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How to Talk to your Boss about Analytics
Presenter: James ParrySr. Systems EngineerSPSS Inc.
Are these your senior executives speaking?
“There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot
cards, or crystal balls.
Collectively, these methods are known as "nutty methods."
Or you can put well-researched facts into sophisticated computer models, more commonly referred to as "a
complete waste of time."
Scott Adams, The Dilbert Future
“There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot
cards, or crystal balls.
Collectively, these methods are known as "nutty methods."
Or you can put well-researched facts into sophisticated computer models, more commonly referred to as "a
complete waste of time."
Scott Adams, The Dilbert Future
Why predictive analytics is not used in many organizations?
“The entry barrier is no longer technology, but whether you
have executives who understand this”
Thomas Davenport, “Competing on Analytics”
“The entry barrier is no longer technology, but whether you
have executives who understand this”
Thomas Davenport, “Competing on Analytics”
Agenda
Why data mine: Demystifying and myth busting
Four steps to planning and presenting your data mining project plan
Reporting: Conveying the strength of a data mining model
What is lift? Considerations for efficient reporting
Tips for when talking to your boss about data mining
Q & A
Close
Demystifying and myth busting
Myth # 1: It’s not for me
“Predictive analytics is rocket science– it’s way above and beyond what I need to do.”
Analytics is now a “hit” in the Top 50 Best-Selling Business Books
And is catching on in institutional fundraising as well…
Predictive analytics becomes mainstream
Myth # 2: I don’t understand it.
“The idea of predictive analytics sounds good, but I really don’t understand what it does, and I couldn’t possibly explain it to anyone else to get their buy-in.”
Predictive Analytics: Defined
Data driven approach to problem solving
Focused on business objectives
Leverages organizational data
Uncovers patterns using predictive and descriptive techniques
Uses results to help improve organizational performance
What Does Predictive Analytics Do?
Predictive Analytics uses existing data to: Predict Group Associate Find outliers
Predictive Analytics: What it isn’t
A product, a particular piece of software, or a given algorithm
It is a business process that is enabled by technology
A model, segmentation scheme or business rules Those are some outputs from the Predictive Analytics process It is a method of discovery that yields information and insight
leading to some action
An end product in and of itself It is a means of harnessing the insight often trapped in large
masses of data It is an iterative, ever improving, feedback cycle
A SQL query, an OLAP hub, or a BI Dashboard
Statistics per se
Predictive Analytics is Part of CRISP-DM, the Industry Standard
Phases Business
Understanding Data Understanding Data Preparation Modeling Evaluation Deployment
Myth #3: I’ve got one already!
“We already do analytics through our business intelligence tools and corporate dashboards.”
Key Differences between BI and Predictive Analytics (PA) BI supplies the core facts of an organization:
Core business metrics KPI’s Factual reporting
PA helps you to interpret these facts as actionable information Predictive associations Optimized models Causal reporting Key Performance Predictors
Strategic Viewpoint Differences between BI and PA Typical BI applications provide a great picture
of what has happened… a rear view perspective
Dashboards in real time show current conditions and metrics… a clear windshield view
Predictive analytics enables future views and forecasting… a peek around the approaching corner and can create new metrics for closing the feedback
loop into the BI system
Myth #4: It won’t pay off
“Our organization is under constant pressure to lower the amount spent to raise a dollar. Predictive analytics will never pay back in time to make a real impact on our campaigns.”
Predictive analytics is important because it delivers value
“The median ROI for the projects that incorporated predictive technologies was 145%, compared with a median ROI of 89% for those projects that did not.”
Source: IDC, “Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study”
Nucleus Research . . .
Nucleus Research: The Real ROI from SPSS Inc.
•94% of customers achieved a positive ROI, with an average payback period of 10.7 months
•Key benefits achieved include reduced costs, increased productivity, improved customer & employee satisfaction, and greater visibility into operations
•81% of projects deployed on time, 75% on or under
budget
Nucleus Research: The Real ROI from SPSS Inc.
•94% of customers achieved a positive ROI, with an average payback period of 10.7 months
•Key benefits achieved include reduced costs, increased productivity, improved customer & employee satisfaction, and greater visibility into operations
•81% of projects deployed on time, 75% on or under
budget
“This is one of the highest ROI scores Nucleus has ever seen
in its Real ROI series of research reports.”
Rebecca Wettemann, Vice President of Research, Nucleus Research
Why is Predictive Analytics so critical to business decisions?
Performance of analytics targeted to certain consumers cross-industry and channel, research from Forrester, Jupiter, Amazon.com and Ovum (DM Review, Feb 11, 2003)
Beforeanalytics
Banner ad click through rates 0.3%Mail response rates 0.5%Conversion rates (post-response) 0.9%Buyer repeat rates 2.0%
Afteranalytics
21%18%10%60%
Four steps to planning and presenting your data mining project plan
Step 1: Determine Business Objectives
Thoroughly understand what you want to accomplish
Describe the criteria for a successful or useful outcome to the project from a business point of view EG: Increase the number of transfers from low to
medium donation groups.
