163.34
Planned Gifts 14 14 $1,442,472 $ 103,033.72
Special Events 316 575 $383,560 $ 1,213.80
Major Donors 129 171 $6,127,802 $ 47,502.34
Gatherings 46 63 $14,647 $ 318.41
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Background: Chapter-based environmental organization that wants to
attract and engage younger membership.
Using age overlay information, we identified that the most valuable
group on a per member basis and the most active across
engagement/giving opportunities were 45-54 year olds.
Opportunity to target awareness-raising messaging to further
engage ‘younger’ members in a variety of ways in the cause.
25. Creating more multi-channel, multi-activity donors
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Background: Large national
health-based, signature events
generate more than half of total
donors and revenue.
Internal tension to protect
donors and revenue by channel
Donors naturally migrate –
50% of revenue generated by
event sourced donors came from
another channel.
Opportunity to proactively,
strategically migrate, upgrade
donors from one
engagement/giving opportunity to
another.
26. Optimizing known and inferred relationships
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Background: Regional health
organization, markets
nationally with premiums,
mission-based offers.
Converted warm prospects
from services, outreach prove
more valuable than those with
unknown affinity
Newsletter responders, a
proxy for mission affinity
highest overall value
Leverage for messaging,
offer development, contact
cadence.
27. Long-term value by channel acquired & donor profiles
DIAMONDS
TO GO Direct Mail Zip Walk FSI SS Co-Op Other RR Web
Donors 44,464 7,396 1,969 1,493 515 288 2,352
% of File
76.04% 12.65% 3.37% 2.55% 0.88% 0.49% 4.02%
Avg. Age
58 55 61 60 56 58 51
First Gift $32.25 $36.00 $32.85 $31.51 $43.41 $53.52 $117.89
Life Value
Per Donor
$286.76 $380.03 $338.31 $457.53 $993.13 $614.30 $423.65
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Background: Diamonds to Go Missions overlay cluster with both
highest number of donors and value per donor.
Utilize information to determine investment by channel
In cultivations, test modified more aggressive gift array
Though average gift may be lower than mid level, major donor
consider investing in higher touch treatment
29. Data equity will take its place next to brand equity,
financial equity and human capital as a key business
asset.
- McKinsey Global Institute ‘Big Data’, June 2011
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I’d like to add this slide to set expectations on my skills/knowledge and areas of expertise which I think will be important in a session with this title.
Will be delivered in a self-deprecating, but confident way.
Visual graphic of how things have changed over the course of my own career in DM with the most dramatic occurring in 2007 and beyond.
In many ways though non profits are caught in the middle pane, in part due to generational preferences i.e., most income still coming from traditional sources, also budget constraints, org structure/siloes, other.
*A NoSQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. NoSQL databases are finding significant and growing industry use in big data and real-time web applications.[1] NoSQL systems are also referred to as "Not only SQL" to emphasize that they may in fact allow SQL-like query languages to be used
* Apache Hadoop is open system software enabling multiple end users to modify coding structure to fit needs … structure unstructured data to make accessible to analysts
Prescriptive analytics uses modeling to specify optimal behavior and actions
‘world’s largest operations research project’ per HBR author
Telematics typically is any integrated use of telecommunications and informatics, also known as ICT (Information and Communications Technology). GPS systems.
Well paced advertisement for product/service offering from IBM, target is CMO/marketers.
Clip is about 3 minutes
Analytic 2.0 example Linkedin, ‘People you may know’, ‘Companies you may want to follow’, ‘Network updates’
3.0 online retailers w/ recommendations from friends, targeted ads, etc.
Cincinnati Zoo on IBM site… pseudo ‘retail’
* $2.1billion from 116,000 non profits reporting June 2013 Charity Navigator
Our job is to send the right offer to the right donor at the right time and cost.
TNC data, about 50% of total membership is over 65, but decent number <65, some due to prospecting beyond direct mail (cavassing).
Gender view on same information also shows that Males are more engaged across all giving channels (v. females).
This information is from NMSS. They work in a data warehouse environment and have consolidated events, other channels along with the direct mail program.
Although much larger than any single mission, the concept here is about strategically moving donors from one channel to another (v. protecting them). MS still holds large event names for 2 years before they will release to the direct mail program ie. 2 year lapsed.
For missions this could include multi activity relationships like Volunteer donors, Event attendees/Direct Mail responsive, other
This data is from BIC team, specific to Missions.
I need to understand how we are currently utilizing in our planning/segmentation.
