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How to Talk to your Boss about Analytics
Presenter: James Parry Sr. Systems Engineer SPSS 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

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 ”

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
“ 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)
Before analytics Banner ad click through rates 0.3% Mail response rates 0.5% Conversion rates (post-response) 0.9% Buyer repeat rates 2.0% After analytics 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 Computing Resources 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

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% % of return 100% 20% 20% 50% 50% 20% 70% ROI

The Perfect Model Doesn’t Exist, But …
The perfect model

Further Comparison – Business Rules
Business rules

Picking Our Model
Compare the C5.1 decision tree model to the others at the 40 th 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

SPSS User Publishes to Web

Consumer Log-in

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?
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

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 Parry Sr. Systems Engineer SPSS Inc. P. 800.543.2185 extension 2092 e-mail: [email_address] website: www.spss.com

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