Step 2: Assess Your Situation
Create an inventory of your available resources, including:
Personnel Data ComputingResources
Software
Step 3: Determine Data Mining Goals
Describe the intended project outputs and how you will arrive at them
Business goals vs. Data Mining Goals Example business goal: Increase the average gift
amount among annual fund donors by X%. Corresponding data mining goal: Predict the
propensity of annual fund donors to give more than they gave last year, using their giving history, demographic information, and stated level of satisfaction with your advancement program.
Step 4: Prepare and Present Your Project Plan List and describe each project stage, including:
Who’s involved? What other resources are required? What is the outcome or objective? When will it be completed?
Remember to include in your plan specific points in time to regroup and review progress and make updates as necessary
Create and follow a strategic plan to secure executive buy-in- Recap
Determine Business Objectives
Assess your Situation
Determine Data Mining Goals
Present your Project Plan
1
2
3
4
Data Mining and Reporting
29
Generated Models
The gold nuggets.
Reporting Considerations
Visually Explaining Competing Models Model lift
Eliminating Tedious, Repetitive, Time-Consuming Edits (3 D’s . . .) Design reusable graphs and graph templates
Getting the Right Information into the Right Hands, Securely Socializing/Publishing results - quickly
Self-Service Reporting Portal Create secure, online reporting environment Place the onus on the end-user, not the analyst
Automate!!
Data Mining: Who’s Involved?
The Power User More hands-on Understand how to connect to the data Understands data preparation Creates Report Templates
Ad-Hoc Reporter/Analyst Runs graphs and tables upon request (many, many) Socializes/Publishes Results
Consumer Usually stake-holder or C-level Does not license desktop application Relies on thin client
After you run some models . . . then what?
Measuring Lift
% of people 100%0%
% o
f re
turn
100%
20%
20%
50%
50%
20%
70%
ROI
34
The Perfect Model Doesn’t Exist, But …
The perfect model
35
Further Comparison – Business Rules
Business rules
36
Picking Our Model
Compare the C5.1 decision tree model to the others at the 40th percentile engagement point.
Presenting the Results
PASW Statistics Base
PASW Modeler
PASW Collaboration &Deployment Services(Predictive Enterprise
Browser)
Design a Template (Analyst/IT)
Pre-Template Chart
Post-Template Chart
Post-Template Chart
SPSS User Publishes to Web
Consumer Log-in
Predictive Enterprise Browser
Predictive Enterprise Browser
Results Rendered in Browser
Reporting Recap
Model Lift – conveys in $$ why using a predictive algorithm makes sense.
Graph Templates – decrease busy work, save $$$ in efficiency
Publishing to the Web Self-Service Reporting Platform – takes the burden
off the IR office thus making it more efficient $$$
Additional tips for talking to your boss about data mining
Laying the communication groundwork There is a communication gap between the
analyst (the maker) and the executive (user) Consumer of analytics is usually non-technical
prefers simple answers to complex explanations Analyst methods are treated like a black box of
information or voodoo but now more than ever, analysts are being called upon to explain how they arrived at an answer
Important first steps
Set proper expectation levels as soon as possible Bosses can have expectations which are too high –
“It’s magic” and will work perfectly They need to be brought down to earth before they
get disappointed and it reflects negatively on you
Bosses can also have mistakenly low expectations They don’t realize the potential of powerful analytics
and set their sights to low to demonstrate significant impact
Remember the audience at all times
Make all output relevant to the consumer Use business terms, not math, tech, stat verbiage Use graphs not words Turn everything into prospects or dollars Place everything into a problem-solving context Consider the price of inaction or not knowing
Words to avoid at all costs
Logistical regression
Hierarchical clustering
Algorithm
Coefficient
R-squared
Neural networks
Words to use frequently
ROI
Prospects
Stewardship
YIELD
Affinity
Growth
Capacity Ranking
Efficiency
Cost reduction
You are not alone in the struggle
Look beyond your own domain Other departments within your institution may
already be employing predictive analytics and/or using SPSS solutions.
List-servs and professional groups such as Prospect DMM, APRA, and CASE, AACRAO, AIR.
Befriend the IT organization Bridge the gap between data expertise and domain
expertise Involve IT to align goals and communicate needs
Over-arching principles
Demystify
Others are doing it
It has been proven
You can do it in small bites
Have a strong plan in place before you start!
Seek help
Questions?
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Key Take-aways
Remove the jargon and rocket science
Stay focused on the goal or business objective
Use external sources as support
Automate insight
Identify internal allies
Contact Information
James ParrySr. Systems Engineer
SPSS Inc.P. 800.543.2185 extension 2092
e-mail: [email protected]: www.spss.com